From c6b817809eeb6893f9b65315d807a55517d7167a Mon Sep 17 00:00:00 2001 From: Daniel Danis Date: Tue, 27 Aug 2024 14:39:09 +0200 Subject: [PATCH 01/47] Allow to wrap a scoring funcion and produce `PhenotypeScorer`. --- src/gpsea/analysis/pscore/_api.py | 46 +++++++++++++++++++++++++++++++ 1 file changed, 46 insertions(+) diff --git a/src/gpsea/analysis/pscore/_api.py b/src/gpsea/analysis/pscore/_api.py index a2899734..5ddce121 100644 --- a/src/gpsea/analysis/pscore/_api.py +++ b/src/gpsea/analysis/pscore/_api.py @@ -11,8 +11,35 @@ class PhenotypeScorer(metaclass=abc.ABCMeta): """ `PhenotypeScorer` assigns the patient with a phenotype score. + + The score can be :attr:`math.nan` if it is not possible to compute the score for a patient. + + The scorer can be created by wrapping a scoring function (see :func:`~PhenotypeScorer.wrap_scoring_function`). """ + @staticmethod + def wrap_scoring_function( + func: typing.Callable[[Patient], float], + ) -> "PhenotypeScorer": + """ + Create a `PhenotypeScorer` by wrap the provided scoring function `func`. + + The function must take exactly one argument of type :class:`~gpsea.model.Patient` + and return a `float` with the corresponding phenotype score. + + Example + ^^^^^^^ + + >>> from gpsea.analysis.pscore import PhenotypeScorer + >>> def f(p): 123.4 + >>> phenotype_scorer = PhenotypeScorer.wrap_scoring_function(f) + + `phenotype_scorer` will assign all patients a score of `123.4`. + + :param func: the scoring function. + """ + return FunctionPhenotypeScorer(func=func) + def score(self, patient: Patient) -> float: """ Compute the score for the `patient`. @@ -20,6 +47,25 @@ def score(self, patient: Patient) -> float: pass +class FunctionPhenotypeScorer(PhenotypeScorer): + """ + `FunctionPhenotypeScorer` computes the phenotype score using the provided function/closure. + """ + # NOT PART OF THE PUBLIC API + + def __init__( + self, + func: typing.Callable[[Patient], float], + ): + self._func = func + + def score(self, patient: Patient) -> float: + """ + Apply the function to compute the phenotype score. + """ + return self._func(patient) + + class PhenotypeScoreAnalysisResult: """ `PhenotypeScoreAnalysisResult` is a container for :class:`PhenotypeScoreAnalysis` results. From e0c70b612830d912c5776f03edcc91ba48fc2bff Mon Sep 17 00:00:00 2001 From: Daniel Danis Date: Tue, 27 Aug 2024 14:39:28 +0200 Subject: [PATCH 02/47] Create `DeVriesPhenotypeScorer` stub. --- src/gpsea/analysis/pscore/__init__.py | 4 ++-- src/gpsea/analysis/pscore/_impl.py | 13 +++++++++++++ 2 files changed, 15 insertions(+), 2 deletions(-) diff --git a/src/gpsea/analysis/pscore/__init__.py b/src/gpsea/analysis/pscore/__init__.py index d03f6fd9..ce9c0e89 100644 --- a/src/gpsea/analysis/pscore/__init__.py +++ b/src/gpsea/analysis/pscore/__init__.py @@ -1,7 +1,7 @@ from ._api import PhenotypeScorer, PhenotypeScoreAnalysis, PhenotypeScoreAnalysisResult -from ._impl import CountingPhenotypeScorer +from ._impl import CountingPhenotypeScorer, DeVriesPhenotypeScorer __all__ = [ 'PhenotypeScorer', 'PhenotypeScoreAnalysis', 'PhenotypeScoreAnalysisResult', - 'CountingPhenotypeScorer', + 'CountingPhenotypeScorer', 'DeVriesPhenotypeScorer', ] diff --git a/src/gpsea/analysis/pscore/_impl.py b/src/gpsea/analysis/pscore/_impl.py index b2ce8bd2..f7cf248c 100644 --- a/src/gpsea/analysis/pscore/_impl.py +++ b/src/gpsea/analysis/pscore/_impl.py @@ -133,3 +133,16 @@ def score( # assert len(args) == 1 and isinstance(args[0], Patient), 'The first argument must be an instance of `Patient`' # assert len(kwds) == 0, 'We do not take any key-word arguments' # return self.score(args[0]) + + +class DeVriesPhenotypeScorer(PhenotypeScorer): + """ + `DeVriesPhenotypeScorer` computes "adapted De Vries Score" + as described in `Feenstra et al `_. + """ + + def score(self, patient: Patient) -> float: + """ + Compute the score for the `patient`. + """ + raise NotImplementedError() From 303858f36cd2fe5edbb0a9f306fbeb7e6c94c534 Mon Sep 17 00:00:00 2001 From: Daniel Danis Date: Tue, 27 Aug 2024 14:44:42 +0200 Subject: [PATCH 03/47] Check genotype categories in boxplot function. --- src/gpsea/analysis/pscore/_api.py | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/src/gpsea/analysis/pscore/_api.py b/src/gpsea/analysis/pscore/_api.py index 5ddce121..991036de 100644 --- a/src/gpsea/analysis/pscore/_api.py +++ b/src/gpsea/analysis/pscore/_api.py @@ -122,8 +122,13 @@ def plot_boxplots( # skip the patients with unassigned genotype group not_na_gts = self._genotype_phenotype_scores["genotype"].notna() data = self._genotype_phenotype_scores.loc[not_na_gts] + + # Check that the provided genotype predicate defines the same categories + # as those found in `data.` actual = set(data["genotype"].unique()) - expected = gt_predicate.get_categorizations() + expected = set(gt_predicate.get_categorizations()) + assert actual == expected, 'Mismatch in the genotype categories' + x = [ data.loc[data["genotype"] == c.category.cat_id, "phenotype"].to_list() for c in gt_predicate.get_categorizations() From 92800cb752b0371080b55a50cd3281fbe0996d79 Mon Sep 17 00:00:00 2001 From: Daniel Danis Date: Tue, 27 Aug 2024 14:49:42 +0200 Subject: [PATCH 04/47] Fix bug in the check. --- src/gpsea/analysis/pscore/_api.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/gpsea/analysis/pscore/_api.py b/src/gpsea/analysis/pscore/_api.py index 991036de..36caa0e9 100644 --- a/src/gpsea/analysis/pscore/_api.py +++ b/src/gpsea/analysis/pscore/_api.py @@ -126,7 +126,7 @@ def plot_boxplots( # Check that the provided genotype predicate defines the same categories # as those found in `data.` actual = set(data["genotype"].unique()) - expected = set(gt_predicate.get_categorizations()) + expected = set(c.cat_id for c in gt_predicate.get_categories()) assert actual == expected, 'Mismatch in the genotype categories' x = [ From 5e81498e199179554ef63cb62cdab7540f54c0e1 Mon Sep 17 00:00:00 2001 From: Daniel Danis Date: Tue, 27 Aug 2024 15:23:50 +0200 Subject: [PATCH 05/47] Next development iteration `v0.2.1.dev0`. --- src/gpsea/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/gpsea/__init__.py b/src/gpsea/__init__.py index 1497e5fb..c52b5596 100644 --- a/src/gpsea/__init__.py +++ b/src/gpsea/__init__.py @@ -2,4 +2,4 @@ GPSEA is a library for analyzing genotype-phenotype correlations in cohorts of rare disease patients. """ -__version__ = "0.2.0" +__version__ = "0.2.1.dev0" From d4e6ffaeea35f58febb08b2fc65a6f1e8fb56e52 Mon Sep 17 00:00:00 2001 From: Daniel Danis Date: Tue, 27 Aug 2024 16:36:05 +0200 Subject: [PATCH 06/47] Improve the MTC user guide section. --- docs/report/tbx5_frameshift_vs_missense.csv | 267 +++++++------- ...bx5_frameshift_vs_missense.mtc_report.html | 10 +- docs/tutorial.rst | 17 +- docs/user-guide/input-data.rst | 2 +- docs/user-guide/mtc.rst | 111 +++--- docs/user-guide/predicates.rst | 2 +- docs/user-guide/report/tbx5_frameshift.csv | 342 +++++++++--------- .../report/tbx5_frameshift.mtc_report.html | 4 +- docs/user-guide/stats.rst | 4 +- src/gpsea/analysis/mtc_filter/_impl.py | 4 - src/gpsea/analysis/pcats/_impl.py | 23 +- src/gpsea/analysis/pscore/_impl.py | 23 -- tests/analysis/test_mtc_filter.py | 11 +- 13 files changed, 405 insertions(+), 415 deletions(-) diff --git a/docs/report/tbx5_frameshift_vs_missense.csv b/docs/report/tbx5_frameshift_vs_missense.csv index e4a80ba2..421e6927 100644 --- a/docs/report/tbx5_frameshift_vs_missense.csv +++ b/docs/report/tbx5_frameshift_vs_missense.csv @@ -14,19 +14,21 @@ Pulmonary arterial hypertension [HP:0002092],4/6,67%,0/2,0%,0.6233766233766234,0 Hypoplasia of the ulna [HP:0003022],1/12,8%,2/10,20%,0.7619047619047618,0.5714285714285713 Hypoplasia of the radius [HP:0002984],30/62,48%,6/14,43%,0.9520604334894502,0.7735491022101784 Atrial septal defect [HP:0001631],42/44,95%,20/20,100%,1.0,1.0 -Short humerus [HP:0005792],7/17,41%,4/9,44%,1.0,1.0 Absent radius [HP:0003974],7/32,22%,6/25,24%,1.0,1.0 -Abnormal atrial septum morphology [HP:0011994],43/43,100%,20/20,100%,, -Abnormal cardiac septum morphology [HP:0001671],62/62,100%,28/28,100%,, -Abnormal heart morphology [HP:0001627],62/62,100%,30/30,100%,, -Abnormal cardiovascular system morphology [HP:0030680],63/63,100%,30/30,100%,, -Abnormality of the cardiovascular system [HP:0001626],65/65,100%,32/32,100%,, +Short humerus [HP:0005792],7/17,41%,4/9,44%,1.0,1.0 +Abnormal carpal morphology [HP:0001191],30/32,94%,0/0,0%,, +Abnormal hand morphology [HP:0005922],53/53,100%,20/20,100%,, +Abnormality of the hand [HP:0001155],60/60,100%,31/31,100%,, +Abnormality of the upper limb [HP:0002817],73/73,100%,34/34,100%,, +Abnormality of limbs [HP:0040064],73/73,100%,34/34,100%,, Phenotypic abnormality [HP:0000118],82/82,100%,38/38,100%,, All [HP:0000001],82/82,100%,38/38,100%,, -Abnormal cardiac atrium morphology [HP:0005120],43/43,100%,20/20,100%,, -Abnormal ventricular septum morphology [HP:0010438],31/31,100%,19/19,100%,, -Abnormal cardiac ventricle morphology [HP:0001713],31/31,100%,19/19,100%,, -Perimembranous ventricular septal defect [HP:0011682],3/59,5%,3/25,12%,, +Abnormality of the wrist [HP:0003019],30/30,100%,0/0,0%,, +Abnormality of upper limb joint [HP:0009810],30/30,100%,6/6,100%,, +Abnormal joint morphology [HP:0001367],31/31,100%,6/6,100%,, +Abnormal skeletal morphology [HP:0011842],73/73,100%,35/35,100%,, +Abnormality of the skeletal system [HP:0000924],73/73,100%,35/35,100%,, +Abnormality of the musculoskeletal system [HP:0033127],74/74,100%,35/35,100%,, Short thumb [HP:0009778],11/41,27%,8/30,27%,, Aplasia/Hypoplasia of the thumb [HP:0009601],20/20,100%,19/19,100%,, Aplasia/Hypoplasia of fingers [HP:0006265],22/22,100%,19/19,100%,, @@ -34,63 +36,49 @@ Aplasia/hypoplasia involving bones of the hand [HP:0005927],22/22,100%,19/19,100 Aplasia/hypoplasia involving bones of the upper limbs [HP:0006496],55/55,100%,22/22,100%,, Aplasia/hypoplasia involving bones of the extremities [HP:0045060],55/55,100%,22/22,100%,, Aplasia/hypoplasia of the extremities [HP:0009815],55/55,100%,22/22,100%,, -Abnormality of limbs [HP:0040064],73/73,100%,34/34,100%,, Aplasia/hypoplasia involving the skeleton [HP:0009115],56/56,100%,23/23,100%,, -Abnormal skeletal morphology [HP:0011842],73/73,100%,35/35,100%,, -Abnormality of the skeletal system [HP:0000924],73/73,100%,35/35,100%,, -Abnormality of the musculoskeletal system [HP:0033127],74/74,100%,35/35,100%,, Abnormal limb bone morphology [HP:0002813],63/63,100%,34/34,100%,, Abnormality of limb bone [HP:0040068],63/63,100%,34/34,100%,, Abnormal appendicular skeleton morphology [HP:0011844],64/64,100%,34/34,100%,, -Abnormality of the upper limb [HP:0002817],73/73,100%,34/34,100%,, -Abnormal hand morphology [HP:0005922],53/53,100%,20/20,100%,, -Abnormality of the hand [HP:0001155],60/60,100%,31/31,100%,, Abnormal finger morphology [HP:0001167],36/36,100%,31/31,100%,, Abnormal digit morphology [HP:0011297],38/38,100%,33/33,100%,, Abnormal thumb morphology [HP:0001172],30/30,100%,31/31,100%,, Short finger [HP:0009381],11/11,100%,8/8,100%,, Short digit [HP:0011927],11/11,100%,10/10,100%,, -Abnormality of thumb phalanx [HP:0009602],13/13,100%,13/13,100%,, -Forearm undergrowth [HP:0009821],30/30,100%,7/7,100%,, -Upper limb undergrowth [HP:0009824],33/33,100%,7/7,100%,, -Limb undergrowth [HP:0009826],33/33,100%,7/7,100%,, +Complete atrioventricular canal defect [HP:0001674],5/37,14%,3/36,8%,, +Atrioventricular canal defect [HP:0006695],5/5,100%,3/3,100%,, +Abnormal cardiac septum morphology [HP:0001671],62/62,100%,28/28,100%,, +Abnormal heart morphology [HP:0001627],62/62,100%,30/30,100%,, +Abnormal cardiovascular system morphology [HP:0030680],63/63,100%,30/30,100%,, +Abnormality of the cardiovascular system [HP:0001626],65/65,100%,32/32,100%,, +11 pairs of ribs [HP:0000878],1/1,100%,0/0,0%,, +Missing ribs [HP:0000921],1/1,100%,0/0,0%,, +Aplasia/Hypoplasia of the ribs [HP:0006712],1/1,100%,0/0,0%,, +Aplasia/Hypoplasia involving bones of the thorax [HP:0006711],1/1,100%,2/2,100%,, +Aplasia/hypoplasia affecting bones of the axial skeleton [HP:0009122],2/2,100%,2/2,100%,, +Abnormal axial skeleton morphology [HP:0009121],8/8,100%,5/5,100%,, +Abnormal thorax morphology [HP:0000765],6/6,100%,5/5,100%,, +Abnormal rib morphology [HP:0000772],1/1,100%,0/0,0%,, +Abnormal rib cage morphology [HP:0001547],4/4,100%,0/0,0%,, +Abnormal cardiac ventricle morphology [HP:0001713],31/31,100%,19/19,100%,, +Abnormal atrial septum morphology [HP:0011994],43/43,100%,20/20,100%,, +Abnormal cardiac atrium morphology [HP:0005120],43/43,100%,20/20,100%,, +Finger aplasia [HP:0009380],15/15,100%,14/14,100%,, +Aplasia involving forearm bones [HP:0009822],7/7,100%,6/6,100%,, Aplasia/hypoplasia involving forearm bones [HP:0006503],37/37,100%,12/12,100%,, Abnormal forearm bone morphology [HP:0040072],37/37,100%,14/14,100%,, Abnormal upper limb bone morphology [HP:0040070],40/40,100%,14/14,100%,, Abnormal forearm morphology [HP:0002973],37/37,100%,14/14,100%,, -Short long bone [HP:0003026],35/35,100%,9/9,100%,, -Abnormal long bone morphology [HP:0011314],44/44,100%,13/13,100%,, Aplasia/Hypoplasia of the radius [HP:0006501],37/37,100%,11/11,100%,, Abnormal morphology of the radius [HP:0002818],37/37,100%,13/13,100%,, -Aplasia of the fingers [HP:0009380],15/15,100%,14/14,100%,, -Tricuspid regurgitation [HP:0005180],3/3,100%,0/0,0%,, -Atrioventricular valve regurgitation [HP:0034376],4/4,100%,2/2,100%,, -Abnormal atrioventricular valve physiology [HP:0031650],4/4,100%,2/2,100%,, -Abnormal heart valve physiology [HP:0031653],4/4,100%,2/2,100%,, -Abnormal cardiovascular system physiology [HP:0011025],23/23,100%,5/5,100%,, -Abnormal tricuspid valve physiology [HP:0031651],3/3,100%,0/0,0%,, -Deviation of the hand or of fingers of the hand [HP:0009484],2/2,100%,2/2,100%,, -Cleft soft palate [HP:0000185],2/2,100%,0/0,0%,, -Abnormal soft palate morphology [HP:0100736],2/2,100%,0/0,0%,, -Abnormal palate morphology [HP:0000174],5/5,100%,0/0,0%,, -Abnormal oral cavity morphology [HP:0000163],5/5,100%,1/1,100%,, -Abnormal oral morphology [HP:0031816],5/5,100%,1/1,100%,, -Abnormality of the mouth [HP:0000153],5/5,100%,1/1,100%,, -Abnormality of the face [HP:0000271],5/5,100%,1/1,100%,, -Abnormality of the head [HP:0000234],5/5,100%,2/2,100%,, -Abnormality of head or neck [HP:0000152],5/5,100%,2/2,100%,, -Cleft palate [HP:0000175],2/2,100%,0/0,0%,, -Orofacial cleft [HP:0000202],2/2,100%,0/0,0%,, -Abnormal carpal morphology [HP:0001191],30/32,94%,0/0,0%,, -Abnormality of the wrist [HP:0003019],30/30,100%,0/0,0%,, -Abnormality of upper limb joint [HP:0009810],30/30,100%,6/6,100%,, -Abnormal joint morphology [HP:0001367],31/31,100%,6/6,100%,, -Mitral valve prolapse [HP:0001634],0/0,0%,1/1,100%,, -Abnormal mitral valve morphology [HP:0001633],0/0,0%,1/1,100%,, -Abnormal atrioventricular valve morphology [HP:0006705],0/0,0%,1/1,100%,, -Abnormal heart valve morphology [HP:0001654],0/0,0%,1/1,100%,, -Mitral regurgitation [HP:0001653],1/1,100%,2/2,100%,, -Abnormal mitral valve physiology [HP:0031481],1/1,100%,2/2,100%,, +Absent forearm bone [HP:0003953],7/7,100%,6/6,100%,, +Forearm undergrowth [HP:0009821],30/30,100%,7/7,100%,, +Upper limb undergrowth [HP:0009824],33/33,100%,7/7,100%,, +Limb undergrowth [HP:0009826],33/33,100%,7/7,100%,, +Short long bone [HP:0003026],35/35,100%,9/9,100%,, +Abnormal long bone morphology [HP:0011314],44/44,100%,13/13,100%,, +Abnormal ventricular septum morphology [HP:0010438],31/31,100%,19/19,100%,, +Abnormality of thumb phalanx [HP:0009602],13/13,100%,13/13,100%,, Limited pronation/supination of forearm [HP:0006394],0/0,0%,3/3,100%,, Limited elbow movement [HP:0002996],0/0,0%,4/4,100%,, Abnormality of the elbow [HP:0009811],0/0,0%,5/5,100%,, @@ -99,8 +87,7 @@ Abnormality of joint mobility [HP:0011729],1/1,100%,5/5,100%,, Abnormal joint physiology [HP:0034430],1/1,100%,5/5,100%,, Abnormal musculoskeletal physiology [HP:0011843],1/1,100%,5/5,100%,, Abnormality of cardiovascular system electrophysiology [HP:0030956],15/15,100%,3/3,100%,, -Upper limb phocomelia [HP:0009813],8/85,9%,2/37,5%,, -Phocomelia [HP:0009829],8/8,100%,2/2,100%,, +Abnormal cardiovascular system physiology [HP:0011025],23/23,100%,5/5,100%,, Pre-capillary pulmonary hypertension [HP:0033578],4/4,100%,0/0,0%,, Elevated pulmonary artery pressure [HP:0004890],4/4,100%,0/0,0%,, Abnormality of pulmonary circulation [HP:0030875],4/4,100%,0/0,0%,, @@ -108,6 +95,11 @@ Abnormal vascular physiology [HP:0030163],4/4,100%,0/0,0%,, Abnormality of the vasculature [HP:0002597],10/10,100%,2/2,100%,, Abnormal respiratory system physiology [HP:0002795],4/4,100%,0/0,0%,, Abnormality of the respiratory system [HP:0002086],4/4,100%,0/0,0%,, +Tricuspid regurgitation [HP:0005180],3/3,100%,0/0,0%,, +Atrioventricular valve regurgitation [HP:0034376],4/4,100%,2/2,100%,, +Abnormal atrioventricular valve physiology [HP:0031650],4/4,100%,2/2,100%,, +Abnormal heart valve physiology [HP:0031653],4/4,100%,2/2,100%,, +Abnormal tricuspid valve physiology [HP:0031651],3/3,100%,0/0,0%,, Aplasia of the 1st metacarpal [HP:0010035],0/0,0%,0/0,0%,, Aplasia of the proximal phalanges of the hand [HP:0010242],0/0,0%,0/0,0%,, Aplasia/Hypoplasia of the proximal phalanges of the hand [HP:0009851],0/0,0%,0/0,0%,, @@ -127,18 +119,25 @@ Partial absence of thumb [HP:0009659],0/0,0%,0/0,0%,, 1-2 finger syndactyly [HP:0010704],3/3,100%,1/1,100%,, Finger syndactyly [HP:0006101],4/4,100%,2/2,100%,, Syndactyly [HP:0001159],4/4,100%,2/2,100%,, -Complete atrioventricular canal defect [HP:0001674],5/37,14%,3/36,8%,, -Atrioventricular canal defect [HP:0006695],5/5,100%,3/3,100%,, -Persistent left superior vena cava [HP:0005301],2/37,5%,0/0,0%,, -Abnormal superior vena cava morphology [HP:0025575],2/2,100%,0/0,0%,, -Abnormal vena cava morphology [HP:0005345],2/2,100%,0/0,0%,, -Abnormal morphology of the great vessels [HP:0030962],6/6,100%,2/2,100%,, -Abnormal blood vessel morphology [HP:0033353],6/6,100%,2/2,100%,, -Abnormal vascular morphology [HP:0025015],6/6,100%,2/2,100%,, -Abnormal venous morphology [HP:0002624],2/2,100%,0/0,0%,, -Abnormal shoulder morphology [HP:0003043],1/1,100%,1/1,100%,, -Abnormal thorax morphology [HP:0000765],6/6,100%,5/5,100%,, -Abnormal axial skeleton morphology [HP:0009121],8/8,100%,5/5,100%,, +Upper limb phocomelia [HP:0009813],8/85,9%,2/37,5%,, +Phocomelia [HP:0009829],8/8,100%,2/2,100%,, +Clinodactyly of the 5th finger [HP:0004209],1/1,100%,0/0,0%,, +Finger clinodactyly [HP:0040019],1/1,100%,0/0,0%,, +Clinodactyly [HP:0030084],1/1,100%,0/0,0%,, +Deviation of finger [HP:0004097],1/1,100%,2/2,100%,, +Deviation of the hand or of fingers of the hand [HP:0009484],2/2,100%,2/2,100%,, +Deviation of the 5th finger [HP:0009179],1/1,100%,0/0,0%,, +Abnormal 5th finger morphology [HP:0004207],4/4,100%,0/0,0%,, +Patent foramen ovale [HP:0001655],4/40,10%,0/36,0%,, +Small hypothenar eminence [HP:0010487],2/2,100%,0/0,0%,, +Abnormality of the hypothenar eminence [HP:0010486],2/2,100%,0/0,0%,, +Abnormality of the musculature of the hand [HP:0001421],2/2,100%,0/0,0%,, +Abnormality of the musculature of the upper limbs [HP:0001446],2/2,100%,0/0,0%,, +Abnormality of the musculature of the limbs [HP:0009127],2/2,100%,0/0,0%,, +Abnormal skeletal muscle morphology [HP:0011805],2/2,100%,0/0,0%,, +Abnormality of the musculature [HP:0003011],2/2,100%,0/0,0%,, +Perimembranous ventricular septal defect [HP:0011682],3/59,5%,3/25,12%,, +Deviation of the thumb [HP:0009603],0/0,0%,2/2,100%,, Aplasia/hypoplasia of the humerus [HP:0006507],7/7,100%,4/4,100%,, Abnormality of the humerus [HP:0003063],7/7,100%,4/4,100%,, Abnormality of the upper arm [HP:0001454],7/7,100%,4/4,100%,, @@ -150,12 +149,18 @@ Abnormal sternum morphology [HP:0000766],3/3,100%,2/2,100%,, Postaxial hand polydactyly [HP:0001162],3/4,75%,0/0,0%,, Postaxial polydactyly [HP:0100259],3/3,100%,0/0,0%,, Polydactyly [HP:0010442],3/3,100%,0/0,0%,, -Abnormal 5th finger morphology [HP:0004207],4/4,100%,0/0,0%,, Hand polydactyly [HP:0001161],3/3,100%,0/0,0%,, Duplication of phalanx of hand [HP:0009997],3/3,100%,0/0,0%,, Duplication of hand bones [HP:0004275],3/3,100%,0/0,0%,, Duplication of bones involving the upper extremities [HP:0009142],3/3,100%,0/0,0%,, High palate [HP:0000218],3/3,100%,0/0,0%,, +Abnormal palate morphology [HP:0000174],5/5,100%,0/0,0%,, +Abnormal oral cavity morphology [HP:0000163],5/5,100%,1/1,100%,, +Abnormal oral morphology [HP:0031816],5/5,100%,1/1,100%,, +Abnormality of the mouth [HP:0000153],5/5,100%,1/1,100%,, +Abnormality of the face [HP:0000271],5/5,100%,1/1,100%,, +Abnormality of the head [HP:0000234],5/5,100%,2/2,100%,, +Abnormality of head or neck [HP:0000152],5/5,100%,2/2,100%,, Short neck [HP:0000470],3/3,100%,0/0,0%,, Abnormal neck morphology [HP:0025668],3/3,100%,0/0,0%,, Abnormality of the neck [HP:0000464],3/3,100%,0/0,0%,, @@ -163,99 +168,95 @@ Abnormality of the cervical spine [HP:0003319],3/3,100%,0/0,0%,, Abnormality of the vertebral column [HP:0000925],4/4,100%,1/1,100%,, Shield chest [HP:0000914],3/3,100%,0/0,0%,, Enlarged thorax [HP:0100625],3/3,100%,0/0,0%,, -Abnormal rib cage morphology [HP:0001547],4/4,100%,0/0,0%,, Y-shaped metatarsals [HP:0010567],3/3,100%,0/0,0%,, Abnormal metatarsal morphology [HP:0001832],3/3,100%,0/0,0%,, Abnormal lower limb bone morphology [HP:0040069],3/3,100%,0/0,0%,, Abnormality of the lower limb [HP:0002814],3/3,100%,0/0,0%,, Abnormal foot morphology [HP:0001760],3/3,100%,0/0,0%,, -Upper extremity joint dislocation [HP:0030310],0/0,0%,2/2,100%,, -Joint dislocation [HP:0001373],0/0,0%,2/2,100%,, -Clinodactyly of the 5th finger [HP:0004209],1/1,100%,0/0,0%,, -Finger clinodactyly [HP:0040019],1/1,100%,0/0,0%,, -Clinodactyly [HP:0030084],1/1,100%,0/0,0%,, -Deviation of finger [HP:0004097],1/1,100%,2/2,100%,, -Deviation of the 5th finger [HP:0009179],1/1,100%,0/0,0%,, -Proximal placement of thumb [HP:0009623],0/0,0%,0/0,0%,, -Deviation of the thumb [HP:0009603],0/0,0%,2/2,100%,, -Abnormal 2nd finger morphology [HP:0004100],1/1,100%,0/0,0%,, -Aplasia/Hypoplasia of the 2nd finger [HP:0006264],1/1,100%,0/0,0%,, -Short middle phalanx of finger [HP:0005819],0/0,0%,0/0,0%,, -Aplasia/Hypoplasia of the middle phalanges of the hand [HP:0009843],0/0,0%,0/0,0%,, -Abnormal middle phalanx morphology of the hand [HP:0009833],0/0,0%,0/0,0%,, -Short phalanx of finger [HP:0009803],0/0,0%,0/0,0%,, -Short middle phalanx of the 5th finger [HP:0004220],0/0,0%,0/0,0%,, -Type A brachydactyly [HP:0009370],0/0,0%,0/0,0%,, -Brachydactyly [HP:0001156],0/0,0%,0/0,0%,, -Short 5th finger [HP:0009237],0/0,0%,0/0,0%,, -Aplasia/Hypoplasia of the 5th finger [HP:0006262],0/0,0%,0/0,0%,, -Aplasia/Hypoplasia of the middle phalanx of the 5th finger [HP:0009161],0/0,0%,0/0,0%,, -Aplasia/Hypoplasia of the phalanges of the 5th finger [HP:0009376],0/0,0%,0/0,0%,, -Abnormal 5th finger phalanx morphology [HP:0004213],0/0,0%,0/0,0%,, -Abnormality of the middle phalanx of the 5th finger [HP:0004219],0/0,0%,0/0,0%,, +Mitral regurgitation [HP:0001653],1/1,100%,2/2,100%,, +Abnormal mitral valve physiology [HP:0031481],1/1,100%,2/2,100%,, +First degree atrioventricular block [HP:0011705],0/22,0%,1/1,100%,, Congenital malformation of the great arteries [HP:0011603],4/4,100%,2/2,100%,, -Aplasia involving forearm bones [HP:0009822],7/7,100%,6/6,100%,, -Absent forearm bone [HP:0003953],7/7,100%,6/6,100%,, -Aplasia/Hypoplasia of the ulna [HP:0006495],2/2,100%,2/2,100%,, +Abnormal morphology of the great vessels [HP:0030962],6/6,100%,2/2,100%,, +Abnormal blood vessel morphology [HP:0033353],6/6,100%,2/2,100%,, +Abnormal vascular morphology [HP:0025015],6/6,100%,2/2,100%,, +Abnormal skull morphology [HP:0000929],1/1,100%,2/2,100%,, +Sinus bradycardia [HP:0001688],0/0,0%,1/1,100%,, +Abnormal electrophysiology of sinoatrial node origin [HP:0011702],0/0,0%,1/1,100%,, +Arrhythmia [HP:0011675],1/1,100%,1/1,100%,, +Bradycardia [HP:0001662],0/0,0%,1/1,100%,, +Abnormal skin morphology [HP:0011121],0/0,0%,1/1,100%,, +Abnormality of the skin [HP:0000951],0/0,0%,1/1,100%,, +Abnormality of the integument [HP:0001574],0/0,0%,1/1,100%,, +Sinus venosus atrial septal defect [HP:0011567],0/2,0%,1/1,100%,, Abnormal morphology of ulna [HP:0040071],2/2,100%,4/4,100%,, +Aplasia/Hypoplasia of the ulna [HP:0006495],2/2,100%,2/2,100%,, +Persistent left superior vena cava [HP:0005301],2/37,5%,0/0,0%,, +Abnormal superior vena cava morphology [HP:0025575],2/2,100%,0/0,0%,, +Abnormal vena cava morphology [HP:0005345],2/2,100%,0/0,0%,, +Abnormal venous morphology [HP:0002624],2/2,100%,0/0,0%,, +Abnormal shoulder morphology [HP:0003043],1/1,100%,1/1,100%,, Hypoplasia of deltoid muscle [HP:0030241],0/0,0%,0/0,0%,, Shoulder muscle hypoplasia [HP:0008952],0/0,0%,0/0,0%,, Hypoplasia of the musculature [HP:0009004],0/0,0%,0/0,0%,, Aplasia/Hypoplasia involving the skeletal musculature [HP:0001460],0/0,0%,0/0,0%,, Abnormality of muscle size [HP:0030236],0/0,0%,0/0,0%,, -Abnormal skeletal muscle morphology [HP:0011805],2/2,100%,0/0,0%,, -Abnormality of the musculature [HP:0003011],2/2,100%,0/0,0%,, Aplasia/Hypoplasia involving the shoulder musculature [HP:0001464],0/0,0%,0/0,0%,, Aplasia/Hypoplasia involving the musculature of the upper limbs [HP:0001467],0/0,0%,0/0,0%,, Aplasia/Hypoplasia involving the musculature of the extremities [HP:0009128],0/0,0%,0/0,0%,, -Abnormality of the musculature of the limbs [HP:0009127],2/2,100%,0/0,0%,, -Abnormality of the musculature of the upper limbs [HP:0001446],2/2,100%,0/0,0%,, Abnormality of the shoulder girdle musculature [HP:0001435],0/0,0%,0/0,0%,, +Common atrium [HP:0011565],0/83,0%,0/38,0%,, +Unroofed coronary sinus [HP:0031297],0/85,0%,0/38,0%,, Atrioventricular dissociation [HP:0011709],0/22,0%,1/1,100%,, -Abnormal toe morphology [HP:0001780],0/0,0%,0/0,0%,, -Abnormal toe phalanx morphology [HP:0010161],0/0,0%,0/0,0%,, -Abnormality of the distal phalanges of the toes [HP:0010182],0/0,0%,0/0,0%,, -Patent foramen ovale [HP:0001655],4/40,10%,0/36,0%,, -Synostosis of joints [HP:0100240],1/1,100%,1/1,100%,, -Short 1st metacarpal [HP:0010034],0/30,0%,0/22,0%,, -Short phalanx of the thumb [HP:0009660],0/30,0%,0/22,0%,, -11 pairs of ribs [HP:0000878],1/1,100%,0/0,0%,, -Missing ribs [HP:0000921],1/1,100%,0/0,0%,, -Aplasia/Hypoplasia of the ribs [HP:0006712],1/1,100%,0/0,0%,, -Aplasia/Hypoplasia involving bones of the thorax [HP:0006711],1/1,100%,2/2,100%,, -Aplasia/hypoplasia affecting bones of the axial skeleton [HP:0009122],2/2,100%,2/2,100%,, -Abnormal rib morphology [HP:0000772],1/1,100%,0/0,0%,, -First degree atrioventricular block [HP:0011705],0/22,0%,1/1,100%,, +Bowed forearm bones [HP:0003956],0/0,0%,1/1,100%,, +Bowing of the arm [HP:0006488],0/0,0%,1/1,100%,, +Bowing of the long bones [HP:0006487],0/0,0%,1/1,100%,, +Abnormal diaphysis morphology [HP:0000940],0/0,0%,1/1,100%,, +Proximal placement of thumb [HP:0009623],0/0,0%,0/0,0%,, +Short middle phalanx of the 5th finger [HP:0004220],0/0,0%,0/0,0%,, +Type A brachydactyly [HP:0009370],0/0,0%,0/0,0%,, +Brachydactyly [HP:0001156],0/0,0%,0/0,0%,, +Short 5th finger [HP:0009237],0/0,0%,0/0,0%,, +Aplasia/Hypoplasia of the 5th finger [HP:0006262],0/0,0%,0/0,0%,, +Aplasia/Hypoplasia of the middle phalanx of the 5th finger [HP:0009161],0/0,0%,0/0,0%,, +Aplasia/Hypoplasia of the phalanges of the 5th finger [HP:0009376],0/0,0%,0/0,0%,, +Abnormal 5th finger phalanx morphology [HP:0004213],0/0,0%,0/0,0%,, +Abnormality of the middle phalanx of the 5th finger [HP:0004219],0/0,0%,0/0,0%,, +Short middle phalanx of finger [HP:0005819],0/0,0%,0/0,0%,, +Aplasia/Hypoplasia of the middle phalanges of the hand [HP:0009843],0/0,0%,0/0,0%,, +Abnormal middle phalanx morphology of the hand [HP:0009833],0/0,0%,0/0,0%,, +Short phalanx of finger [HP:0009803],0/0,0%,0/0,0%,, Micrognathia [HP:0000347],1/1,100%,1/1,100%,, Aplasia/Hypoplasia of the mandible [HP:0009118],1/1,100%,1/1,100%,, Aplasia/Hypoplasia involving bones of the skull [HP:0009116],1/1,100%,1/1,100%,, -Abnormal skull morphology [HP:0000929],1/1,100%,2/2,100%,, Abnormal mandible morphology [HP:0000277],1/1,100%,1/1,100%,, Abnormal jaw morphology [HP:0030791],1/1,100%,1/1,100%,, Abnormal facial skeleton morphology [HP:0011821],1/1,100%,1/1,100%,, -Bowed forearm bones [HP:0003956],0/0,0%,1/1,100%,, -Bowing of the arm [HP:0006488],0/0,0%,1/1,100%,, -Bowing of the long bones [HP:0006487],0/0,0%,1/1,100%,, -Abnormal diaphysis morphology [HP:0000940],0/0,0%,1/1,100%,, -Common atrium [HP:0011565],0/83,0%,0/38,0%,, -Unroofed coronary sinus [HP:0031297],0/85,0%,0/38,0%,, -Amelia involving the upper limbs [HP:0009812],0/83,0%,1/37,3%,, -Third degree atrioventricular block [HP:0001709],0/22,0%,1/1,100%,, -Small hypothenar eminence [HP:0010487],2/2,100%,0/0,0%,, -Abnormality of the hypothenar eminence [HP:0010486],2/2,100%,0/0,0%,, -Abnormality of the musculature of the hand [HP:0001421],2/2,100%,0/0,0%,, +Cleft soft palate [HP:0000185],2/2,100%,0/0,0%,, +Abnormal soft palate morphology [HP:0100736],2/2,100%,0/0,0%,, +Cleft palate [HP:0000175],2/2,100%,0/0,0%,, +Orofacial cleft [HP:0000202],2/2,100%,0/0,0%,, +Craniofacial cleft [HP:5201015],2/2,100%,0/0,0%,, Hypoplastic scapulae [HP:0000882],1/1,100%,1/1,100%,, Aplasia/Hypoplasia of the scapulae [HP:0006713],1/1,100%,1/1,100%,, Abnormal scapula morphology [HP:0000782],1/1,100%,1/1,100%,, +Amelia involving the upper limbs [HP:0009812],0/83,0%,1/37,3%,, +Third degree atrioventricular block [HP:0001709],0/22,0%,1/1,100%,, +Upper extremity joint dislocation [HP:0030310],0/0,0%,2/2,100%,, +Joint dislocation [HP:0001373],0/0,0%,2/2,100%,, +Abnormal toe morphology [HP:0001780],0/0,0%,0/0,0%,, +Abnormal toe phalanx morphology [HP:0010161],0/0,0%,0/0,0%,, +Abnormality of the distal phalanges of the toes [HP:0010182],0/0,0%,0/0,0%,, +Aplasia/Hypoplasia of the 2nd finger [HP:0006264],1/1,100%,0/0,0%,, +Abnormal 2nd finger morphology [HP:0004100],1/1,100%,0/0,0%,, +Synostosis of joints [HP:0100240],1/1,100%,1/1,100%,, Left ventricular noncompaction cardiomyopathy [HP:0011664],0/1,0%,1/5,20%,, Noncompaction cardiomyopathy [HP:0012817],0/0,0%,1/1,100%,, Cardiomyopathy [HP:0001638],0/0,0%,1/1,100%,, Abnormal myocardium morphology [HP:0001637],0/0,0%,1/1,100%,, -Sinus bradycardia [HP:0001688],0/0,0%,1/1,100%,, -Abnormal electrophysiology of sinoatrial node origin [HP:0011702],0/0,0%,1/1,100%,, -Arrhythmia [HP:0011675],1/1,100%,1/1,100%,, -Bradycardia [HP:0001662],0/0,0%,1/1,100%,, -Abnormal skin morphology [HP:0011121],0/0,0%,1/1,100%,, -Abnormality of the skin [HP:0000951],0/0,0%,1/1,100%,, -Abnormality of the integument [HP:0001574],0/0,0%,1/1,100%,, -Sinus venosus atrial septal defect [HP:0011567],0/2,0%,1/1,100%,, +Mitral valve prolapse [HP:0001634],0/0,0%,1/1,100%,, +Abnormal mitral valve morphology [HP:0001633],0/0,0%,1/1,100%,, +Abnormal atrioventricular valve morphology [HP:0006705],0/0,0%,1/1,100%,, +Abnormal heart valve morphology [HP:0001654],0/0,0%,1/1,100%,, +Short 1st metacarpal [HP:0010034],0/30,0%,0/22,0%,, +Short phalanx of the thumb [HP:0009660],0/30,0%,0/22,0%,, diff --git a/docs/report/tbx5_frameshift_vs_missense.mtc_report.html b/docs/report/tbx5_frameshift_vs_missense.mtc_report.html index 78345301..5b149af7 100644 --- a/docs/report/tbx5_frameshift_vs_missense.mtc_report.html +++ b/docs/report/tbx5_frameshift_vs_missense.mtc_report.html @@ -48,9 +48,9 @@

Phenotype testing report

Phenotype MTC filter: HPO MTC filter

Multiple testing correction: fdr_bh

-

Performed statistical tests for 16 out of the total of 259 HPO terms.

+

Performed statistical tests for 16 out of the total of 260 HPO terms.

- + @@ -62,7 +62,7 @@

Phenotype testing report

- + @@ -83,7 +83,7 @@

Phenotype testing report

- + @@ -111,7 +111,7 @@

Phenotype testing report

- + diff --git a/docs/tutorial.rst b/docs/tutorial.rst index a2bc3230..10ce6a23 100644 --- a/docs/tutorial.rst +++ b/docs/tutorial.rst @@ -50,11 +50,11 @@ Load HPO ^^^^^^^^ GPSEA needs HPO to do the analysis. -We use HPO toolkit to load HPO version `v2023-10-09`: +We use HPO toolkit to load HPO version `v2024-07-01`: >>> import hpotk ->>> ontology_store = hpotk.configure_ontology_store() ->>> hpo = ontology_store.load_minimal_hpo(release='v2023-10-09') +>>> store = hpotk.configure_ontology_store() +>>> hpo = store.load_minimal_hpo(release='v2024-07-01') .. tip:: @@ -249,12 +249,12 @@ Now we can perform the analysis and investigate the results. 16 We only tested 16 HPO terms. This is despite the individuals being collectively annotated with -259 direct and indirect HPO terms +260 direct and indirect HPO terms >>> len(result.phenotypes) -259 +260 -We can show the reasoning behind *not* testing 243 (`259 - 16`) HPO terms +We can show the reasoning behind *not* testing 244 (`260 - 16`) HPO terms by exploring the phenotype MTC filtering report. >>> from gpsea.view import MtcStatsViewer @@ -266,11 +266,6 @@ by exploring the phenotype MTC filtering report. .. raw:: html :file: report/tbx5_frameshift_vs_missense.mtc_report.html -.. - - TODO: - Show how to write out the tested HPO terms. - and these are the HPO terms ordered by the p value corrected with the Benjamini-Hochberg procedure: >>> from gpsea.analysis.predicate import PatientCategories diff --git a/docs/user-guide/input-data.rst b/docs/user-guide/input-data.rst index 646df52a..f0413295 100644 --- a/docs/user-guide/input-data.rst +++ b/docs/user-guide/input-data.rst @@ -32,7 +32,7 @@ the standard `gpsea` installation: >>> import hpotk >>> store = hpotk.configure_ontology_store() ->>> hpo = store.load_minimal_hpo(release='v2024-03-06') +>>> hpo = store.load_minimal_hpo(release='v2024-07-01') Next, let's prepare a :class:`~gpsea.preprocessing.CohortCreator` that will turn a collection of phenopacket into a :class:`~gpsea.model.Cohort`, required in the downstream steps. diff --git a/docs/user-guide/mtc.rst b/docs/user-guide/mtc.rst index 7aa3e23e..f8958b8a 100644 --- a/docs/user-guide/mtc.rst +++ b/docs/user-guide/mtc.rst @@ -4,8 +4,9 @@ Multiple-testing correction =========================== +********** Background -~~~~~~~~~~ +********** A p-value is the probability that a test result, under the null hypothesis, assumes the observed or a more extreme value. It is important to realize that if we @@ -25,33 +26,28 @@ tests, the probability that none will be significant is least one significant result. +*********************** Implementation in GPSEA -~~~~~~~~~~~~~~~~~~~~~~~ +*********************** By default, GPSEA performs a hypothesis test for each HPO term found at least twice in the cohort, meaning that we may perform up to hundreds of tests. Therefore, unless we take into account the fact that multiple statistical tests are being performed, it is likely that we will obtain one or more false-positive results. -Genephenocorr offers two approaches to mitigate this problem: multiple-testing correction (MTC) procedures +GPSEA offers two approaches to mitigate this problem: multiple-testing correction (MTC) procedures and MTC filters to choose the terms to be tested. -Here we will show how to configure the MTC approach -using :class:`~gpsea.analysis.CohortAnalysisConfiguration` class. - ->>> from gpsea.analysis import CohortAnalysisConfiguration ->>> config = CohortAnalysisConfiguration() - Multiple-testing correction procedures -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +====================================== A number of MTC procedures have been developed to limit the probability of false-positive results. The MTC procedures differ in complexity, in their assumptions about the data, and in the type of control they provide. -The genephenocorr package uses the Python package `statsmodels `_ to implement +The GPSEA package uses the Python package `statsmodels `_ to implement MTC. See the `documentation `_ for details; the following table shows allowable options. @@ -83,31 +79,35 @@ the following table shows allowable options. The oldest and simplest MTC procedure is the Bonferroni -correction. The Bonferroni procedure thus provides control of the family-wise +correction (``bonferroni``). The Bonferroni procedure thus provides control of the family-wise error rate (FWER), which is the probability of at least one Type I error. The Bonferroni method multiplies the p-value returned by each test (which is call the *nominal* p-value) -by the number of tests performed (the result is capped at 1.0). This is the *default* method in genephenocorr. - ->>> config.pval_correction -'bonferroni' +by the number of tests performed (the result is capped at 1.0). Alternatively, procedures that control the false-discovery rate (FDR), limit the proportion of significant results that are type I errors (false discoveries). The Benjamini and Hochberg method (``fdr_bh``) is probably the most commonly used one. - -This is how we can set an alternative MTC correction procedure: - ->>> config.pval_correction = 'fdr_bh' ->>> config.pval_correction -'fdr_bh' +This is the *default* method in GPSEA. + +To set an alternative MTC procedure, we use the `mtc_correction` option +when creating an instance of :class:`~gpsea.analysis.pcats.HpoTermAnalysis`: + +>>> from gpsea.analysis.mtc_filter import UseAllTermsMtcFilter +>>> from gpsea.analysis.pcats import HpoTermAnalysis +>>> from gpsea.analysis.pcats.stats import ScipyFisherExact +>>> analysis = HpoTermAnalysis( +... count_statistic=ScipyFisherExact(), +... mtc_filter=UseAllTermsMtcFilter(), +... mtc_correction='bonferroni', # <--- The MTC correction setup +... ) .. _mtc-filters: MTC filters: Choosing which terms to test -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +========================================= We can reduce the overall MTC burden by choosing which terms to test. For example, if we choose to test only ten terms out of 450, @@ -116,8 +116,19 @@ is only 10 instead of 450, and more p-values may "survive" the multiple-testing correction. In the context of GPSEA, we represent the concept of phenotype filtering -by :class:`~gpsea.analysis.PhenotypeMtcFilter`. -We describe the three filtering strategies in the next sections. +by :class:`~gpsea.analysis.mtc_filter.PhenotypeMtcFilter`. +The filter must be chosen before the :class:`~gpsea.analysis.pcats.MultiPhenotypeAnalysis`, +such as :class:`~gpsea.analysis.pcats.HpoTermAnalysis`, is run: + +>>> from gpsea.analysis.pcats import HpoTermAnalysis +>>> analysis = HpoTermAnalysis() # doctest: +ELLIPSIS +Traceback (most recent call last): + ... +TypeError: HpoTermAnalysis.__init__() missing 2 required positional arguments: 'count_statistic' and 'mtc_filter' + +Note the missing `mtc_filter` option. + +We describe the three filtering strategies in the following sections. .. _use-all-terms-strategy: @@ -129,13 +140,10 @@ The first MTC filtering strategy is the simplest - do not apply any filtering at This will result in testing all terms. We do not recommend this strategy, but it can be useful to disable MTC filtering. -The strategy is invoked by default, -or explicitly by :func:`~gpsea.analysis.CohortAnalysisConfiguration.all_terms_strategy` method: - ->>> config.all_terms_strategy() ->>> config.mtc_strategy - +The strategy is implemented in :class:`~gpsea.analysis.mtc_filter.UseAllTermsMtcFilter`. +>>> from gpsea.analysis.mtc_filter import UseAllTermsMtcFilter +>>> use_all = UseAllTermsMtcFilter() .. _specify-terms-strategy: @@ -143,27 +151,20 @@ Specify terms strategy ---------------------- In presence of a specific hypothesis as to which terms may be different between groups, -then you can specify these terms using -the :func:`~gpsea.analysis.CohortAnalysisConfiguration.specify_terms_strategy` method. +then you can specify these terms in :class:`~gpsea.analysis.mtc_filter.SpecifiedTermsMtcFilter`. For example if we want to specifically test `Abnormal putamen morphology (HP:0031982) `_ and `Abnormal caudate nucleus morphology (HP:0002339) `_ we pass an iterable (e.g. a tuple) with these two terms as an argument: ->>> config.specify_terms_strategy( +>>> from gpsea.analysis.mtc_filter import SpecifiedTermsMtcFilter +>>> specified_terms = SpecifiedTermsMtcFilter( ... terms_to_test=( ... "HP:0031982", # Abnormal putamen morphology ... "HP:0002339", # Abnormal caudate nucleus morphology ... ) ... ) ->>> config.mtc_strategy - ->>> config.terms_to_test -('HP:0031982', 'HP:0002339') - -Later, when the `config` is used in analysis, -GPSEA will only perform two hypothesis tests, one for each of the two terms. .. _hpo-mtc-filter-strategy: @@ -172,26 +173,32 @@ HPO MTC filter strategy ----------------------- Last, the HPO MTC strategy involves making several domain judgments to take advantage of the HPO structure. +The strategy needs access to HPO: -The strategy is chosen by invoking -:func:`~gpsea.analysis.CohortAnalysisConfiguration.hpo_mtc_strategy` method: +>>> import hpotk +>>> store = hpotk.configure_ontology_store() +>>> hpo = store.load_minimal_hpo(release='v2024-07-01') + +and it is implemented in the :class:`~gpsea.analysis.mtc_filter.HpoMtcFilter` class: + +>>> from gpsea.analysis.mtc_filter import HpoMtcFilter +>>> hpo_mtc = HpoMtcFilter.default_filter( +... hpo=hpo, +... term_frequency_threshold=0.2, +... ) ->>> config = CohortAnalysisConfiguration() ->>> config.hpo_mtc_strategy(min_patients_w_hpo=0.5) ->>> config.mtc_strategy - ->>> config.min_patients_w_hpo -0.5 -HPO MTC takes a threshold as an argument (e.g. 50% in the example above) +We use static constructor :func:`~gpsea.analysis.mtc_filter.HpoMtcFilter.default_filter` +for creating :class:`~gpsea.analysis.mtc_filter.HpoMtcFilter`. +The constructor takes a threshold as an argument (e.g. 20% in the example above) and the method's logic is made up of 8 individual heuristics designed to skip testing the HPO terms that are unlikely to yield significant or interesting results: #. Skip terms that occur very rarely - The ``min_patients_w_hpo`` determines the mininum proportion of individuals + The ``term_frequency_threshold`` determines the mininum proportion of individuals with direct or indirect annotation by the HPO term to test. We check each of the genotype groups (e.g., MISSENSE vs. not-MISSENSE), and we only retain a term for testing - if the proportion of individuals in at least one genotype group is at least ``min_patients_w_hpo``. + if the proportion of individuals in at least one genotype group is greater than or equal to ``term_frequency_threshold``. This is because of our assumption that even if there is statistical significance, if a term is only seen in (for example) 7% of individuals in the MISSENSE group and 2% in the not-MISSENSE group, diff --git a/docs/user-guide/predicates.rst b/docs/user-guide/predicates.rst index e3393847..00647f1f 100644 --- a/docs/user-guide/predicates.rst +++ b/docs/user-guide/predicates.rst @@ -27,7 +27,7 @@ Briefly, we first load HPO: >>> import hpotk >>> store = hpotk.configure_ontology_store() ->>> hpo = store.load_minimal_hpo(release='v2024-03-06') +>>> hpo = store.load_minimal_hpo(release='v2024-07-01') then, we configure the cohort creator: diff --git a/docs/user-guide/report/tbx5_frameshift.csv b/docs/user-guide/report/tbx5_frameshift.csv index d794cec4..6abe64be 100644 --- a/docs/user-guide/report/tbx5_frameshift.csv +++ b/docs/user-guide/report/tbx5_frameshift.csv @@ -13,42 +13,148 @@ Muscular ventricular septal defect [HP:0011623],6/25,24%,8/84,10%,0.144002047919 Pulmonary arterial hypertension [HP:0002092],0/2,0%,8/14,57%,0.6899307928951144,0.4666666666666667 Short thumb [HP:0009778],8/30,27%,25/69,36%,0.6899307928951144,0.4870099714553749 Absent radius [HP:0003974],6/25,24%,9/43,21%,1.0,0.7703831604944444 -Atrial septal defect [HP:0001631],20/20,100%,63/65,97%,1.0,1.0 Hypoplasia of the radius [HP:0002984],6/14,43%,34/75,45%,1.0,1.0 -Short humerus [HP:0005792],4/9,44%,8/21,38%,1.0,1.0 +Atrial septal defect [HP:0001631],20/20,100%,63/65,97%,1.0,1.0 Hypoplasia of the ulna [HP:0003022],2/10,20%,3/17,18%,1.0,1.0 -Aplasia/Hypoplasia of the thumb [HP:0009601],19/19,100%,40/40,100%,, -Aplasia/Hypoplasia of fingers [HP:0006265],19/19,100%,44/44,100%,, -Aplasia/hypoplasia involving bones of the hand [HP:0005927],19/19,100%,44/44,100%,, -Aplasia/hypoplasia involving bones of the upper limbs [HP:0006496],22/22,100%,78/78,100%,, -Aplasia/hypoplasia involving bones of the extremities [HP:0045060],22/22,100%,78/78,100%,, -Aplasia/hypoplasia of the extremities [HP:0009815],22/22,100%,78/78,100%,, -Abnormality of limbs [HP:0040064],34/34,100%,102/102,100%,, +Short humerus [HP:0005792],4/9,44%,8/21,38%,1.0,1.0 +Abnormal ventricular septum morphology [HP:0010438],19/19,100%,42/42,100%,, +Abnormal cardiac ventricle morphology [HP:0001713],19/19,100%,43/43,100%,, +Abnormal heart morphology [HP:0001627],30/30,100%,89/89,100%,, +Abnormal cardiovascular system morphology [HP:0030680],30/30,100%,92/92,100%,, +Abnormality of the cardiovascular system [HP:0001626],32/32,100%,94/94,100%,, Phenotypic abnormality [HP:0000118],38/38,100%,114/114,100%,, All [HP:0000001],38/38,100%,114/114,100%,, -Aplasia/hypoplasia involving the skeleton [HP:0009115],23/23,100%,80/80,100%,, -Abnormal skeletal morphology [HP:0011842],35/35,100%,103/103,100%,, -Abnormality of the skeletal system [HP:0000924],35/35,100%,103/103,100%,, -Abnormality of the musculoskeletal system [HP:0033127],35/35,100%,104/104,100%,, +Abnormal cardiac septum morphology [HP:0001671],28/28,100%,89/89,100%,, +Forearm undergrowth [HP:0009821],7/7,100%,35/35,100%,, +Abnormal upper limb bone morphology [HP:0040070],14/14,100%,50/50,100%,, +Abnormality of the upper limb [HP:0002817],34/34,100%,102/102,100%,, +Abnormality of limbs [HP:0040064],34/34,100%,102/102,100%,, Abnormal limb bone morphology [HP:0002813],34/34,100%,92/92,100%,, Abnormality of limb bone [HP:0040068],34/34,100%,92/92,100%,, +Abnormality of the skeletal system [HP:0000924],35/35,100%,103/103,100%,, +Abnormality of the musculoskeletal system [HP:0033127],35/35,100%,104/104,100%,, Abnormal appendicular skeleton morphology [HP:0011844],34/34,100%,93/93,100%,, -Abnormality of the upper limb [HP:0002817],34/34,100%,102/102,100%,, +Abnormal skeletal morphology [HP:0011842],35/35,100%,103/103,100%,, +Upper limb undergrowth [HP:0009824],7/7,100%,38/38,100%,, +Limb undergrowth [HP:0009826],7/7,100%,38/38,100%,, +Aplasia/hypoplasia of the extremities [HP:0009815],22/22,100%,78/78,100%,, +Aplasia/hypoplasia involving the skeleton [HP:0009115],23/23,100%,80/80,100%,, +Aplasia/hypoplasia involving bones of the upper limbs [HP:0006496],22/22,100%,78/78,100%,, +Aplasia/hypoplasia involving bones of the extremities [HP:0045060],22/22,100%,78/78,100%,, +Aplasia/hypoplasia involving forearm bones [HP:0006503],12/12,100%,43/43,100%,, +Abnormal forearm bone morphology [HP:0040072],14/14,100%,43/43,100%,, +Abnormal forearm morphology [HP:0002973],14/14,100%,43/43,100%,, +Short long bone [HP:0003026],9/9,100%,41/41,100%,, +Abnormal long bone morphology [HP:0011314],13/13,100%,50/50,100%,, +Aplasia/Hypoplasia of the radius [HP:0006501],11/11,100%,43/43,100%,, +Abnormal morphology of the radius [HP:0002818],13/13,100%,43/43,100%,, +1-2 finger syndactyly [HP:0010704],1/1,100%,4/4,100%,, +Finger syndactyly [HP:0006101],2/2,100%,5/5,100%,, +Syndactyly [HP:0001159],2/2,100%,5/5,100%,, +Abnormal digit morphology [HP:0011297],33/33,100%,67/67,100%,, +Abnormal atrial septum morphology [HP:0011994],20/20,100%,64/64,100%,, +Abnormal cardiac atrium morphology [HP:0005120],20/20,100%,64/64,100%,, +Perimembranous ventricular septal defect [HP:0011682],3/25,12%,6/84,7%,, +Aplasia/Hypoplasia of the thumb [HP:0009601],19/19,100%,40/40,100%,, +Aplasia/Hypoplasia of fingers [HP:0006265],19/19,100%,44/44,100%,, +Aplasia/hypoplasia involving bones of the hand [HP:0005927],19/19,100%,44/44,100%,, Abnormal hand morphology [HP:0005922],20/20,100%,75/75,100%,, Abnormality of the hand [HP:0001155],31/31,100%,88/88,100%,, Abnormal finger morphology [HP:0001167],31/31,100%,64/64,100%,, -Abnormal digit morphology [HP:0011297],33/33,100%,67/67,100%,, Abnormal thumb morphology [HP:0001172],31/31,100%,58/58,100%,, +Finger aplasia [HP:0009380],14/14,100%,23/23,100%,, +Aplasia involving forearm bones [HP:0009822],6/6,100%,9/9,100%,, +Absent forearm bone [HP:0003953],6/6,100%,9/9,100%,, +Aplasia/Hypoplasia of the ulna [HP:0006495],2/2,100%,4/4,100%,, +Abnormal morphology of ulna [HP:0040071],4/4,100%,4/4,100%,, +Abnormal carpal morphology [HP:0001191],0/0,0%,30/32,94%,, +Abnormality of the wrist [HP:0003019],0/0,0%,30/30,100%,, +Abnormality of upper limb joint [HP:0009810],6/6,100%,32/32,100%,, +Abnormal joint morphology [HP:0001367],6/6,100%,33/33,100%,, +Abnormality of cardiovascular system electrophysiology [HP:0030956],3/3,100%,18/18,100%,, +Abnormal cardiovascular system physiology [HP:0011025],5/5,100%,30/30,100%,, +Abnormality of thumb phalanx [HP:0009602],13/13,100%,26/26,100%,, +Upper limb phocomelia [HP:0009813],2/37,5%,8/116,7%,, +Phocomelia [HP:0009829],2/2,100%,8/8,100%,, Short finger [HP:0009381],8/8,100%,27/27,100%,, Short digit [HP:0011927],10/10,100%,28/28,100%,, -Abnormality of thumb phalanx [HP:0009602],13/13,100%,26/26,100%,, +Pre-capillary pulmonary hypertension [HP:0033578],0/0,0%,8/8,100%,, +Elevated pulmonary artery pressure [HP:0004890],0/0,0%,8/8,100%,, +Abnormality of pulmonary circulation [HP:0030875],0/0,0%,8/8,100%,, +Abnormal vascular physiology [HP:0030163],0/0,0%,8/8,100%,, +Abnormality of the vasculature [HP:0002597],2/2,100%,17/17,100%,, +Abnormal respiratory system physiology [HP:0002795],0/0,0%,8/8,100%,, +Abnormality of the respiratory system [HP:0002086],0/0,0%,8/8,100%,, Congenital malformation of the great arteries [HP:0011603],2/2,100%,7/7,100%,, Abnormal morphology of the great vessels [HP:0030962],2/2,100%,10/10,100%,, Abnormal blood vessel morphology [HP:0033353],2/2,100%,11/11,100%,, Abnormal vascular morphology [HP:0025015],2/2,100%,11/11,100%,, -Abnormal cardiovascular system morphology [HP:0030680],30/30,100%,92/92,100%,, -Abnormality of the cardiovascular system [HP:0001626],32/32,100%,94/94,100%,, -Abnormality of the vasculature [HP:0002597],2/2,100%,17/17,100%,, +Patent foramen ovale [HP:0001655],0/36,0%,4/69,6%,, +Synostosis of joints [HP:0100240],1/1,100%,1/1,100%,, +Abnormality of joint mobility [HP:0011729],5/5,100%,3/3,100%,, +Abnormal joint physiology [HP:0034430],5/5,100%,3/3,100%,, +Abnormal musculoskeletal physiology [HP:0011843],5/5,100%,3/3,100%,, +Abnormality of the vertebral column [HP:0000925],1/1,100%,4/4,100%,, +Abnormal axial skeleton morphology [HP:0009121],5/5,100%,9/9,100%,, +Complete atrioventricular canal defect [HP:0001674],3/36,8%,6/67,9%,, +Atrioventricular canal defect [HP:0006695],3/3,100%,6/6,100%,, +Hypoplasia of deltoid muscle [HP:0030241],0/0,0%,6/6,100%,, +Shoulder muscle hypoplasia [HP:0008952],0/0,0%,6/6,100%,, +Hypoplasia of the musculature [HP:0009004],0/0,0%,6/6,100%,, +Aplasia/Hypoplasia involving the skeletal musculature [HP:0001460],0/0,0%,6/6,100%,, +Abnormality of muscle size [HP:0030236],0/0,0%,6/6,100%,, +Abnormal skeletal muscle morphology [HP:0011805],0/0,0%,8/8,100%,, +Abnormality of the musculature [HP:0003011],0/0,0%,8/8,100%,, +Aplasia/Hypoplasia involving the shoulder musculature [HP:0001464],0/0,0%,6/6,100%,, +Aplasia/Hypoplasia involving the musculature of the upper limbs [HP:0001467],0/0,0%,6/6,100%,, +Aplasia/Hypoplasia involving the musculature of the extremities [HP:0009128],0/0,0%,6/6,100%,, +Abnormality of the musculature of the limbs [HP:0009127],0/0,0%,8/8,100%,, +Abnormality of the musculature of the upper limbs [HP:0001446],0/0,0%,8/8,100%,, +Abnormality of the shoulder girdle musculature [HP:0001435],0/0,0%,6/6,100%,, +Bowed forearm bones [HP:0003956],1/1,100%,0/0,0%,, +Bowing of the arm [HP:0006488],1/1,100%,0/0,0%,, +Bowing of the long bones [HP:0006487],1/1,100%,0/0,0%,, +Abnormal diaphysis morphology [HP:0000940],1/1,100%,0/0,0%,, +Micrognathia [HP:0000347],1/1,100%,1/1,100%,, +Aplasia/Hypoplasia of the mandible [HP:0009118],1/1,100%,1/1,100%,, +Aplasia/Hypoplasia involving bones of the skull [HP:0009116],1/1,100%,1/1,100%,, +Aplasia/hypoplasia affecting bones of the axial skeleton [HP:0009122],2/2,100%,3/3,100%,, +Abnormal skull morphology [HP:0000929],2/2,100%,1/1,100%,, +Abnormality of the head [HP:0000234],2/2,100%,5/5,100%,, +Abnormality of head or neck [HP:0000152],2/2,100%,5/5,100%,, +Abnormal mandible morphology [HP:0000277],1/1,100%,1/1,100%,, +Abnormal jaw morphology [HP:0030791],1/1,100%,1/1,100%,, +Abnormal facial skeleton morphology [HP:0011821],1/1,100%,1/1,100%,, +Abnormal oral cavity morphology [HP:0000163],1/1,100%,5/5,100%,, +Abnormal oral morphology [HP:0031816],1/1,100%,5/5,100%,, +Abnormality of the mouth [HP:0000153],1/1,100%,5/5,100%,, +Abnormality of the face [HP:0000271],1/1,100%,5/5,100%,, +Aplasia/Hypoplasia involving bones of the thorax [HP:0006711],2/2,100%,2/2,100%,, +Abnormal thorax morphology [HP:0000765],5/5,100%,7/7,100%,, +Persistent left superior vena cava [HP:0005301],0/0,0%,4/39,10%,, +Abnormal superior vena cava morphology [HP:0025575],0/0,0%,4/4,100%,, +Abnormal vena cava morphology [HP:0005345],0/0,0%,4/4,100%,, +Abnormal venous morphology [HP:0002624],0/0,0%,4/4,100%,, +Aplasia/hypoplasia of the humerus [HP:0006507],4/4,100%,8/8,100%,, +Abnormality of the humerus [HP:0003063],4/4,100%,8/8,100%,, +Abnormality of the upper arm [HP:0001454],4/4,100%,8/8,100%,, +Right atrial enlargement [HP:0030718],0/0,0%,4/4,100%,, +Abnormal right atrium morphology [HP:0025580],0/0,0%,4/4,100%,, +Atrial septal dilatation [HP:0011995],0/0,0%,4/4,100%,, +Left ventricular noncompaction cardiomyopathy [HP:0011664],1/5,20%,1/7,14%,, +Noncompaction cardiomyopathy [HP:0012817],1/1,100%,1/1,100%,, +Cardiomyopathy [HP:0001638],1/1,100%,1/1,100%,, +Abnormal myocardium morphology [HP:0001637],1/1,100%,1/1,100%,, +Hypoplastic scapulae [HP:0000882],1/1,100%,1/1,100%,, +Aplasia/Hypoplasia of the scapulae [HP:0006713],1/1,100%,1/1,100%,, +Abnormal scapula morphology [HP:0000782],1/1,100%,1/1,100%,, +Abnormal toe morphology [HP:0001780],0/0,0%,1/1,100%,, +Abnormal foot morphology [HP:0001760],0/0,0%,4/4,100%,, +Abnormality of the lower limb [HP:0002814],0/0,0%,4/4,100%,, +Abnormal toe phalanx morphology [HP:0010161],0/0,0%,1/1,100%,, +Abnormality of the distal phalanges of the toes [HP:0010182],0/0,0%,1/1,100%,, +Abnormal lower limb bone morphology [HP:0040069],0/0,0%,4/4,100%,, +Aplasia involving bones of the extremities [HP:0009825],0/0,0%,3/3,100%,, Aplasia of the 1st metacarpal [HP:0010035],0/0,0%,3/3,100%,, Aplasia of the proximal phalanges of the hand [HP:0010242],0/0,0%,3/3,100%,, Aplasia/Hypoplasia of the proximal phalanges of the hand [HP:0009851],0/0,0%,3/3,100%,, @@ -57,137 +163,44 @@ Abnormal finger phalanx morphology [HP:0005918],0/0,0%,9/9,100%,, Aplasia/Hypoplasia of the phalanges of the hand [HP:0009767],0/0,0%,6/6,100%,, Aplasia of the phalanges of the hand [HP:0009802],0/0,0%,3/3,100%,, Aplasia involving bones of the upper limbs [HP:0009823],0/0,0%,3/3,100%,, -Aplasia involving bones of the extremities [HP:0009825],0/0,0%,3/3,100%,, -Finger aplasia [HP:0009380],14/14,100%,23/23,100%,, Aplasia of metacarpal bones [HP:0010048],0/0,0%,3/3,100%,, Aplasia/Hypoplasia involving the metacarpal bones [HP:0005914],0/0,0%,4/4,100%,, Abnormal metacarpal morphology [HP:0005916],0/0,0%,4/4,100%,, -Abnormal upper limb bone morphology [HP:0040070],14/14,100%,50/50,100%,, Aplasia/Hypoplasia of the 1st metacarpal [HP:0010026],0/0,0%,4/4,100%,, Abnormal 1st metacarpal morphology [HP:0010009],0/0,0%,4/4,100%,, Aplasia/Hypoplasia of the phalanges of the thumb [HP:0009658],0/0,0%,4/4,100%,, Partial absence of thumb [HP:0009659],0/0,0%,3/3,100%,, -Abnormal atrial septum morphology [HP:0011994],20/20,100%,64/64,100%,, -Abnormal cardiac septum morphology [HP:0001671],28/28,100%,89/89,100%,, -Abnormal heart morphology [HP:0001627],30/30,100%,89/89,100%,, -Abnormal cardiac atrium morphology [HP:0005120],20/20,100%,64/64,100%,, -Perimembranous ventricular septal defect [HP:0011682],3/25,12%,6/84,7%,, -Abnormal ventricular septum morphology [HP:0010438],19/19,100%,42/42,100%,, -Abnormal cardiac ventricle morphology [HP:0001713],19/19,100%,43/43,100%,, -Forearm undergrowth [HP:0009821],7/7,100%,35/35,100%,, -Upper limb undergrowth [HP:0009824],7/7,100%,38/38,100%,, -Limb undergrowth [HP:0009826],7/7,100%,38/38,100%,, -Aplasia/hypoplasia involving forearm bones [HP:0006503],12/12,100%,43/43,100%,, -Abnormal forearm bone morphology [HP:0040072],14/14,100%,43/43,100%,, -Abnormal forearm morphology [HP:0002973],14/14,100%,43/43,100%,, -Short long bone [HP:0003026],9/9,100%,41/41,100%,, -Abnormal long bone morphology [HP:0011314],13/13,100%,50/50,100%,, -Aplasia/Hypoplasia of the radius [HP:0006501],11/11,100%,43/43,100%,, -Abnormal morphology of the radius [HP:0002818],13/13,100%,43/43,100%,, -Abnormal carpal morphology [HP:0001191],0/0,0%,30/32,94%,, -Abnormality of the wrist [HP:0003019],0/0,0%,30/30,100%,, -Abnormality of upper limb joint [HP:0009810],6/6,100%,32/32,100%,, -Abnormal joint morphology [HP:0001367],6/6,100%,33/33,100%,, -Persistent left superior vena cava [HP:0005301],0/0,0%,4/39,10%,, -Abnormal superior vena cava morphology [HP:0025575],0/0,0%,4/4,100%,, -Abnormal vena cava morphology [HP:0005345],0/0,0%,4/4,100%,, -Abnormal venous morphology [HP:0002624],0/0,0%,4/4,100%,, -Tricuspid regurgitation [HP:0005180],0/0,0%,5/5,100%,, +Limited pronation/supination of forearm [HP:0006394],3/3,100%,2/2,100%,, +Limited elbow movement [HP:0002996],4/4,100%,2/2,100%,, +Abnormality of the elbow [HP:0009811],5/5,100%,2/2,100%,, +Limitation of joint mobility [HP:0001376],4/4,100%,2/2,100%,, +Mitral regurgitation [HP:0001653],2/2,100%,2/2,100%,, Atrioventricular valve regurgitation [HP:0034376],2/2,100%,7/7,100%,, Abnormal atrioventricular valve physiology [HP:0031650],2/2,100%,7/7,100%,, Abnormal heart valve physiology [HP:0031653],2/2,100%,7/7,100%,, -Abnormal cardiovascular system physiology [HP:0011025],5/5,100%,30/30,100%,, +Abnormal mitral valve physiology [HP:0031481],2/2,100%,2/2,100%,, +Tricuspid regurgitation [HP:0005180],0/0,0%,5/5,100%,, Abnormal tricuspid valve physiology [HP:0031651],0/0,0%,5/5,100%,, -Micrognathia [HP:0000347],1/1,100%,1/1,100%,, -Aplasia/Hypoplasia of the mandible [HP:0009118],1/1,100%,1/1,100%,, -Aplasia/Hypoplasia involving bones of the skull [HP:0009116],1/1,100%,1/1,100%,, -Aplasia/hypoplasia affecting bones of the axial skeleton [HP:0009122],2/2,100%,3/3,100%,, -Abnormal axial skeleton morphology [HP:0009121],5/5,100%,9/9,100%,, -Abnormal skull morphology [HP:0000929],2/2,100%,1/1,100%,, -Abnormality of the head [HP:0000234],2/2,100%,5/5,100%,, -Abnormality of head or neck [HP:0000152],2/2,100%,5/5,100%,, -Abnormal mandible morphology [HP:0000277],1/1,100%,1/1,100%,, -Abnormal jaw morphology [HP:0030791],1/1,100%,1/1,100%,, -Abnormal facial skeleton morphology [HP:0011821],1/1,100%,1/1,100%,, Cleft soft palate [HP:0000185],0/0,0%,2/2,100%,, Abnormal soft palate morphology [HP:0100736],0/0,0%,2/2,100%,, Abnormal palate morphology [HP:0000174],0/0,0%,5/5,100%,, -Abnormal oral cavity morphology [HP:0000163],1/1,100%,5/5,100%,, -Abnormal oral morphology [HP:0031816],1/1,100%,5/5,100%,, -Abnormality of the mouth [HP:0000153],1/1,100%,5/5,100%,, -Abnormality of the face [HP:0000271],1/1,100%,5/5,100%,, Cleft palate [HP:0000175],0/0,0%,2/2,100%,, Orofacial cleft [HP:0000202],0/0,0%,2/2,100%,, Craniofacial cleft [HP:5201015],0/0,0%,2/2,100%,, -Aplasia involving forearm bones [HP:0009822],6/6,100%,9/9,100%,, -Absent forearm bone [HP:0003953],6/6,100%,9/9,100%,, -Aplasia/hypoplasia of the humerus [HP:0006507],4/4,100%,8/8,100%,, -Abnormality of the humerus [HP:0003063],4/4,100%,8/8,100%,, -Abnormality of the upper arm [HP:0001454],4/4,100%,8/8,100%,, -1-2 finger syndactyly [HP:0010704],1/1,100%,4/4,100%,, -Finger syndactyly [HP:0006101],2/2,100%,5/5,100%,, -Syndactyly [HP:0001159],2/2,100%,5/5,100%,, -Complete atrioventricular canal defect [HP:0001674],3/36,8%,6/67,9%,, -Atrioventricular canal defect [HP:0006695],3/3,100%,6/6,100%,, -Upper limb phocomelia [HP:0009813],2/37,5%,8/116,7%,, -Phocomelia [HP:0009829],2/2,100%,8/8,100%,, -Abnormality of cardiovascular system electrophysiology [HP:0030956],3/3,100%,18/18,100%,, -Atrioventricular dissociation [HP:0011709],1/1,100%,0/22,0%,, -Abnormal morphology of ulna [HP:0040071],4/4,100%,4/4,100%,, -Aplasia/Hypoplasia of the ulna [HP:0006495],2/2,100%,4/4,100%,, -Mitral valve prolapse [HP:0001634],1/1,100%,1/1,100%,, -Abnormal mitral valve morphology [HP:0001633],1/1,100%,1/1,100%,, -Abnormal atrioventricular valve morphology [HP:0006705],1/1,100%,1/1,100%,, -Abnormal heart valve morphology [HP:0001654],1/1,100%,1/1,100%,, -Proximal placement of thumb [HP:0009623],0/0,0%,3/3,100%,, -Deviation of the thumb [HP:0009603],2/2,100%,3/3,100%,, -Deviation of finger [HP:0004097],2/2,100%,4/4,100%,, -Deviation of the hand or of fingers of the hand [HP:0009484],2/2,100%,5/5,100%,, -Short 1st metacarpal [HP:0010034],0/22,0%,1/45,2%,, -Short phalanx of the thumb [HP:0009660],0/22,0%,1/45,2%,, Sinus bradycardia [HP:0001688],1/1,100%,2/2,100%,, Abnormal electrophysiology of sinoatrial node origin [HP:0011702],1/1,100%,2/2,100%,, Arrhythmia [HP:0011675],1/1,100%,3/3,100%,, Bradycardia [HP:0001662],1/1,100%,2/2,100%,, -Hypoplasia of deltoid muscle [HP:0030241],0/0,0%,6/6,100%,, -Shoulder muscle hypoplasia [HP:0008952],0/0,0%,6/6,100%,, -Hypoplasia of the musculature [HP:0009004],0/0,0%,6/6,100%,, -Aplasia/Hypoplasia involving the skeletal musculature [HP:0001460],0/0,0%,6/6,100%,, -Abnormality of muscle size [HP:0030236],0/0,0%,6/6,100%,, -Abnormal skeletal muscle morphology [HP:0011805],0/0,0%,8/8,100%,, -Abnormality of the musculature [HP:0003011],0/0,0%,8/8,100%,, -Aplasia/Hypoplasia involving the shoulder musculature [HP:0001464],0/0,0%,6/6,100%,, -Aplasia/Hypoplasia involving the musculature of the upper limbs [HP:0001467],0/0,0%,6/6,100%,, -Aplasia/Hypoplasia involving the musculature of the extremities [HP:0009128],0/0,0%,6/6,100%,, -Abnormality of the musculature of the limbs [HP:0009127],0/0,0%,8/8,100%,, -Abnormality of the musculature of the upper limbs [HP:0001446],0/0,0%,8/8,100%,, -Abnormality of the shoulder girdle musculature [HP:0001435],0/0,0%,6/6,100%,, -Patent foramen ovale [HP:0001655],0/36,0%,4/69,6%,, -Synostosis of joints [HP:0100240],1/1,100%,1/1,100%,, -Abnormality of joint mobility [HP:0011729],5/5,100%,3/3,100%,, -Abnormal joint physiology [HP:0034430],5/5,100%,3/3,100%,, -Abnormal musculoskeletal physiology [HP:0011843],5/5,100%,3/3,100%,, -Abnormality of the vertebral column [HP:0000925],1/1,100%,4/4,100%,, -Upper extremity joint dislocation [HP:0030310],2/2,100%,0/0,0%,, -Joint dislocation [HP:0001373],2/2,100%,0/0,0%,, -Abnormal shoulder morphology [HP:0003043],1/1,100%,1/1,100%,, -Abnormal thorax morphology [HP:0000765],5/5,100%,7/7,100%,, -Limited elbow movement [HP:0002996],4/4,100%,2/2,100%,, -Abnormality of the elbow [HP:0009811],5/5,100%,2/2,100%,, -Limitation of joint mobility [HP:0001376],4/4,100%,2/2,100%,, -Mitral regurgitation [HP:0001653],2/2,100%,2/2,100%,, -Abnormal mitral valve physiology [HP:0031481],2/2,100%,2/2,100%,, -Pre-capillary pulmonary hypertension [HP:0033578],0/0,0%,8/8,100%,, -Elevated pulmonary artery pressure [HP:0004890],0/0,0%,8/8,100%,, -Abnormality of pulmonary circulation [HP:0030875],0/0,0%,8/8,100%,, -Abnormal vascular physiology [HP:0030163],0/0,0%,8/8,100%,, -Abnormal respiratory system physiology [HP:0002795],0/0,0%,8/8,100%,, -Abnormality of the respiratory system [HP:0002086],0/0,0%,8/8,100%,, -Right atrial enlargement [HP:0030718],0/0,0%,4/4,100%,, -Abnormal right atrium morphology [HP:0025580],0/0,0%,4/4,100%,, -Atrial septal dilatation [HP:0011995],0/0,0%,4/4,100%,, Pectus excavatum [HP:0000767],2/2,100%,3/4,75%,, Abnormal sternum morphology [HP:0000766],2/2,100%,3/3,100%,, +Deviation of the thumb [HP:0009603],2/2,100%,3/3,100%,, +Deviation of finger [HP:0004097],2/2,100%,4/4,100%,, +Deviation of the hand or of fingers of the hand [HP:0009484],2/2,100%,5/5,100%,, +Abnormal skin morphology [HP:0011121],1/1,100%,0/0,0%,, +Abnormality of the skin [HP:0000951],1/1,100%,0/0,0%,, +Abnormality of the integument [HP:0001574],1/1,100%,0/0,0%,, +Sinus venosus atrial septal defect [HP:0011567],1/1,100%,0/2,0%,, +First degree atrioventricular block [HP:0011705],1/1,100%,1/23,4%,, Postaxial hand polydactyly [HP:0001162],0/0,0%,3/4,75%,, Postaxial polydactyly [HP:0100259],0/0,0%,3/3,100%,, Polydactyly [HP:0010442],0/0,0%,3/3,100%,, @@ -206,27 +219,22 @@ Enlarged thorax [HP:0100625],0/0,0%,3/3,100%,, Abnormal rib cage morphology [HP:0001547],0/0,0%,5/5,100%,, Y-shaped metatarsals [HP:0010567],0/0,0%,3/3,100%,, Abnormal metatarsal morphology [HP:0001832],0/0,0%,3/3,100%,, -Abnormal lower limb bone morphology [HP:0040069],0/0,0%,4/4,100%,, -Abnormality of the lower limb [HP:0002814],0/0,0%,4/4,100%,, -Abnormal foot morphology [HP:0001760],0/0,0%,4/4,100%,, -Common atrium [HP:0011565],0/38,0%,1/115,1%,, -Unroofed coronary sinus [HP:0031297],0/38,0%,1/117,1%,, -Limited pronation/supination of forearm [HP:0006394],3/3,100%,2/2,100%,, -First degree atrioventricular block [HP:0011705],1/1,100%,1/23,4%,, -Hypoplastic scapulae [HP:0000882],1/1,100%,1/1,100%,, -Aplasia/Hypoplasia of the scapulae [HP:0006713],1/1,100%,1/1,100%,, -Aplasia/Hypoplasia involving bones of the thorax [HP:0006711],2/2,100%,2/2,100%,, -Abnormal scapula morphology [HP:0000782],1/1,100%,1/1,100%,, -Clinodactyly of the 5th finger [HP:0004209],0/0,0%,2/2,100%,, -Finger clinodactyly [HP:0040019],0/0,0%,2/2,100%,, -Clinodactyly [HP:0030084],0/0,0%,2/2,100%,, -Deviation of the 5th finger [HP:0009179],0/0,0%,2/2,100%,, -Abnormal 2nd finger morphology [HP:0004100],0/0,0%,2/2,100%,, +11 pairs of ribs [HP:0000878],0/0,0%,2/2,100%,, +Missing ribs [HP:0000921],0/0,0%,2/2,100%,, +Aplasia/Hypoplasia of the ribs [HP:0006712],0/0,0%,2/2,100%,, +Abnormal rib morphology [HP:0000772],0/0,0%,2/2,100%,, Aplasia/Hypoplasia of the 2nd finger [HP:0006264],0/0,0%,2/2,100%,, -Short middle phalanx of finger [HP:0005819],0/0,0%,2/2,100%,, -Aplasia/Hypoplasia of the middle phalanges of the hand [HP:0009843],0/0,0%,2/2,100%,, -Abnormal middle phalanx morphology of the hand [HP:0009833],0/0,0%,2/2,100%,, -Short phalanx of finger [HP:0009803],0/0,0%,2/2,100%,, +Abnormal 2nd finger morphology [HP:0004100],0/0,0%,2/2,100%,, +Small hypothenar eminence [HP:0010487],0/0,0%,2/2,100%,, +Abnormality of the hypothenar eminence [HP:0010486],0/0,0%,2/2,100%,, +Abnormality of the musculature of the hand [HP:0001421],0/0,0%,2/2,100%,, +Upper extremity joint dislocation [HP:0030310],2/2,100%,0/0,0%,, +Joint dislocation [HP:0001373],2/2,100%,0/0,0%,, +Amelia involving the upper limbs [HP:0009812],1/37,3%,0/114,0%,, +Third degree atrioventricular block [HP:0001709],1/1,100%,0/22,0%,, +Atrioventricular dissociation [HP:0011709],1/1,100%,0/22,0%,, +Abnormal shoulder morphology [HP:0003043],1/1,100%,1/1,100%,, +Proximal placement of thumb [HP:0009623],0/0,0%,3/3,100%,, Short middle phalanx of the 5th finger [HP:0004220],0/0,0%,2/2,100%,, Type A brachydactyly [HP:0009370],0/0,0%,2/2,100%,, Brachydactyly [HP:0001156],0/0,0%,2/2,100%,, @@ -236,27 +244,19 @@ Aplasia/Hypoplasia of the middle phalanx of the 5th finger [HP:0009161],0/0,0%,2 Aplasia/Hypoplasia of the phalanges of the 5th finger [HP:0009376],0/0,0%,2/2,100%,, Abnormal 5th finger phalanx morphology [HP:0004213],0/0,0%,2/2,100%,, Abnormality of the middle phalanx of the 5th finger [HP:0004219],0/0,0%,2/2,100%,, -11 pairs of ribs [HP:0000878],0/0,0%,2/2,100%,, -Missing ribs [HP:0000921],0/0,0%,2/2,100%,, -Aplasia/Hypoplasia of the ribs [HP:0006712],0/0,0%,2/2,100%,, -Abnormal rib morphology [HP:0000772],0/0,0%,2/2,100%,, -Bowed forearm bones [HP:0003956],1/1,100%,0/0,0%,, -Bowing of the arm [HP:0006488],1/1,100%,0/0,0%,, -Bowing of the long bones [HP:0006487],1/1,100%,0/0,0%,, -Abnormal diaphysis morphology [HP:0000940],1/1,100%,0/0,0%,, -Abnormal skin morphology [HP:0011121],1/1,100%,0/0,0%,, -Abnormality of the skin [HP:0000951],1/1,100%,0/0,0%,, -Abnormality of the integument [HP:0001574],1/1,100%,0/0,0%,, -Sinus venosus atrial septal defect [HP:0011567],1/1,100%,0/2,0%,, -Left ventricular noncompaction cardiomyopathy [HP:0011664],1/5,20%,1/7,14%,, -Noncompaction cardiomyopathy [HP:0012817],1/1,100%,1/1,100%,, -Cardiomyopathy [HP:0001638],1/1,100%,1/1,100%,, -Abnormal myocardium morphology [HP:0001637],1/1,100%,1/1,100%,, -Abnormal toe morphology [HP:0001780],0/0,0%,1/1,100%,, -Abnormal toe phalanx morphology [HP:0010161],0/0,0%,1/1,100%,, -Abnormality of the distal phalanges of the toes [HP:0010182],0/0,0%,1/1,100%,, -Amelia involving the upper limbs [HP:0009812],1/37,3%,0/114,0%,, -Third degree atrioventricular block [HP:0001709],1/1,100%,0/22,0%,, -Small hypothenar eminence [HP:0010487],0/0,0%,2/2,100%,, -Abnormality of the hypothenar eminence [HP:0010486],0/0,0%,2/2,100%,, -Abnormality of the musculature of the hand [HP:0001421],0/0,0%,2/2,100%,, +Short middle phalanx of finger [HP:0005819],0/0,0%,2/2,100%,, +Aplasia/Hypoplasia of the middle phalanges of the hand [HP:0009843],0/0,0%,2/2,100%,, +Abnormal middle phalanx morphology of the hand [HP:0009833],0/0,0%,2/2,100%,, +Short phalanx of finger [HP:0009803],0/0,0%,2/2,100%,, +Common atrium [HP:0011565],0/38,0%,1/115,1%,, +Unroofed coronary sinus [HP:0031297],0/38,0%,1/117,1%,, +Clinodactyly of the 5th finger [HP:0004209],0/0,0%,2/2,100%,, +Finger clinodactyly [HP:0040019],0/0,0%,2/2,100%,, +Clinodactyly [HP:0030084],0/0,0%,2/2,100%,, +Deviation of the 5th finger [HP:0009179],0/0,0%,2/2,100%,, +Mitral valve prolapse [HP:0001634],1/1,100%,1/1,100%,, +Abnormal mitral valve morphology [HP:0001633],1/1,100%,1/1,100%,, +Abnormal atrioventricular valve morphology [HP:0006705],1/1,100%,1/1,100%,, +Abnormal heart valve morphology [HP:0001654],1/1,100%,1/1,100%,, +Short 1st metacarpal [HP:0010034],0/22,0%,1/45,2%,, +Short phalanx of the thumb [HP:0009660],0/22,0%,1/45,2%,, diff --git a/docs/user-guide/report/tbx5_frameshift.mtc_report.html b/docs/user-guide/report/tbx5_frameshift.mtc_report.html index 3d00300a..fbc4a9de 100644 --- a/docs/user-guide/report/tbx5_frameshift.mtc_report.html +++ b/docs/user-guide/report/tbx5_frameshift.mtc_report.html @@ -103,14 +103,14 @@

Phenotype testing report

 - + - + diff --git a/docs/user-guide/stats.rst b/docs/user-guide/stats.rst index 993dd592..3eeca2bf 100644 --- a/docs/user-guide/stats.rst +++ b/docs/user-guide/stats.rst @@ -13,8 +13,6 @@ and explanations of how they are implemented by our software. Compare genotype and phenotype groups ************************************* -TODO - .. _fisher-exact-test: Fisher exact test (FET) @@ -214,7 +212,7 @@ We can now execute the analysis: 17 -Thanks to Phenotype MTC filter, we only tested 16 out of 260 terms. +Thanks to Phenotype MTC filter, we only tested 17 out of 260 terms. We can learn more by showing the MTC filter report: >>> from gpsea.view import MtcStatsViewer diff --git a/src/gpsea/analysis/mtc_filter/_impl.py b/src/gpsea/analysis/mtc_filter/_impl.py index b15e4bc6..0aca8cc7 100644 --- a/src/gpsea/analysis/mtc_filter/_impl.py +++ b/src/gpsea/analysis/mtc_filter/_impl.py @@ -181,18 +181,14 @@ class SpecifiedTermsMtcFilter(PhenotypeMtcFilter[hpotk.TermId]): def __init__( self, - hpo: hpotk.MinimalOntology, terms_to_test: typing.Iterable[hpotk.TermId], ): """ - Args: - hpo: reference to HPO ontology object terms_to_test: an iterable of TermIds representing the terms to test """ self._ok = PhenotypeMtcResult.ok() self._fail = PhenotypeMtcResult.fail("Non-specified term") - self._hpo = hpo self._terms_to_test_set = set(terms_to_test) def filter( diff --git a/src/gpsea/analysis/pcats/_impl.py b/src/gpsea/analysis/pcats/_impl.py index 61841a51..e4513fa0 100644 --- a/src/gpsea/analysis/pcats/_impl.py +++ b/src/gpsea/analysis/pcats/_impl.py @@ -14,10 +14,16 @@ from gpsea.analysis.pcats.stats import CountStatistic from ..predicate import PatientCategory from ..predicate.genotype import GenotypePolyPredicate -from ..predicate.phenotype import P, PhenotypePolyPredicate, prepare_predicates_for_terms_of_interest +from ..predicate.phenotype import P, PhenotypePolyPredicate from ..mtc_filter import PhenotypeMtcFilter, PhenotypeMtcResult +DEFAULT_MTC_PROCEDURE = 'fdr_bh' +""" +Use Benjamini-Hochberg as the default MTC procedure. +""" + + def apply_predicates_on_patients( patients: typing.Iterable[Patient], gt_predicate: GenotypePolyPredicate, @@ -253,12 +259,23 @@ class MultiPhenotypeAnalysis(typing.Generic[P], metaclass=abc.ABCMeta): def __init__( self, count_statistic: CountStatistic, - mtc_correction: typing.Optional[str] = None, + mtc_correction: typing.Optional[str] = DEFAULT_MTC_PROCEDURE, mtc_alpha: float = 0.05, ): + """ + Create the analysis. + + See the :func:`~statsmodels.stats.multitest.multipletests` for the accepted `mtc_correction` values. + + :param count_statistic: the statistical test for computing p value for genotype-phenotype contingency table. + :param mtc_correction: a `str` with the MTC procedure code or `None` if no MTC should be performed. + :param mtc_alpha: a `float` with the family-wise error rate for FWER controlling procedures + (e.g. Bonferroni MTC) or false discovery rate for the FDR procedures (e.g. Benjamini-Hochberg). + """ assert isinstance(count_statistic, CountStatistic) self._count_statistic = count_statistic self._mtc_correction = mtc_correction + assert isinstance(mtc_alpha, float) and 0. <= mtc_alpha <= 1. self._mtc_alpha = mtc_alpha @abc.abstractmethod @@ -485,7 +502,7 @@ def __init__( self, count_statistic: CountStatistic, mtc_filter: PhenotypeMtcFilter, - mtc_correction: typing.Optional[str] = None, + mtc_correction: typing.Optional[str] = DEFAULT_MTC_PROCEDURE, mtc_alpha: float = 0.05, ): super().__init__( diff --git a/src/gpsea/analysis/pscore/_impl.py b/src/gpsea/analysis/pscore/_impl.py index b2ce8bd2..3d3adbaa 100644 --- a/src/gpsea/analysis/pscore/_impl.py +++ b/src/gpsea/analysis/pscore/_impl.py @@ -16,29 +16,6 @@ class CountingPhenotypeScorer(PhenotypeScorer): In the case, the query would include the corresponding terms (e.g., Abnormal brain morphology HP:0012443). An individual can then have between 0 and 4 phenotype group abnormalities. This predicate is intended to be used with the Mann Whitney U test. - - Example - ^^^^^^^ - We first need to load HPO using HPO toolkit: - - >>> import hpotk - >>> store = hpotk.configure_ontology_store() - >>> hpo = store.load_minimal_hpo(release='v2024-07-01') - - Now we can create `CountingPhenotypeScorer` to test for presence of brain, liver, kidney and skin abnormalities: - - >>> from gpsea.analysis.pscore import CountingPhenotypeScorer - >>> phenotype_groups = ( - ... "HP:0012443", # Abnormal brain morphology - ... "HP:0410042", # Abnormal liver morphology - ... "HP:0012210", # Abnormal renal morphology - ... "HP:0011121", # Abnormal skin morphology - ... ) - >>> scorer = CountingPhenotypeScorer.from_query_curies( - ... hpo=hpo, - ... query=phenotype_groups, - ... ) - """ @staticmethod diff --git a/tests/analysis/test_mtc_filter.py b/tests/analysis/test_mtc_filter.py index 0b060516..c07600ef 100644 --- a/tests/analysis/test_mtc_filter.py +++ b/tests/analysis/test_mtc_filter.py @@ -13,7 +13,7 @@ from gpsea.model import Cohort -class TestHeuristicSamplerMtcFilter: +class TestHpoMtcFilter: @pytest.fixture def mtc_filter( @@ -75,7 +75,7 @@ def test_one_genotype_has_zero_hpo_observations( gt_categories: pd.Index, pheno_categories: pd.Index, ): - counts_df = TestHeuristicSamplerMtcFilter.prepare_counts_df(counts, gt_categories, pheno_categories) + counts_df = TestHpoMtcFilter.prepare_counts_df(counts, gt_categories, pheno_categories) actual = HpoMtcFilter.one_genotype_has_zero_hpo_observations( counts=counts_df, @@ -104,7 +104,7 @@ def test_some_cell_has_greater_than_one_count( gt_categories: pd.Index, pheno_categories: pd.Index, ): - counts_df = TestHeuristicSamplerMtcFilter.prepare_counts_df(counts, gt_categories, pheno_categories) + counts_df = TestHpoMtcFilter.prepare_counts_df(counts, gt_categories, pheno_categories) actual = HpoMtcFilter.some_cell_has_greater_than_one_count(counts=counts_df) @@ -125,7 +125,7 @@ def test_genotypes_have_same_hpo_proportions( gt_categories: pd.Index, pheno_categories: pd.Index, ): - counts_df = TestHeuristicSamplerMtcFilter.prepare_counts_df(counts, gt_categories, pheno_categories) + counts_df = TestHpoMtcFilter.prepare_counts_df(counts, gt_categories, pheno_categories) actual = HpoMtcFilter.genotypes_have_same_hpo_proportions( counts=counts_df, @@ -165,7 +165,6 @@ def test_filter_terms_to_test( def test_specified_term_mtc_filter( self, - hpo: hpotk.MinimalOntology, suox_gt_predicate: GenotypePolyPredicate, patient_counts: typing.Tuple[ typing.Mapping[hpotk.TermId, int], @@ -178,7 +177,7 @@ def test_specified_term_mtc_filter( but after our filter, only one survives (filtered_n_usable == 1), and we have four cases in which the reason for filtering out is 'Skipping non-specified term' """ - specified_filter = SpecifiedTermsMtcFilter(hpo=hpo, terms_to_test={hpotk.TermId.from_curie("HP:0032350")}) + specified_filter = SpecifiedTermsMtcFilter(terms_to_test={hpotk.TermId.from_curie("HP:0032350")}) n_usable, all_counts = patient_counts mtc_report = specified_filter.filter_terms_to_test( suox_gt_predicate, From abd5545bd48af0234e127c45eff941f2b2427b76 Mon Sep 17 00:00:00 2001 From: Lauren Rekerle Date: Tue, 27 Aug 2024 12:29:16 -0500 Subject: [PATCH 07/47] Made updates to all the notebooks to reflect the rebrand --- .../CLDN16_genotype_phenotype_corr.ipynb | 741 +- case_studies/KMT2D/KMT2D_correlations.ipynb | 6690 ++--------------- .../KMT2D/input/PMID_24019847_patient.json | 352 - .../KMT2D/input/PMID_24739679_patientK1.json | 356 - .../KMT2D/input/PMID_24739679_patientK10.json | 351 - 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a/case_studies/CLDN16/CLDN16_genotype_phenotype_corr.ipynb +++ b/case_studies/CLDN16/CLDN16_genotype_phenotype_corr.ipynb @@ -10,7 +10,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "id": "3441db9f-ed83-422e-ba15-e78b525f0359", "metadata": {}, "outputs": [ @@ -18,23 +18,23 @@ "name": "stdout", "output_type": "stream", "text": [ - "Using genophenocorr version 0.1.1dev\n" + "Using gpsea version 0.1.1dev\n" ] } ], "source": [ - "import genophenocorr\n", - "from genophenocorr.preprocessing import configure_caching_cohort_creator, load_phenopacket_folder\n", - "from genophenocorr.preprocessing import UniprotProteinMetadataService, VVTranscriptCoordinateService\n", - "from genophenocorr.analysis import configure_cohort_analysis, CohortAnalysisConfiguration\n", - "from genophenocorr.model import VariantEffect\n", - "from genophenocorr.model.genome import GRCh38\n", - "from genophenocorr.analysis.predicate import PatientCategories\n", - "from genophenocorr.view import CohortViewable, ProteinVisualizable, ProteinVisualizer\n", + "import gpsea\n", + "from gpsea.preprocessing import configure_caching_cohort_creator, load_phenopacket_folder\n", + "from gpsea.preprocessing import UniprotProteinMetadataService, VVMultiCoordinateService\n", + "from gpsea.analysis import configure_cohort_analysis, CohortAnalysisConfiguration\n", + "from gpsea.model import VariantEffect\n", + "from gpsea.model.genome import GRCh38\n", + "from gpsea.analysis.predicate import PatientCategories\n", + "from gpsea.view import CohortViewable, ProteinVisualizable, ProteinVisualizer, ProteinViewable\n", "from IPython.display import HTML, display\n", "import hpotk\n", "\n", - "print(f\"Using genophenocorr version {genophenocorr.__version__}\")" + "print(f\"Using gpsea version {gpsea.__version__}\")" ] }, { @@ -66,36 +66,34 @@ "metadata": {}, "outputs": [ { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "Patients Created: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████| 46/46 [00:00<00:00, 252.09it/s]\n", + "Patients Created: 100%|██████████| 46/46 [00:38<00:00, 1.19it/s]\n", "Validated under none policy\n", - "Showing errors and warnings\n", - "46 phenopacket(s) found at `../../../phenopacket-store/notebooks/CLDN16/phenopackets/`\n", - " patient #36\n", - " variants\n", - " warnings:\n", - " ·Expected a VCF record, a VRS CNV, or an expression with `hgvs.c` but had an error retrieving any from patient nan[PMID_32869508_nan]. Remove variant from testing\n", - " patient #39\n", - " variants\n", - " warnings:\n", - " ·Expected a VCF record, a VRS CNV, or an expression with `hgvs.c` but had an error retrieving any from patient patient[PMID_26136118_patient]. Remove variant from testing\n", - " ·Patient PMID_26136118_patient has no variants to work with\n", - " patient #40\n", - " variants\n", - " warnings:\n", - " ·Expected a VCF record, a VRS CNV, or an expression with `hgvs.c` but had an error retrieving any from patient Patient 5[PMID_26426912_Patient_5]. Remove variant from testing\n", - " ·Patient PMID_26426912_Patient_5 has no variants to work with\n" + "No errors or warnings were found\n" ] } ], "source": [ - "from genophenocorr.preprocessing import configure_caching_cohort_creator, load_phenopacket_folder\n", + "from gpsea.preprocessing import configure_caching_cohort_creator, load_phenopackets\n", + "from ppktstore.registry import configure_phenopacket_registry\n", "\n", - "fpath_phenopackets = '../../../phenopacket-store/notebooks/CLDN16/phenopackets/'\n", "cohort_creator = configure_caching_cohort_creator(hpo)\n", - "cohort = load_phenopacket_folder(fpath_phenopackets, cohort_creator)" + "\n", + "cohort_name = 'CLDN16'\n", + "phenopacket_store_release = '0.1.19'\n", + "\n", + "registry = configure_phenopacket_registry()\n", + "with registry.open_phenopacket_store(phenopacket_store_release) as ps:\n", + " phenopackets = tuple(ps.iter_cohort_phenopackets(cohort_name))\n", + "\n", + "cohort, validation = load_phenopackets(\n", + " phenopackets=phenopackets, \n", + " cohort_creator=cohort_creator,\n", + ")\n", + "del phenopackets\n", + "validation.summarize()" ] }, { @@ -110,28 +108,7 @@ }, { "cell_type": "code", - "execution_count": 11, - "id": "d1bc40a6", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[('NP_001365421.1', 57), ('NP_001365422.1', 57), ('NP_006571.2', 57)]" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cohort.list_all_proteins()" - ] - }, - { - "cell_type": "code", - "execution_count": 10, + "execution_count": 7, "id": "fe5344f4-3cf4-49c5-8bf8-810c0bb17c18", "metadata": {}, "outputs": [ @@ -143,87 +120,88 @@ "\n", " \n", " Cohort\n", - " \n", "\n", "\n", "\n", - "

genophenocorr cohort analysis

\n", - "

Successfully ingested 46 phenopackets.

\n", + "

GPSEA cohort analysis

\n", + "

Successfully loaded 46 individuals.

\n", " \n", - "

Not able to ingest 2 individuals.

\n", + "

No errors encountered.

\n", " \n", "\n", - "
Using HPO MTC filter, 243 term(s) were omitted from statistical analysis.Using HPO MTC filter, 244 term(s) were omitted from statistical analysis.
Code TODO Skipping general term4344
TODO Skipping term with only 2 observations (not powered for 2x2)2526
TODO Skipping term with only 6 observations (not powered for 2x2)1312
TODOSkipping term with maximum frequency that was less than threshold 0.2Skipping term with only 5 observations (not powered for 2x2) 10
TODOSkipping term with only 5 observations (not powered for 2x2)Skipping term with maximum frequency that was less than threshold 0.2 10
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\n", "

Top 10 HPO Terms

\n", - " A total of 82 HPO terms were used to annotated the cohort. \n", + " A total of 79 HPO terms were used to annotated the cohort.\n", "
HPO TermIDAnnotation CountSeen in n individuals
NephrocalcinosisHP:00001214546
Renal insufficiencyHP:000008332
Renal magnesium wastingHP:000556730
Renal insufficiencyHP:000008328
PolyuriaHP:000010323
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Top 10 Variants

\n", - " Variants are shown according to NM_006580.4. A total of 57 unique variants were identified in the cohort.\n", + " Variants are shown according to NM_006580.4. A total of 60 unique variants were identified in the cohort.\n", "
VariantVariant nameVariant CountCountVariant keyVariant NameProtein VariantVariant Class
173_190404787_190404787_G_Tc.243G>T17
p.Leu81PheMISSENSE_VARIANT
63_190408368_190408368_G_Ac.437G>A6
p.Arg146HisMISSENSE_VARIANT
53_190402342_190402342_C_Gc.120C>G5
p.Ser40ArgMISSENSE_VARIANT
43_190408423_190408423_G_Tc.492G>T4
p.Trp164CysMISSENSE_VARIANT
33_190404768_190404768_T_Cc.224T>C3
p.Leu75ProMISSENSE_VARIANT
3_190408506_190408506_G_Tc.574+1G>T2
3SO:1000029_HGNC:2037_CLDN161000029_HGNCNoneTRANSCRIPT_ABLATION
3_190409949_190409949_T_Gc.621T>G2
3_190402370_190402370_T_Cc.148T>Cp.Cys50ArgMISSENSE_VARIANT
3_190404908_190404923_GCCACGTTACTAATAG_TACCGGTCTGGCTGGACTAGCAAc.364_379delinsTACCGGTCTGGCTGGACTAGCAA2
3_190409982_190409982_C_Gc.654C>Gp.Tyr218TerSTOP_GAINED
3_190408347_190408347_C_Tc.416C>T2
3_190404908_190404923_--16bp--_--23bp--c.364_379delinsTACCGGTCTGGCTGGACTAGCAAp.Ala122TyrfsTer22FRAMESHIFT_VARIANT
3_190408436_190408436_G_Ac.505G>A2
3_190409949_190409949_T_Gc.621T>Gp.Tyr207TerSTOP_GAINED
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Diseases

\n", "
Disease IDDisease NameDisease NameDisease IDAnnotation Count
OMIM:248250Hypomagnesemia 3, renalHypomagnesemia 3, renalOMIM:24825046
\n", - "\n", " \n", - " \n", + "
\n", " \n", @@ -393,7 +388,12 @@ " \n", " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -403,13 +403,13 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -439,14 +439,14 @@ } ], "source": [ - "from genophenocorr.view import CohortViewable\n", + "from gpsea.view import CohortViewable\n", "viewer = CohortViewable(hpo)\n", "display(HTML(viewer.process(cohort=cohort, transcript_id=CLDN16_transcript)))" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 8, "id": "c79a33b1-53a8-4f78-aeb8-9939d245e73d", "metadata": {}, "outputs": [ @@ -456,13 +456,13 @@ "" ] }, - "execution_count": 13, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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"text/plain": [ "
" ] @@ -472,14 +472,14 @@ } ], "source": [ - "from genophenocorr.preprocessing import UniprotProteinMetadataService\n", - "from genophenocorr.model.genome import GRCh38\n", - "from genophenocorr.preprocessing import VVTranscriptCoordinateService\n", + "from gpsea.preprocessing import UniprotProteinMetadataService\n", + "from gpsea.model.genome import GRCh38\n", + "from gpsea.preprocessing import VVMultiCoordinateService\n", "pms = UniprotProteinMetadataService()\n", "protein_meta = pms.annotate('NP_006571.1')\n", - "txc_service = VVTranscriptCoordinateService(genome_build=GRCh38)\n", + "txc_service = VVMultiCoordinateService(genome_build=GRCh38)\n", "tx_coordinates = txc_service.fetch(CLDN16_transcript)\n", - "from genophenocorr.view import ProteinVisualizable, ProteinVisualizer\n", + "from gpsea.view import ProteinVisualizable, ProteinVisualizer\n", "pvis = ProteinVisualizable(tx_coordinates=tx_coordinates, protein_meta=protein_meta, cohort=cohort)\n", "drawer = ProteinVisualizer()\n", "drawer.draw_fig(pvis=pvis)" @@ -487,20 +487,134 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 11, + "id": "6bba515c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " \n", + " Cohort\n", + " \n", + "\n", + "\n", + "\n", + "

GPSEA protein analysis

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The UniProt API successfully returned protein information for ID: NP_006571.1

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Protein Name: Claudin-16

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Variant categories for NM_006580.4

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FRAMESHIFT_VARIANTSTOP_GAINED6
TRANSCRIPT_ABLATION3
SPLICE_DONOR_VARIANT2FRAMESHIFT_VARIANT3
STOP_GAINED6SPLICE_DONOR_VARIANT2
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Protein Features

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Feature NameFeature TypeFeature CoordinatesVariants in Feature
Interaction with TJP1motif233 - 2350
\n", + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "viewer = ProteinViewable()\n", + "html_prot = viewer.process(cohort, pvis)\n", + "display(HTML(html_prot))" + ] + }, + { + "cell_type": "code", + "execution_count": 12, "id": "fd352277-9cab-494d-aad2-0e528a2d0e73", "metadata": {}, "outputs": [], "source": [ - "from genophenocorr.analysis import configure_cohort_analysis\n", - "from genophenocorr.analysis.predicate import GenotypeBooleanPredicate, PatientCategories\n", + "from gpsea.analysis import configure_cohort_analysis, CohortAnalysisConfiguration\n", + "from gpsea.analysis.predicate import PatientCategories\n", + "from gpsea.model.genome import Region\n", "\n", - "analysis = configure_cohort_analysis(cohort, hpo)" + "analysis_config = CohortAnalysisConfiguration()\n", + "analysis_config.missing_implies_excluded = False\n", + "analysis_config.hpo_mtc_strategy()\n", + "analysis = configure_cohort_analysis(cohort, hpo, config=analysis_config)" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 13, "id": "e3b7bfd7-c4b1-4a4f-ba91-62ffc359350e", "metadata": {}, "outputs": [ @@ -525,9 +639,8 @@ " \n", " \n", " MISSENSE_VARIANT on NM_006580.4\n", - " Both\n", - " One\n", - " Neither\n", + " Yes\n", + " No\n", " \n", " \n", " \n", @@ -536,232 +649,190 @@ " Percent\n", " Count\n", " Percent\n", - " Count\n", - " Percent\n", " p value\n", " Corrected p value\n", " \n", " \n", " \n", " \n", - " Nephrolithiasis [HP:0000787]\n", - " 0/9\n", - " 0%\n", - " 7/21\n", - " 33%\n", - " 1/1\n", - " 100%\n", - " 0.020655\n", - " 1.0\n", + " Stage 5 chronic kidney disease [HP:0003774]\n", + " 2/35\n", + " 6%\n", + " 3/8\n", + " 38%\n", + " 0.037218\n", + " 0.446616\n", " \n", " \n", - " Stage 5 chronic kidney disease [HP:0003774]\n", - " 0/0\n", - " 0%\n", - " 1/8\n", - " 12%\n", - " 3/3\n", - " 100%\n", - " 0.024242\n", - " 1.0\n", + " Hypocalcemia [HP:0002901]\n", + " 8/9\n", + " 89%\n", + " 2/6\n", + " 33%\n", + " 0.088911\n", + " 1.000000\n", " \n", " \n", - " Chronic kidney disease [HP:0012622]\n", - " 0/0\n", - " 0%\n", - " 2/8\n", - " 25%\n", - " 3/3\n", + " Nephrolithiasis [HP:0000787]\n", + " 7/30\n", + " 23%\n", + " 1/1\n", " 100%\n", - " 0.060606\n", - " 1.0\n", + " 0.258065\n", + " 1.000000\n", " \n", " \n", " Failure to thrive [HP:0001508]\n", - " 1/9\n", - " 11%\n", - " 6/15\n", - " 40%\n", + " 7/24\n", + " 29%\n", " 1/1\n", " 100%\n", - " 0.075640\n", - " 1.0\n", + " 0.320000\n", + " 1.000000\n", " \n", " \n", - " Hypocalcemia [HP:0002901]\n", - " 0/0\n", - " 0%\n", - " 8/9\n", - " 89%\n", - " 2/6\n", - " 33%\n", - " 0.088911\n", - " 1.0\n", + " Recurrent urinary tract infections [HP:0000010]\n", + " 13/26\n", + " 50%\n", + " 3/4\n", + " 75%\n", + " 0.601533\n", + " 1.000000\n", " \n", " \n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", + " Polyuria [HP:0000103]\n", + " 20/25\n", + " 80%\n", + " 3/3\n", + " 100%\n", + " 1.000000\n", + " 1.000000\n", " \n", " \n", - " Abnormal urine output [HP:0012590]\n", + " Hypermagnesiuria [HP:0012608]\n", + " 25/27\n", + " 93%\n", " 7/7\n", " 100%\n", - " 13/13\n", - " 100%\n", - " 3/3\n", + " 1.000000\n", + " 1.000000\n", + " \n", + " \n", + " Hypomagnesemia [HP:0002917]\n", + " 11/12\n", + " 92%\n", + " 7/7\n", " 100%\n", " 1.000000\n", - " 1.0\n", + " 1.000000\n", " \n", " \n", - " Hypocitraturia [HP:0012405]\n", - " 4/7\n", - " 57%\n", - " 6/13\n", - " 46%\n", - " 0/0\n", - " 0%\n", + " Hypocalcemic seizures [HP:0002199]\n", + " 2/5\n", + " 40%\n", + " 1/5\n", + " 20%\n", + " 1.000000\n", " 1.000000\n", - " 1.0\n", " \n", " \n", - " Abnormal bone structure [HP:0003330]\n", - " 1/1\n", - " 100%\n", - " 1/1\n", + " Polydipsia [HP:0001959]\n", + " 19/24\n", + " 79%\n", + " 3/3\n", " 100%\n", - " 0/0\n", - " 0%\n", " 1.000000\n", - " 1.0\n", + " 1.000000\n", " \n", " \n", - " Abnormal enzyme concentration or activity [HP:0034684]\n", - " 0/0\n", - " 0%\n", - " 0/0\n", - " 0%\n", - " 1/1\n", + " Hypercalciuria [HP:0002150]\n", + " 32/36\n", + " 89%\n", + " 7/7\n", " 100%\n", " 1.000000\n", - " 1.0\n", + " 1.000000\n", " \n", " \n", - " Abnormality of the hand [HP:0001155]\n", - " 0/0\n", - " 0%\n", - " 1/1\n", + " Elevated circulating parathyroid hormone level [HP:0003165]\n", + " 25/26\n", + " 96%\n", + " 3/3\n", " 100%\n", - " 0/0\n", - " 0%\n", " 1.000000\n", - " 1.0\n", + " 1.000000\n", " \n", " \n", "\n", - "

173 rows × 8 columns

\n", "" ], "text/plain": [ - "MISSENSE_VARIANT on NM_006580.4 Both One \\\n", - " Count Percent Count \n", - "Nephrolithiasis [HP:0000787] 0/9 0% 7/21 \n", - "Stage 5 chronic kidney disease [HP:0003774] 0/0 0% 1/8 \n", - "Chronic kidney disease [HP:0012622] 0/0 0% 2/8 \n", - "Failure to thrive [HP:0001508] 1/9 11% 6/15 \n", - "Hypocalcemia [HP:0002901] 0/0 0% 8/9 \n", - "... ... ... ... \n", - "Abnormal urine output [HP:0012590] 7/7 100% 13/13 \n", - "Hypocitraturia [HP:0012405] 4/7 57% 6/13 \n", - "Abnormal bone structure [HP:0003330] 1/1 100% 1/1 \n", - "Abnormal enzyme concentration or activity [HP:0... 0/0 0% 0/0 \n", - "Abnormality of the hand [HP:0001155] 0/0 0% 1/1 \n", - "\n", - "MISSENSE_VARIANT on NM_006580.4 Neither \\\n", - " Percent Count Percent \n", - "Nephrolithiasis [HP:0000787] 33% 1/1 100% \n", - "Stage 5 chronic kidney disease [HP:0003774] 12% 3/3 100% \n", - "Chronic kidney disease [HP:0012622] 25% 3/3 100% \n", - "Failure to thrive [HP:0001508] 40% 1/1 100% \n", - "Hypocalcemia [HP:0002901] 89% 2/6 33% \n", - "... ... ... ... \n", - "Abnormal urine output [HP:0012590] 100% 3/3 100% \n", - "Hypocitraturia [HP:0012405] 46% 0/0 0% \n", - "Abnormal bone structure [HP:0003330] 100% 0/0 0% \n", - "Abnormal enzyme concentration or activity [HP:0... 0% 1/1 100% \n", - "Abnormality of the hand [HP:0001155] 100% 0/0 0% \n", + "MISSENSE_VARIANT on NM_006580.4 Yes No \\\n", + " Count Percent Count \n", + "Stage 5 chronic kidney disease [HP:0003774] 2/35 6% 3/8 \n", + "Hypocalcemia [HP:0002901] 8/9 89% 2/6 \n", + "Nephrolithiasis [HP:0000787] 7/30 23% 1/1 \n", + "Failure to thrive [HP:0001508] 7/24 29% 1/1 \n", + "Recurrent urinary tract infections [HP:0000010] 13/26 50% 3/4 \n", + "Polyuria [HP:0000103] 20/25 80% 3/3 \n", + "Hypermagnesiuria [HP:0012608] 25/27 93% 7/7 \n", + "Hypomagnesemia [HP:0002917] 11/12 92% 7/7 \n", + "Hypocalcemic seizures [HP:0002199] 2/5 40% 1/5 \n", + "Polydipsia [HP:0001959] 19/24 79% 3/3 \n", + "Hypercalciuria [HP:0002150] 32/36 89% 7/7 \n", + "Elevated circulating parathyroid hormone level ... 25/26 96% 3/3 \n", "\n", - "MISSENSE_VARIANT on NM_006580.4 \n", - " p value Corrected p value \n", - "Nephrolithiasis [HP:0000787] 0.020655 1.0 \n", - "Stage 5 chronic kidney disease [HP:0003774] 0.024242 1.0 \n", - "Chronic kidney disease [HP:0012622] 0.060606 1.0 \n", - "Failure to thrive [HP:0001508] 0.075640 1.0 \n", - "Hypocalcemia [HP:0002901] 0.088911 1.0 \n", - "... ... ... \n", - "Abnormal urine output [HP:0012590] 1.000000 1.0 \n", - "Hypocitraturia [HP:0012405] 1.000000 1.0 \n", - "Abnormal bone structure [HP:0003330] 1.000000 1.0 \n", - "Abnormal enzyme concentration or activity [HP:0... 1.000000 1.0 \n", - "Abnormality of the hand [HP:0001155] 1.000000 1.0 \n", + "MISSENSE_VARIANT on NM_006580.4 \\\n", + " Percent p value \n", + "Stage 5 chronic kidney disease [HP:0003774] 38% 0.037218 \n", + "Hypocalcemia [HP:0002901] 33% 0.088911 \n", + "Nephrolithiasis [HP:0000787] 100% 0.258065 \n", + "Failure to thrive [HP:0001508] 100% 0.320000 \n", + "Recurrent urinary tract infections [HP:0000010] 75% 0.601533 \n", + "Polyuria [HP:0000103] 100% 1.000000 \n", + "Hypermagnesiuria [HP:0012608] 100% 1.000000 \n", + "Hypomagnesemia [HP:0002917] 100% 1.000000 \n", + "Hypocalcemic seizures [HP:0002199] 20% 1.000000 \n", + "Polydipsia [HP:0001959] 100% 1.000000 \n", + "Hypercalciuria [HP:0002150] 100% 1.000000 \n", + "Elevated circulating parathyroid hormone level ... 100% 1.000000 \n", "\n", - "[173 rows x 8 columns]" + "MISSENSE_VARIANT on NM_006580.4 \n", + " Corrected p value \n", + "Stage 5 chronic kidney disease [HP:0003774] 0.446616 \n", + "Hypocalcemia [HP:0002901] 1.000000 \n", + "Nephrolithiasis [HP:0000787] 1.000000 \n", + "Failure to thrive [HP:0001508] 1.000000 \n", + "Recurrent urinary tract infections [HP:0000010] 1.000000 \n", + "Polyuria [HP:0000103] 1.000000 \n", + "Hypermagnesiuria [HP:0012608] 1.000000 \n", + "Hypomagnesemia [HP:0002917] 1.000000 \n", + "Hypocalcemic seizures [HP:0002199] 1.000000 \n", + "Polydipsia [HP:0001959] 1.000000 \n", + "Hypercalciuria [HP:0002150] 1.000000 \n", + "Elevated circulating parathyroid hormone level ... 1.000000 " ] }, - "execution_count": 18, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "from genophenocorr.model import VariantEffect\n", + "from gpsea.model import VariantEffect\n", + "from gpsea.analysis.predicate.genotype import VariantPredicates\n", "\n", - "missense = analysis.compare_by_recessive_variant_effect(VariantEffect.MISSENSE_VARIANT, CLDN16_transcript)\n", + "is_missense = VariantPredicates.variant_effect(VariantEffect.MISSENSE_VARIANT, tx_id=CLDN16_transcript)\n", + "missense = analysis.compare_hpo_vs_genotype(is_missense)\n", "missense.summarize(hpo, PatientCategories.YES)" ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "28fd459b-c95b-442b-8227-4c214c07fb4b", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'Phenotypic abnormality'" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "hpo.get_term(\"HP:0000118\").name" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7938d75d-bdb8-4652-becf-cb1bdd891d3f", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "enviro", "language": "python", - "name": "python3" + "name": "enviro" }, "language_info": { "codemirror_mode": { diff --git a/case_studies/KMT2D/KMT2D_correlations.ipynb b/case_studies/KMT2D/KMT2D_correlations.ipynb index 34f74281..caadd52d 100644 --- a/case_studies/KMT2D/KMT2D_correlations.ipynb +++ b/case_studies/KMT2D/KMT2D_correlations.ipynb @@ -17,23 +17,18 @@ "output_type": "stream", "text": [ "Loaded HPO v2023-10-09\n", - "Using genophenocorr version 0.1.1dev\n" + "Using gpsea version 0.1.1dev\n" ] } ], "source": [ - "import genophenocorr\n", + "import gpsea\n", "import hpotk\n", - "from IPython.display import HTML, display\n", - "from genophenocorr.preprocessing import configure_caching_cohort_creator, load_phenopacket_folder\n", - "from genophenocorr.analysis import configure_cohort_analysis, CohortAnalysisConfiguration\n", - "from genophenocorr.view import CohortViewable, ProteinVisualizer\n", + "\n", "store = hpotk.configure_ontology_store()\n", - "import pandas as pd\n", - "pd.set_option('display.max_rows', None)\n", "hpo = store.load_minimal_hpo(release='v2023-10-09')\n", "print(f'Loaded HPO v{hpo.version}')\n", - "print(f\"Using genophenocorr version {genophenocorr.__version__}\")" + "print(f\"Using gpsea version {gpsea.__version__}\")" ] }, { @@ -52,19 +47,34 @@ "metadata": {}, "outputs": [ { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "Patients Created: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████| 65/65 [00:00<00:00, 189.81it/s]\n", + "Patients Created: 100%|██████████| 65/65 [02:01<00:00, 1.87s/it]\n", "Validated under none policy\n", "No errors or warnings were found\n" ] } ], "source": [ - "fpath_phenopackets = 'input'\n", - "cohort_creator = configure_caching_cohort_creator(hpo, timeout=20)\n", - "cohort = load_phenopacket_folder(fpath_phenopackets, cohort_creator)" + "from ppktstore.registry import configure_phenopacket_registry\n", + "from gpsea.preprocessing import configure_caching_cohort_creator, load_phenopackets\n", + "\n", + "cohort_creator = configure_caching_cohort_creator(hpo)\n", + "\n", + "cohort_name = 'KMT2D'\n", + "phenopacket_store_release = '0.1.19'\n", + "\n", + "registry = configure_phenopacket_registry()\n", + "with registry.open_phenopacket_store(phenopacket_store_release) as ps:\n", + " phenopackets = tuple(ps.iter_cohort_phenopackets(cohort_name))\n", + "\n", + "cohort, validation = load_phenopackets(\n", + " phenopackets=phenopackets, \n", + " cohort_creator=cohort_creator,\n", + ")\n", + "del phenopackets\n", + "validation.summarize()" ] }, { @@ -80,80 +90,82 @@ "\n", " \n", " Cohort\n", - " \n", "\n", "\n", "\n", - "

genophenocorr cohort analysis

\n", - "

Successfully ingested 65 phenopackets.

\n", + "

GPSEA cohort analysis

\n", + "

Successfully loaded 65 individuals.

\n", " \n", "

No errors encountered.

\n", " \n", "\n", - " \n", + "
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\n", "

Top 10 HPO Terms

\n", - " A total of 239 HPO terms were used to annotated the cohort. \n", + " A total of 239 HPO terms were used to annotated the cohort.\n", "
HPO TermIDAnnotation CountSeen in n individuals
Global developmental delayHP:0001263Prominent fingertip padsHP:000121230
Prominent fingertip padsHP:0001212Global developmental delayHP:000126330
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\n", "

Top 10 Variants

\n", " Variants are shown according to NM_003482.4. A total of 65 unique variants were identified in the cohort.\n", "
VariantVariant nameVariant CountCountVariant keyVariant NameProtein VariantVariant Class
412_49034182_49034182_A_Gc.10625T>C4
p.Leu3542ProMISSENSE_VARIANT
412_49022655_49022655_C_Tc.16273G>A4
p.Glu5425LysMISSENSE_VARIANT
12_49034225_49034225_G_Cc.10582C>G3
12_49034082_49034082_C_Gc.10725G>Cp.Gln3575HisMISSENSE_VARIANT
3
12_49034225_49034225_G_Cc.10582C>Gp.Leu3528ValMISSENSE_VARIANT
312_49033960_49033960_C_Tc.10745G>A3
p.Arg3582GlnMISSENSE_VARIANT
212_49022634_49022634_G_Ac.16294C>T2
p.Arg5432TrpMISSENSE_VARIANT
12_49055320_49055320_T_Cc.5A>G1
12_49032140_49032140_C_Ac.12565G>Tp.Gly4189TerSTOP_GAINED
12_49034117_49034117_G_Cc.10690C>G1
12_49033067_49033067_G_Tc.11638C>Ap.Leu3880MetMISSENSE_VARIANT
12_49046631_49046631_T_TGc.4395dup1
12_49032398_49032398_G_Ac.12307C>Tp.Gln4103TerSTOP_GAINED
12_49034149_49034149_C_Ac.10658G>T1
12_49031446_49031446_C_Tc.13259G>Ap.Arg4420GlnMISSENSE_VARIANT
\n", - " \n", + "
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\n", "

Diseases

\n", "
Disease IDDisease NameDisease NameDisease IDAnnotation Count
OMIM:147920Kabuki Syndrome 1Kabuki Syndrome 1OMIM:14792065
\n", " \n", - "

Call this function with transcript to see table with variant effect counts.

\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
\n", + "

Variant categories for NM_003482.4

\n", + "
Variant effectAnnotation Count
MISSENSE_VARIANT36
STOP_GAINED15
FRAMESHIFT_VARIANT14
\n", " \n", "\n", "\n", @@ -329,9 +389,11 @@ } ], "source": [ - "cohort_viewable = CohortViewable(hpo=hpo)\n", - "html = cohort_viewable.process(cohort=cohort, transcript_id=KTM2D_transcript_id)\n", - "display(HTML(html))" + "from gpsea.view import CohortViewable\n", + "from IPython.display import HTML, display\n", + "\n", + "viewer = CohortViewable(hpo)\n", + "display(HTML(viewer.process(cohort=cohort, transcript_id=KTM2D_transcript_id)))" ] }, { @@ -340,14 +402,14 @@ "metadata": {}, "outputs": [], "source": [ - "from genophenocorr.preprocessing import UniprotProteinMetadataService\n", - "from genophenocorr.model.genome import GRCh38\n", - "from genophenocorr.preprocessing import VVTranscriptCoordinateService\n", - "txc_service = VVTranscriptCoordinateService(genome_build=GRCh38)\n", - "tx_coordinates = txc_service.fetch(KTM2D_transcript_id)\n", - "from genophenocorr.preprocessing import UniprotProteinMetadataService\n", - "pms = UniprotProteinMetadataService()\n", - "protein_meta = pms.annotate(KTM2D_protein_id)\n" + "from gpsea.preprocessing import configure_protein_metadata_service\n", + "from gpsea.preprocessing import VVMultiCoordinateService\n", + "from gpsea.model.genome import GRCh38\n", + "\n", + "pms = configure_protein_metadata_service()\n", + "protein_meta = pms.annotate(KTM2D_protein_id)\n", + "txc_service = VVMultiCoordinateService(genome_build=GRCh38)\n", + "tx_coordinates = txc_service.fetch(KTM2D_transcript_id)\n" ] }, { @@ -356,7 +418,8 @@ "metadata": {}, "outputs": [], "source": [ - "from genophenocorr.view import ProteinVisualizable\n", + "from gpsea.view import ProteinVisualizable, ProteinVisualizer, ProteinViewable\n", + "\n", "pvis = ProteinVisualizable(tx_coordinates=tx_coordinates, protein_meta=protein_meta, cohort=cohort)" ] }, @@ -365,5543 +428,514 @@ "execution_count": 7, "metadata": {}, "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "drawer = ProteinVisualizer()\n", - "drawer.draw_fig(pvis=pvis)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "from genophenocorr.analysis import configure_cohort_analysis, CohortAnalysisConfiguration\n", - "from genophenocorr.analysis.predicate import PatientCategories\n", - "from genophenocorr.model import VariantEffect\n", - "\n", - "analysis_config = CohortAnalysisConfiguration()\n", - "analysis_config.missing_implies_excluded = True\n", - "analysis_config.pval_correction = 'fdr_bh'\n", - "analysis_config.min_perc_patients_w_hpo = 0.1\n", - "analysis = configure_cohort_analysis(cohort, hpo, config=analysis_config)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "450\n" - ] - }, { "data": { "text/html": [ - "
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" \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
MISSENSE_VARIANT on NM_003482.4YesNo
CountPercentCountPercentp valueCorrected p value
Sensorineural hearing impairment [HP:0000407]6/1155%0/130%0.0034321.0
Hearing impairment [HP:0000365]14/1974%8/2138%0.0308711.0
Ventricular septal defect [HP:0001629]0/150%5/2124%0.0619431.0
High forehead [HP:0000348]5/683%1/520%0.0800871.0
Motor delay [HP:0001270]2/825%2/2100%0.1333331.0
Hypotonia [HP:0001252]2/729%10/1471%0.1588241.0
Hypoglycemia [HP:0001943]1/1100%0/50%0.1666671.0
Patent ductus arteriosus [HP:0001643]1/244%4/2119%0.1690131.0
Decreased proportion of CD4-positive, alpha-beta memory T cells [HP:0410386]6/875%2/540%0.2929291.0
Depressed nasal ridge [HP:0000457]1/520%1/1100%0.3333331.0
Blue sclerae [HP:0000592]4/757%3/1030%0.3499591.0
Highly arched eyebrow [HP:0002553]8/989%14/2167%0.3741901.0
Atrial septal defect [HP:0001631]5/771%6/1346%0.3742261.0
Eversion of lateral third of lower eyelids [HP:0007655]2/367%16/1889%0.3864661.0
Sacral dimple [HP:0000960]0/20%6/1060%0.4545451.0
Recurrent otitis media [HP:0000403]8/1553%6/1735%0.4764511.0
Decreased proportion of memory B cells [HP:0030374]6/875%5/5100%0.4871791.0
Renal duplication [HP:0000075]0/50%2/825%0.4871791.0
Ptosis [HP:0000508]0/70%2/1118%0.4967321.0
Intellectual disability, mild [HP:0001256]3/1225%1/250%0.5054951.0
Long palpebral fissure [HP:0000637]8/8100%17/2181%0.5520191.0
Sparse lateral eyebrow [HP:0005338]6/786%5/862%0.5692311.0
Seizure [HP:0001250]2/825%2/1712%0.5699601.0
Congenital hip dislocation [HP:0001374]1/714%5/1631%0.6213851.0
Coarctation of aorta [HP:0001680]1/157%3/2114%0.6256681.0
Clinodactyly of the 5th finger [HP:0004209]2/922%4/1136%0.6424151.0
Patent foramen ovale [HP:0001655]2/248%3/2015%0.6458961.0
Proportionate short stature [HP:0003508]7/1164%4/944%0.6534171.0
Severe short stature [HP:0003510]7/1258%4/1040%0.6699211.0
Scoliosis [HP:0002650]6/1932%4/1724%0.7169251.0
Depressed nasal tip [HP:0000437]16/2080%20/2774%0.7365041.0
Abnormality of the endocrine system [HP:0000818]7/7100%1/1100%1.0000001.0
Abnormal middle ear morphology [HP:0008609]0/00%1/1100%1.0000001.0
Growth abnormality [HP:0001507]13/13100%16/16100%1.0000001.0
Abnormality of the upper limb [HP:0002817]15/15100%20/20100%1.0000001.0
Orofacial cleft [HP:0000202]9/9100%8/8100%1.0000001.0
Abnormal ocular adnexa morphology [HP:0030669]13/13100%21/21100%1.0000001.0
Abnormal renal morphology [HP:0012210]1/1100%4/4100%1.0000001.0
Abnormality of the hand [HP:0001155]15/15100%20/20100%1.0000001.0
Abnormal soft palate morphology [HP:0100736]2/2100%0/00%1.0000001.0
Abnormality of metabolism/homeostasis [HP:0001939]5/5100%3/3100%1.0000001.0
Abnormal skeletal morphology [HP:0011842]23/23100%26/26100%1.0000001.0
Abnormality of the gastrointestinal tract [HP:0011024]5/5100%0/00%1.0000001.0
Abnormality of the scalp hair [HP:0100037]0/00%2/2100%1.0000001.0
Abnormality of the abdominal organs [HP:0002012]0/00%1/1100%1.0000001.0
Neoplasm by anatomical site [HP:0011793]0/00%1/1100%1.0000001.0
Cupped ear [HP:0000378]1/333%0/00%1.0000001.0
Abnormality of the genitourinary system [HP:0000119]4/4100%6/6100%1.0000001.0
Choanal atresia [HP:0000453]8/8100%0/00%1.0000001.0
Strabismus [HP:0000486]2/825%4/1625%1.0000001.0
Abnormal hair pattern [HP:0010720]0/00%2/2100%1.0000001.0
Periauricular skin pits [HP:0100277]3/3100%0/00%1.0000001.0
Abnormal lower lip morphology [HP:0000178]3/3100%1/1100%1.0000001.0
Abnormal hair morphology [HP:0001595]8/8100%17/17100%1.0000001.0
Single transverse palmar crease [HP:0000954]0/00%2/367%1.0000001.0
Abnormality of the breast [HP:0000769]10/10100%0/00%1.0000001.0
Abnormal spermatogenesis [HP:0008669]2/2100%1/1100%1.0000001.0
Abnormality of the upper urinary tract [HP:0010935]2/2100%5/5100%1.0000001.0
Abnormality of the choanae [HP:0000415]8/8100%0/00%1.0000001.0
Abnormal conjugate eye movement [HP:0000549]2/2100%4/4100%1.0000001.0
Tented upper lip vermilion [HP:0010804]1/1100%0/00%1.0000001.0
Abnormal eyelash morphology [HP:0000499]0/00%3/3100%1.0000001.0
Aplasia/hypoplasia involving the skeleton [HP:0009115]11/11100%11/11100%1.0000001.0
Language impairment [HP:0002463]8/8100%2/2100%1.0000001.0
Abnormal axial skeleton morphology [HP:0009121]13/13100%15/15100%1.0000001.0
Abnormal semicircular canal morphology [HP:0011380]3/3100%0/00%1.0000001.0
Abnormal T cell count [HP:0011839]6/6100%2/2100%1.0000001.0
Functional abnormality of the inner ear [HP:0011389]6/6100%0/00%1.0000001.0
Abnormal palate morphology [HP:0000174]12/12100%10/10100%1.0000001.0
Abnormality of the genital system [HP:0000078]2/2100%1/1100%1.0000001.0
Abnormality of the immune system [HP:0002715]15/15100%17/17100%1.0000001.0
Abnormality of the parathyroid gland [HP:0000828]4/4100%0/00%1.0000001.0
Global developmental delay [HP:0001263]14/1593%16/1794%1.0000001.0
Phenotypic abnormality [HP:0000118]33/33100%29/29100%1.0000001.0
Functional abnormality of male internal genitalia [HP:0000025]2/2100%1/1100%1.0000001.0
Abnormality of the cardiovascular system [HP:0001626]8/8100%12/12100%1.0000001.0
Abnormal inner ear morphology [HP:0011390]3/3100%0/00%1.0000001.0
Regional abnormality of skin [HP:0011356]12/12100%18/18100%1.0000001.0
Abnormality of the hairline [HP:0009553]0/00%2/2100%1.0000001.0
Type II pneumocyte hyperplasia [HP:0033328]1/1100%0/00%1.0000001.0
Abnormality of the integument [HP:0001574]19/19100%26/26100%1.0000001.0
Aplasia/hypoplasia affecting bones of the axial skeleton [HP:0009122]3/3100%4/4100%1.0000001.0
Abnormal nasal morphology [HP:0005105]24/24100%25/25100%1.0000001.0
Abnormal oral cavity morphology [HP:0000163]16/16100%13/13100%1.0000001.0
Abnormal digestive system morphology [HP:0025033]1/1100%1/1100%1.0000001.0
Abnormal limb bone morphology [HP:0002813]15/15100%21/21100%1.0000001.0
Abnormal ventricular septum morphology [HP:0010438]0/00%5/5100%1.0000001.0
Abnormal vascular morphology [HP:0025015]3/3100%6/6100%1.0000001.0
Underdeveloped nasal alae [HP:0000430]4/667%0/00%1.0000001.0
Sacrococcygeal pilonidal abnormality [HP:0010767]0/00%6/6100%1.0000001.0
Abnormal B cell count [HP:0010975]6/6100%5/5100%1.0000001.0
Abnormal hand morphology [HP:0005922]12/12100%18/18100%1.0000001.0
Microcephaly [HP:0000252]3/743%4/1136%1.0000001.0
Cleft palate [HP:0000175]9/1090%8/8100%1.0000001.0
Abnormal heart valve physiology [HP:0031653]0/00%1/1100%1.0000001.0
Thick vermilion border [HP:0012471]3/3100%1/1100%1.0000001.0
Ventricular hypertrophy [HP:0001714]1/1100%0/00%1.0000001.0
Neurodevelopmental abnormality [HP:0012759]22/22100%22/22100%1.0000001.0
Abnormal size of the palpebral fissures [HP:0200007]8/8100%17/17100%1.0000001.0
Finger clinodactyly [HP:0040019]4/4100%12/12100%1.0000001.0
Otitis media [HP:0000388]8/8100%6/6100%1.0000001.0
Abnormal morphology of the great vessels [HP:0030962]3/3100%6/6100%1.0000001.0
Abnormal palmar dermatoglyphics [HP:0001018]0/00%2/2100%1.0000001.0
Abnormal spleen morphology [HP:0025408]0/00%1/1100%1.0000001.0
Abnormal heart valve morphology [HP:0001654]0/00%1/1100%1.0000001.0
Abnormal aortic valve morphology [HP:0001646]0/00%1/1100%1.0000001.0
Abnormal leukocyte physiology [HP:0033796]4/4100%3/3100%1.0000001.0
Low-set ears [HP:0000369]2/540%0/00%1.0000001.0
Abnormal aortic valve physiology [HP:0031652]0/00%1/1100%1.0000001.0
Deviation of the 5th finger [HP:0009179]2/2100%4/4100%1.0000001.0
Abnormal nasolacrimal system morphology [HP:0000614]1/1100%0/00%1.0000001.0
Abnormal male reproductive system physiology [HP:0012874]2/2100%1/1100%1.0000001.0
Abnormal circulating IgG level [HP:0410242]2/2100%1/1100%1.0000001.0
Obesity [HP:0001513]0/00%1/1100%1.0000001.0
Abnormal leukocyte morphology [HP:0001881]7/7100%5/5100%1.0000001.0
Aplasia of the semicircular canal [HP:0011381]3/475%0/00%1.0000001.0
Joint hypermobility [HP:0001382]2/729%3/1225%1.0000001.0
Abnormal cellular physiology [HP:0011017]4/4100%3/3100%1.0000001.0
Abnormality of the skeletal system [HP:0000924]23/23100%26/26100%1.0000001.0
Abnormal atrial septum morphology [HP:0011994]5/5100%6/6100%1.0000001.0
Neoplasm of the middle ear [HP:0100799]0/00%1/1100%1.0000001.0
Abnormal nostril morphology [HP:0005288]3/3100%0/00%1.0000001.0
Abnormal jaw morphology [HP:0030791]4/4100%4/4100%1.0000001.0
Aplasia/Hypoplasia involving the nose [HP:0009924]4/4100%0/00%1.0000001.0
Abnormal alveolar type II pneumocyte morphology [HP:0033245]1/1100%0/00%1.0000001.0
Tooth malposition [HP:0000692]2/2100%0/00%1.0000001.0
Abnormal cardiac atrium morphology [HP:0005120]5/5100%6/6100%1.0000001.0
Abnormal skin adnexa morphology [HP:0011138]8/8100%17/17100%1.0000001.0
Abnormal anus morphology [HP:0034915]1/1100%1/1100%1.0000001.0
Abnormality of the thyroid gland [HP:0000820]7/7100%0/00%1.0000001.0
Abnormal sacrum morphology [HP:0005107]0/00%6/6100%1.0000001.0
Abnormal mandible morphology [HP:0000277]4/4100%4/4100%1.0000001.0
Widely spaced teeth [HP:0000687]1/425%0/00%1.0000001.0
Aplasia/hypoplasia involving bones of the hand [HP:0005927]8/8100%7/7100%1.0000001.0
Abnormal T cell morphology [HP:0002843]6/6100%2/2100%1.0000001.0
Abnormal uvula morphology [HP:0000172]2/2100%0/00%1.0000001.0
Aplasia/Hypoplasia of fingers [HP:0006265]8/8100%7/7100%1.0000001.0
Aplasia/Hypoplasia of the nipples [HP:0006709]10/10100%0/00%1.0000001.0
Renal hypoplasia/aplasia [HP:0008678]1/1100%0/00%1.0000001.0
Oligodontia [HP:0000677]3/3100%0/00%1.0000001.0
Congenital malformation of the left heart [HP:0045017]0/00%1/1100%1.0000001.0
High palate [HP:0000218]10/1191%8/8100%1.0000001.0
Abnormal 5th finger morphology [HP:0004207]10/10100%10/10100%1.0000001.0
Abnormal earlobe morphology [HP:0000363]8/8100%6/6100%1.0000001.0
Abnormal eyelid morphology [HP:0000492]12/12100%20/20100%1.0000001.0
Cryptorchidism [HP:0000028]2/2100%1/1100%1.0000001.0
Abnormal muscle physiology [HP:0011804]2/2100%10/10100%1.0000001.0
Abnormality of the dentition [HP:0000164]8/8100%0/00%1.0000001.0
Vertebral clefting [HP:0008428]0/00%1/1100%1.0000001.0
Short digit [HP:0011927]8/8100%11/11100%1.0000001.0
Abnormal skin morphology of the palm [HP:0040211]0/00%2/2100%1.0000001.0
Nasolacrimal duct obstruction [HP:0000579]1/1100%0/00%1.0000001.0
Abnormal vertebral morphology [HP:0003468]0/00%1/1100%1.0000001.0
Telecanthus [HP:0000506]1/1100%0/00%1.0000001.0
Abnormality of the fontanelles or cranial sutures [HP:0000235]0/00%1/1100%1.0000001.0
Low posterior hairline [HP:0002162]0/10%2/633%1.0000001.0
Deviation of finger [HP:0004097]4/4100%12/12100%1.0000001.0
Abnormal ear morphology [HP:0031703]21/21100%23/23100%1.0000001.0
Abnormality of reproductive system physiology [HP:0000080]2/2100%1/1100%1.0000001.0
Long eyelashes [HP:0000527]0/10%3/650%1.0000001.0
Abnormal leukocyte count [HP:0011893]7/7100%5/5100%1.0000001.0
Abnormality of the inner ear [HP:0000359]3/3100%0/00%1.0000001.0
Abnormal hip joint morphology [HP:0001384]1/1100%5/5100%1.0000001.0
Abnormality of the palmar creases [HP:0010490]0/00%2/2100%1.0000001.0
Decreased proportion of memory T cells [HP:0032183]6/6100%2/2100%1.0000001.0
Abnormal cardiac ventricle morphology [HP:0001713]1/1100%5/5100%1.0000001.0
Persistence of primary teeth [HP:0006335]2/450%0/00%1.0000001.0
Abnormality of the posterior hairline [HP:0030141]0/00%2/2100%1.0000001.0
Abnormal esophagus physiology [HP:0025270]5/5100%0/00%1.0000001.0
Abnormality of humoral immunity [HP:0005368]4/4100%3/3100%1.0000001.0
Abnormal respiratory system morphology [HP:0012252]2/2100%1/1100%1.0000001.0
Abnormality of the orbital region [HP:0000315]13/13100%21/21100%1.0000001.0
Abnormal lacrimal duct morphology [HP:0011481]1/1100%0/00%1.0000001.0
Abnormal location of ears [HP:0000357]2/2100%0/00%1.0000001.0
Unusual infection [HP:0032101]15/15100%15/15100%1.0000001.0
Abnormality of digestive system physiology [HP:0025032]9/9100%3/3100%1.0000001.0
Abnormal glucose homeostasis [HP:0011014]1/1100%0/00%1.0000001.0
Depressed nasal bridge [HP:0005280]1/250%1/1100%1.0000001.0
Abnormal calvaria morphology [HP:0002683]0/00%1/1100%1.0000001.0
Abnormal pulmonary vein morphology [HP:0030968]1/1100%0/00%1.0000001.0
Abnormal respiratory system physiology [HP:0002795]3/3100%0/00%1.0000001.0
Abnormal number of teeth [HP:0006483]3/3100%0/00%1.0000001.0
Morphological central nervous system abnormality [HP:0002011]3/3100%5/5100%1.0000001.0
Abnormality of the lymphatic system [HP:0100763]0/00%1/1100%1.0000001.0
Abnormality of the pulmonary vasculature [HP:0004930]1/1100%0/00%1.0000001.0
Posteriorly rotated ears [HP:0000358]1/520%0/00%1.0000001.0
Abnormality of the face [HP:0000271]26/26100%28/28100%1.0000001.0
Abnormal fingertip morphology [HP:0001211]12/12100%18/18100%1.0000001.0
Abnormality of the palpebral fissures [HP:0008050]11/11100%18/18100%1.0000001.0
Decreased circulating IgG subclass level [HP:0032135]1/1100%0/00%1.0000001.0
Deviation of the hand or of fingers of the hand [HP:0009484]4/4100%12/12100%1.0000001.0
Partial anomalous pulmonary venous return [HP:0010773]1/1100%0/00%1.0000001.0
Bifid uvula [HP:0000193]2/367%0/00%1.0000001.0
Decreased circulating antibody level [HP:0004313]4/4100%3/3100%1.0000001.0
Cleft lip [HP:0410030]0/00%1/1100%1.0000001.0
Abnormal eyebrow morphology [HP:0000534]8/8100%16/16100%1.0000001.0
Abnormality of mental function [HP:0011446]19/19100%14/14100%1.0000001.0
Brachydactyly [HP:0001156]0/00%4/4100%1.0000001.0
Branchial anomaly [HP:0009794]2/2100%0/00%1.0000001.0
Abnormal pituitary gland morphology [HP:0012503]0/00%1/1100%1.0000001.0
Short nose [HP:0003196]2/2100%0/00%1.0000001.0
Ectopic anus [HP:0004397]1/1100%0/00%1.0000001.0
Cholesteatoma [HP:0009797]0/10%1/520%1.0000001.0
Decreased head circumference [HP:0040195]3/3100%4/4100%1.0000001.0
Prominent fingertip pads [HP:0001212]12/1580%18/2378%1.0000001.0
Abnormal pulmonary alveolar system morphology [HP:0033137]1/1100%0/00%1.0000001.0
Abnormal abdomen morphology [HP:0001438]0/00%1/1100%1.0000001.0
Abnormal nervous system morphology [HP:0012639]3/3100%5/5100%1.0000001.0
Microtia [HP:0008551]1/1100%0/00%1.0000001.0
Abnormal circulating IgA level [HP:0410240]2/2100%2/2100%1.0000001.0
Abnormality of the amniotic fluid [HP:0001560]1/1100%0/00%1.0000001.0
Aplasia/Hypoplasia of the external ear [HP:0008772]1/1100%0/00%1.0000001.0
Abnormal thorax morphology [HP:0000765]1/1100%0/00%1.0000001.0
Abnormal dental morphology [HP:0006482]3/3100%0/00%1.0000001.0
Abnormal periauricular region morphology [HP:0000383]3/3100%0/00%1.0000001.0
Abnormality of pulmonary circulation [HP:0030875]3/3100%0/00%1.0000001.0
Pulmonary arterial hypertension [HP:0002092]3/1030%0/00%1.0000001.0
Wide nasal bridge [HP:0000431]3/3100%11/1479%1.0000001.0
Abnormality of lower limb joint [HP:0100491]1/1100%5/5100%1.0000001.0
Hypoplastic heart [HP:0001961]0/00%1/1100%1.0000001.0
Postnatal growth retardation [HP:0008897]5/862%4/580%1.0000001.0
Abnormal ear physiology [HP:0031704]14/14100%8/8100%1.0000001.0
Renal hypoplasia [HP:0000089]1/520%0/00%1.0000001.0
Abnormal testis morphology [HP:0000035]2/2100%1/1100%1.0000001.0
Abnormal upper lip morphology [HP:0000177]4/4100%5/5100%1.0000001.0
Holoprosencephaly [HP:0001360]1/1100%2/2100%1.0000001.0
Wide nose [HP:0000445]0/00%1/1100%1.0000001.0
Neoplasm [HP:0002664]0/00%1/1100%1.0000001.0
Abnormal cerebral morphology [HP:0002060]3/3100%4/4100%1.0000001.0
Hypotelorism [HP:0000601]1/1100%2/2100%1.0000001.0
Abnormal cardiovascular system morphology [HP:0030680]7/7100%11/11100%1.0000001.0
Polyhydramnios [HP:0001561]1/1100%0/10%1.0000001.0
Abnormality of thyroid physiology [HP:0002926]7/7100%0/00%1.0000001.0
Abnormality of body weight [HP:0004323]0/00%1/1100%1.0000001.0
Abnormality of eye movement [HP:0000496]2/2100%4/4100%1.0000001.0
Abnormality of the eye [HP:0000478]8/8100%9/9100%1.0000001.0
Joint dislocation [HP:0001373]1/1100%5/5100%1.0000001.0
Abnormal blood glucose concentration [HP:0011015]1/1100%0/00%1.0000001.0
Abnormal lymphocyte physiology [HP:0031409]4/4100%3/3100%1.0000001.0
Branchial sinus [HP:0100272]2/450%0/00%1.0000001.0
Abnormal thoracic spine morphology [HP:0100711]1/1100%0/00%1.0000001.0
Abnormality of the head [HP:0000234]26/26100%28/28100%1.0000001.0
Abdominal symptom [HP:0011458]5/5100%3/3100%1.0000001.0
Abnormal lung morphology [HP:0002088]2/2100%1/1100%1.0000001.0
Abnormal cellular phenotype [HP:0025354]4/4100%3/3100%1.0000001.0
Abnormal skin morphology [HP:0011121]14/14100%18/18100%1.0000001.0
Abnormal musculoskeletal physiology [HP:0011843]2/2100%3/3100%1.0000001.0
Abnormal eye physiology [HP:0012373]2/2100%4/4100%1.0000001.0
Intrauterine growth retardation [HP:0001511]1/1100%0/00%1.0000001.0
Smooth philtrum [HP:0000319]3/3100%0/00%1.0000001.0
Subpleural honeycombing [HP:0031631]1/1100%0/00%1.0000001.0
Aplasia/Hypoplasia involving bones of the skull [HP:0009116]3/3100%4/4100%1.0000001.0
Abnormal endocrine physiology [HP:0031072]0/00%1/1100%1.0000001.0
Aplasia/Hypoplasia of the mandible [HP:0009118]3/3100%4/4100%1.0000001.0
Abnormal ureter morphology [HP:0025633]0/00%1/1100%1.0000001.0
Thin upper lip vermilion [HP:0000219]3/475%4/667%1.0000001.0
Splenomegaly [HP:0001744]0/20%1/617%1.0000001.0
Abnormal skull morphology [HP:0000929]7/7100%8/8100%1.0000001.0
Abnormal neck morphology [HP:0025668]2/2100%2/2100%1.0000001.0
Delayed speech and language development [HP:0000750]8/1080%2/2100%1.0000001.0
Abnormal cardiovascular system physiology [HP:0011025]3/3100%1/1100%1.0000001.0
Abnormal oral morphology [HP:0031816]16/16100%13/13100%1.0000001.0
Abnormality of joint mobility [HP:0011729]2/2100%3/3100%1.0000001.0
Cleft upper lip [HP:0000204]0/00%1/1100%1.0000001.0
Abnormal eye morphology [HP:0012372]6/6100%6/6100%1.0000001.0
Abnormality of the forehead [HP:0000290]5/5100%1/1100%1.0000001.0
Short 5th finger [HP:0009237]8/8100%7/7100%1.0000001.0
Downslanted palpebral fissures [HP:0000494]3/3100%1/1100%1.0000001.0
Bilateral renal hypoplasia [HP:0012584]1/520%0/00%1.0000001.0
Abnormality of the mouth [HP:0000153]16/16100%13/13100%1.0000001.0
Abnormal morphology of the nasal alae [HP:0000429]6/6100%0/00%1.0000001.0
Midface retrusion [HP:0011800]1/1100%1/1100%1.0000001.0
Abnormal lymphocyte morphology [HP:0004332]7/7100%5/5100%1.0000001.0
Abnormal proportion of memory T cells [HP:0032182]6/6100%2/2100%1.0000001.0
Abnormal aortic morphology [HP:0001679]1/1100%4/4100%1.0000001.0
Abnormal muscle tone [HP:0003808]2/2100%10/10100%1.0000001.0
Abnormal midface morphology [HP:0000309]4/4100%1/1100%1.0000001.0
Proboscis [HP:0012806]1/1100%0/10%1.0000001.0
Abnormality of B cell physiology [HP:0005372]4/4100%3/3100%1.0000001.0
Aplasia/Hypoplasia of the 5th finger [HP:0006262]8/8100%7/7100%1.0000001.0
Lobar holoprosencephaly [HP:0006870]0/00%1/1100%1.0000001.0
Abnormality of the vasculature [HP:0002597]5/5100%6/6100%1.0000001.0
Abnormal cheek morphology [HP:0004426]3/3100%0/00%1.0000001.0
Aplasia/hypoplasia involving bones of the extremities [HP:0045060]8/8100%7/7100%1.0000001.0
Abnormality of body height [HP:0000002]9/9100%13/13100%1.0000001.0
Abnormal nasal tip morphology [HP:0000436]16/16100%20/20100%1.0000001.0
Craniosynostosis [HP:0001363]0/00%1/1100%1.0000001.0
Dimple chin [HP:0010751]1/1100%0/00%1.0000001.0
Abnormal joint morphology [HP:0001367]1/1100%5/5100%1.0000001.0
Abnormal systemic arterial morphology [HP:0011004]1/1100%4/4100%1.0000001.0
Anterior hypopituitarism [HP:0000830]0/00%1/1100%1.0000001.0
Abnormal nasal dorsum morphology [HP:0011119]1/1100%1/1100%1.0000001.0
Abnormal heart morphology [HP:0001627]5/5100%10/10100%1.0000001.0
Pre-capillary pulmonary hypertension [HP:0033578]3/3100%0/00%1.0000001.0
Abnormal inflammatory response [HP:0012647]8/8100%6/6100%1.0000001.0
Anteriorly placed anus [HP:0001545]1/1100%0/00%1.0000001.0
Hearing abnormality [HP:0000364]14/14100%8/8100%1.0000001.0
Epicanthus [HP:0000286]0/10%1/520%1.0000001.0
Hip dysplasia [HP:0001385]0/00%2/2100%1.0000001.0
Abnormality of the nervous system [HP:0000707]26/26100%25/25100%1.0000001.0
Abnormal pinna morphology [HP:0000377]19/19100%22/22100%1.0000001.0
Abnormal joint physiology [HP:0034430]2/2100%3/3100%1.0000001.0
Prominent digit pad [HP:0011298]12/12100%18/18100%1.0000001.0
Hypoparathyroidism [HP:0000829]4/4100%0/00%1.0000001.0
Aplasia/hypoplasia involving bones of the upper limbs [HP:0006496]8/8100%7/7100%1.0000001.0
Abnormal sclera morphology [HP:0000591]4/4100%3/3100%1.0000001.0
Decreased circulating total IgG [HP:0032132]1/812%1/520%1.0000001.0
Abnormal palm morphology [HP:0100871]0/00%2/2100%1.0000001.0
Renal cyst [HP:0000107]0/00%1/1100%1.0000001.0
Abnormal hip bone morphology [HP:0003272]1/1100%7/7100%1.0000001.0
Ectropion [HP:0000656]2/2100%16/16100%1.0000001.0
Abnormality of the musculoskeletal system [HP:0033127]23/23100%26/26100%1.0000001.0
Abnormal blood vessel morphology [HP:0033353]3/3100%6/6100%1.0000001.0
Abnormality of blood and blood-forming tissues [HP:0001871]8/8100%5/5100%1.0000001.0
Increased inflammatory response [HP:0012649]8/8100%6/6100%1.0000001.0
Abnormality of the respiratory system [HP:0002086]5/5100%1/1100%1.0000001.0
Abnormality of prenatal development or birth [HP:0001197]1/1100%0/00%1.0000001.0
Short stature [HP:0004322]9/1369%13/1872%1.0000001.0
Abnormal B cell subset distribution [HP:0025539]6/6100%5/5100%1.0000001.0
Median cleft lip [HP:0000161]0/10%1/1100%1.0000001.0
Aplasia/Hypoplasia of the ear [HP:0008771]1/1100%0/00%1.0000001.0
Abnormality of the spleen [HP:0001743]0/00%1/1100%1.0000001.0
Abnormality of the chin [HP:0000306]1/1100%0/00%1.0000001.0
Thin vermilion border [HP:0000233]5/5100%4/4100%1.0000001.0
Growth delay [HP:0001510]13/13100%16/16100%1.0000001.0
Dental malocclusion [HP:0000689]1/425%0/00%1.0000001.0
Recurrent infections [HP:0002719]15/2171%15/2075%1.0000001.0
Clinodactyly [HP:0030084]4/4100%12/12100%1.0000001.0
Abnormality of the outer ear [HP:0000356]19/19100%22/22100%1.0000001.0
Aplasia/Hypoplasia of the eyebrow [HP:0100840]6/6100%8/8100%1.0000001.0
Abnormal nipple morphology [HP:0004404]10/10100%0/00%1.0000001.0
Increased body weight [HP:0004324]0/00%1/1100%1.0000001.0
Abnormal breast morphology [HP:0031093]10/10100%0/00%1.0000001.0
Abnormality of the anterior pituitary [HP:0011747]0/00%1/1100%1.0000001.0
Abnormal dermatoglyphics [HP:0007477]0/00%2/2100%1.0000001.0
Lower extremity joint dislocation [HP:0030311]1/1100%5/5100%1.0000001.0
Abnormal nervous system physiology [HP:0012638]24/24100%23/23100%1.0000001.0
Abnormality of the kidney [HP:0000077]2/2100%4/4100%1.0000001.0
Abnormal digit morphology [HP:0011297]15/15100%21/21100%1.0000001.0
Abnormal cardiac septum morphology [HP:0001671]5/5100%8/8100%1.0000001.0
Microdontia [HP:0000691]3/560%0/00%1.0000001.0
Aplasia/Hypoplasia involving the central nervous system [HP:0002977]3/3100%4/4100%1.0000001.0
Azoospermia [HP:0000027]2/2100%1/1100%1.0000001.0
Aplasia/hypoplasia of the extremities [HP:0009815]8/8100%7/7100%1.0000001.0
Abnormal immune system morphology [HP:0032251]7/7100%5/5100%1.0000001.0
Abnormal B cell morphology [HP:0002846]6/6100%5/5100%1.0000001.0
Abnormal lip morphology [HP:0000159]6/6100%5/5100%1.0000001.0
Abnormality of the urinary system physiology [HP:0011277]1/1100%0/00%1.0000001.0
Macrotia [HP:0000400]8/1080%16/2080%1.0000001.0
Neurodevelopmental delay [HP:0012758]21/21100%18/18100%1.0000001.0
Abnormal homeostasis [HP:0012337]1/1100%0/00%1.0000001.0
Large earlobe [HP:0009748]8/8100%6/6100%1.0000001.0
Abnormal cellular immune system morphology [HP:0010987]7/7100%5/5100%1.0000001.0
Respiratory tract infection [HP:0011947]0/00%1/1100%1.0000001.0
Anal atresia [HP:0002023]0/10%1/520%1.0000001.0
Preauricular pit [HP:0004467]3/3100%0/00%1.0000001.0
Abnormal pelvic girdle bone morphology [HP:0002644]1/1100%7/7100%1.0000001.0
Anomalous pulmonary venous return [HP:0010772]1/1100%0/00%1.0000001.0
Abnormality of the anus [HP:0004378]1/1100%1/1100%1.0000001.0
Localized skin lesion [HP:0011355]3/3100%6/6100%1.0000001.0
Abnormality of the nose [HP:0000366]25/25100%25/25100%1.0000001.0
Ectopic kidney [HP:0000086]0/20%2/922%1.0000001.0
Congenital malformation of the great arteries [HP:0011603]1/1100%4/4100%1.0000001.0
Abnormal curvature of the vertebral column [HP:0010674]6/6100%4/4100%1.0000001.0
Slanting of the palpebral fissure [HP:0200006]3/3100%1/1100%1.0000001.0
Abnormality of speech or vocalization [HP:0002167]8/8100%2/2100%1.0000001.0
Abnormal proportion of memory B cells [HP:0030373]6/6100%5/5100%1.0000001.0
Abnormal response to endocrine stimulation test [HP:0031073]0/00%1/1100%1.0000001.0
Abnormality of the lower limb [HP:0002814]1/1100%5/5100%1.0000001.0
Decreased circulating IgA level [HP:0002720]2/825%2/540%1.0000001.0
Abnormal finger morphology [HP:0001167]15/15100%20/20100%1.0000001.0
Abnormality of limbs [HP:0040064]15/15100%23/23100%1.0000001.0
Non-obstructive azoospermia [HP:0011961]2/2100%1/1100%1.0000001.0
Abnormal communication [HP:0034434]8/8100%2/2100%1.0000001.0
Abnormality of the neck [HP:0000464]2/2100%2/2100%1.0000001.0
Hypothyroidism [HP:0000821]7/7100%0/00%1.0000001.0
Abnormal aortic valve cusp morphology [HP:0031567]0/00%1/1100%1.0000001.0
Visceromegaly [HP:0003271]0/00%1/1100%1.0000001.0
Abnormality of the philtrum [HP:0000288]3/3100%0/00%1.0000001.0
Tooth agenesis [HP:0009804]3/3100%0/00%1.0000001.0
Abnormality of globe location [HP:0100886]2/2100%3/3100%1.0000001.0
Abnormal vascular physiology [HP:0030163]3/3100%0/00%1.0000001.0
Renal insufficiency [HP:0000083]1/1100%0/00%1.0000001.0
Abnormal scalp morphology [HP:0001965]0/00%2/2100%1.0000001.0
Abnormality of immune system physiology [HP:0010978]15/15100%16/16100%1.0000001.0
Abnormal nasal bridge morphology [HP:0000422]4/4100%12/12100%1.0000001.0
Abnormal brain morphology [HP:0012443]3/3100%5/5100%1.0000001.0
Feeding difficulties [HP:0011968]5/1436%3/1127%1.0000001.0
Abnormality of skull size [HP:0000240]3/3100%4/4100%1.0000001.0
Micrognathia [HP:0000347]3/650%4/944%1.0000001.0
Abnormal form of the vertebral bodies [HP:0003312]0/00%1/1100%1.0000001.0
Neoplasm of the ear [HP:0012780]0/00%1/1100%1.0000001.0
Abnormality of the ear [HP:0000598]24/24100%23/23100%1.0000001.0
Decreased circulating IgG level [HP:0004315]2/2100%1/1100%1.0000001.0
Hypoplastic nipples [HP:0002557]5/1242%0/00%1.0000001.0
Abnormality of the musculature [HP:0003011]2/2100%10/10100%1.0000001.0
Alobar holoprosencephaly [HP:0006988]1/1100%1/250%1.0000001.0
Aplasia/Hypoplasia of the cerebrum [HP:0007364]3/3100%4/4100%1.0000001.0
Gastroesophageal reflux [HP:0002020]5/1729%0/00%1.0000001.0
Abnormal lymphocyte count [HP:0040088]7/7100%5/5100%1.0000001.0
Abnormal appendicular skeleton morphology [HP:0011844]15/15100%23/23100%1.0000001.0
Abnormality of the digestive system [HP:0025031]10/10100%4/4100%1.0000001.0
Abnormal subpleural morphology [HP:0031630]1/1100%0/00%1.0000001.0
Sparse eyebrow [HP:0045075]6/786%8/1173%1.0000001.0
Abnormal right ventricle morphology [HP:0001707]1/1100%0/00%1.0000001.0
Narrow palate [HP:0000189]8/8100%6/6100%1.0000001.0
Elevated pulmonary artery pressure [HP:0004890]3/3100%0/00%1.0000001.0
Protruding ear [HP:0000411]0/00%1/1100%1.0000001.0
Abnormality of primary teeth [HP:0006481]2/2100%0/00%1.0000001.0
Hip dislocation [HP:0002827]1/1100%5/5100%1.0000001.0
Hypertelorism [HP:0000316]1/250%1/520%1.0000001.0
Abnormal aortic arch morphology [HP:0012303]0/00%1/1100%1.0000001.0
Abnormality of the skin [HP:0000951]14/14100%18/18100%1.0000001.0
Butterfly vertebrae [HP:0003316]0/00%1/1100%1.0000001.0
Anteverted nares [HP:0000463]3/3100%0/00%1.0000001.0
Abnormality of upper lip vermillion [HP:0011339]4/4100%4/4100%1.0000001.0
Congenital hypothyroidism [HP:0000851]3/650%0/00%1.0000001.0
Abnormal T cell subset distribution [HP:0025540]6/6100%2/2100%1.0000001.0
Abnormal localization of kidney [HP:0100542]0/00%2/2100%1.0000001.0
Abnormal forebrain morphology [HP:0100547]3/3100%4/4100%1.0000001.0
High, narrow palate [HP:0002705]8/989%6/6100%1.0000001.0
Abnormality of limb bone [HP:0040068]15/15100%21/21100%1.0000001.0
Abnormality of the ocular adnexa [HP:0032039]13/13100%21/21100%1.0000001.0
Decreased response to growth hormone stimulation test [HP:0000824]0/00%1/1100%1.0000001.0
Abnormal morphology of the vestibule of the inner ear [HP:0011376]3/3100%0/00%1.0000001.0
Congenital abnormal hair pattern [HP:0011361]0/00%2/2100%1.0000001.0
Abnormality of the ureter [HP:0000069]0/00%1/1100%1.0000001.0
Short finger [HP:0009381]8/8100%7/7100%1.0000001.0
All [HP:0000001]33/33100%29/29100%1.0000001.0
Abnormality of dental eruption [HP:0006292]2/2100%0/00%1.0000001.0
Abnormality of the hypothalamus-pituitary axis [HP:0000864]0/00%1/1100%1.0000001.0
Thick lower lip vermilion [HP:0000179]3/3100%1/1100%1.0000001.0
Abnormal facial skeleton morphology [HP:0011821]4/4100%4/4100%1.0000001.0
Absent nipple [HP:0002561]5/1242%0/00%1.0000001.0
Intellectual disability [HP:0001249]12/1392%13/1493%1.0000001.0
Abnormal renal physiology [HP:0012211]1/1100%0/00%1.0000001.0
Abnormality of the pulmonary veins [HP:0011718]1/1100%0/00%1.0000001.0
Hypopituitarism [HP:0040075]0/00%1/1100%1.0000001.0
Abnormality of head or neck [HP:0000152]26/26100%28/28100%1.0000001.0
Abnormality of the parathyroid physiology [HP:0011767]4/4100%0/00%1.0000001.0
Skin dimple [HP:0010781]0/00%6/6100%1.0000001.0
Abnormality of the middle ear [HP:0000370]8/8100%7/7100%1.0000001.0
Retrognathia [HP:0000278]1/1100%0/00%1.0000001.0
Abnormal immunoglobulin level [HP:0010701]4/4100%3/3100%1.0000001.0
Functional abnormality of the gastrointestinal tract [HP:0012719]5/5100%0/00%1.0000001.0
Abnormal external nose morphology [HP:0010938]22/22100%21/21100%1.0000001.0
Skin pit [HP:0100276]3/3100%0/00%1.0000001.0
Abnormality of cranial sutures [HP:0011329]0/00%1/1100%1.0000001.0
Abnormality of the vertebral column [HP:0000925]6/6100%10/10100%1.0000001.0
Abnormality of the urinary system [HP:0000079]2/2100%5/5100%1.0000001.0
\n", - "
" + "\n", + "\n", + "\n", + "

GPSEA protein analysis

\n", + "

The UniProt API successfully returned protein information for ID: NP_003473.3

\n", + "

Protein Name: Histone-lysine N-methyltransferase 2D

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\n", + "

Protein Features

\n", + "
Feature NameFeature TypeFeature CoordinatesVariants in Feature
Disorderedregion1 - 602
Disorderedregion368 - 3870
Disorderedregion393 - 4160
Disorderedregion436 - 13313
15 X 5 AA repeats of S/P-P-P-E/P-E/Aregion439 - 6680
1repeat442 - 4460
2repeat460 - 4640
3repeat469 - 4730
4repeat496 - 5000
5repeat504 - 5080
6repeat521 - 5250
7repeat555 - 5590
8repeat564 - 5680
9repeat573 - 5770
10repeat582 - 5860
11repeat609 - 6130
12repeat618 - 6220
13repeat627 - 6310
14repeat645 - 6490
15repeat663 - 6670
Disorderedregion1340 - 13590
Disorderedregion1610 - 17673
Disorderedregion1793 - 18890
Disorderedregion1904 - 20023
Disorderedregion2165 - 26833
LXXLL motif 1motif2686 - 26900
Disorderedregion2697 - 28142
Disorderedregion2835 - 29962
LXXLL motif 2motif3038 - 30420
Disorderedregion3078 - 31100
Disorderedregion3147 - 32090
Disorderedregion3263 - 33390
Disorderedregion3462 - 34990
Disorderedregion3596 - 36730
Disorderedregion3758 - 38020
Disorderedregion3984 - 41912
LXXLL motif 3motif4222 - 42260
Disorderedregion4233 - 43980
LXXLL motif 4motif4253 - 42570
Disorderedregion4410 - 44521
LXXLL motif 5motif4463 - 44670
Disorderedregion4503 - 45440
Disorderedregion4613 - 47270
Disorderedregion4822 - 48570
Disorderedregion4905 - 49800
LXXLL motif 6motif4990 - 49940
FYR N-terminaldomain5175 - 52350
FYR C-terminaldomain5236 - 53210
WDR5 interaction motif (WIN)motif5337 - 53420
SETdomain5397 - 55139
Post-SETdomain5521 - 55370
\n", + "\n", + "" ], "text/plain": [ - "MISSENSE_VARIANT on NM_003482.4 Yes No \\\n", - " Count Percent Count \n", - "Sensorineural hearing impairment [HP:0000407] 6/11 55% 0/13 \n", - "Hearing impairment [HP:0000365] 14/19 74% 8/21 \n", - "Ventricular septal defect [HP:0001629] 0/15 0% 5/21 \n", - "High forehead [HP:0000348] 5/6 83% 1/5 \n", - "Motor delay [HP:0001270] 2/8 25% 2/2 \n", - "Hypotonia [HP:0001252] 2/7 29% 10/14 \n", - "Hypoglycemia [HP:0001943] 1/1 100% 0/5 \n", - "Patent ductus arteriosus [HP:0001643] 1/24 4% 4/21 \n", - "Decreased proportion of CD4-positive, alpha-bet... 6/8 75% 2/5 \n", - "Depressed nasal ridge [HP:0000457] 1/5 20% 1/1 \n", - "Blue sclerae [HP:0000592] 4/7 57% 3/10 \n", - "Highly arched eyebrow [HP:0002553] 8/9 89% 14/21 \n", - "Atrial septal defect [HP:0001631] 5/7 71% 6/13 \n", - "Eversion of lateral third of lower eyelids [HP:... 2/3 67% 16/18 \n", - "Sacral dimple [HP:0000960] 0/2 0% 6/10 \n", - "Recurrent otitis media [HP:0000403] 8/15 53% 6/17 \n", - "Decreased proportion of memory B cells [HP:0030... 6/8 75% 5/5 \n", - "Renal duplication [HP:0000075] 0/5 0% 2/8 \n", - "Ptosis [HP:0000508] 0/7 0% 2/11 \n", - "Intellectual disability, mild [HP:0001256] 3/12 25% 1/2 \n", - "Long palpebral fissure [HP:0000637] 8/8 100% 17/21 \n", - "Sparse lateral eyebrow [HP:0005338] 6/7 86% 5/8 \n", - "Seizure [HP:0001250] 2/8 25% 2/17 \n", - "Congenital hip dislocation [HP:0001374] 1/7 14% 5/16 \n", - "Coarctation of aorta [HP:0001680] 1/15 7% 3/21 \n", - "Clinodactyly of the 5th finger [HP:0004209] 2/9 22% 4/11 \n", - "Patent foramen ovale [HP:0001655] 2/24 8% 3/20 \n", - "Proportionate short stature [HP:0003508] 7/11 64% 4/9 \n", - "Severe short stature [HP:0003510] 7/12 58% 4/10 \n", - "Scoliosis [HP:0002650] 6/19 32% 4/17 \n", - "Depressed nasal tip [HP:0000437] 16/20 80% 20/27 \n", - "Abnormality of the endocrine system [HP:0000818] 7/7 100% 1/1 \n", - "Abnormal middle ear morphology [HP:0008609] 0/0 0% 1/1 \n", - "Growth abnormality [HP:0001507] 13/13 100% 16/16 \n", - "Abnormality of the upper limb [HP:0002817] 15/15 100% 20/20 \n", - "Orofacial cleft [HP:0000202] 9/9 100% 8/8 \n", - "Abnormal ocular adnexa morphology [HP:0030669] 13/13 100% 21/21 \n", - "Abnormal renal morphology [HP:0012210] 1/1 100% 4/4 \n", - "Abnormality of the hand [HP:0001155] 15/15 100% 20/20 \n", - "Abnormal soft palate morphology [HP:0100736] 2/2 100% 0/0 \n", - "Abnormality of metabolism/homeostasis [HP:0001939] 5/5 100% 3/3 \n", - "Abnormal skeletal morphology [HP:0011842] 23/23 100% 26/26 \n", - "Abnormality of the gastrointestinal tract [HP:0... 5/5 100% 0/0 \n", - "Abnormality of the scalp hair [HP:0100037] 0/0 0% 2/2 \n", - "Abnormality of the abdominal organs [HP:0002012] 0/0 0% 1/1 \n", - "Neoplasm by anatomical site [HP:0011793] 0/0 0% 1/1 \n", - "Cupped ear [HP:0000378] 1/3 33% 0/0 \n", - "Abnormality of the genitourinary system [HP:000... 4/4 100% 6/6 \n", - "Choanal atresia [HP:0000453] 8/8 100% 0/0 \n", - "Strabismus [HP:0000486] 2/8 25% 4/16 \n", - "Abnormal hair pattern [HP:0010720] 0/0 0% 2/2 \n", - "Periauricular skin pits [HP:0100277] 3/3 100% 0/0 \n", - "Abnormal lower lip morphology [HP:0000178] 3/3 100% 1/1 \n", - "Abnormal hair morphology [HP:0001595] 8/8 100% 17/17 \n", - "Single transverse palmar crease [HP:0000954] 0/0 0% 2/3 \n", - "Abnormality of the breast [HP:0000769] 10/10 100% 0/0 \n", - "Abnormal spermatogenesis [HP:0008669] 2/2 100% 1/1 \n", - "Abnormality of the upper urinary tract [HP:0010... 2/2 100% 5/5 \n", - "Abnormality of the choanae [HP:0000415] 8/8 100% 0/0 \n", - "Abnormal conjugate eye movement [HP:0000549] 2/2 100% 4/4 \n", - "Tented upper lip vermilion [HP:0010804] 1/1 100% 0/0 \n", - "Abnormal eyelash morphology [HP:0000499] 0/0 0% 3/3 \n", - "Aplasia/hypoplasia involving the skeleton [HP:0... 11/11 100% 11/11 \n", - "Language impairment [HP:0002463] 8/8 100% 2/2 \n", - "Abnormal axial skeleton morphology [HP:0009121] 13/13 100% 15/15 \n", - "Abnormal semicircular canal morphology [HP:0011... 3/3 100% 0/0 \n", - "Abnormal T cell count [HP:0011839] 6/6 100% 2/2 \n", - "Functional abnormality of the inner ear [HP:001... 6/6 100% 0/0 \n", - "Abnormal palate morphology [HP:0000174] 12/12 100% 10/10 \n", - "Abnormality of the genital system [HP:0000078] 2/2 100% 1/1 \n", - "Abnormality of the immune system [HP:0002715] 15/15 100% 17/17 \n", - "Abnormality of the parathyroid gland [HP:0000828] 4/4 100% 0/0 \n", - "Global developmental delay [HP:0001263] 14/15 93% 16/17 \n", - "Phenotypic abnormality [HP:0000118] 33/33 100% 29/29 \n", - "Functional abnormality of male internal genital... 2/2 100% 1/1 \n", - "Abnormality of the cardiovascular system [HP:00... 8/8 100% 12/12 \n", - "Abnormal inner ear morphology [HP:0011390] 3/3 100% 0/0 \n", - "Regional abnormality of skin [HP:0011356] 12/12 100% 18/18 \n", - "Abnormality of the hairline [HP:0009553] 0/0 0% 2/2 \n", - "Type II pneumocyte hyperplasia [HP:0033328] 1/1 100% 0/0 \n", - "Abnormality of the integument [HP:0001574] 19/19 100% 26/26 \n", - "Aplasia/hypoplasia affecting bones of the axial... 3/3 100% 4/4 \n", - "Abnormal nasal morphology [HP:0005105] 24/24 100% 25/25 \n", - "Abnormal oral cavity morphology [HP:0000163] 16/16 100% 13/13 \n", - "Abnormal digestive system morphology [HP:0025033] 1/1 100% 1/1 \n", - "Abnormal limb bone morphology [HP:0002813] 15/15 100% 21/21 \n", - "Abnormal ventricular septum morphology [HP:0010... 0/0 0% 5/5 \n", - "Abnormal vascular morphology [HP:0025015] 3/3 100% 6/6 \n", - "Underdeveloped nasal alae [HP:0000430] 4/6 67% 0/0 \n", - "Sacrococcygeal pilonidal abnormality [HP:0010767] 0/0 0% 6/6 \n", - "Abnormal B cell count [HP:0010975] 6/6 100% 5/5 \n", - "Abnormal hand morphology [HP:0005922] 12/12 100% 18/18 \n", - "Microcephaly [HP:0000252] 3/7 43% 4/11 \n", - "Cleft palate [HP:0000175] 9/10 90% 8/8 \n", - "Abnormal heart valve physiology [HP:0031653] 0/0 0% 1/1 \n", - "Thick vermilion border [HP:0012471] 3/3 100% 1/1 \n", - "Ventricular hypertrophy [HP:0001714] 1/1 100% 0/0 \n", - "Neurodevelopmental abnormality [HP:0012759] 22/22 100% 22/22 \n", - "Abnormal size of the palpebral fissures [HP:020... 8/8 100% 17/17 \n", - "Finger clinodactyly [HP:0040019] 4/4 100% 12/12 \n", - "Otitis media [HP:0000388] 8/8 100% 6/6 \n", - "Abnormal morphology of the great vessels [HP:00... 3/3 100% 6/6 \n", - "Abnormal palmar dermatoglyphics [HP:0001018] 0/0 0% 2/2 \n", - "Abnormal spleen morphology [HP:0025408] 0/0 0% 1/1 \n", - "Abnormal heart valve morphology [HP:0001654] 0/0 0% 1/1 \n", - "Abnormal aortic valve morphology [HP:0001646] 0/0 0% 1/1 \n", - "Abnormal leukocyte physiology [HP:0033796] 4/4 100% 3/3 \n", - "Low-set ears [HP:0000369] 2/5 40% 0/0 \n", - "Abnormal aortic valve physiology [HP:0031652] 0/0 0% 1/1 \n", - "Deviation of the 5th finger [HP:0009179] 2/2 100% 4/4 \n", - "Abnormal nasolacrimal system morphology [HP:000... 1/1 100% 0/0 \n", - "Abnormal male reproductive system physiology [H... 2/2 100% 1/1 \n", - "Abnormal circulating IgG level [HP:0410242] 2/2 100% 1/1 \n", - "Obesity [HP:0001513] 0/0 0% 1/1 \n", - "Abnormal leukocyte morphology [HP:0001881] 7/7 100% 5/5 \n", - "Aplasia of the semicircular canal [HP:0011381] 3/4 75% 0/0 \n", - "Joint hypermobility [HP:0001382] 2/7 29% 3/12 \n", - "Abnormal cellular physiology [HP:0011017] 4/4 100% 3/3 \n", - "Abnormality of the skeletal system [HP:0000924] 23/23 100% 26/26 \n", - "Abnormal atrial septum morphology [HP:0011994] 5/5 100% 6/6 \n", - "Neoplasm of the middle ear [HP:0100799] 0/0 0% 1/1 \n", - "Abnormal nostril morphology [HP:0005288] 3/3 100% 0/0 \n", - "Abnormal jaw morphology [HP:0030791] 4/4 100% 4/4 \n", - "Aplasia/Hypoplasia involving the nose [HP:0009924] 4/4 100% 0/0 \n", - "Abnormal alveolar type II pneumocyte morphology... 1/1 100% 0/0 \n", - "Tooth malposition [HP:0000692] 2/2 100% 0/0 \n", - "Abnormal cardiac atrium morphology [HP:0005120] 5/5 100% 6/6 \n", - "Abnormal skin adnexa morphology [HP:0011138] 8/8 100% 17/17 \n", - "Abnormal anus morphology [HP:0034915] 1/1 100% 1/1 \n", - "Abnormality of the thyroid gland [HP:0000820] 7/7 100% 0/0 \n", - "Abnormal sacrum morphology [HP:0005107] 0/0 0% 6/6 \n", - "Abnormal mandible morphology [HP:0000277] 4/4 100% 4/4 \n", - "Widely spaced teeth [HP:0000687] 1/4 25% 0/0 \n", - "Aplasia/hypoplasia involving bones of the hand ... 8/8 100% 7/7 \n", - "Abnormal T cell morphology [HP:0002843] 6/6 100% 2/2 \n", - "Abnormal uvula morphology [HP:0000172] 2/2 100% 0/0 \n", - "Aplasia/Hypoplasia of fingers [HP:0006265] 8/8 100% 7/7 \n", - "Aplasia/Hypoplasia of the nipples [HP:0006709] 10/10 100% 0/0 \n", - "Renal hypoplasia/aplasia [HP:0008678] 1/1 100% 0/0 \n", - "Oligodontia [HP:0000677] 3/3 100% 0/0 \n", - "Congenital malformation of the left heart [HP:0... 0/0 0% 1/1 \n", - "High palate [HP:0000218] 10/11 91% 8/8 \n", - "Abnormal 5th finger morphology [HP:0004207] 10/10 100% 10/10 \n", - "Abnormal earlobe morphology [HP:0000363] 8/8 100% 6/6 \n", - "Abnormal eyelid morphology [HP:0000492] 12/12 100% 20/20 \n", - "Cryptorchidism [HP:0000028] 2/2 100% 1/1 \n", - "Abnormal muscle physiology [HP:0011804] 2/2 100% 10/10 \n", - "Abnormality of the dentition [HP:0000164] 8/8 100% 0/0 \n", - "Vertebral clefting [HP:0008428] 0/0 0% 1/1 \n", - "Short digit [HP:0011927] 8/8 100% 11/11 \n", - "Abnormal skin morphology of the palm [HP:0040211] 0/0 0% 2/2 \n", - "Nasolacrimal duct obstruction [HP:0000579] 1/1 100% 0/0 \n", - "Abnormal vertebral morphology [HP:0003468] 0/0 0% 1/1 \n", - "Telecanthus [HP:0000506] 1/1 100% 0/0 \n", - "Abnormality of the fontanelles or cranial sutur... 0/0 0% 1/1 \n", - "Low posterior hairline [HP:0002162] 0/1 0% 2/6 \n", - "Deviation of finger [HP:0004097] 4/4 100% 12/12 \n", - "Abnormal ear morphology [HP:0031703] 21/21 100% 23/23 \n", - "Abnormality of reproductive system physiology [... 2/2 100% 1/1 \n", - "Long eyelashes [HP:0000527] 0/1 0% 3/6 \n", - "Abnormal leukocyte count [HP:0011893] 7/7 100% 5/5 \n", - "Abnormality of the inner ear [HP:0000359] 3/3 100% 0/0 \n", - "Abnormal hip joint morphology [HP:0001384] 1/1 100% 5/5 \n", - "Abnormality of the palmar creases [HP:0010490] 0/0 0% 2/2 \n", - "Decreased proportion of memory T cells [HP:0032... 6/6 100% 2/2 \n", - "Abnormal cardiac ventricle morphology [HP:0001713] 1/1 100% 5/5 \n", - "Persistence of primary teeth [HP:0006335] 2/4 50% 0/0 \n", - "Abnormality of the posterior hairline [HP:0030141] 0/0 0% 2/2 \n", - "Abnormal esophagus physiology [HP:0025270] 5/5 100% 0/0 \n", - "Abnormality of humoral immunity [HP:0005368] 4/4 100% 3/3 \n", - "Abnormal respiratory system morphology [HP:0012... 2/2 100% 1/1 \n", - "Abnormality of the orbital region [HP:0000315] 13/13 100% 21/21 \n", - "Abnormal lacrimal duct morphology [HP:0011481] 1/1 100% 0/0 \n", - "Abnormal location of ears [HP:0000357] 2/2 100% 0/0 \n", - "Unusual infection [HP:0032101] 15/15 100% 15/15 \n", - "Abnormality of digestive system physiology [HP:... 9/9 100% 3/3 \n", - "Abnormal glucose homeostasis [HP:0011014] 1/1 100% 0/0 \n", - "Depressed nasal bridge [HP:0005280] 1/2 50% 1/1 \n", - "Abnormal calvaria morphology [HP:0002683] 0/0 0% 1/1 \n", - "Abnormal pulmonary vein morphology [HP:0030968] 1/1 100% 0/0 \n", - "Abnormal respiratory system physiology [HP:0002... 3/3 100% 0/0 \n", - "Abnormal number of teeth [HP:0006483] 3/3 100% 0/0 \n", - "Morphological central nervous system abnormalit... 3/3 100% 5/5 \n", - "Abnormality of the lymphatic system [HP:0100763] 0/0 0% 1/1 \n", - "Abnormality of the pulmonary vasculature [HP:00... 1/1 100% 0/0 \n", - "Posteriorly rotated ears [HP:0000358] 1/5 20% 0/0 \n", - "Abnormality of the face [HP:0000271] 26/26 100% 28/28 \n", - "Abnormal fingertip morphology [HP:0001211] 12/12 100% 18/18 \n", - "Abnormality of the palpebral fissures [HP:0008050] 11/11 100% 18/18 \n", - "Decreased circulating IgG subclass level [HP:00... 1/1 100% 0/0 \n", - "Deviation of the hand or of fingers of the hand... 4/4 100% 12/12 \n", - "Partial anomalous pulmonary venous return [HP:0... 1/1 100% 0/0 \n", - "Bifid uvula [HP:0000193] 2/3 67% 0/0 \n", - "Decreased circulating antibody level [HP:0004313] 4/4 100% 3/3 \n", - "Cleft lip [HP:0410030] 0/0 0% 1/1 \n", - "Abnormal eyebrow morphology [HP:0000534] 8/8 100% 16/16 \n", - "Abnormality of mental function [HP:0011446] 19/19 100% 14/14 \n", - "Brachydactyly [HP:0001156] 0/0 0% 4/4 \n", - "Branchial anomaly [HP:0009794] 2/2 100% 0/0 \n", - "Abnormal pituitary gland morphology [HP:0012503] 0/0 0% 1/1 \n", - "Short nose [HP:0003196] 2/2 100% 0/0 \n", - "Ectopic anus [HP:0004397] 1/1 100% 0/0 \n", - "Cholesteatoma [HP:0009797] 0/1 0% 1/5 \n", - "Decreased head circumference [HP:0040195] 3/3 100% 4/4 \n", - "Prominent fingertip pads [HP:0001212] 12/15 80% 18/23 \n", - "Abnormal pulmonary alveolar system morphology [... 1/1 100% 0/0 \n", - "Abnormal abdomen morphology [HP:0001438] 0/0 0% 1/1 \n", - "Abnormal nervous system morphology [HP:0012639] 3/3 100% 5/5 \n", - "Microtia [HP:0008551] 1/1 100% 0/0 \n", - "Abnormal circulating IgA level [HP:0410240] 2/2 100% 2/2 \n", - "Abnormality of the amniotic fluid [HP:0001560] 1/1 100% 0/0 \n", - "Aplasia/Hypoplasia of the external ear [HP:0008... 1/1 100% 0/0 \n", - "Abnormal thorax morphology [HP:0000765] 1/1 100% 0/0 \n", - "Abnormal dental morphology [HP:0006482] 3/3 100% 0/0 \n", - "Abnormal periauricular region morphology [HP:00... 3/3 100% 0/0 \n", - "Abnormality of pulmonary circulation [HP:0030875] 3/3 100% 0/0 \n", - "Pulmonary arterial hypertension [HP:0002092] 3/10 30% 0/0 \n", - "Wide nasal bridge [HP:0000431] 3/3 100% 11/14 \n", - "Abnormality of lower limb joint [HP:0100491] 1/1 100% 5/5 \n", - "Hypoplastic heart [HP:0001961] 0/0 0% 1/1 \n", - "Postnatal growth retardation [HP:0008897] 5/8 62% 4/5 \n", - "Abnormal ear physiology [HP:0031704] 14/14 100% 8/8 \n", - "Renal hypoplasia [HP:0000089] 1/5 20% 0/0 \n", - "Abnormal testis morphology [HP:0000035] 2/2 100% 1/1 \n", - "Abnormal upper lip morphology [HP:0000177] 4/4 100% 5/5 \n", - "Holoprosencephaly [HP:0001360] 1/1 100% 2/2 \n", - "Wide nose [HP:0000445] 0/0 0% 1/1 \n", - "Neoplasm [HP:0002664] 0/0 0% 1/1 \n", - "Abnormal cerebral morphology [HP:0002060] 3/3 100% 4/4 \n", - "Hypotelorism [HP:0000601] 1/1 100% 2/2 \n", - "Abnormal cardiovascular system morphology [HP:0... 7/7 100% 11/11 \n", - "Polyhydramnios [HP:0001561] 1/1 100% 0/1 \n", - "Abnormality of thyroid physiology [HP:0002926] 7/7 100% 0/0 \n", - "Abnormality of body weight [HP:0004323] 0/0 0% 1/1 \n", - "Abnormality of eye movement [HP:0000496] 2/2 100% 4/4 \n", - "Abnormality of the eye [HP:0000478] 8/8 100% 9/9 \n", - "Joint dislocation [HP:0001373] 1/1 100% 5/5 \n", - "Abnormal blood glucose concentration [HP:0011015] 1/1 100% 0/0 \n", - "Abnormal lymphocyte physiology [HP:0031409] 4/4 100% 3/3 \n", - "Branchial sinus [HP:0100272] 2/4 50% 0/0 \n", - "Abnormal thoracic spine morphology [HP:0100711] 1/1 100% 0/0 \n", - "Abnormality of the head [HP:0000234] 26/26 100% 28/28 \n", - "Abdominal symptom [HP:0011458] 5/5 100% 3/3 \n", - "Abnormal lung morphology [HP:0002088] 2/2 100% 1/1 \n", - "Abnormal cellular phenotype [HP:0025354] 4/4 100% 3/3 \n", - "Abnormal skin morphology [HP:0011121] 14/14 100% 18/18 \n", - "Abnormal musculoskeletal physiology [HP:0011843] 2/2 100% 3/3 \n", - "Abnormal eye physiology [HP:0012373] 2/2 100% 4/4 \n", - "Intrauterine growth retardation [HP:0001511] 1/1 100% 0/0 \n", - "Smooth philtrum [HP:0000319] 3/3 100% 0/0 \n", - "Subpleural honeycombing [HP:0031631] 1/1 100% 0/0 \n", - "Aplasia/Hypoplasia involving bones of the skull... 3/3 100% 4/4 \n", - "Abnormal endocrine physiology [HP:0031072] 0/0 0% 1/1 \n", - "Aplasia/Hypoplasia of the mandible [HP:0009118] 3/3 100% 4/4 \n", - "Abnormal ureter morphology [HP:0025633] 0/0 0% 1/1 \n", - "Thin upper lip vermilion [HP:0000219] 3/4 75% 4/6 \n", - "Splenomegaly [HP:0001744] 0/2 0% 1/6 \n", - "Abnormal skull morphology [HP:0000929] 7/7 100% 8/8 \n", - "Abnormal neck morphology [HP:0025668] 2/2 100% 2/2 \n", - "Delayed speech and language development [HP:000... 8/10 80% 2/2 \n", - "Abnormal cardiovascular system physiology [HP:0... 3/3 100% 1/1 \n", - "Abnormal oral morphology [HP:0031816] 16/16 100% 13/13 \n", - "Abnormality of joint mobility [HP:0011729] 2/2 100% 3/3 \n", - "Cleft upper lip [HP:0000204] 0/0 0% 1/1 \n", - "Abnormal eye morphology [HP:0012372] 6/6 100% 6/6 \n", - "Abnormality of the forehead [HP:0000290] 5/5 100% 1/1 \n", - "Short 5th finger [HP:0009237] 8/8 100% 7/7 \n", - "Downslanted palpebral fissures [HP:0000494] 3/3 100% 1/1 \n", - "Bilateral renal hypoplasia [HP:0012584] 1/5 20% 0/0 \n", - "Abnormality of the mouth [HP:0000153] 16/16 100% 13/13 \n", - "Abnormal morphology of the nasal alae [HP:0000429] 6/6 100% 0/0 \n", - "Midface retrusion [HP:0011800] 1/1 100% 1/1 \n", - "Abnormal lymphocyte morphology [HP:0004332] 7/7 100% 5/5 \n", - "Abnormal proportion of memory T cells [HP:0032182] 6/6 100% 2/2 \n", - "Abnormal aortic morphology [HP:0001679] 1/1 100% 4/4 \n", - "Abnormal muscle tone [HP:0003808] 2/2 100% 10/10 \n", - "Abnormal midface morphology [HP:0000309] 4/4 100% 1/1 \n", - "Proboscis [HP:0012806] 1/1 100% 0/1 \n", - "Abnormality of B cell physiology [HP:0005372] 4/4 100% 3/3 \n", - "Aplasia/Hypoplasia of the 5th finger [HP:0006262] 8/8 100% 7/7 \n", - "Lobar holoprosencephaly [HP:0006870] 0/0 0% 1/1 \n", - "Abnormality of the vasculature [HP:0002597] 5/5 100% 6/6 \n", - "Abnormal cheek morphology [HP:0004426] 3/3 100% 0/0 \n", - "Aplasia/hypoplasia involving bones of the extre... 8/8 100% 7/7 \n", - "Abnormality of body height [HP:0000002] 9/9 100% 13/13 \n", - "Abnormal nasal tip morphology [HP:0000436] 16/16 100% 20/20 \n", - "Craniosynostosis [HP:0001363] 0/0 0% 1/1 \n", - "Dimple chin [HP:0010751] 1/1 100% 0/0 \n", - "Abnormal joint morphology [HP:0001367] 1/1 100% 5/5 \n", - "Abnormal systemic arterial morphology [HP:0011004] 1/1 100% 4/4 \n", - "Anterior hypopituitarism [HP:0000830] 0/0 0% 1/1 \n", - "Abnormal nasal dorsum morphology [HP:0011119] 1/1 100% 1/1 \n", - "Abnormal heart morphology [HP:0001627] 5/5 100% 10/10 \n", - "Pre-capillary pulmonary hypertension [HP:0033578] 3/3 100% 0/0 \n", - "Abnormal inflammatory response [HP:0012647] 8/8 100% 6/6 \n", - "Anteriorly placed anus [HP:0001545] 1/1 100% 0/0 \n", - "Hearing abnormality [HP:0000364] 14/14 100% 8/8 \n", - "Epicanthus [HP:0000286] 0/1 0% 1/5 \n", - "Hip dysplasia [HP:0001385] 0/0 0% 2/2 \n", - "Abnormality of the nervous system [HP:0000707] 26/26 100% 25/25 \n", - "Abnormal pinna morphology [HP:0000377] 19/19 100% 22/22 \n", - "Abnormal joint physiology [HP:0034430] 2/2 100% 3/3 \n", - "Prominent digit pad [HP:0011298] 12/12 100% 18/18 \n", - "Hypoparathyroidism [HP:0000829] 4/4 100% 0/0 \n", - "Aplasia/hypoplasia involving bones of the upper... 8/8 100% 7/7 \n", - "Abnormal sclera morphology [HP:0000591] 4/4 100% 3/3 \n", - "Decreased circulating total IgG [HP:0032132] 1/8 12% 1/5 \n", - "Abnormal palm morphology [HP:0100871] 0/0 0% 2/2 \n", - "Renal cyst [HP:0000107] 0/0 0% 1/1 \n", - "Abnormal hip bone morphology [HP:0003272] 1/1 100% 7/7 \n", - "Ectropion [HP:0000656] 2/2 100% 16/16 \n", - "Abnormality of the musculoskeletal system [HP:0... 23/23 100% 26/26 \n", - "Abnormal blood vessel morphology [HP:0033353] 3/3 100% 6/6 \n", - "Abnormality of blood and blood-forming tissues ... 8/8 100% 5/5 \n", - "Increased inflammatory response [HP:0012649] 8/8 100% 6/6 \n", - "Abnormality of the respiratory system [HP:0002086] 5/5 100% 1/1 \n", - "Abnormality of prenatal development or birth [H... 1/1 100% 0/0 \n", - "Short stature [HP:0004322] 9/13 69% 13/18 \n", - "Abnormal B cell subset distribution [HP:0025539] 6/6 100% 5/5 \n", - "Median cleft lip [HP:0000161] 0/1 0% 1/1 \n", - "Aplasia/Hypoplasia of the ear [HP:0008771] 1/1 100% 0/0 \n", - "Abnormality of the spleen [HP:0001743] 0/0 0% 1/1 \n", - "Abnormality of the chin [HP:0000306] 1/1 100% 0/0 \n", - "Thin vermilion border [HP:0000233] 5/5 100% 4/4 \n", - "Growth delay [HP:0001510] 13/13 100% 16/16 \n", - "Dental malocclusion [HP:0000689] 1/4 25% 0/0 \n", - "Recurrent infections [HP:0002719] 15/21 71% 15/20 \n", - "Clinodactyly [HP:0030084] 4/4 100% 12/12 \n", - "Abnormality of the outer ear [HP:0000356] 19/19 100% 22/22 \n", - "Aplasia/Hypoplasia of the eyebrow [HP:0100840] 6/6 100% 8/8 \n", - "Abnormal nipple morphology [HP:0004404] 10/10 100% 0/0 \n", - "Increased body weight [HP:0004324] 0/0 0% 1/1 \n", - "Abnormal breast morphology [HP:0031093] 10/10 100% 0/0 \n", - "Abnormality of the anterior pituitary [HP:0011747] 0/0 0% 1/1 \n", - "Abnormal dermatoglyphics [HP:0007477] 0/0 0% 2/2 \n", - "Lower extremity joint dislocation [HP:0030311] 1/1 100% 5/5 \n", - "Abnormal nervous system physiology [HP:0012638] 24/24 100% 23/23 \n", - "Abnormality of the kidney [HP:0000077] 2/2 100% 4/4 \n", - "Abnormal digit morphology [HP:0011297] 15/15 100% 21/21 \n", - "Abnormal cardiac septum morphology [HP:0001671] 5/5 100% 8/8 \n", - "Microdontia [HP:0000691] 3/5 60% 0/0 \n", - "Aplasia/Hypoplasia involving the central nervou... 3/3 100% 4/4 \n", - "Azoospermia [HP:0000027] 2/2 100% 1/1 \n", - "Aplasia/hypoplasia of the extremities [HP:0009815] 8/8 100% 7/7 \n", - "Abnormal immune system morphology [HP:0032251] 7/7 100% 5/5 \n", - "Abnormal B cell morphology [HP:0002846] 6/6 100% 5/5 \n", - "Abnormal lip morphology [HP:0000159] 6/6 100% 5/5 \n", - "Abnormality of the urinary system physiology [H... 1/1 100% 0/0 \n", - "Macrotia [HP:0000400] 8/10 80% 16/20 \n", - "Neurodevelopmental delay [HP:0012758] 21/21 100% 18/18 \n", - "Abnormal homeostasis [HP:0012337] 1/1 100% 0/0 \n", - "Large earlobe [HP:0009748] 8/8 100% 6/6 \n", - "Abnormal cellular immune system morphology [HP:... 7/7 100% 5/5 \n", - "Respiratory tract infection [HP:0011947] 0/0 0% 1/1 \n", - "Anal atresia [HP:0002023] 0/1 0% 1/5 \n", - "Preauricular pit [HP:0004467] 3/3 100% 0/0 \n", - "Abnormal pelvic girdle bone morphology [HP:0002... 1/1 100% 7/7 \n", - "Anomalous pulmonary venous return [HP:0010772] 1/1 100% 0/0 \n", - "Abnormality of the anus [HP:0004378] 1/1 100% 1/1 \n", - "Localized skin lesion [HP:0011355] 3/3 100% 6/6 \n", - "Abnormality of the nose [HP:0000366] 25/25 100% 25/25 \n", - "Ectopic kidney [HP:0000086] 0/2 0% 2/9 \n", - "Congenital malformation of the great arteries [... 1/1 100% 4/4 \n", - "Abnormal curvature of the vertebral column [HP:... 6/6 100% 4/4 \n", - "Slanting of the palpebral fissure [HP:0200006] 3/3 100% 1/1 \n", - "Abnormality of speech or vocalization [HP:0002167] 8/8 100% 2/2 \n", - "Abnormal proportion of memory B cells [HP:0030373] 6/6 100% 5/5 \n", - "Abnormal response to endocrine stimulation test... 0/0 0% 1/1 \n", - "Abnormality of the lower limb [HP:0002814] 1/1 100% 5/5 \n", - "Decreased circulating IgA level [HP:0002720] 2/8 25% 2/5 \n", - "Abnormal finger morphology [HP:0001167] 15/15 100% 20/20 \n", - "Abnormality of limbs [HP:0040064] 15/15 100% 23/23 \n", - "Non-obstructive azoospermia [HP:0011961] 2/2 100% 1/1 \n", - "Abnormal communication [HP:0034434] 8/8 100% 2/2 \n", - "Abnormality of the neck [HP:0000464] 2/2 100% 2/2 \n", - "Hypothyroidism [HP:0000821] 7/7 100% 0/0 \n", - "Abnormal aortic valve cusp morphology [HP:0031567] 0/0 0% 1/1 \n", - "Visceromegaly [HP:0003271] 0/0 0% 1/1 \n", - "Abnormality of the philtrum [HP:0000288] 3/3 100% 0/0 \n", - "Tooth agenesis [HP:0009804] 3/3 100% 0/0 \n", - "Abnormality of globe location [HP:0100886] 2/2 100% 3/3 \n", - "Abnormal vascular physiology [HP:0030163] 3/3 100% 0/0 \n", - "Renal insufficiency [HP:0000083] 1/1 100% 0/0 \n", - "Abnormal scalp morphology [HP:0001965] 0/0 0% 2/2 \n", - "Abnormality of immune system physiology [HP:001... 15/15 100% 16/16 \n", - "Abnormal nasal bridge morphology [HP:0000422] 4/4 100% 12/12 \n", - "Abnormal brain morphology [HP:0012443] 3/3 100% 5/5 \n", - "Feeding difficulties [HP:0011968] 5/14 36% 3/11 \n", - "Abnormality of skull size [HP:0000240] 3/3 100% 4/4 \n", - "Micrognathia [HP:0000347] 3/6 50% 4/9 \n", - "Abnormal form of the vertebral bodies [HP:0003312] 0/0 0% 1/1 \n", - "Neoplasm of the ear [HP:0012780] 0/0 0% 1/1 \n", - "Abnormality of the ear [HP:0000598] 24/24 100% 23/23 \n", - "Decreased circulating IgG level [HP:0004315] 2/2 100% 1/1 \n", - "Hypoplastic nipples [HP:0002557] 5/12 42% 0/0 \n", - "Abnormality of the musculature [HP:0003011] 2/2 100% 10/10 \n", - "Alobar holoprosencephaly [HP:0006988] 1/1 100% 1/2 \n", - "Aplasia/Hypoplasia of the cerebrum [HP:0007364] 3/3 100% 4/4 \n", - "Gastroesophageal reflux [HP:0002020] 5/17 29% 0/0 \n", - "Abnormal lymphocyte count [HP:0040088] 7/7 100% 5/5 \n", - "Abnormal appendicular skeleton morphology [HP:0... 15/15 100% 23/23 \n", - "Abnormality of the digestive system [HP:0025031] 10/10 100% 4/4 \n", - "Abnormal subpleural morphology [HP:0031630] 1/1 100% 0/0 \n", - "Sparse eyebrow [HP:0045075] 6/7 86% 8/11 \n", - "Abnormal right ventricle morphology [HP:0001707] 1/1 100% 0/0 \n", - "Narrow palate [HP:0000189] 8/8 100% 6/6 \n", - "Elevated pulmonary artery pressure [HP:0004890] 3/3 100% 0/0 \n", - "Protruding ear [HP:0000411] 0/0 0% 1/1 \n", - "Abnormality of primary teeth [HP:0006481] 2/2 100% 0/0 \n", - "Hip dislocation [HP:0002827] 1/1 100% 5/5 \n", - "Hypertelorism [HP:0000316] 1/2 50% 1/5 \n", - "Abnormal aortic arch morphology [HP:0012303] 0/0 0% 1/1 \n", - "Abnormality of the skin [HP:0000951] 14/14 100% 18/18 \n", - "Butterfly vertebrae [HP:0003316] 0/0 0% 1/1 \n", - "Anteverted nares [HP:0000463] 3/3 100% 0/0 \n", - "Abnormality of upper lip vermillion [HP:0011339] 4/4 100% 4/4 \n", - "Congenital hypothyroidism [HP:0000851] 3/6 50% 0/0 \n", - "Abnormal T cell subset distribution [HP:0025540] 6/6 100% 2/2 \n", - "Abnormal localization of kidney [HP:0100542] 0/0 0% 2/2 \n", - "Abnormal forebrain morphology [HP:0100547] 3/3 100% 4/4 \n", - "High, narrow palate [HP:0002705] 8/9 89% 6/6 \n", - "Abnormality of limb bone [HP:0040068] 15/15 100% 21/21 \n", - "Abnormality of the ocular adnexa [HP:0032039] 13/13 100% 21/21 \n", - "Decreased response to growth hormone stimulatio... 0/0 0% 1/1 \n", - "Abnormal morphology of the vestibule of the inn... 3/3 100% 0/0 \n", - "Congenital abnormal hair pattern [HP:0011361] 0/0 0% 2/2 \n", - "Abnormality of the ureter [HP:0000069] 0/0 0% 1/1 \n", - "Short finger [HP:0009381] 8/8 100% 7/7 \n", - "All [HP:0000001] 33/33 100% 29/29 \n", - "Abnormality of dental eruption [HP:0006292] 2/2 100% 0/0 \n", - "Abnormality of the hypothalamus-pituitary axis ... 0/0 0% 1/1 \n", - "Thick lower lip vermilion [HP:0000179] 3/3 100% 1/1 \n", - "Abnormal facial skeleton morphology [HP:0011821] 4/4 100% 4/4 \n", - "Absent nipple [HP:0002561] 5/12 42% 0/0 \n", - "Intellectual disability [HP:0001249] 12/13 92% 13/14 \n", - "Abnormal renal physiology [HP:0012211] 1/1 100% 0/0 \n", - "Abnormality of the pulmonary veins [HP:0011718] 1/1 100% 0/0 \n", - "Hypopituitarism [HP:0040075] 0/0 0% 1/1 \n", - "Abnormality of head or neck [HP:0000152] 26/26 100% 28/28 \n", - "Abnormality of the parathyroid physiology [HP:0... 4/4 100% 0/0 \n", - "Skin dimple [HP:0010781] 0/0 0% 6/6 \n", - "Abnormality of the middle ear [HP:0000370] 8/8 100% 7/7 \n", - "Retrognathia [HP:0000278] 1/1 100% 0/0 \n", - "Abnormal immunoglobulin level [HP:0010701] 4/4 100% 3/3 \n", - "Functional abnormality of the gastrointestinal ... 5/5 100% 0/0 \n", - "Abnormal external nose morphology [HP:0010938] 22/22 100% 21/21 \n", - "Skin pit [HP:0100276] 3/3 100% 0/0 \n", - "Abnormality of cranial sutures [HP:0011329] 0/0 0% 1/1 \n", - "Abnormality of the vertebral column [HP:0000925] 6/6 100% 10/10 \n", - "Abnormality of the urinary system [HP:0000079] 2/2 100% 5/5 \n", - "\n", - "MISSENSE_VARIANT on NM_003482.4 \\\n", - " Percent p value \n", - "Sensorineural hearing impairment [HP:0000407] 0% 0.003432 \n", - "Hearing impairment [HP:0000365] 38% 0.030871 \n", - "Ventricular septal defect [HP:0001629] 24% 0.061943 \n", - "High forehead [HP:0000348] 20% 0.080087 \n", - "Motor delay [HP:0001270] 100% 0.133333 \n", - "Hypotonia [HP:0001252] 71% 0.158824 \n", - "Hypoglycemia [HP:0001943] 0% 0.166667 \n", - "Patent ductus arteriosus [HP:0001643] 19% 0.169013 \n", - "Decreased proportion of CD4-positive, alpha-bet... 40% 0.292929 \n", - "Depressed nasal ridge [HP:0000457] 100% 0.333333 \n", - "Blue sclerae [HP:0000592] 30% 0.349959 \n", - "Highly arched eyebrow [HP:0002553] 67% 0.374190 \n", - "Atrial septal defect [HP:0001631] 46% 0.374226 \n", - "Eversion of lateral third of lower eyelids [HP:... 89% 0.386466 \n", - "Sacral dimple [HP:0000960] 60% 0.454545 \n", - "Recurrent otitis media [HP:0000403] 35% 0.476451 \n", - "Decreased proportion of memory B cells [HP:0030... 100% 0.487179 \n", - "Renal duplication [HP:0000075] 25% 0.487179 \n", - "Ptosis [HP:0000508] 18% 0.496732 \n", - "Intellectual disability, mild [HP:0001256] 50% 0.505495 \n", - "Long palpebral fissure [HP:0000637] 81% 0.552019 \n", - "Sparse lateral eyebrow [HP:0005338] 62% 0.569231 \n", - "Seizure [HP:0001250] 12% 0.569960 \n", - "Congenital hip dislocation [HP:0001374] 31% 0.621385 \n", - "Coarctation of aorta [HP:0001680] 14% 0.625668 \n", - "Clinodactyly of the 5th finger [HP:0004209] 36% 0.642415 \n", - "Patent foramen ovale [HP:0001655] 15% 0.645896 \n", - "Proportionate short stature [HP:0003508] 44% 0.653417 \n", - "Severe short stature [HP:0003510] 40% 0.669921 \n", - "Scoliosis [HP:0002650] 24% 0.716925 \n", - "Depressed nasal tip [HP:0000437] 74% 0.736504 \n", - "Abnormality of the endocrine system [HP:0000818] 100% 1.000000 \n", - "Abnormal middle ear morphology [HP:0008609] 100% 1.000000 \n", - "Growth abnormality [HP:0001507] 100% 1.000000 \n", - "Abnormality of the upper limb [HP:0002817] 100% 1.000000 \n", - "Orofacial cleft [HP:0000202] 100% 1.000000 \n", - "Abnormal ocular adnexa morphology [HP:0030669] 100% 1.000000 \n", - "Abnormal renal morphology [HP:0012210] 100% 1.000000 \n", - "Abnormality of the hand [HP:0001155] 100% 1.000000 \n", - "Abnormal soft palate morphology [HP:0100736] 0% 1.000000 \n", - "Abnormality of metabolism/homeostasis [HP:0001939] 100% 1.000000 \n", - "Abnormal skeletal morphology [HP:0011842] 100% 1.000000 \n", - "Abnormality of the gastrointestinal tract [HP:0... 0% 1.000000 \n", - "Abnormality of the scalp hair [HP:0100037] 100% 1.000000 \n", - "Abnormality of the abdominal organs [HP:0002012] 100% 1.000000 \n", - "Neoplasm by anatomical site [HP:0011793] 100% 1.000000 \n", - "Cupped ear [HP:0000378] 0% 1.000000 \n", - "Abnormality of the genitourinary system [HP:000... 100% 1.000000 \n", - "Choanal atresia [HP:0000453] 0% 1.000000 \n", - "Strabismus [HP:0000486] 25% 1.000000 \n", - "Abnormal hair pattern [HP:0010720] 100% 1.000000 \n", - "Periauricular skin pits [HP:0100277] 0% 1.000000 \n", - "Abnormal lower lip morphology [HP:0000178] 100% 1.000000 \n", - "Abnormal hair morphology [HP:0001595] 100% 1.000000 \n", - "Single transverse palmar crease [HP:0000954] 67% 1.000000 \n", - "Abnormality of the breast [HP:0000769] 0% 1.000000 \n", - "Abnormal spermatogenesis [HP:0008669] 100% 1.000000 \n", - "Abnormality of the upper urinary tract [HP:0010... 100% 1.000000 \n", - "Abnormality of the choanae [HP:0000415] 0% 1.000000 \n", - "Abnormal conjugate eye movement [HP:0000549] 100% 1.000000 \n", - "Tented upper lip vermilion [HP:0010804] 0% 1.000000 \n", - "Abnormal eyelash morphology [HP:0000499] 100% 1.000000 \n", - "Aplasia/hypoplasia involving the skeleton [HP:0... 100% 1.000000 \n", - "Language impairment [HP:0002463] 100% 1.000000 \n", - "Abnormal axial skeleton morphology [HP:0009121] 100% 1.000000 \n", - "Abnormal semicircular canal morphology [HP:0011... 0% 1.000000 \n", - "Abnormal T cell count [HP:0011839] 100% 1.000000 \n", - "Functional abnormality of the inner ear [HP:001... 0% 1.000000 \n", - "Abnormal palate morphology [HP:0000174] 100% 1.000000 \n", - "Abnormality of the genital system [HP:0000078] 100% 1.000000 \n", - "Abnormality of the immune system [HP:0002715] 100% 1.000000 \n", - "Abnormality of the parathyroid gland [HP:0000828] 0% 1.000000 \n", - "Global developmental delay [HP:0001263] 94% 1.000000 \n", - "Phenotypic abnormality [HP:0000118] 100% 1.000000 \n", - "Functional abnormality of male internal genital... 100% 1.000000 \n", - "Abnormality of the cardiovascular system [HP:00... 100% 1.000000 \n", - "Abnormal inner ear morphology [HP:0011390] 0% 1.000000 \n", - "Regional abnormality of skin [HP:0011356] 100% 1.000000 \n", - "Abnormality of the hairline [HP:0009553] 100% 1.000000 \n", - "Type II pneumocyte hyperplasia [HP:0033328] 0% 1.000000 \n", - "Abnormality of the integument [HP:0001574] 100% 1.000000 \n", - "Aplasia/hypoplasia affecting bones of the axial... 100% 1.000000 \n", - "Abnormal nasal morphology [HP:0005105] 100% 1.000000 \n", - "Abnormal oral cavity morphology [HP:0000163] 100% 1.000000 \n", - "Abnormal digestive system morphology [HP:0025033] 100% 1.000000 \n", - "Abnormal limb bone morphology [HP:0002813] 100% 1.000000 \n", - "Abnormal ventricular septum morphology [HP:0010... 100% 1.000000 \n", - "Abnormal vascular morphology [HP:0025015] 100% 1.000000 \n", - "Underdeveloped nasal alae [HP:0000430] 0% 1.000000 \n", - "Sacrococcygeal pilonidal abnormality [HP:0010767] 100% 1.000000 \n", - "Abnormal B cell count [HP:0010975] 100% 1.000000 \n", - "Abnormal hand morphology [HP:0005922] 100% 1.000000 \n", - "Microcephaly [HP:0000252] 36% 1.000000 \n", - "Cleft palate [HP:0000175] 100% 1.000000 \n", - "Abnormal heart valve physiology [HP:0031653] 100% 1.000000 \n", - "Thick vermilion border [HP:0012471] 100% 1.000000 \n", - "Ventricular hypertrophy [HP:0001714] 0% 1.000000 \n", - "Neurodevelopmental abnormality [HP:0012759] 100% 1.000000 \n", - "Abnormal size of the palpebral fissures [HP:020... 100% 1.000000 \n", - "Finger clinodactyly [HP:0040019] 100% 1.000000 \n", - "Otitis media [HP:0000388] 100% 1.000000 \n", - "Abnormal morphology of the great vessels [HP:00... 100% 1.000000 \n", - "Abnormal palmar dermatoglyphics [HP:0001018] 100% 1.000000 \n", - "Abnormal spleen morphology [HP:0025408] 100% 1.000000 \n", - "Abnormal heart valve morphology [HP:0001654] 100% 1.000000 \n", - "Abnormal aortic valve morphology [HP:0001646] 100% 1.000000 \n", - "Abnormal leukocyte physiology [HP:0033796] 100% 1.000000 \n", - "Low-set ears [HP:0000369] 0% 1.000000 \n", - "Abnormal aortic valve physiology [HP:0031652] 100% 1.000000 \n", - "Deviation of the 5th finger [HP:0009179] 100% 1.000000 \n", - "Abnormal nasolacrimal system morphology [HP:000... 0% 1.000000 \n", - "Abnormal male reproductive system physiology [H... 100% 1.000000 \n", - "Abnormal circulating IgG level [HP:0410242] 100% 1.000000 \n", - "Obesity [HP:0001513] 100% 1.000000 \n", - "Abnormal leukocyte morphology [HP:0001881] 100% 1.000000 \n", - "Aplasia of the semicircular canal [HP:0011381] 0% 1.000000 \n", - "Joint hypermobility [HP:0001382] 25% 1.000000 \n", - "Abnormal cellular physiology [HP:0011017] 100% 1.000000 \n", - "Abnormality of the skeletal system [HP:0000924] 100% 1.000000 \n", - "Abnormal atrial septum morphology [HP:0011994] 100% 1.000000 \n", - "Neoplasm of the middle ear [HP:0100799] 100% 1.000000 \n", - "Abnormal nostril morphology [HP:0005288] 0% 1.000000 \n", - "Abnormal jaw morphology [HP:0030791] 100% 1.000000 \n", - "Aplasia/Hypoplasia involving the nose [HP:0009924] 0% 1.000000 \n", - "Abnormal alveolar type II pneumocyte morphology... 0% 1.000000 \n", - "Tooth malposition [HP:0000692] 0% 1.000000 \n", - "Abnormal cardiac atrium morphology [HP:0005120] 100% 1.000000 \n", - "Abnormal skin adnexa morphology [HP:0011138] 100% 1.000000 \n", - "Abnormal anus morphology [HP:0034915] 100% 1.000000 \n", - "Abnormality of the thyroid gland [HP:0000820] 0% 1.000000 \n", - "Abnormal sacrum morphology [HP:0005107] 100% 1.000000 \n", - "Abnormal mandible morphology [HP:0000277] 100% 1.000000 \n", - "Widely spaced teeth [HP:0000687] 0% 1.000000 \n", - "Aplasia/hypoplasia involving bones of the hand ... 100% 1.000000 \n", - "Abnormal T cell morphology [HP:0002843] 100% 1.000000 \n", - "Abnormal uvula morphology [HP:0000172] 0% 1.000000 \n", - "Aplasia/Hypoplasia of fingers [HP:0006265] 100% 1.000000 \n", - "Aplasia/Hypoplasia of the nipples [HP:0006709] 0% 1.000000 \n", - "Renal hypoplasia/aplasia [HP:0008678] 0% 1.000000 \n", - "Oligodontia [HP:0000677] 0% 1.000000 \n", - "Congenital malformation of the left heart [HP:0... 100% 1.000000 \n", - "High palate [HP:0000218] 100% 1.000000 \n", - "Abnormal 5th finger morphology [HP:0004207] 100% 1.000000 \n", - "Abnormal earlobe morphology [HP:0000363] 100% 1.000000 \n", - "Abnormal eyelid morphology [HP:0000492] 100% 1.000000 \n", - "Cryptorchidism [HP:0000028] 100% 1.000000 \n", - "Abnormal muscle physiology [HP:0011804] 100% 1.000000 \n", - "Abnormality of the dentition [HP:0000164] 0% 1.000000 \n", - "Vertebral clefting [HP:0008428] 100% 1.000000 \n", - "Short digit [HP:0011927] 100% 1.000000 \n", - "Abnormal skin morphology of the palm [HP:0040211] 100% 1.000000 \n", - "Nasolacrimal duct obstruction [HP:0000579] 0% 1.000000 \n", - "Abnormal vertebral morphology [HP:0003468] 100% 1.000000 \n", - "Telecanthus [HP:0000506] 0% 1.000000 \n", - "Abnormality of the fontanelles or cranial sutur... 100% 1.000000 \n", - "Low posterior hairline [HP:0002162] 33% 1.000000 \n", - "Deviation of finger [HP:0004097] 100% 1.000000 \n", - "Abnormal ear morphology [HP:0031703] 100% 1.000000 \n", - "Abnormality of reproductive system physiology [... 100% 1.000000 \n", - "Long eyelashes [HP:0000527] 50% 1.000000 \n", - "Abnormal leukocyte count [HP:0011893] 100% 1.000000 \n", - "Abnormality of the inner ear [HP:0000359] 0% 1.000000 \n", - "Abnormal hip joint morphology [HP:0001384] 100% 1.000000 \n", - "Abnormality of the palmar creases [HP:0010490] 100% 1.000000 \n", - "Decreased proportion of memory T cells [HP:0032... 100% 1.000000 \n", - "Abnormal cardiac ventricle morphology [HP:0001713] 100% 1.000000 \n", - "Persistence of primary teeth [HP:0006335] 0% 1.000000 \n", - "Abnormality of the posterior hairline [HP:0030141] 100% 1.000000 \n", - "Abnormal esophagus physiology [HP:0025270] 0% 1.000000 \n", - "Abnormality of humoral immunity [HP:0005368] 100% 1.000000 \n", - "Abnormal respiratory system morphology [HP:0012... 100% 1.000000 \n", - "Abnormality of the orbital region [HP:0000315] 100% 1.000000 \n", - "Abnormal lacrimal duct morphology [HP:0011481] 0% 1.000000 \n", - "Abnormal location of ears [HP:0000357] 0% 1.000000 \n", - "Unusual infection [HP:0032101] 100% 1.000000 \n", - "Abnormality of digestive system physiology [HP:... 100% 1.000000 \n", - "Abnormal glucose homeostasis [HP:0011014] 0% 1.000000 \n", - "Depressed nasal bridge [HP:0005280] 100% 1.000000 \n", - "Abnormal calvaria morphology [HP:0002683] 100% 1.000000 \n", - "Abnormal pulmonary vein morphology [HP:0030968] 0% 1.000000 \n", - "Abnormal respiratory system physiology [HP:0002... 0% 1.000000 \n", - "Abnormal number of teeth [HP:0006483] 0% 1.000000 \n", - "Morphological central nervous system abnormalit... 100% 1.000000 \n", - "Abnormality of the lymphatic system [HP:0100763] 100% 1.000000 \n", - "Abnormality of the pulmonary vasculature [HP:00... 0% 1.000000 \n", - "Posteriorly rotated ears [HP:0000358] 0% 1.000000 \n", - "Abnormality of the face [HP:0000271] 100% 1.000000 \n", - "Abnormal fingertip morphology [HP:0001211] 100% 1.000000 \n", - "Abnormality of the palpebral fissures [HP:0008050] 100% 1.000000 \n", - "Decreased circulating IgG subclass level [HP:00... 0% 1.000000 \n", - "Deviation of the hand or of fingers of the hand... 100% 1.000000 \n", - "Partial anomalous pulmonary venous return [HP:0... 0% 1.000000 \n", - "Bifid uvula [HP:0000193] 0% 1.000000 \n", - "Decreased circulating antibody level [HP:0004313] 100% 1.000000 \n", - "Cleft lip [HP:0410030] 100% 1.000000 \n", - "Abnormal eyebrow morphology [HP:0000534] 100% 1.000000 \n", - "Abnormality of mental function [HP:0011446] 100% 1.000000 \n", - "Brachydactyly [HP:0001156] 100% 1.000000 \n", - "Branchial anomaly [HP:0009794] 0% 1.000000 \n", - "Abnormal pituitary gland morphology [HP:0012503] 100% 1.000000 \n", - "Short nose [HP:0003196] 0% 1.000000 \n", - "Ectopic anus [HP:0004397] 0% 1.000000 \n", - "Cholesteatoma [HP:0009797] 20% 1.000000 \n", - "Decreased head circumference [HP:0040195] 100% 1.000000 \n", - "Prominent fingertip pads [HP:0001212] 78% 1.000000 \n", - "Abnormal pulmonary alveolar system morphology [... 0% 1.000000 \n", - "Abnormal abdomen morphology [HP:0001438] 100% 1.000000 \n", - "Abnormal nervous system morphology [HP:0012639] 100% 1.000000 \n", - "Microtia [HP:0008551] 0% 1.000000 \n", - "Abnormal circulating IgA level [HP:0410240] 100% 1.000000 \n", - "Abnormality of the amniotic fluid [HP:0001560] 0% 1.000000 \n", - "Aplasia/Hypoplasia of the external ear [HP:0008... 0% 1.000000 \n", - "Abnormal thorax morphology [HP:0000765] 0% 1.000000 \n", - "Abnormal dental morphology [HP:0006482] 0% 1.000000 \n", - "Abnormal periauricular region morphology [HP:00... 0% 1.000000 \n", - "Abnormality of pulmonary circulation [HP:0030875] 0% 1.000000 \n", - "Pulmonary arterial hypertension [HP:0002092] 0% 1.000000 \n", - "Wide nasal bridge [HP:0000431] 79% 1.000000 \n", - "Abnormality of lower limb joint [HP:0100491] 100% 1.000000 \n", - "Hypoplastic heart [HP:0001961] 100% 1.000000 \n", - "Postnatal growth retardation [HP:0008897] 80% 1.000000 \n", - "Abnormal ear physiology [HP:0031704] 100% 1.000000 \n", - "Renal hypoplasia [HP:0000089] 0% 1.000000 \n", - "Abnormal testis morphology [HP:0000035] 100% 1.000000 \n", - "Abnormal upper lip morphology [HP:0000177] 100% 1.000000 \n", - "Holoprosencephaly [HP:0001360] 100% 1.000000 \n", - "Wide nose [HP:0000445] 100% 1.000000 \n", - "Neoplasm [HP:0002664] 100% 1.000000 \n", - "Abnormal cerebral morphology [HP:0002060] 100% 1.000000 \n", - "Hypotelorism [HP:0000601] 100% 1.000000 \n", - "Abnormal cardiovascular system morphology [HP:0... 100% 1.000000 \n", - "Polyhydramnios [HP:0001561] 0% 1.000000 \n", - "Abnormality of thyroid physiology [HP:0002926] 0% 1.000000 \n", - "Abnormality of body weight [HP:0004323] 100% 1.000000 \n", - "Abnormality of eye movement [HP:0000496] 100% 1.000000 \n", - "Abnormality of the eye [HP:0000478] 100% 1.000000 \n", - "Joint dislocation [HP:0001373] 100% 1.000000 \n", - "Abnormal blood glucose concentration [HP:0011015] 0% 1.000000 \n", - "Abnormal lymphocyte physiology [HP:0031409] 100% 1.000000 \n", - "Branchial sinus [HP:0100272] 0% 1.000000 \n", - "Abnormal thoracic spine morphology [HP:0100711] 0% 1.000000 \n", - "Abnormality of the head [HP:0000234] 100% 1.000000 \n", - "Abdominal symptom [HP:0011458] 100% 1.000000 \n", - "Abnormal lung morphology [HP:0002088] 100% 1.000000 \n", - "Abnormal cellular phenotype [HP:0025354] 100% 1.000000 \n", - "Abnormal skin morphology [HP:0011121] 100% 1.000000 \n", - "Abnormal musculoskeletal physiology [HP:0011843] 100% 1.000000 \n", - "Abnormal eye physiology [HP:0012373] 100% 1.000000 \n", - "Intrauterine growth retardation [HP:0001511] 0% 1.000000 \n", - "Smooth philtrum [HP:0000319] 0% 1.000000 \n", - "Subpleural honeycombing [HP:0031631] 0% 1.000000 \n", - "Aplasia/Hypoplasia involving bones of the skull... 100% 1.000000 \n", - "Abnormal endocrine physiology [HP:0031072] 100% 1.000000 \n", - "Aplasia/Hypoplasia of the mandible [HP:0009118] 100% 1.000000 \n", - "Abnormal ureter morphology [HP:0025633] 100% 1.000000 \n", - "Thin upper lip vermilion [HP:0000219] 67% 1.000000 \n", - "Splenomegaly [HP:0001744] 17% 1.000000 \n", - "Abnormal skull morphology [HP:0000929] 100% 1.000000 \n", - "Abnormal neck morphology [HP:0025668] 100% 1.000000 \n", - "Delayed speech and language development [HP:000... 100% 1.000000 \n", - "Abnormal cardiovascular system physiology [HP:0... 100% 1.000000 \n", - "Abnormal oral morphology [HP:0031816] 100% 1.000000 \n", - "Abnormality of joint mobility [HP:0011729] 100% 1.000000 \n", - "Cleft upper lip [HP:0000204] 100% 1.000000 \n", - "Abnormal eye morphology [HP:0012372] 100% 1.000000 \n", - "Abnormality of the forehead [HP:0000290] 100% 1.000000 \n", - "Short 5th finger [HP:0009237] 100% 1.000000 \n", - "Downslanted palpebral fissures [HP:0000494] 100% 1.000000 \n", - "Bilateral renal hypoplasia [HP:0012584] 0% 1.000000 \n", - "Abnormality of the mouth [HP:0000153] 100% 1.000000 \n", - "Abnormal morphology of the nasal alae [HP:0000429] 0% 1.000000 \n", - "Midface retrusion [HP:0011800] 100% 1.000000 \n", - "Abnormal lymphocyte morphology [HP:0004332] 100% 1.000000 \n", - "Abnormal proportion of memory T cells [HP:0032182] 100% 1.000000 \n", - "Abnormal aortic morphology [HP:0001679] 100% 1.000000 \n", - "Abnormal muscle tone [HP:0003808] 100% 1.000000 \n", - "Abnormal midface morphology [HP:0000309] 100% 1.000000 \n", - "Proboscis [HP:0012806] 0% 1.000000 \n", - "Abnormality of B cell physiology [HP:0005372] 100% 1.000000 \n", - "Aplasia/Hypoplasia of the 5th finger [HP:0006262] 100% 1.000000 \n", - "Lobar holoprosencephaly [HP:0006870] 100% 1.000000 \n", - "Abnormality of the vasculature [HP:0002597] 100% 1.000000 \n", - "Abnormal cheek morphology [HP:0004426] 0% 1.000000 \n", - "Aplasia/hypoplasia involving bones of the extre... 100% 1.000000 \n", - "Abnormality of body height [HP:0000002] 100% 1.000000 \n", - "Abnormal nasal tip morphology [HP:0000436] 100% 1.000000 \n", - "Craniosynostosis [HP:0001363] 100% 1.000000 \n", - "Dimple chin [HP:0010751] 0% 1.000000 \n", - "Abnormal joint morphology [HP:0001367] 100% 1.000000 \n", - "Abnormal systemic arterial morphology [HP:0011004] 100% 1.000000 \n", - "Anterior hypopituitarism [HP:0000830] 100% 1.000000 \n", - "Abnormal nasal dorsum morphology [HP:0011119] 100% 1.000000 \n", - "Abnormal heart morphology [HP:0001627] 100% 1.000000 \n", - "Pre-capillary pulmonary hypertension [HP:0033578] 0% 1.000000 \n", - "Abnormal inflammatory response [HP:0012647] 100% 1.000000 \n", - "Anteriorly placed anus [HP:0001545] 0% 1.000000 \n", - "Hearing abnormality [HP:0000364] 100% 1.000000 \n", - "Epicanthus [HP:0000286] 20% 1.000000 \n", - "Hip dysplasia [HP:0001385] 100% 1.000000 \n", - "Abnormality of the nervous system [HP:0000707] 100% 1.000000 \n", - "Abnormal pinna morphology [HP:0000377] 100% 1.000000 \n", - "Abnormal joint physiology [HP:0034430] 100% 1.000000 \n", - "Prominent digit pad [HP:0011298] 100% 1.000000 \n", - "Hypoparathyroidism [HP:0000829] 0% 1.000000 \n", - "Aplasia/hypoplasia involving bones of the upper... 100% 1.000000 \n", - "Abnormal sclera morphology [HP:0000591] 100% 1.000000 \n", - "Decreased circulating total IgG [HP:0032132] 20% 1.000000 \n", - "Abnormal palm morphology [HP:0100871] 100% 1.000000 \n", - "Renal cyst [HP:0000107] 100% 1.000000 \n", - "Abnormal hip bone morphology [HP:0003272] 100% 1.000000 \n", - "Ectropion [HP:0000656] 100% 1.000000 \n", - "Abnormality of the musculoskeletal system [HP:0... 100% 1.000000 \n", - "Abnormal blood vessel morphology [HP:0033353] 100% 1.000000 \n", - "Abnormality of blood and blood-forming tissues ... 100% 1.000000 \n", - "Increased inflammatory response [HP:0012649] 100% 1.000000 \n", - "Abnormality of the respiratory system [HP:0002086] 100% 1.000000 \n", - "Abnormality of prenatal development or birth [H... 0% 1.000000 \n", - "Short stature [HP:0004322] 72% 1.000000 \n", - "Abnormal B cell subset distribution [HP:0025539] 100% 1.000000 \n", - "Median cleft lip [HP:0000161] 100% 1.000000 \n", - "Aplasia/Hypoplasia of the ear [HP:0008771] 0% 1.000000 \n", - "Abnormality of the spleen [HP:0001743] 100% 1.000000 \n", - "Abnormality of the chin [HP:0000306] 0% 1.000000 \n", - "Thin vermilion border [HP:0000233] 100% 1.000000 \n", - "Growth delay [HP:0001510] 100% 1.000000 \n", - "Dental malocclusion [HP:0000689] 0% 1.000000 \n", - "Recurrent infections [HP:0002719] 75% 1.000000 \n", - "Clinodactyly [HP:0030084] 100% 1.000000 \n", - "Abnormality of the outer ear [HP:0000356] 100% 1.000000 \n", - "Aplasia/Hypoplasia of the eyebrow [HP:0100840] 100% 1.000000 \n", - "Abnormal nipple morphology [HP:0004404] 0% 1.000000 \n", - "Increased body weight [HP:0004324] 100% 1.000000 \n", - "Abnormal breast morphology [HP:0031093] 0% 1.000000 \n", - "Abnormality of the anterior pituitary [HP:0011747] 100% 1.000000 \n", - "Abnormal dermatoglyphics [HP:0007477] 100% 1.000000 \n", - "Lower extremity joint dislocation [HP:0030311] 100% 1.000000 \n", - "Abnormal nervous system physiology [HP:0012638] 100% 1.000000 \n", - "Abnormality of the kidney [HP:0000077] 100% 1.000000 \n", - "Abnormal digit morphology [HP:0011297] 100% 1.000000 \n", - "Abnormal cardiac septum morphology [HP:0001671] 100% 1.000000 \n", - "Microdontia [HP:0000691] 0% 1.000000 \n", - "Aplasia/Hypoplasia involving the central nervou... 100% 1.000000 \n", - "Azoospermia [HP:0000027] 100% 1.000000 \n", - "Aplasia/hypoplasia of the extremities [HP:0009815] 100% 1.000000 \n", - "Abnormal immune system morphology [HP:0032251] 100% 1.000000 \n", - "Abnormal B cell morphology [HP:0002846] 100% 1.000000 \n", - "Abnormal lip morphology [HP:0000159] 100% 1.000000 \n", - "Abnormality of the urinary system physiology [H... 0% 1.000000 \n", - "Macrotia [HP:0000400] 80% 1.000000 \n", - "Neurodevelopmental delay [HP:0012758] 100% 1.000000 \n", - "Abnormal homeostasis [HP:0012337] 0% 1.000000 \n", - "Large earlobe [HP:0009748] 100% 1.000000 \n", - "Abnormal cellular immune system morphology [HP:... 100% 1.000000 \n", - "Respiratory tract infection [HP:0011947] 100% 1.000000 \n", - "Anal atresia [HP:0002023] 20% 1.000000 \n", - "Preauricular pit [HP:0004467] 0% 1.000000 \n", - "Abnormal pelvic girdle bone morphology [HP:0002... 100% 1.000000 \n", - "Anomalous pulmonary venous return [HP:0010772] 0% 1.000000 \n", - "Abnormality of the anus [HP:0004378] 100% 1.000000 \n", - "Localized skin lesion [HP:0011355] 100% 1.000000 \n", - "Abnormality of the nose [HP:0000366] 100% 1.000000 \n", - "Ectopic kidney [HP:0000086] 22% 1.000000 \n", - "Congenital malformation of the great arteries [... 100% 1.000000 \n", - "Abnormal curvature of the vertebral column [HP:... 100% 1.000000 \n", - "Slanting of the palpebral fissure [HP:0200006] 100% 1.000000 \n", - "Abnormality of speech or vocalization [HP:0002167] 100% 1.000000 \n", - "Abnormal proportion of memory B cells [HP:0030373] 100% 1.000000 \n", - "Abnormal response to endocrine stimulation test... 100% 1.000000 \n", - "Abnormality of the lower limb [HP:0002814] 100% 1.000000 \n", - "Decreased circulating IgA level [HP:0002720] 40% 1.000000 \n", - "Abnormal finger morphology [HP:0001167] 100% 1.000000 \n", - "Abnormality of limbs [HP:0040064] 100% 1.000000 \n", - "Non-obstructive azoospermia [HP:0011961] 100% 1.000000 \n", - "Abnormal communication [HP:0034434] 100% 1.000000 \n", - "Abnormality of the neck [HP:0000464] 100% 1.000000 \n", - "Hypothyroidism [HP:0000821] 0% 1.000000 \n", - "Abnormal aortic valve cusp morphology [HP:0031567] 100% 1.000000 \n", - "Visceromegaly [HP:0003271] 100% 1.000000 \n", - "Abnormality of the philtrum [HP:0000288] 0% 1.000000 \n", - "Tooth agenesis [HP:0009804] 0% 1.000000 \n", - "Abnormality of globe location [HP:0100886] 100% 1.000000 \n", - "Abnormal vascular physiology [HP:0030163] 0% 1.000000 \n", - "Renal insufficiency [HP:0000083] 0% 1.000000 \n", - "Abnormal scalp morphology [HP:0001965] 100% 1.000000 \n", - "Abnormality of immune system physiology [HP:001... 100% 1.000000 \n", - "Abnormal nasal bridge morphology [HP:0000422] 100% 1.000000 \n", - "Abnormal brain morphology [HP:0012443] 100% 1.000000 \n", - "Feeding difficulties [HP:0011968] 27% 1.000000 \n", - "Abnormality of skull size [HP:0000240] 100% 1.000000 \n", - "Micrognathia [HP:0000347] 44% 1.000000 \n", - "Abnormal form of the vertebral bodies [HP:0003312] 100% 1.000000 \n", - "Neoplasm of the ear [HP:0012780] 100% 1.000000 \n", - "Abnormality of the ear [HP:0000598] 100% 1.000000 \n", - "Decreased circulating IgG level [HP:0004315] 100% 1.000000 \n", - "Hypoplastic nipples [HP:0002557] 0% 1.000000 \n", - "Abnormality of the musculature [HP:0003011] 100% 1.000000 \n", - "Alobar holoprosencephaly [HP:0006988] 50% 1.000000 \n", - "Aplasia/Hypoplasia of the cerebrum [HP:0007364] 100% 1.000000 \n", - "Gastroesophageal reflux [HP:0002020] 0% 1.000000 \n", - "Abnormal lymphocyte count [HP:0040088] 100% 1.000000 \n", - "Abnormal appendicular skeleton morphology [HP:0... 100% 1.000000 \n", - "Abnormality of the digestive system [HP:0025031] 100% 1.000000 \n", - "Abnormal subpleural morphology [HP:0031630] 0% 1.000000 \n", - "Sparse eyebrow [HP:0045075] 73% 1.000000 \n", - "Abnormal right ventricle morphology [HP:0001707] 0% 1.000000 \n", - "Narrow palate [HP:0000189] 100% 1.000000 \n", - "Elevated pulmonary artery pressure [HP:0004890] 0% 1.000000 \n", - "Protruding ear [HP:0000411] 100% 1.000000 \n", - "Abnormality of primary teeth [HP:0006481] 0% 1.000000 \n", - "Hip dislocation [HP:0002827] 100% 1.000000 \n", - "Hypertelorism [HP:0000316] 20% 1.000000 \n", - "Abnormal aortic arch morphology [HP:0012303] 100% 1.000000 \n", - "Abnormality of the skin [HP:0000951] 100% 1.000000 \n", - "Butterfly vertebrae [HP:0003316] 100% 1.000000 \n", - "Anteverted nares [HP:0000463] 0% 1.000000 \n", - "Abnormality of upper lip vermillion [HP:0011339] 100% 1.000000 \n", - "Congenital hypothyroidism [HP:0000851] 0% 1.000000 \n", - "Abnormal T cell subset distribution [HP:0025540] 100% 1.000000 \n", - "Abnormal localization of kidney [HP:0100542] 100% 1.000000 \n", - "Abnormal forebrain morphology [HP:0100547] 100% 1.000000 \n", - "High, narrow palate [HP:0002705] 100% 1.000000 \n", - "Abnormality of limb bone [HP:0040068] 100% 1.000000 \n", - "Abnormality of the ocular adnexa [HP:0032039] 100% 1.000000 \n", - "Decreased response to growth hormone stimulatio... 100% 1.000000 \n", - "Abnormal morphology of the vestibule of the inn... 0% 1.000000 \n", - "Congenital abnormal hair pattern [HP:0011361] 100% 1.000000 \n", - "Abnormality of the ureter [HP:0000069] 100% 1.000000 \n", - "Short finger [HP:0009381] 100% 1.000000 \n", - "All [HP:0000001] 100% 1.000000 \n", - "Abnormality of dental eruption [HP:0006292] 0% 1.000000 \n", - "Abnormality of the hypothalamus-pituitary axis ... 100% 1.000000 \n", - "Thick lower lip vermilion [HP:0000179] 100% 1.000000 \n", - "Abnormal facial skeleton morphology [HP:0011821] 100% 1.000000 \n", - "Absent nipple [HP:0002561] 0% 1.000000 \n", - "Intellectual disability [HP:0001249] 93% 1.000000 \n", - "Abnormal renal physiology [HP:0012211] 0% 1.000000 \n", - "Abnormality of the pulmonary veins [HP:0011718] 0% 1.000000 \n", - "Hypopituitarism [HP:0040075] 100% 1.000000 \n", - "Abnormality of head or neck [HP:0000152] 100% 1.000000 \n", - "Abnormality of the parathyroid physiology [HP:0... 0% 1.000000 \n", - "Skin dimple [HP:0010781] 100% 1.000000 \n", - "Abnormality of the middle ear [HP:0000370] 100% 1.000000 \n", - "Retrognathia [HP:0000278] 0% 1.000000 \n", - "Abnormal immunoglobulin level [HP:0010701] 100% 1.000000 \n", - "Functional abnormality of the gastrointestinal ... 0% 1.000000 \n", - "Abnormal external nose morphology [HP:0010938] 100% 1.000000 \n", - "Skin pit [HP:0100276] 0% 1.000000 \n", - "Abnormality of cranial sutures [HP:0011329] 100% 1.000000 \n", - "Abnormality of the vertebral column [HP:0000925] 100% 1.000000 \n", - "Abnormality of the urinary system [HP:0000079] 100% 1.000000 \n", - "\n", - "MISSENSE_VARIANT on NM_003482.4 \n", - " Corrected p value \n", - "Sensorineural hearing impairment [HP:0000407] 1.0 \n", - "Hearing impairment [HP:0000365] 1.0 \n", - "Ventricular septal defect [HP:0001629] 1.0 \n", - "High forehead [HP:0000348] 1.0 \n", - "Motor delay [HP:0001270] 1.0 \n", - "Hypotonia [HP:0001252] 1.0 \n", - "Hypoglycemia [HP:0001943] 1.0 \n", - "Patent ductus arteriosus [HP:0001643] 1.0 \n", - "Decreased proportion of CD4-positive, alpha-bet... 1.0 \n", - "Depressed nasal ridge [HP:0000457] 1.0 \n", - "Blue sclerae [HP:0000592] 1.0 \n", - "Highly arched eyebrow [HP:0002553] 1.0 \n", - "Atrial septal defect [HP:0001631] 1.0 \n", - "Eversion of lateral third of lower eyelids [HP:... 1.0 \n", - "Sacral dimple [HP:0000960] 1.0 \n", - "Recurrent otitis media [HP:0000403] 1.0 \n", - "Decreased proportion of memory B cells [HP:0030... 1.0 \n", - "Renal duplication [HP:0000075] 1.0 \n", - "Ptosis [HP:0000508] 1.0 \n", - "Intellectual disability, mild [HP:0001256] 1.0 \n", - "Long palpebral fissure [HP:0000637] 1.0 \n", - "Sparse lateral eyebrow [HP:0005338] 1.0 \n", - "Seizure [HP:0001250] 1.0 \n", - "Congenital hip dislocation [HP:0001374] 1.0 \n", - "Coarctation of aorta [HP:0001680] 1.0 \n", - "Clinodactyly of the 5th finger [HP:0004209] 1.0 \n", - "Patent foramen ovale [HP:0001655] 1.0 \n", - "Proportionate short stature [HP:0003508] 1.0 \n", - "Severe short stature [HP:0003510] 1.0 \n", - "Scoliosis [HP:0002650] 1.0 \n", - "Depressed nasal tip [HP:0000437] 1.0 \n", - "Abnormality of the endocrine system [HP:0000818] 1.0 \n", - "Abnormal middle ear morphology [HP:0008609] 1.0 \n", - "Growth abnormality [HP:0001507] 1.0 \n", - "Abnormality of the upper limb [HP:0002817] 1.0 \n", - "Orofacial cleft [HP:0000202] 1.0 \n", - "Abnormal ocular adnexa morphology [HP:0030669] 1.0 \n", - "Abnormal renal morphology [HP:0012210] 1.0 \n", - "Abnormality of the hand [HP:0001155] 1.0 \n", - "Abnormal soft palate morphology [HP:0100736] 1.0 \n", - "Abnormality of metabolism/homeostasis [HP:0001939] 1.0 \n", - "Abnormal skeletal morphology [HP:0011842] 1.0 \n", - "Abnormality of the gastrointestinal tract [HP:0... 1.0 \n", - "Abnormality of the scalp hair [HP:0100037] 1.0 \n", - "Abnormality of the abdominal organs [HP:0002012] 1.0 \n", - "Neoplasm by anatomical site [HP:0011793] 1.0 \n", - "Cupped ear [HP:0000378] 1.0 \n", - "Abnormality of the genitourinary system [HP:000... 1.0 \n", - "Choanal atresia [HP:0000453] 1.0 \n", - "Strabismus [HP:0000486] 1.0 \n", - "Abnormal hair pattern [HP:0010720] 1.0 \n", - "Periauricular skin pits [HP:0100277] 1.0 \n", - "Abnormal lower lip morphology [HP:0000178] 1.0 \n", - "Abnormal hair morphology [HP:0001595] 1.0 \n", - "Single transverse palmar crease [HP:0000954] 1.0 \n", - "Abnormality of the breast [HP:0000769] 1.0 \n", - "Abnormal spermatogenesis [HP:0008669] 1.0 \n", - "Abnormality of the upper urinary tract [HP:0010... 1.0 \n", - "Abnormality of the choanae [HP:0000415] 1.0 \n", - "Abnormal conjugate eye movement [HP:0000549] 1.0 \n", - "Tented upper lip vermilion [HP:0010804] 1.0 \n", - "Abnormal eyelash morphology [HP:0000499] 1.0 \n", - "Aplasia/hypoplasia involving the skeleton [HP:0... 1.0 \n", - "Language impairment [HP:0002463] 1.0 \n", - "Abnormal axial skeleton morphology [HP:0009121] 1.0 \n", - "Abnormal semicircular canal morphology [HP:0011... 1.0 \n", - "Abnormal T cell count [HP:0011839] 1.0 \n", - "Functional abnormality of the inner ear [HP:001... 1.0 \n", - "Abnormal palate morphology [HP:0000174] 1.0 \n", - "Abnormality of the genital system [HP:0000078] 1.0 \n", - "Abnormality of the immune system [HP:0002715] 1.0 \n", - "Abnormality of the parathyroid gland [HP:0000828] 1.0 \n", - "Global developmental delay [HP:0001263] 1.0 \n", - "Phenotypic abnormality [HP:0000118] 1.0 \n", - "Functional abnormality of male internal genital... 1.0 \n", - "Abnormality of the cardiovascular system [HP:00... 1.0 \n", - "Abnormal inner ear morphology [HP:0011390] 1.0 \n", - "Regional abnormality of skin [HP:0011356] 1.0 \n", - "Abnormality of the hairline [HP:0009553] 1.0 \n", - "Type II pneumocyte hyperplasia [HP:0033328] 1.0 \n", - "Abnormality of the integument [HP:0001574] 1.0 \n", - "Aplasia/hypoplasia affecting bones of the axial... 1.0 \n", - "Abnormal nasal morphology [HP:0005105] 1.0 \n", - "Abnormal oral cavity morphology [HP:0000163] 1.0 \n", - "Abnormal digestive system morphology [HP:0025033] 1.0 \n", - "Abnormal limb bone morphology [HP:0002813] 1.0 \n", - "Abnormal ventricular septum morphology [HP:0010... 1.0 \n", - "Abnormal vascular morphology [HP:0025015] 1.0 \n", - "Underdeveloped nasal alae [HP:0000430] 1.0 \n", - "Sacrococcygeal pilonidal abnormality [HP:0010767] 1.0 \n", - "Abnormal B cell count [HP:0010975] 1.0 \n", - "Abnormal hand morphology [HP:0005922] 1.0 \n", - "Microcephaly [HP:0000252] 1.0 \n", - "Cleft palate [HP:0000175] 1.0 \n", - "Abnormal heart valve physiology [HP:0031653] 1.0 \n", - "Thick vermilion border [HP:0012471] 1.0 \n", - "Ventricular hypertrophy [HP:0001714] 1.0 \n", - "Neurodevelopmental abnormality [HP:0012759] 1.0 \n", - "Abnormal size of the palpebral fissures [HP:020... 1.0 \n", - "Finger clinodactyly [HP:0040019] 1.0 \n", - "Otitis media [HP:0000388] 1.0 \n", - "Abnormal morphology of the great vessels [HP:00... 1.0 \n", - "Abnormal palmar dermatoglyphics [HP:0001018] 1.0 \n", - "Abnormal spleen morphology [HP:0025408] 1.0 \n", - "Abnormal heart valve morphology [HP:0001654] 1.0 \n", - "Abnormal aortic valve morphology [HP:0001646] 1.0 \n", - "Abnormal leukocyte physiology [HP:0033796] 1.0 \n", - "Low-set ears [HP:0000369] 1.0 \n", - "Abnormal aortic valve physiology [HP:0031652] 1.0 \n", - "Deviation of the 5th finger [HP:0009179] 1.0 \n", - "Abnormal nasolacrimal system morphology [HP:000... 1.0 \n", - "Abnormal male reproductive system physiology [H... 1.0 \n", - "Abnormal circulating IgG level [HP:0410242] 1.0 \n", - "Obesity [HP:0001513] 1.0 \n", - "Abnormal leukocyte morphology [HP:0001881] 1.0 \n", - "Aplasia of the semicircular canal [HP:0011381] 1.0 \n", - "Joint hypermobility [HP:0001382] 1.0 \n", - "Abnormal cellular physiology [HP:0011017] 1.0 \n", - "Abnormality of the skeletal system [HP:0000924] 1.0 \n", - "Abnormal atrial septum morphology [HP:0011994] 1.0 \n", - "Neoplasm of the middle ear [HP:0100799] 1.0 \n", - "Abnormal nostril morphology [HP:0005288] 1.0 \n", - "Abnormal jaw morphology [HP:0030791] 1.0 \n", - "Aplasia/Hypoplasia involving the nose [HP:0009924] 1.0 \n", - "Abnormal alveolar type II pneumocyte morphology... 1.0 \n", - "Tooth malposition [HP:0000692] 1.0 \n", - "Abnormal cardiac atrium morphology [HP:0005120] 1.0 \n", - "Abnormal skin adnexa morphology [HP:0011138] 1.0 \n", - "Abnormal anus morphology [HP:0034915] 1.0 \n", - "Abnormality of the thyroid gland [HP:0000820] 1.0 \n", - "Abnormal sacrum morphology [HP:0005107] 1.0 \n", - "Abnormal mandible morphology [HP:0000277] 1.0 \n", - "Widely spaced teeth [HP:0000687] 1.0 \n", - "Aplasia/hypoplasia involving bones of the hand ... 1.0 \n", - "Abnormal T cell morphology [HP:0002843] 1.0 \n", - "Abnormal uvula morphology [HP:0000172] 1.0 \n", - "Aplasia/Hypoplasia of fingers [HP:0006265] 1.0 \n", - "Aplasia/Hypoplasia of the nipples [HP:0006709] 1.0 \n", - "Renal hypoplasia/aplasia [HP:0008678] 1.0 \n", - "Oligodontia [HP:0000677] 1.0 \n", - "Congenital malformation of the left heart [HP:0... 1.0 \n", - "High palate [HP:0000218] 1.0 \n", - "Abnormal 5th finger morphology [HP:0004207] 1.0 \n", - "Abnormal earlobe morphology [HP:0000363] 1.0 \n", - "Abnormal eyelid morphology [HP:0000492] 1.0 \n", - "Cryptorchidism [HP:0000028] 1.0 \n", - "Abnormal muscle physiology [HP:0011804] 1.0 \n", - "Abnormality of the dentition [HP:0000164] 1.0 \n", - "Vertebral clefting [HP:0008428] 1.0 \n", - "Short digit [HP:0011927] 1.0 \n", - "Abnormal skin morphology of the palm [HP:0040211] 1.0 \n", - "Nasolacrimal duct obstruction [HP:0000579] 1.0 \n", - "Abnormal vertebral morphology [HP:0003468] 1.0 \n", - "Telecanthus [HP:0000506] 1.0 \n", - "Abnormality of the fontanelles or cranial sutur... 1.0 \n", - "Low posterior hairline [HP:0002162] 1.0 \n", - "Deviation of finger [HP:0004097] 1.0 \n", - "Abnormal ear morphology [HP:0031703] 1.0 \n", - "Abnormality of reproductive system physiology [... 1.0 \n", - "Long eyelashes [HP:0000527] 1.0 \n", - "Abnormal leukocyte count [HP:0011893] 1.0 \n", - "Abnormality of the inner ear [HP:0000359] 1.0 \n", - "Abnormal hip joint morphology [HP:0001384] 1.0 \n", - "Abnormality of the palmar creases [HP:0010490] 1.0 \n", - "Decreased proportion of memory T cells [HP:0032... 1.0 \n", - "Abnormal cardiac ventricle morphology [HP:0001713] 1.0 \n", - "Persistence of primary teeth [HP:0006335] 1.0 \n", - "Abnormality of the posterior hairline [HP:0030141] 1.0 \n", - "Abnormal esophagus physiology [HP:0025270] 1.0 \n", - "Abnormality of humoral immunity [HP:0005368] 1.0 \n", - "Abnormal respiratory system morphology [HP:0012... 1.0 \n", - "Abnormality of the orbital region [HP:0000315] 1.0 \n", - "Abnormal lacrimal duct morphology [HP:0011481] 1.0 \n", - "Abnormal location of ears [HP:0000357] 1.0 \n", - "Unusual infection [HP:0032101] 1.0 \n", - "Abnormality of digestive system physiology [HP:... 1.0 \n", - "Abnormal glucose homeostasis [HP:0011014] 1.0 \n", - "Depressed nasal bridge [HP:0005280] 1.0 \n", - "Abnormal calvaria morphology [HP:0002683] 1.0 \n", - "Abnormal pulmonary vein morphology [HP:0030968] 1.0 \n", - "Abnormal respiratory system physiology [HP:0002... 1.0 \n", - "Abnormal number of teeth [HP:0006483] 1.0 \n", - "Morphological central nervous system abnormalit... 1.0 \n", - "Abnormality of the lymphatic system [HP:0100763] 1.0 \n", - "Abnormality of the pulmonary vasculature [HP:00... 1.0 \n", - "Posteriorly rotated ears [HP:0000358] 1.0 \n", - "Abnormality of the face [HP:0000271] 1.0 \n", - "Abnormal fingertip morphology [HP:0001211] 1.0 \n", - "Abnormality of the palpebral fissures [HP:0008050] 1.0 \n", - "Decreased circulating IgG subclass level [HP:00... 1.0 \n", - "Deviation of the hand or of fingers of the hand... 1.0 \n", - "Partial anomalous pulmonary venous return [HP:0... 1.0 \n", - "Bifid uvula [HP:0000193] 1.0 \n", - "Decreased circulating antibody level [HP:0004313] 1.0 \n", - "Cleft lip [HP:0410030] 1.0 \n", - "Abnormal eyebrow morphology [HP:0000534] 1.0 \n", - "Abnormality of mental function [HP:0011446] 1.0 \n", - "Brachydactyly [HP:0001156] 1.0 \n", - "Branchial anomaly [HP:0009794] 1.0 \n", - "Abnormal pituitary gland morphology [HP:0012503] 1.0 \n", - "Short nose [HP:0003196] 1.0 \n", - "Ectopic anus [HP:0004397] 1.0 \n", - "Cholesteatoma [HP:0009797] 1.0 \n", - "Decreased head circumference [HP:0040195] 1.0 \n", - "Prominent fingertip pads [HP:0001212] 1.0 \n", - "Abnormal pulmonary alveolar system morphology [... 1.0 \n", - "Abnormal abdomen morphology [HP:0001438] 1.0 \n", - "Abnormal nervous system morphology [HP:0012639] 1.0 \n", - "Microtia [HP:0008551] 1.0 \n", - "Abnormal circulating IgA level [HP:0410240] 1.0 \n", - "Abnormality of the amniotic fluid [HP:0001560] 1.0 \n", - "Aplasia/Hypoplasia of the external ear [HP:0008... 1.0 \n", - "Abnormal thorax morphology [HP:0000765] 1.0 \n", - "Abnormal dental morphology [HP:0006482] 1.0 \n", - "Abnormal periauricular region morphology [HP:00... 1.0 \n", - "Abnormality of pulmonary circulation [HP:0030875] 1.0 \n", - "Pulmonary arterial hypertension [HP:0002092] 1.0 \n", - "Wide nasal bridge [HP:0000431] 1.0 \n", - "Abnormality of lower limb joint [HP:0100491] 1.0 \n", - "Hypoplastic heart [HP:0001961] 1.0 \n", - "Postnatal growth retardation [HP:0008897] 1.0 \n", - "Abnormal ear physiology [HP:0031704] 1.0 \n", - "Renal hypoplasia [HP:0000089] 1.0 \n", - "Abnormal testis morphology [HP:0000035] 1.0 \n", - "Abnormal upper lip morphology [HP:0000177] 1.0 \n", - "Holoprosencephaly [HP:0001360] 1.0 \n", - "Wide nose [HP:0000445] 1.0 \n", - "Neoplasm [HP:0002664] 1.0 \n", - "Abnormal cerebral morphology [HP:0002060] 1.0 \n", - "Hypotelorism [HP:0000601] 1.0 \n", - "Abnormal cardiovascular system morphology [HP:0... 1.0 \n", - "Polyhydramnios [HP:0001561] 1.0 \n", - "Abnormality of thyroid physiology [HP:0002926] 1.0 \n", - "Abnormality of body weight [HP:0004323] 1.0 \n", - "Abnormality of eye movement [HP:0000496] 1.0 \n", - "Abnormality of the eye [HP:0000478] 1.0 \n", - "Joint dislocation [HP:0001373] 1.0 \n", - "Abnormal blood glucose concentration [HP:0011015] 1.0 \n", - "Abnormal lymphocyte physiology [HP:0031409] 1.0 \n", - "Branchial sinus [HP:0100272] 1.0 \n", - "Abnormal thoracic spine morphology [HP:0100711] 1.0 \n", - "Abnormality of the head [HP:0000234] 1.0 \n", - "Abdominal symptom [HP:0011458] 1.0 \n", - "Abnormal lung morphology [HP:0002088] 1.0 \n", - "Abnormal cellular phenotype [HP:0025354] 1.0 \n", - "Abnormal skin morphology [HP:0011121] 1.0 \n", - "Abnormal musculoskeletal physiology [HP:0011843] 1.0 \n", - "Abnormal eye physiology [HP:0012373] 1.0 \n", - "Intrauterine growth retardation [HP:0001511] 1.0 \n", - "Smooth philtrum [HP:0000319] 1.0 \n", - "Subpleural honeycombing [HP:0031631] 1.0 \n", - "Aplasia/Hypoplasia involving bones of the skull... 1.0 \n", - "Abnormal endocrine physiology [HP:0031072] 1.0 \n", - "Aplasia/Hypoplasia of the mandible [HP:0009118] 1.0 \n", - "Abnormal ureter morphology [HP:0025633] 1.0 \n", - "Thin upper lip vermilion [HP:0000219] 1.0 \n", - "Splenomegaly [HP:0001744] 1.0 \n", - "Abnormal skull morphology [HP:0000929] 1.0 \n", - "Abnormal neck morphology [HP:0025668] 1.0 \n", - "Delayed speech and language development [HP:000... 1.0 \n", - "Abnormal cardiovascular system physiology [HP:0... 1.0 \n", - "Abnormal oral morphology [HP:0031816] 1.0 \n", - "Abnormality of joint mobility [HP:0011729] 1.0 \n", - "Cleft upper lip [HP:0000204] 1.0 \n", - "Abnormal eye morphology [HP:0012372] 1.0 \n", - "Abnormality of the forehead [HP:0000290] 1.0 \n", - "Short 5th finger [HP:0009237] 1.0 \n", - "Downslanted palpebral fissures [HP:0000494] 1.0 \n", - "Bilateral renal hypoplasia [HP:0012584] 1.0 \n", - "Abnormality of the mouth [HP:0000153] 1.0 \n", - "Abnormal morphology of the nasal alae [HP:0000429] 1.0 \n", - "Midface retrusion [HP:0011800] 1.0 \n", - "Abnormal lymphocyte morphology [HP:0004332] 1.0 \n", - "Abnormal proportion of memory T cells [HP:0032182] 1.0 \n", - "Abnormal aortic morphology [HP:0001679] 1.0 \n", - "Abnormal muscle tone [HP:0003808] 1.0 \n", - "Abnormal midface morphology [HP:0000309] 1.0 \n", - "Proboscis [HP:0012806] 1.0 \n", - "Abnormality of B cell physiology [HP:0005372] 1.0 \n", - "Aplasia/Hypoplasia of the 5th finger [HP:0006262] 1.0 \n", - "Lobar holoprosencephaly [HP:0006870] 1.0 \n", - "Abnormality of the vasculature [HP:0002597] 1.0 \n", - "Abnormal cheek morphology [HP:0004426] 1.0 \n", - "Aplasia/hypoplasia involving bones of the extre... 1.0 \n", - "Abnormality of body height [HP:0000002] 1.0 \n", - "Abnormal nasal tip morphology [HP:0000436] 1.0 \n", - "Craniosynostosis [HP:0001363] 1.0 \n", - "Dimple chin [HP:0010751] 1.0 \n", - "Abnormal joint morphology [HP:0001367] 1.0 \n", - "Abnormal systemic arterial morphology [HP:0011004] 1.0 \n", - "Anterior hypopituitarism [HP:0000830] 1.0 \n", - "Abnormal nasal dorsum morphology [HP:0011119] 1.0 \n", - "Abnormal heart morphology [HP:0001627] 1.0 \n", - "Pre-capillary pulmonary hypertension [HP:0033578] 1.0 \n", - "Abnormal inflammatory response [HP:0012647] 1.0 \n", - "Anteriorly placed anus [HP:0001545] 1.0 \n", - "Hearing abnormality [HP:0000364] 1.0 \n", - "Epicanthus [HP:0000286] 1.0 \n", - "Hip dysplasia [HP:0001385] 1.0 \n", - "Abnormality of the nervous system [HP:0000707] 1.0 \n", - "Abnormal pinna morphology [HP:0000377] 1.0 \n", - "Abnormal joint physiology [HP:0034430] 1.0 \n", - "Prominent digit pad [HP:0011298] 1.0 \n", - "Hypoparathyroidism [HP:0000829] 1.0 \n", - "Aplasia/hypoplasia involving bones of the upper... 1.0 \n", - "Abnormal sclera morphology [HP:0000591] 1.0 \n", - "Decreased circulating total IgG [HP:0032132] 1.0 \n", - "Abnormal palm morphology [HP:0100871] 1.0 \n", - "Renal cyst [HP:0000107] 1.0 \n", - "Abnormal hip bone morphology [HP:0003272] 1.0 \n", - "Ectropion [HP:0000656] 1.0 \n", - "Abnormality of the musculoskeletal system [HP:0... 1.0 \n", - "Abnormal blood vessel morphology [HP:0033353] 1.0 \n", - "Abnormality of blood and blood-forming tissues ... 1.0 \n", - "Increased inflammatory response [HP:0012649] 1.0 \n", - "Abnormality of the respiratory system [HP:0002086] 1.0 \n", - "Abnormality of prenatal development or birth [H... 1.0 \n", - "Short stature [HP:0004322] 1.0 \n", - "Abnormal B cell subset distribution [HP:0025539] 1.0 \n", - "Median cleft lip [HP:0000161] 1.0 \n", - "Aplasia/Hypoplasia of the ear [HP:0008771] 1.0 \n", - "Abnormality of the spleen [HP:0001743] 1.0 \n", - "Abnormality of the chin [HP:0000306] 1.0 \n", - "Thin vermilion border [HP:0000233] 1.0 \n", - "Growth delay [HP:0001510] 1.0 \n", - "Dental malocclusion [HP:0000689] 1.0 \n", - "Recurrent infections [HP:0002719] 1.0 \n", - "Clinodactyly [HP:0030084] 1.0 \n", - "Abnormality of the outer ear [HP:0000356] 1.0 \n", - "Aplasia/Hypoplasia of the eyebrow [HP:0100840] 1.0 \n", - "Abnormal nipple morphology [HP:0004404] 1.0 \n", - "Increased body weight [HP:0004324] 1.0 \n", - "Abnormal breast morphology [HP:0031093] 1.0 \n", - "Abnormality of the anterior pituitary [HP:0011747] 1.0 \n", - "Abnormal dermatoglyphics [HP:0007477] 1.0 \n", - "Lower extremity joint dislocation [HP:0030311] 1.0 \n", - "Abnormal nervous system physiology [HP:0012638] 1.0 \n", - "Abnormality of the kidney [HP:0000077] 1.0 \n", - "Abnormal digit morphology [HP:0011297] 1.0 \n", - "Abnormal cardiac septum morphology [HP:0001671] 1.0 \n", - "Microdontia [HP:0000691] 1.0 \n", - "Aplasia/Hypoplasia involving the central nervou... 1.0 \n", - "Azoospermia [HP:0000027] 1.0 \n", - "Aplasia/hypoplasia of the extremities [HP:0009815] 1.0 \n", - "Abnormal immune system morphology [HP:0032251] 1.0 \n", - "Abnormal B cell morphology [HP:0002846] 1.0 \n", - "Abnormal lip morphology [HP:0000159] 1.0 \n", - "Abnormality of the urinary system physiology [H... 1.0 \n", - "Macrotia [HP:0000400] 1.0 \n", - "Neurodevelopmental delay [HP:0012758] 1.0 \n", - "Abnormal homeostasis [HP:0012337] 1.0 \n", - "Large earlobe [HP:0009748] 1.0 \n", - "Abnormal cellular immune system morphology [HP:... 1.0 \n", - "Respiratory tract infection [HP:0011947] 1.0 \n", - "Anal atresia [HP:0002023] 1.0 \n", - "Preauricular pit [HP:0004467] 1.0 \n", - "Abnormal pelvic girdle bone morphology [HP:0002... 1.0 \n", - "Anomalous pulmonary venous return [HP:0010772] 1.0 \n", - "Abnormality of the anus [HP:0004378] 1.0 \n", - "Localized skin lesion [HP:0011355] 1.0 \n", - "Abnormality of the nose [HP:0000366] 1.0 \n", - "Ectopic kidney [HP:0000086] 1.0 \n", - "Congenital malformation of the great arteries [... 1.0 \n", - "Abnormal curvature of the vertebral column [HP:... 1.0 \n", - "Slanting of the palpebral fissure [HP:0200006] 1.0 \n", - "Abnormality of speech or vocalization [HP:0002167] 1.0 \n", - "Abnormal proportion of memory B cells [HP:0030373] 1.0 \n", - "Abnormal response to endocrine stimulation test... 1.0 \n", - "Abnormality of the lower limb [HP:0002814] 1.0 \n", - "Decreased circulating IgA level [HP:0002720] 1.0 \n", - "Abnormal finger morphology [HP:0001167] 1.0 \n", - "Abnormality of limbs [HP:0040064] 1.0 \n", - "Non-obstructive azoospermia [HP:0011961] 1.0 \n", - "Abnormal communication [HP:0034434] 1.0 \n", - "Abnormality of the neck [HP:0000464] 1.0 \n", - "Hypothyroidism [HP:0000821] 1.0 \n", - "Abnormal aortic valve cusp morphology [HP:0031567] 1.0 \n", - "Visceromegaly [HP:0003271] 1.0 \n", - "Abnormality of the philtrum [HP:0000288] 1.0 \n", - "Tooth agenesis [HP:0009804] 1.0 \n", - "Abnormality of globe location [HP:0100886] 1.0 \n", - "Abnormal vascular physiology [HP:0030163] 1.0 \n", - "Renal insufficiency [HP:0000083] 1.0 \n", - "Abnormal scalp morphology [HP:0001965] 1.0 \n", - "Abnormality of immune system physiology [HP:001... 1.0 \n", - "Abnormal nasal bridge morphology [HP:0000422] 1.0 \n", - "Abnormal brain morphology [HP:0012443] 1.0 \n", - "Feeding difficulties [HP:0011968] 1.0 \n", - "Abnormality of skull size [HP:0000240] 1.0 \n", - "Micrognathia [HP:0000347] 1.0 \n", - "Abnormal form of the vertebral bodies [HP:0003312] 1.0 \n", - "Neoplasm of the ear [HP:0012780] 1.0 \n", - "Abnormality of the ear [HP:0000598] 1.0 \n", - "Decreased circulating IgG level [HP:0004315] 1.0 \n", - "Hypoplastic nipples [HP:0002557] 1.0 \n", - "Abnormality of the musculature [HP:0003011] 1.0 \n", - "Alobar holoprosencephaly [HP:0006988] 1.0 \n", - "Aplasia/Hypoplasia of the cerebrum [HP:0007364] 1.0 \n", - "Gastroesophageal reflux [HP:0002020] 1.0 \n", - "Abnormal lymphocyte count [HP:0040088] 1.0 \n", - "Abnormal appendicular skeleton morphology [HP:0... 1.0 \n", - "Abnormality of the digestive system [HP:0025031] 1.0 \n", - "Abnormal subpleural morphology [HP:0031630] 1.0 \n", - "Sparse eyebrow [HP:0045075] 1.0 \n", - "Abnormal right ventricle morphology [HP:0001707] 1.0 \n", - "Narrow palate [HP:0000189] 1.0 \n", - "Elevated pulmonary artery pressure [HP:0004890] 1.0 \n", - "Protruding ear [HP:0000411] 1.0 \n", - "Abnormality of primary teeth [HP:0006481] 1.0 \n", - "Hip dislocation [HP:0002827] 1.0 \n", - "Hypertelorism [HP:0000316] 1.0 \n", - "Abnormal aortic arch morphology [HP:0012303] 1.0 \n", - "Abnormality of the skin [HP:0000951] 1.0 \n", - "Butterfly vertebrae [HP:0003316] 1.0 \n", - "Anteverted nares [HP:0000463] 1.0 \n", - "Abnormality of upper lip vermillion [HP:0011339] 1.0 \n", - "Congenital hypothyroidism [HP:0000851] 1.0 \n", - "Abnormal T cell subset distribution [HP:0025540] 1.0 \n", - "Abnormal localization of kidney [HP:0100542] 1.0 \n", - "Abnormal forebrain morphology [HP:0100547] 1.0 \n", - "High, narrow palate [HP:0002705] 1.0 \n", - "Abnormality of limb bone [HP:0040068] 1.0 \n", - "Abnormality of the ocular adnexa [HP:0032039] 1.0 \n", - "Decreased response to growth hormone stimulatio... 1.0 \n", - "Abnormal morphology of the vestibule of the inn... 1.0 \n", - "Congenital abnormal hair pattern [HP:0011361] 1.0 \n", - "Abnormality of the ureter [HP:0000069] 1.0 \n", - "Short finger [HP:0009381] 1.0 \n", - "All [HP:0000001] 1.0 \n", - "Abnormality of dental eruption [HP:0006292] 1.0 \n", - "Abnormality of the hypothalamus-pituitary axis ... 1.0 \n", - "Thick lower lip vermilion [HP:0000179] 1.0 \n", - "Abnormal facial skeleton morphology [HP:0011821] 1.0 \n", - "Absent nipple [HP:0002561] 1.0 \n", - "Intellectual disability [HP:0001249] 1.0 \n", - "Abnormal renal physiology [HP:0012211] 1.0 \n", - "Abnormality of the pulmonary veins [HP:0011718] 1.0 \n", - "Hypopituitarism [HP:0040075] 1.0 \n", - "Abnormality of head or neck [HP:0000152] 1.0 \n", - "Abnormality of the parathyroid physiology [HP:0... 1.0 \n", - "Skin dimple [HP:0010781] 1.0 \n", - "Abnormality of the middle ear [HP:0000370] 1.0 \n", - "Retrognathia [HP:0000278] 1.0 \n", - "Abnormal immunoglobulin level [HP:0010701] 1.0 \n", - "Functional abnormality of the gastrointestinal ... 1.0 \n", - "Abnormal external nose morphology [HP:0010938] 1.0 \n", - "Skin pit [HP:0100276] 1.0 \n", - "Abnormality of cranial sutures [HP:0011329] 1.0 \n", - "Abnormality of the vertebral column [HP:0000925] 1.0 \n", - "Abnormality of the urinary system [HP:0000079] 1.0 " + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "viewer = ProteinViewable()\n", + "html_prot = viewer.process(cohort, pvis)\n", + "display(HTML(html_prot))" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" ] }, - "execution_count": 9, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "missense_var = analysis.compare_by_variant_effect(VariantEffect.MISSENSE_VARIANT, tx_id=KTM2D_transcript_id)\n", - "print(len(missense_var.pvals))\n", - "missense_var.summarize(hpo, PatientCategories.YES)" + "drawer = ProteinVisualizer()\n", + "drawer.draw_fig(pvis=pvis)" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ + "from gpsea.analysis import configure_cohort_analysis, CohortAnalysisConfiguration\n", + "from gpsea.analysis.predicate import PatientCategories\n", + "from gpsea.model.genome import Region\n", + "\n", "analysis_config = CohortAnalysisConfiguration()\n", - "analysis_config.missing_implies_excluded = True\n", - "analysis_config.pval_correction = 'fdr_bh'\n", - "analysis_config.min_perc_patients_w_hpo = 0.1\n", - "analysis_config.heuristic_strategy()\n", + "analysis_config.missing_implies_excluded = False\n", + "analysis_config.hpo_mtc_strategy()\n", + "\n", "analysis = configure_cohort_analysis(cohort, hpo, config=analysis_config)" ] }, { "cell_type": "code", - "execution_count": 11, - "metadata": {}, + "execution_count": 10, + "metadata": { + "scrolled": true + }, "outputs": [ { "data": { @@ -5946,7 +980,7 @@ " 0/13\n", " 0%\n", " 0.003432\n", - " 0.164760\n", + " 0.15103\n", " \n", " \n", " Hearing impairment [HP:0000365]\n", @@ -5955,7 +989,7 @@ " 8/21\n", " 38%\n", " 0.030871\n", - " 0.740896\n", + " 1.00000\n", " \n", " \n", " Ventricular septal defect [HP:0001629]\n", @@ -5964,7 +998,7 @@ " 5/21\n", " 24%\n", " 0.061943\n", - " 0.961039\n", + " 1.00000\n", " \n", " \n", " High forehead [HP:0000348]\n", @@ -5973,7 +1007,7 @@ " 1/5\n", " 20%\n", " 0.080087\n", - " 0.961039\n", + " 1.00000\n", " \n", " \n", " Motor delay [HP:0001270]\n", @@ -5982,7 +1016,7 @@ " 2/2\n", " 100%\n", " 0.133333\n", - " 1.000000\n", + " 1.00000\n", " \n", " \n", " Hypotonia [HP:0001252]\n", @@ -5991,16 +1025,7 @@ " 10/14\n", " 71%\n", " 0.158824\n", - " 1.000000\n", - " \n", - " \n", - " Patent ductus arteriosus [HP:0001643]\n", - " 1/24\n", - " 4%\n", - " 4/21\n", - " 19%\n", - " 0.169013\n", - " 1.000000\n", + " 1.00000\n", " \n", " \n", " Decreased proportion of CD4-positive, alpha-beta memory T cells [HP:0410386]\n", @@ -6009,7 +1034,7 @@ " 2/5\n", " 40%\n", " 0.292929\n", - " 1.000000\n", + " 1.00000\n", " \n", " \n", " Blue sclerae [HP:0000592]\n", @@ -6018,7 +1043,7 @@ " 3/10\n", " 30%\n", " 0.349959\n", - " 1.000000\n", + " 1.00000\n", " \n", " \n", " Highly arched eyebrow [HP:0002553]\n", @@ -6027,7 +1052,7 @@ " 14/21\n", " 67%\n", " 0.374190\n", - " 1.000000\n", + " 1.00000\n", " \n", " \n", " Atrial septal defect [HP:0001631]\n", @@ -6036,7 +1061,7 @@ " 6/13\n", " 46%\n", " 0.374226\n", - " 1.000000\n", + " 1.00000\n", " \n", " \n", " Eversion of lateral third of lower eyelids [HP:0007655]\n", @@ -6045,7 +1070,7 @@ " 16/18\n", " 89%\n", " 0.386466\n", - " 1.000000\n", + " 1.00000\n", " \n", " \n", " Sacral dimple [HP:0000960]\n", @@ -6054,7 +1079,7 @@ " 6/10\n", " 60%\n", " 0.454545\n", - " 1.000000\n", + " 1.00000\n", " \n", " \n", " Recurrent otitis media [HP:0000403]\n", @@ -6063,7 +1088,7 @@ " 6/17\n", " 35%\n", " 0.476451\n", - " 1.000000\n", + " 1.00000\n", " \n", " \n", " Decreased proportion of memory B cells [HP:0030374]\n", @@ -6072,7 +1097,7 @@ " 5/5\n", " 100%\n", " 0.487179\n", - " 1.000000\n", + " 1.00000\n", " \n", " \n", " Renal duplication [HP:0000075]\n", @@ -6081,16 +1106,7 @@ " 2/8\n", " 25%\n", " 0.487179\n", - " 1.000000\n", - " \n", - " \n", - " Ptosis [HP:0000508]\n", - " 0/7\n", - " 0%\n", - " 2/11\n", - " 18%\n", - " 0.496732\n", - " 1.000000\n", + " 1.00000\n", " \n", " \n", " Intellectual disability, mild [HP:0001256]\n", @@ -6099,7 +1115,7 @@ " 1/2\n", " 50%\n", " 0.505495\n", - " 1.000000\n", + " 1.00000\n", " \n", " \n", " Long palpebral fissure [HP:0000637]\n", @@ -6108,7 +1124,7 @@ " 17/21\n", " 81%\n", " 0.552019\n", - " 1.000000\n", + " 1.00000\n", " \n", " \n", " Sparse lateral eyebrow [HP:0005338]\n", @@ -6117,7 +1133,7 @@ " 5/8\n", " 62%\n", " 0.569231\n", - " 1.000000\n", + " 1.00000\n", " \n", " \n", " Seizure [HP:0001250]\n", @@ -6126,7 +1142,7 @@ " 2/17\n", " 12%\n", " 0.569960\n", - " 1.000000\n", + " 1.00000\n", " \n", " \n", " Congenital hip dislocation [HP:0001374]\n", @@ -6135,16 +1151,7 @@ " 5/16\n", " 31%\n", " 0.621385\n", - " 1.000000\n", - " \n", - " \n", - " Coarctation of aorta [HP:0001680]\n", - " 1/15\n", - " 7%\n", - " 3/21\n", - " 14%\n", - " 0.625668\n", - " 1.000000\n", + " 1.00000\n", " \n", " \n", " Clinodactyly of the 5th finger [HP:0004209]\n", @@ -6153,16 +1160,7 @@ " 4/11\n", " 36%\n", " 0.642415\n", - " 1.000000\n", - " \n", - " \n", - " Patent foramen ovale [HP:0001655]\n", - " 2/24\n", - " 8%\n", - " 3/20\n", - " 15%\n", - " 0.645896\n", - " 1.000000\n", + " 1.00000\n", " \n", " \n", " Proportionate short stature [HP:0003508]\n", @@ -6171,7 +1169,7 @@ " 4/9\n", " 44%\n", " 0.653417\n", - " 1.000000\n", + " 1.00000\n", " \n", " \n", " Severe short stature [HP:0003510]\n", @@ -6180,7 +1178,7 @@ " 4/10\n", " 40%\n", " 0.669921\n", - " 1.000000\n", + " 1.00000\n", " \n", " \n", " Scoliosis [HP:0002650]\n", @@ -6189,7 +1187,7 @@ " 4/17\n", " 24%\n", " 0.716925\n", - " 1.000000\n", + " 1.00000\n", " \n", " \n", " Depressed nasal tip [HP:0000437]\n", @@ -6198,25 +1196,7 @@ " 20/27\n", " 74%\n", " 0.736504\n", - " 1.000000\n", - " \n", - " \n", - " Ectopic kidney [HP:0000086]\n", - " 0/2\n", - " 0%\n", - " 2/9\n", - " 22%\n", - " 1.000000\n", - " 1.000000\n", - " \n", - " \n", - " Decreased circulating IgA level [HP:0002720]\n", - " 2/8\n", - " 25%\n", - " 2/5\n", - " 40%\n", - " 1.000000\n", - " 1.000000\n", + " 1.00000\n", " \n", " \n", " Thin upper lip vermilion [HP:0000219]\n", @@ -6225,7 +1205,7 @@ " 4/6\n", " 67%\n", " 1.000000\n", - " 1.000000\n", + " 1.00000\n", " \n", " \n", " Delayed speech and language development [HP:0000750]\n", @@ -6234,16 +1214,7 @@ " 2/2\n", " 100%\n", " 1.000000\n", - " 1.000000\n", - " \n", - " \n", - " Low posterior hairline [HP:0002162]\n", - " 0/1\n", - " 0%\n", - " 2/6\n", - " 33%\n", - " 1.000000\n", - " 1.000000\n", + " 1.00000\n", " \n", " \n", " Feeding difficulties [HP:0011968]\n", @@ -6252,34 +1223,34 @@ " 3/11\n", " 27%\n", " 1.000000\n", - " 1.000000\n", + " 1.00000\n", " \n", " \n", - " Long eyelashes [HP:0000527]\n", - " 0/1\n", - " 0%\n", - " 3/6\n", - " 50%\n", - " 1.000000\n", + " High, narrow palate [HP:0002705]\n", + " 8/9\n", + " 89%\n", + " 6/6\n", + " 100%\n", " 1.000000\n", + " 1.00000\n", " \n", " \n", - " Micrognathia [HP:0000347]\n", - " 3/6\n", - " 50%\n", - " 4/9\n", - " 44%\n", - " 1.000000\n", + " Joint hypermobility [HP:0001382]\n", + " 2/7\n", + " 29%\n", + " 3/12\n", + " 25%\n", " 1.000000\n", + " 1.00000\n", " \n", " \n", - " Microcephaly [HP:0000252]\n", - " 3/7\n", - " 43%\n", - " 4/11\n", - " 36%\n", - " 1.000000\n", + " Long eyelashes [HP:0000527]\n", + " 0/1\n", + " 0%\n", + " 3/6\n", + " 50%\n", " 1.000000\n", + " 1.00000\n", " \n", " \n", " Cleft palate [HP:0000175]\n", @@ -6288,16 +1259,16 @@ " 8/8\n", " 100%\n", " 1.000000\n", - " 1.000000\n", + " 1.00000\n", " \n", " \n", - " High, narrow palate [HP:0002705]\n", - " 8/9\n", - " 89%\n", - " 6/6\n", - " 100%\n", - " 1.000000\n", + " Low posterior hairline [HP:0002162]\n", + " 0/1\n", + " 0%\n", + " 2/6\n", + " 33%\n", " 1.000000\n", + " 1.00000\n", " \n", " \n", " Prominent fingertip pads [HP:0001212]\n", @@ -6306,7 +1277,25 @@ " 18/23\n", " 78%\n", " 1.000000\n", + " 1.00000\n", + " \n", + " \n", + " Ectopic kidney [HP:0000086]\n", + " 0/2\n", + " 0%\n", + " 2/9\n", + " 22%\n", + " 1.000000\n", + " 1.00000\n", + " \n", + " \n", + " Decreased circulating IgA level [HP:0002720]\n", + " 2/8\n", + " 25%\n", + " 2/5\n", + " 40%\n", " 1.000000\n", + " 1.00000\n", " \n", " \n", " Wide nasal bridge [HP:0000431]\n", @@ -6315,7 +1304,7 @@ " 11/14\n", " 79%\n", " 1.000000\n", - " 1.000000\n", + " 1.00000\n", " \n", " \n", " Postnatal growth retardation [HP:0008897]\n", @@ -6324,16 +1313,34 @@ " 4/5\n", " 80%\n", " 1.000000\n", + " 1.00000\n", + " \n", + " \n", + " Micrognathia [HP:0000347]\n", + " 3/6\n", + " 50%\n", + " 4/9\n", + " 44%\n", " 1.000000\n", + " 1.00000\n", " \n", " \n", - " Joint hypermobility [HP:0001382]\n", - " 2/7\n", - " 29%\n", - " 3/12\n", - " 25%\n", + " Microcephaly [HP:0000252]\n", + " 3/7\n", + " 43%\n", + " 4/11\n", + " 36%\n", " 1.000000\n", + " 1.00000\n", + " \n", + " \n", + " High palate [HP:0000218]\n", + " 10/11\n", + " 91%\n", + " 8/8\n", + " 100%\n", " 1.000000\n", + " 1.00000\n", " \n", " \n", " Sparse eyebrow [HP:0045075]\n", @@ -6342,7 +1349,7 @@ " 8/11\n", " 73%\n", " 1.000000\n", - " 1.000000\n", + " 1.00000\n", " \n", " \n", " Recurrent infections [HP:0002719]\n", @@ -6351,16 +1358,7 @@ " 15/20\n", " 75%\n", " 1.000000\n", - " 1.000000\n", - " \n", - " \n", - " High palate [HP:0000218]\n", - " 10/11\n", - " 91%\n", - " 8/8\n", - " 100%\n", - " 1.000000\n", - " 1.000000\n", + " 1.00000\n", " \n", " \n", " Short stature [HP:0004322]\n", @@ -6369,7 +1367,7 @@ " 13/18\n", " 72%\n", " 1.000000\n", - " 1.000000\n", + " 1.00000\n", " \n", " \n", "\n", @@ -6384,7 +1382,6 @@ "High forehead [HP:0000348] 5/6 83% 1/5 \n", "Motor delay [HP:0001270] 2/8 25% 2/2 \n", "Hypotonia [HP:0001252] 2/7 29% 10/14 \n", - "Patent ductus arteriosus [HP:0001643] 1/24 4% 4/21 \n", "Decreased proportion of CD4-positive, alpha-bet... 6/8 75% 2/5 \n", "Blue sclerae [HP:0000592] 4/7 57% 3/10 \n", "Highly arched eyebrow [HP:0002553] 8/9 89% 14/21 \n", @@ -6394,37 +1391,34 @@ "Recurrent otitis media [HP:0000403] 8/15 53% 6/17 \n", "Decreased proportion of memory B cells [HP:0030... 6/8 75% 5/5 \n", "Renal duplication [HP:0000075] 0/5 0% 2/8 \n", - "Ptosis [HP:0000508] 0/7 0% 2/11 \n", "Intellectual disability, mild [HP:0001256] 3/12 25% 1/2 \n", "Long palpebral fissure [HP:0000637] 8/8 100% 17/21 \n", "Sparse lateral eyebrow [HP:0005338] 6/7 86% 5/8 \n", "Seizure [HP:0001250] 2/8 25% 2/17 \n", "Congenital hip dislocation [HP:0001374] 1/7 14% 5/16 \n", - "Coarctation of aorta [HP:0001680] 1/15 7% 3/21 \n", "Clinodactyly of the 5th finger [HP:0004209] 2/9 22% 4/11 \n", - "Patent foramen ovale [HP:0001655] 2/24 8% 3/20 \n", "Proportionate short stature [HP:0003508] 7/11 64% 4/9 \n", "Severe short stature [HP:0003510] 7/12 58% 4/10 \n", "Scoliosis [HP:0002650] 6/19 32% 4/17 \n", "Depressed nasal tip [HP:0000437] 16/20 80% 20/27 \n", - "Ectopic kidney [HP:0000086] 0/2 0% 2/9 \n", - "Decreased circulating IgA level [HP:0002720] 2/8 25% 2/5 \n", "Thin upper lip vermilion [HP:0000219] 3/4 75% 4/6 \n", "Delayed speech and language development [HP:000... 8/10 80% 2/2 \n", - "Low posterior hairline [HP:0002162] 0/1 0% 2/6 \n", "Feeding difficulties [HP:0011968] 5/14 36% 3/11 \n", + "High, narrow palate [HP:0002705] 8/9 89% 6/6 \n", + "Joint hypermobility [HP:0001382] 2/7 29% 3/12 \n", "Long eyelashes [HP:0000527] 0/1 0% 3/6 \n", - "Micrognathia [HP:0000347] 3/6 50% 4/9 \n", - "Microcephaly [HP:0000252] 3/7 43% 4/11 \n", "Cleft palate [HP:0000175] 9/10 90% 8/8 \n", - "High, narrow palate [HP:0002705] 8/9 89% 6/6 \n", + "Low posterior hairline [HP:0002162] 0/1 0% 2/6 \n", "Prominent fingertip pads [HP:0001212] 12/15 80% 18/23 \n", + "Ectopic kidney [HP:0000086] 0/2 0% 2/9 \n", + "Decreased circulating IgA level [HP:0002720] 2/8 25% 2/5 \n", "Wide nasal bridge [HP:0000431] 3/3 100% 11/14 \n", "Postnatal growth retardation [HP:0008897] 5/8 62% 4/5 \n", - "Joint hypermobility [HP:0001382] 2/7 29% 3/12 \n", + "Micrognathia [HP:0000347] 3/6 50% 4/9 \n", + "Microcephaly [HP:0000252] 3/7 43% 4/11 \n", + "High palate [HP:0000218] 10/11 91% 8/8 \n", "Sparse eyebrow [HP:0045075] 6/7 86% 8/11 \n", "Recurrent infections [HP:0002719] 15/21 71% 15/20 \n", - "High palate [HP:0000218] 10/11 91% 8/8 \n", "Short stature [HP:0004322] 9/13 69% 13/18 \n", "\n", "MISSENSE_VARIANT on NM_003482.4 \\\n", @@ -6435,7 +1429,6 @@ "High forehead [HP:0000348] 20% 0.080087 \n", "Motor delay [HP:0001270] 100% 0.133333 \n", "Hypotonia [HP:0001252] 71% 0.158824 \n", - "Patent ductus arteriosus [HP:0001643] 19% 0.169013 \n", "Decreased proportion of CD4-positive, alpha-bet... 40% 0.292929 \n", "Blue sclerae [HP:0000592] 30% 0.349959 \n", "Highly arched eyebrow [HP:0002553] 67% 0.374190 \n", @@ -6445,143 +1438,104 @@ "Recurrent otitis media [HP:0000403] 35% 0.476451 \n", "Decreased proportion of memory B cells [HP:0030... 100% 0.487179 \n", "Renal duplication [HP:0000075] 25% 0.487179 \n", - "Ptosis [HP:0000508] 18% 0.496732 \n", "Intellectual disability, mild [HP:0001256] 50% 0.505495 \n", "Long palpebral fissure [HP:0000637] 81% 0.552019 \n", "Sparse lateral eyebrow [HP:0005338] 62% 0.569231 \n", "Seizure [HP:0001250] 12% 0.569960 \n", "Congenital hip dislocation [HP:0001374] 31% 0.621385 \n", - "Coarctation of aorta [HP:0001680] 14% 0.625668 \n", "Clinodactyly of the 5th finger [HP:0004209] 36% 0.642415 \n", - "Patent foramen ovale [HP:0001655] 15% 0.645896 \n", "Proportionate short stature [HP:0003508] 44% 0.653417 \n", "Severe short stature [HP:0003510] 40% 0.669921 \n", "Scoliosis [HP:0002650] 24% 0.716925 \n", "Depressed nasal tip [HP:0000437] 74% 0.736504 \n", - "Ectopic kidney [HP:0000086] 22% 1.000000 \n", - "Decreased circulating IgA level [HP:0002720] 40% 1.000000 \n", "Thin upper lip vermilion [HP:0000219] 67% 1.000000 \n", "Delayed speech and language development [HP:000... 100% 1.000000 \n", - "Low posterior hairline [HP:0002162] 33% 1.000000 \n", "Feeding difficulties [HP:0011968] 27% 1.000000 \n", + "High, narrow palate [HP:0002705] 100% 1.000000 \n", + "Joint hypermobility [HP:0001382] 25% 1.000000 \n", "Long eyelashes [HP:0000527] 50% 1.000000 \n", - "Micrognathia [HP:0000347] 44% 1.000000 \n", - "Microcephaly [HP:0000252] 36% 1.000000 \n", "Cleft palate [HP:0000175] 100% 1.000000 \n", - "High, narrow palate [HP:0002705] 100% 1.000000 \n", + "Low posterior hairline [HP:0002162] 33% 1.000000 \n", "Prominent fingertip pads [HP:0001212] 78% 1.000000 \n", + "Ectopic kidney [HP:0000086] 22% 1.000000 \n", + "Decreased circulating IgA level [HP:0002720] 40% 1.000000 \n", "Wide nasal bridge [HP:0000431] 79% 1.000000 \n", "Postnatal growth retardation [HP:0008897] 80% 1.000000 \n", - "Joint hypermobility [HP:0001382] 25% 1.000000 \n", + "Micrognathia [HP:0000347] 44% 1.000000 \n", + "Microcephaly [HP:0000252] 36% 1.000000 \n", + "High palate [HP:0000218] 100% 1.000000 \n", "Sparse eyebrow [HP:0045075] 73% 1.000000 \n", "Recurrent infections [HP:0002719] 75% 1.000000 \n", - "High palate [HP:0000218] 100% 1.000000 \n", "Short stature [HP:0004322] 72% 1.000000 \n", "\n", "MISSENSE_VARIANT on NM_003482.4 \n", " Corrected p value \n", - "Sensorineural hearing impairment [HP:0000407] 0.164760 \n", - "Hearing impairment [HP:0000365] 0.740896 \n", - "Ventricular septal defect [HP:0001629] 0.961039 \n", - "High forehead [HP:0000348] 0.961039 \n", - "Motor delay [HP:0001270] 1.000000 \n", - "Hypotonia [HP:0001252] 1.000000 \n", - "Patent ductus arteriosus [HP:0001643] 1.000000 \n", - "Decreased proportion of CD4-positive, alpha-bet... 1.000000 \n", - "Blue sclerae [HP:0000592] 1.000000 \n", - "Highly arched eyebrow [HP:0002553] 1.000000 \n", - "Atrial septal defect [HP:0001631] 1.000000 \n", - "Eversion of lateral third of lower eyelids [HP:... 1.000000 \n", - "Sacral dimple [HP:0000960] 1.000000 \n", - "Recurrent otitis media [HP:0000403] 1.000000 \n", - "Decreased proportion of memory B cells [HP:0030... 1.000000 \n", - "Renal duplication [HP:0000075] 1.000000 \n", - "Ptosis [HP:0000508] 1.000000 \n", - "Intellectual disability, mild [HP:0001256] 1.000000 \n", - "Long palpebral fissure [HP:0000637] 1.000000 \n", - "Sparse lateral eyebrow [HP:0005338] 1.000000 \n", - "Seizure [HP:0001250] 1.000000 \n", - "Congenital hip dislocation [HP:0001374] 1.000000 \n", - "Coarctation of aorta [HP:0001680] 1.000000 \n", - "Clinodactyly of the 5th finger [HP:0004209] 1.000000 \n", - "Patent foramen ovale [HP:0001655] 1.000000 \n", - "Proportionate short stature [HP:0003508] 1.000000 \n", - "Severe short stature [HP:0003510] 1.000000 \n", - "Scoliosis [HP:0002650] 1.000000 \n", - "Depressed nasal tip [HP:0000437] 1.000000 \n", - "Ectopic kidney [HP:0000086] 1.000000 \n", - "Decreased circulating IgA level [HP:0002720] 1.000000 \n", - "Thin upper lip vermilion [HP:0000219] 1.000000 \n", - "Delayed speech and language development [HP:000... 1.000000 \n", - "Low posterior hairline [HP:0002162] 1.000000 \n", - "Feeding difficulties [HP:0011968] 1.000000 \n", - "Long eyelashes [HP:0000527] 1.000000 \n", - "Micrognathia [HP:0000347] 1.000000 \n", - "Microcephaly [HP:0000252] 1.000000 \n", - "Cleft palate [HP:0000175] 1.000000 \n", - "High, narrow palate [HP:0002705] 1.000000 \n", - "Prominent fingertip pads [HP:0001212] 1.000000 \n", - "Wide nasal bridge [HP:0000431] 1.000000 \n", - "Postnatal growth retardation [HP:0008897] 1.000000 \n", - "Joint hypermobility [HP:0001382] 1.000000 \n", - "Sparse eyebrow [HP:0045075] 1.000000 \n", - "Recurrent infections [HP:0002719] 1.000000 \n", - "High palate [HP:0000218] 1.000000 \n", - "Short stature [HP:0004322] 1.000000 " - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "missense_var = analysis.compare_by_variant_effect(VariantEffect.MISSENSE_VARIANT, tx_id=KTM2D_transcript_id)\n", - "missense_var.summarize(hpo, PatientCategories.YES)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "tx_dict = cohort.variant_effect_count_by_tx(tx_id=KTM2D_transcript_id)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[('FRAMESHIFT_VARIANT', 14), ('MISSENSE_VARIANT', 36), ('STOP_GAINED', 15)]" + "Sensorineural hearing impairment [HP:0000407] 0.15103 \n", + "Hearing impairment [HP:0000365] 1.00000 \n", + "Ventricular septal defect [HP:0001629] 1.00000 \n", + "High forehead [HP:0000348] 1.00000 \n", + "Motor delay [HP:0001270] 1.00000 \n", + "Hypotonia [HP:0001252] 1.00000 \n", + "Decreased proportion of CD4-positive, alpha-bet... 1.00000 \n", + "Blue sclerae [HP:0000592] 1.00000 \n", + "Highly arched eyebrow [HP:0002553] 1.00000 \n", + "Atrial septal defect [HP:0001631] 1.00000 \n", + "Eversion of lateral third of lower eyelids [HP:... 1.00000 \n", + "Sacral dimple [HP:0000960] 1.00000 \n", + "Recurrent otitis media [HP:0000403] 1.00000 \n", + "Decreased proportion of memory B cells [HP:0030... 1.00000 \n", + "Renal duplication [HP:0000075] 1.00000 \n", + "Intellectual disability, mild [HP:0001256] 1.00000 \n", + "Long palpebral fissure [HP:0000637] 1.00000 \n", + "Sparse lateral eyebrow [HP:0005338] 1.00000 \n", + "Seizure [HP:0001250] 1.00000 \n", + "Congenital hip dislocation [HP:0001374] 1.00000 \n", + "Clinodactyly of the 5th finger [HP:0004209] 1.00000 \n", + "Proportionate short stature [HP:0003508] 1.00000 \n", + "Severe short stature [HP:0003510] 1.00000 \n", + "Scoliosis [HP:0002650] 1.00000 \n", + "Depressed nasal tip [HP:0000437] 1.00000 \n", + "Thin upper lip vermilion [HP:0000219] 1.00000 \n", + "Delayed speech and language development [HP:000... 1.00000 \n", + "Feeding difficulties [HP:0011968] 1.00000 \n", + "High, narrow palate [HP:0002705] 1.00000 \n", + "Joint hypermobility [HP:0001382] 1.00000 \n", + "Long eyelashes [HP:0000527] 1.00000 \n", + "Cleft palate [HP:0000175] 1.00000 \n", + "Low posterior hairline [HP:0002162] 1.00000 \n", + "Prominent fingertip pads [HP:0001212] 1.00000 \n", + "Ectopic kidney [HP:0000086] 1.00000 \n", + "Decreased circulating IgA level [HP:0002720] 1.00000 \n", + "Wide nasal bridge [HP:0000431] 1.00000 \n", + "Postnatal growth retardation [HP:0008897] 1.00000 \n", + "Micrognathia [HP:0000347] 1.00000 \n", + "Microcephaly [HP:0000252] 1.00000 \n", + "High palate [HP:0000218] 1.00000 \n", + "Sparse eyebrow [HP:0045075] 1.00000 \n", + "Recurrent infections [HP:0002719] 1.00000 \n", + "Short stature [HP:0004322] 1.00000 " ] }, - "execution_count": 17, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "sorted(tx_dict.items())" + "from gpsea.model import VariantEffect\n", + "from gpsea.analysis.predicate.genotype import VariantPredicates\n", + "\n", + "is_missense = VariantPredicates.variant_effect(VariantEffect.MISSENSE_VARIANT, tx_id=KTM2D_transcript_id)\n", + "missense = analysis.compare_hpo_vs_genotype(is_missense)\n", + "missense.summarize(hpo, PatientCategories.YES)" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "enviro", "language": "python", - "name": "python3" + "name": "enviro" }, "language_info": { "codemirror_mode": { diff --git a/case_studies/KMT2D/input/PMID_24019847_patient.json b/case_studies/KMT2D/input/PMID_24019847_patient.json deleted file mode 100644 index cb37309e..00000000 --- a/case_studies/KMT2D/input/PMID_24019847_patient.json +++ /dev/null @@ -1,352 +0,0 @@ -{ - "id": "PMID_24019847_patient", - "subject": { - "id": "patient", - "sex": "MALE" - }, - "phenotypicFeatures": [ - { - "type": { - "id": "HP:0000252", - "label": "Microcephaly" - }, - "onset": { - "ontologyClass": { - "id": "HP:0003577", - "label": "Congenital onset" - } - } - }, - { - "type": { - "id": "HP:0000437", - "label": "Depressed nasal tip" - }, - "onset": { - "age": { - "iso8601duration": "P2Y3M" - } - } - }, - { - "type": { - "id": "HP:0000527", - "label": "Long eyelashes" - }, - "onset": { - "age": { - "iso8601duration": "P2Y3M" - } - } - }, - { - "type": { - "id": "HP:0000637", - "label": "Long palpebral fissure" - }, - "onset": { - "age": { - "iso8601duration": "P2Y3M" - } - } - }, - { - "type": { - "id": "HP:0007655", - "label": "Eversion of lateral third of lower eyelids" - }, - "onset": { - "age": { - "iso8601duration": "P2Y3M" - } - } - }, - { - "type": { - "id": "HP:0001156", - "label": "Brachydactyly" - }, - "onset": { - "age": { - "iso8601duration": "P2Y3M" - } - } - }, - { - "type": { - "id": "HP:0001212", - "label": "Prominent fingertip pads" - }, - "onset": { - "age": { - "iso8601duration": "P2Y3M" - } - } - }, - { - "type": { - "id": "HP:0000750", - "label": "Delayed speech and language development" - }, - "onset": { - "age": { - "iso8601duration": "P2Y3M" - } - } - }, - { - "type": { - "id": "HP:0002553", - "label": "Highly arched eyebrow" - }, - "onset": { - "age": { - "iso8601duration": "P2Y3M" - } - } - }, - { - "type": { - "id": "HP:0001263", - "label": "Global developmental delay" - }, - "onset": { - "age": { - "iso8601duration": "P1Y6M" - } - } - }, - { - "type": { - "id": "HP:0000403", - "label": "Recurrent otitis media" - } - }, - { - "type": { - "id": "HP:0001249", - "label": "Intellectual disability" - } - }, - { - "type": { - "id": "HP:0001647", - "label": "Bicuspid aortic valve" - } - }, - { - "type": { - "id": "HP:0003316", - "label": "Butterfly vertebrae" - } - }, - { - "type": { - "id": "HP:0004383", - "label": "Hypoplastic left heart" - }, - "excluded": true - }, - { - "type": { - "id": "HP:0001629", - "label": "Ventricular septal defect" - }, - "excluded": true - }, - { - "type": { - "id": "HP:0001633", - "label": "Abnormal mitral valve morphology" - }, - "excluded": true - }, - { - "type": { - "id": "HP:0001636", - "label": "Tetralogy of Fallot" - }, - "excluded": true - }, - { - "type": { - "id": "HP:0001643", - "label": "Patent ductus arteriosus" - }, - "excluded": true - }, - { - "type": { - "id": "HP:0001650", - "label": "Aortic valve stenosis" - }, - "excluded": true - }, - { - "type": { - "id": "HP:0001655", - "label": "Patent foramen ovale" - }, - "excluded": true - }, - { - "type": { - "id": "HP:0001667", - "label": "Right ventricular hypertrophy" - }, - "excluded": true - }, - { - "type": { - "id": "HP:0011626", - "label": "Scimitar anomaly" - }, - "excluded": true - }, - { - "type": { - "id": "HP:0011611", - "label": "Interrupted aortic arch" - }, - "excluded": true - }, - { - "type": { - "id": "HP:0001680", - "label": "Coarctation of aorta" - }, - "excluded": true - }, - { - "type": { - "id": "HP:0001684", - "label": "Secundum atrial septal defect" - }, - "excluded": true - }, - { - "type": { - "id": "HP:0001718", - "label": "Mitral stenosis" - }, - "excluded": true - } - ], - "interpretations": [ - { - "id": "patient", - "progressStatus": "SOLVED", - "diagnosis": { - "disease": { - "id": "OMIM:147920", - "label": "Kabuki Syndrome 1" - }, - "genomicInterpretations": [ - { - "subjectOrBiosampleId": "patient", - "interpretationStatus": "CAUSATIVE", - "variantInterpretation": { - "variationDescriptor": { - "id": "var_FrSoMulpanOFuQIxZnppDnjIR", - "geneContext": { - "valueId": "HGNC:7133", - "symbol": "KMT2D" - }, - "expressions": [ - { - "syntax": "hgvs.c", - "value": "NM_003482.3:c.5256_5257del" - }, - { - "syntax": "hgvs.g", - "value": "NC_000012.12:g.49043931_49043932del" - } - ], - "vcfRecord": { - "genomeAssembly": "hg38", - "chrom": "chr12", - "pos": "49043929", - "ref": "TTC", - "alt": "T" - }, - "moleculeContext": "genomic", - "allelicState": { - "id": "GENO:0000135", - "label": "heterozygous" - } - } - } - } - ] - } - } - ], - "diseases": [ - { - "term": { - "id": "OMIM:147920", - "label": "Kabuki Syndrome 1" - }, - "onset": { - "ontologyClass": { - "id": "HP:0003577", - "label": "Congenital onset" - } - } - } - ], - "metaData": { - "created": "2024-05-17T13:48:15.527078866Z", - "createdBy": "ORCID:0000-0002-0736-9199", - "resources": [ - { - "id": "geno", - "name": "Genotype Ontology", - "url": "http://purl.obolibrary.org/obo/geno.owl", - "version": "2022-03-05", - "namespacePrefix": "GENO", - "iriPrefix": "http://purl.obolibrary.org/obo/GENO_" - }, - { - "id": "hgnc", - "name": "HUGO Gene Nomenclature Committee", - "url": "https://www.genenames.org", - "version": "06/01/23", - "namespacePrefix": "HGNC", - "iriPrefix": "https://www.genenames.org/data/gene-symbol-report/#!/hgnc_id/" - }, - { - "id": "omim", - "name": "An Online Catalog of Human Genes and Genetic Disorders", - "url": "https://www.omim.org", - "version": "January 4, 2023", - "namespacePrefix": "OMIM", - "iriPrefix": "https://www.omim.org/entry/" - }, - { - "id": "so", - "name": "Sequence types and features ontology", - "url": "http://purl.obolibrary.org/obo/so.obo", - "version": "2021-11-22", - "namespacePrefix": "SO", - "iriPrefix": "http://purl.obolibrary.org/obo/SO_" - }, - { - "id": "hp", - "name": "human phenotype ontology", - "url": "http://purl.obolibrary.org/obo/hp.owl", - "version": "2024-04-26", - "namespacePrefix": "HP", - "iriPrefix": "http://purl.obolibrary.org/obo/HP_" - } - ], - "phenopacketSchemaVersion": "2.0", - "externalReferences": [ - { - "id": "PMID:24019847", - "reference": "https://pubmed.ncbi.nlm.nih.gov/24019847", - "description": "A novel MLL2 gene mutation in a Korean patient with Kabuki syndrome" - } - ] - } -} \ No newline at end of file diff --git a/case_studies/KMT2D/input/PMID_24739679_patientK1.json b/case_studies/KMT2D/input/PMID_24739679_patientK1.json deleted file mode 100644 index 1c1189dd..00000000 --- a/case_studies/KMT2D/input/PMID_24739679_patientK1.json +++ /dev/null @@ -1,356 +0,0 @@ -{ - "id": "PMID_24739679_patient_K1", - "subject": { - "id": "patient K1", - "timeAtLastEncounter": { - "age": { - "iso8601duration": "P11M" - } - }, - "sex": "MALE" - }, - "phenotypicFeatures": [ - { - "type": { - "id": "HP:0000431", - "label": "Wide nasal bridge" - } - }, - { - "type": { - "id": "HP:0000437", - "label": "Depressed nasal tip" - } - }, - { - "type": { - "id": "HP:0000637", - "label": "Long palpebral fissure" - } - }, - { - "type": { - "id": "HP:0007655", - "label": "Eversion of lateral third of lower eyelids" - } - }, - { - "type": { - "id": "HP:0000592", - "label": "Blue sclerae" - } - }, - { - "type": { - "id": "HP:0001212", - "label": "Prominent fingertip pads" - } - }, - { - "type": { - "id": "HP:0001252", - "label": "Hypotonia" - } - }, - { - "type": { - "id": "HP:0001263", - "label": "Global developmental delay" - } - }, - { - "type": { - "id": "HP:0011626", - "label": "Scimitar anomaly" - } - }, - { - "type": { - "id": "HP:0002553", - "label": "Highly arched eyebrow" - } - }, - { - "type": { - "id": "HP:0004322", - "label": "Short stature" - } - }, - { - "type": { - "id": "HP:0005338", - "label": "Sparse lateral eyebrow" - } - }, - { - "type": { - "id": "HP:0000400", - "label": "Macrotia" - } - }, - { - "type": { - "id": "HP:0040019", - "label": "Finger clinodactyly" - } - }, - { - "type": { - "id": "HP:0000003", - "label": "Multicystic kidney dysplasia" - }, - "excluded": true - }, - { - "type": { - "id": "HP:0000073", - "label": "Ureteral duplication" - }, - "excluded": true - }, - { - "type": { - "id": "HP:0000075", - "label": "Renal duplication" - }, - "excluded": true - }, - { - "type": { - "id": "HP:0000086", - "label": "Ectopic kidney" - }, - "excluded": true - }, - { - "type": { - "id": "HP:0000365", - "label": "Hearing impairment" - }, - "excluded": true - }, - { - "type": { - "id": "HP:0000486", - "label": "Strabismus" - }, - "excluded": true - }, - { - "type": { - "id": "HP:0000508", - "label": "Ptosis" - }, - "excluded": true - }, - { - "type": { - "id": "HP:0000960", - "label": "Sacral dimple" - }, - "excluded": true - }, - { - "type": { - "id": "HP:0001250", - "label": "Seizure" - }, - "excluded": true - }, - { - "type": { - "id": "HP:0001374", - "label": "Congenital hip dislocation" - }, - "excluded": true - }, - { - "type": { - "id": "HP:0001382", - "label": "Joint hypermobility" - }, - "excluded": true - }, - { - "type": { - "id": "HP:0004383", - "label": "Hypoplastic left heart" - }, - "excluded": true - }, - { - "type": { - "id": "HP:0001629", - "label": "Ventricular septal defect" - }, - "excluded": true - }, - { - "type": { - "id": "HP:0001631", - "label": "Atrial septal defect" - }, - "excluded": true - }, - { - "type": { - "id": "HP:0001643", - "label": "Patent ductus arteriosus" - }, - "excluded": true - }, - { - "type": { - "id": "HP:0001650", - "label": "Aortic valve stenosis" - }, - "excluded": true - }, - { - "type": { - "id": "HP:0011611", - "label": "Interrupted aortic arch" - }, - "excluded": true - }, - { - "type": { - "id": "HP:0001680", - "label": "Coarctation of aorta" - }, - "excluded": true - }, - { - "type": { - "id": "HP:0002650", - "label": "Scoliosis" - }, - "excluded": true - }, - { - "type": { - "id": "HP:0002719", - "label": "Recurrent infections" - }, - "excluded": true - }, - { - "type": { - "id": "HP:0007099", - "label": "Chiari type I malformation" - }, - "excluded": true - } - ], - "interpretations": [ - { - "id": "patient K1", - "progressStatus": "SOLVED", - "diagnosis": { - "disease": { - "id": "OMIM:147920", - "label": "Kabuki Syndrome 1" - }, - "genomicInterpretations": [ - { - "subjectOrBiosampleId": "patient K1", - "interpretationStatus": "CAUSATIVE", - "variantInterpretation": { - "variationDescriptor": { - "id": "var_rFZamZBqJxbFRnZeigdzuvaWf", - "geneContext": { - "valueId": "HGNC:7133", - "symbol": "KMT2D" - }, - "expressions": [ - { - "syntax": "hgvs.c", - "value": "NM_003482.3:c.4271G>T" - }, - { - "syntax": "hgvs.g", - "value": "NC_000012.12:g.49046756C>A" - } - ], - "vcfRecord": { - "genomeAssembly": "hg38", - "chrom": "chr12", - "pos": "49046756", - "ref": "C", - "alt": "A" - }, - "moleculeContext": "genomic", - "allelicState": { - "id": "GENO:0000135", - "label": "heterozygous" - } - } - } - } - ] - } - } - ], - "diseases": [ - { - "term": { - "id": "OMIM:147920", - "label": "Kabuki Syndrome 1" - } - } - ], - "metaData": { - "created": "2024-05-17T13:48:15.516162157Z", - "createdBy": "ORCID:0000-0002-0736-9199", - "resources": [ - { - "id": "geno", - "name": "Genotype Ontology", - "url": "http://purl.obolibrary.org/obo/geno.owl", - "version": "2022-03-05", - "namespacePrefix": "GENO", - "iriPrefix": "http://purl.obolibrary.org/obo/GENO_" - }, - { - "id": "hgnc", - "name": "HUGO Gene Nomenclature Committee", - "url": "https://www.genenames.org", - "version": "06/01/23", - "namespacePrefix": "HGNC", - "iriPrefix": "https://www.genenames.org/data/gene-symbol-report/#!/hgnc_id/" - }, - { - "id": "omim", - "name": "An Online Catalog of Human Genes and Genetic Disorders", - "url": "https://www.omim.org", - "version": "January 4, 2023", - "namespacePrefix": "OMIM", - "iriPrefix": "https://www.omim.org/entry/" - }, - { - "id": "so", - "name": "Sequence types and features ontology", - "url": "http://purl.obolibrary.org/obo/so.obo", - "version": "2021-11-22", - "namespacePrefix": "SO", - "iriPrefix": "http://purl.obolibrary.org/obo/SO_" - }, - { - "id": "hp", - "name": "human phenotype ontology", - "url": "http://purl.obolibrary.org/obo/hp.owl", - "version": "2024-04-26", - "namespacePrefix": "HP", - "iriPrefix": "http://purl.obolibrary.org/obo/HP_" - } - ], - "phenopacketSchemaVersion": "2.0", - "externalReferences": [ - { - "id": "PMID:24739679", - "reference": "https://pubmed.ncbi.nlm.nih.gov/24739679", - "description": "Identification of KMT2D and KDM6A mutations by exome sequencing in Korean patients with Kabuki syndrome" - } - ] - } -} \ No newline at end of file diff --git a/case_studies/KMT2D/input/PMID_24739679_patientK10.json b/case_studies/KMT2D/input/PMID_24739679_patientK10.json deleted file mode 100644 index 85c56d93..00000000 --- a/case_studies/KMT2D/input/PMID_24739679_patientK10.json +++ /dev/null @@ -1,351 +0,0 @@ -{ - "id": "PMID_24739679_patient_K10", - "subject": { - "id": "patient K10", - "timeAtLastEncounter": { - "age": { - "iso8601duration": "P10Y" - } - }, - "sex": "FEMALE" - }, - "phenotypicFeatures": [ - { - "type": { - "id": "HP:0000431", - "label": "Wide nasal bridge" - } - }, - { - "type": { - "id": "HP:0000437", - "label": "Depressed nasal tip" - } - }, - { - "type": { - "id": "HP:0000637", - "label": "Long palpebral fissure" - } - }, - { - "type": { - "id": "HP:0007655", - "label": "Eversion of lateral third of lower eyelids" - } - }, - { - "type": { - "id": "HP:0001212", - "label": "Prominent fingertip pads" - } - }, - { - "type": { - "id": "HP:0001252", - "label": "Hypotonia" - } - }, - { - "type": { - "id": "HP:0001263", - "label": "Global developmental delay" - } - }, - { - "type": { - "id": "HP:0001374", - "label": "Congenital hip dislocation" - } - }, - { - "type": { - "id": "HP:0002553", - "label": "Highly arched eyebrow" - } - }, - { - "type": { - "id": "HP:0002719", - "label": "Recurrent infections" - } - }, - { - "type": { - "id": "HP:0000400", - "label": "Macrotia" - } - }, - { - "type": { - "id": "HP:0040019", - "label": "Finger clinodactyly" - } - }, - { - "type": { - "id": "HP:0000003", - "label": "Multicystic kidney dysplasia" - }, - "excluded": true - }, - { - "type": { - "id": "HP:0000073", - "label": "Ureteral duplication" - }, - "excluded": true - }, - { - "type": { - "id": "HP:0000075", - "label": "Renal duplication" - }, - "excluded": true - }, - { - "type": { - "id": "HP:0000086", - "label": "Ectopic kidney" - }, - "excluded": true - }, - { - "type": { - "id": "HP:0000365", - "label": "Hearing impairment" - }, - "excluded": true - }, - { - "type": { - "id": "HP:0000486", - "label": "Strabismus" - }, - "excluded": true - }, - { - "type": { - "id": "HP:0000508", - "label": "Ptosis" - }, - "excluded": true - }, - { - "type": { - "id": "HP:0000592", - "label": "Blue sclerae" - }, - "excluded": true - }, - { - "type": { - "id": "HP:0000960", - "label": "Sacral dimple" - }, - "excluded": true - }, - { - "type": { - "id": "HP:0001250", - "label": "Seizure" - }, - "excluded": true - }, - { - "type": { - "id": "HP:0001382", - "label": "Joint hypermobility" - }, - "excluded": true - }, - { - "type": { - "id": "HP:0004383", - "label": "Hypoplastic left heart" - }, - "excluded": true - }, - { - "type": { - "id": "HP:0001629", - "label": "Ventricular septal defect" - }, - "excluded": true - }, - { - "type": { - "id": "HP:0001631", - "label": "Atrial septal defect" - }, - "excluded": true - }, - { - "type": { - "id": "HP:0001643", - "label": "Patent ductus arteriosus" - }, - "excluded": true - }, - { - "type": { - "id": "HP:0001650", - "label": "Aortic valve stenosis" - }, - "excluded": true - }, - { - "type": { - "id": "HP:0011626", - "label": "Scimitar anomaly" - }, - "excluded": true - }, - { - "type": { - "id": "HP:0011611", - "label": "Interrupted aortic arch" - }, - "excluded": true - }, - { - "type": { - "id": "HP:0001680", - "label": "Coarctation of aorta" - }, - "excluded": true - }, - { - "type": { - "id": "HP:0002650", - "label": "Scoliosis" - }, - "excluded": true - }, - { - "type": { - "id": "HP:0004322", - "label": "Short stature" - }, - "excluded": true - }, - { - "type": { - "id": "HP:0007099", - "label": "Chiari type I malformation" - }, - "excluded": true - } - ], - "interpretations": [ - { - "id": "patient K10", - "progressStatus": "SOLVED", - "diagnosis": { - "disease": { - "id": "OMIM:147920", - "label": "Kabuki Syndrome 1" - }, - "genomicInterpretations": [ - { - "subjectOrBiosampleId": "patient K10", - "interpretationStatus": "CAUSATIVE", - "variantInterpretation": { - "variationDescriptor": { - "id": "var_gTxObsrXEgqErysSXLfxdNSCg", - "geneContext": { - "valueId": "HGNC:7133", - "symbol": "KMT2D" - }, - "expressions": [ - { - "syntax": "hgvs.c", - "value": "NM_003482.3:c.11515C>T" - }, - { - "syntax": "hgvs.g", - "value": "NC_000012.12:g.49033190G>A" - } - ], - "vcfRecord": { - "genomeAssembly": "hg38", - "chrom": "chr12", - "pos": "49033190", - "ref": "G", - "alt": "A" - }, - "moleculeContext": "genomic", - "allelicState": { - "id": "GENO:0000135", - "label": "heterozygous" - } - } - } - } - ] - } - } - ], - "diseases": [ - { - "term": { - "id": "OMIM:147920", - "label": "Kabuki Syndrome 1" - } - } - ], - "metaData": { - "created": "2024-05-17T13:48:15.525057077Z", - "createdBy": "ORCID:0000-0002-0736-9199", - "resources": [ - { - "id": "geno", - "name": "Genotype Ontology", - "url": "http://purl.obolibrary.org/obo/geno.owl", - "version": "2022-03-05", - "namespacePrefix": "GENO", - "iriPrefix": "http://purl.obolibrary.org/obo/GENO_" - }, - { - "id": "hgnc", - "name": "HUGO Gene Nomenclature Committee", - "url": "https://www.genenames.org", - "version": "06/01/23", - "namespacePrefix": "HGNC", - "iriPrefix": "https://www.genenames.org/data/gene-symbol-report/#!/hgnc_id/" - }, - { - "id": "omim", - "name": "An Online Catalog of Human Genes and Genetic Disorders", - "url": "https://www.omim.org", - "version": "January 4, 2023", - 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} - ] - } -} \ No newline at end of file diff --git a/case_studies/KMT2D/input/PMID_35060672_femaleinfantVI1.json b/case_studies/KMT2D/input/PMID_35060672_femaleinfantVI1.json deleted file mode 100644 index 74637bda..00000000 --- a/case_studies/KMT2D/input/PMID_35060672_femaleinfantVI1.json +++ /dev/null @@ -1,229 +0,0 @@ -{ - "id": "PMID_35060672_female_infant_VI1", - "subject": { - "id": "female infant VI1", - "timeAtLastEncounter": { - "age": { - "iso8601duration": "P7Y" - } - }, - "sex": "FEMALE" - }, - "phenotypicFeatures": [ - { - "type": { - "id": "HP:0000347", - "label": "Micrognathia" - }, - "onset": { - "ontologyClass": { - "id": "HP:0003577", - "label": "Congenital onset" - } - } - }, - { - "type": { - "id": "HP:0000453", - "label": "Choanal atresia" - }, - "onset": { - "ontologyClass": { - "id": "HP:0003577", - "label": "Congenital onset" - } - } - }, - { - "type": { - "id": "HP:0000407", - "label": "Sensorineural hearing impairment" - }, - "onset": { - "age": { - "iso8601duration": "P2Y" - } - } - }, - { - "type": { - "id": "HP:0000368", - "label": "Low-set, posteriorly rotated ears" - }, - "excluded": true - }, - { - "type": { - "id": "HP:0000378", - "label": "Cupped ear" - }, - "excluded": true - }, - { - "type": { - "id": "HP:0000396", - "label": "Overfolded helix" - }, - "excluded": true - }, - { - "type": { - "id": "HP:0000480", - "label": "Retinal coloboma" - }, - "excluded": true - }, - { - "type": { - "id": "HP:0001256", - "label": "Intellectual disability, mild" - }, - "excluded": true - }, - { - "type": { - "id": "HP:0001263", - "label": "Global developmental delay" - }, - "excluded": true - }, - { - "type": { - "id": "HP:0001270", - "label": "Motor delay" - }, - "excluded": true - }, - { - "type": { - "id": "HP:0000750", - "label": "Delayed speech and language development" - }, - "excluded": true - }, - { - "type": { - "id": "HP:0000400", - "label": "Macrotia" - }, - "excluded": true - } - ], - "interpretations": [ - { - "id": "female infant VI1", - "progressStatus": "SOLVED", - "diagnosis": { - "disease": { - "id": "OMIM:147920", - "label": "Kabuki Syndrome 1" - }, - "genomicInterpretations": [ - { - "subjectOrBiosampleId": "female infant VI1", - "interpretationStatus": "CAUSATIVE", - "variantInterpretation": { - "variationDescriptor": { - "id": "var_WKYPRGIULVfluZGtZeAZjfmCo", - "geneContext": { - "valueId": "HGNC:7133", - "symbol": "KMT2D" - }, - "expressions": [ - { - "syntax": "hgvs.c", - "value": "NM_003482.3:c.10706A>G" - }, - { - "syntax": "hgvs.g", - "value": "NC_000012.12:g.49034101T>C" - } - ], - "vcfRecord": { - "genomeAssembly": "hg38", - "chrom": "chr12", - "pos": "49034101", - "ref": "T", - "alt": "C" - }, - "moleculeContext": "genomic", - "allelicState": { - "id": "GENO:0000135", - "label": "heterozygous" - } - } - } - } - ] - } - } - ], - "diseases": [ - { - "term": { - "id": "OMIM:147920", - "label": "Kabuki Syndrome 1" - }, - "onset": { - "ontologyClass": { - "id": "HP:0003577", - "label": "Congenital onset" - } - } - } - ], - "metaData": { - "created": "2024-05-17T13:48:15.564396142Z", - "createdBy": "ORCID:0000-0002-0736-9199", - "resources": [ - { - "id": "geno", - "name": "Genotype Ontology", - "url": "http://purl.obolibrary.org/obo/geno.owl", - "version": "2022-03-05", - "namespacePrefix": "GENO", - "iriPrefix": "http://purl.obolibrary.org/obo/GENO_" - }, - { - "id": "hgnc", - "name": "HUGO Gene Nomenclature Committee", - "url": "https://www.genenames.org", - "version": "06/01/23", - "namespacePrefix": "HGNC", - "iriPrefix": "https://www.genenames.org/data/gene-symbol-report/#!/hgnc_id/" - }, - { - "id": "omim", - "name": "An Online Catalog of Human Genes and Genetic Disorders", - "url": "https://www.omim.org", - "version": "January 4, 2023", - "namespacePrefix": "OMIM", - "iriPrefix": "https://www.omim.org/entry/" - }, - { - "id": "so", - "name": "Sequence types and features ontology", - "url": "http://purl.obolibrary.org/obo/so.obo", - "version": "2021-11-22", - "namespacePrefix": "SO", - "iriPrefix": "http://purl.obolibrary.org/obo/SO_" - }, - { - "id": "hp", - "name": "human phenotype ontology", - "url": "http://purl.obolibrary.org/obo/hp.owl", - "version": "2024-04-26", - "namespacePrefix": "HP", - "iriPrefix": "http://purl.obolibrary.org/obo/HP_" - } - ], - "phenopacketSchemaVersion": "2.0", - "externalReferences": [ - { - "id": "PMID:35060672", - "reference": "https://pubmed.ncbi.nlm.nih.gov/35060672", - "description": "Refining the clinical phenotype associated with missense variants in exons 38 and 39 of KMT2D" - } - ] - } -} \ No newline at end of file diff --git a/case_studies/LMNA/LMNA.ipynb b/case_studies/LMNA/LMNA.ipynb index d17069c4..6c9416eb 100644 --- a/case_studies/LMNA/LMNA.ipynb +++ b/case_studies/LMNA/LMNA.ipynb @@ -23,13 +23,19 @@ "name": "stdout", "output_type": "stream", "text": [ - "Using genophenocorr version 0.1.1dev\n" + "Loaded HPO v2023-10-09\n", + "Using gpsea version 0.2.1.dev0\n" ] } ], "source": [ - "import genophenocorr\n", - "print(f\"Using genophenocorr version {genophenocorr.__version__}\")" + "import gpsea\n", + "import hpotk\n", + "\n", + "store = hpotk.configure_ontology_store()\n", + "hpo = store.load_minimal_hpo(release='v2023-10-09')\n", + "print(f'Loaded HPO v{hpo.version}')\n", + "print(f\"Using gpsea version {gpsea.__version__}\")" ] }, { @@ -42,41 +48,36 @@ "start_time": "2024-02-20T14:57:02.248177800Z" } }, - "outputs": [], - "source": [ - "import hpotk\n", - "\n", - "fpath_hpo = 'https://github.com/obophenotype/human-phenotype-ontology/releases/download/v2024-02-08/hp.json'\n", - "hpo = hpotk.load_minimal_ontology(fpath_hpo)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "a3b52e65351030dc", - "metadata": { - "ExecuteTime": { - "end_time": "2024-02-20T14:58:33.257730200Z", - "start_time": "2024-02-20T14:57:06.570095700Z" - } - }, "outputs": [ { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "Patients Created: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████| 127/127 [00:00<00:00, 526.18it/s]\n", + "Patients Created: 100%|██████████| 127/127 [00:00<00:00, 341.23it/s]\n", "Validated under none policy\n", "No errors or warnings were found\n" ] } ], "source": [ - "from genophenocorr.preprocessing import configure_caching_cohort_creator, load_phenopacket_folder\n", + "from ppktstore.registry import configure_phenopacket_registry\n", + "from gpsea.preprocessing import configure_caching_cohort_creator, load_phenopackets\n", "\n", - "fpath_phenopackets = 'input/'\n", "cohort_creator = configure_caching_cohort_creator(hpo)\n", - "cohort = load_phenopacket_folder(fpath_phenopackets, cohort_creator)" + "\n", + "cohort_name = 'LMNA'\n", + "phenopacket_store_release = '0.1.19'\n", + "\n", + "registry = configure_phenopacket_registry()\n", + "with registry.open_phenopacket_store(phenopacket_store_release) as ps:\n", + " phenopackets = tuple(ps.iter_cohort_phenopackets(cohort_name))\n", + "\n", + "cohort, validation = load_phenopackets(\n", + " phenopackets=phenopackets, \n", + " cohort_creator=cohort_creator,\n", + ")\n", + "del phenopackets\n", + "validation.summarize()" ] }, { @@ -93,7 +94,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "id": "624466e48a2046e", "metadata": { "ExecuteTime": { @@ -103,7 +104,8 @@ }, "outputs": [], "source": [ - "tx_id = 'NM_170707.4'" + "LMNA_MANE_transcript = 'NM_170707.4'\n", + "LMNA_protein_id = 'NP_005563.1'" ] }, { @@ -116,27 +118,9 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "id": "5c27ec7e5affa54f", "metadata": {}, - "outputs": [], - "source": [ - "from IPython.display import HTML, display\n", - "from genophenocorr.view import CohortViewable\n", - "\n", - "viewer = CohortViewable(hpo)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "9b23d6563240a28", - "metadata": { - "ExecuteTime": { - "end_time": "2024-02-20T16:12:21.622146100Z", - "start_time": "2024-02-20T16:12:21.594225300Z" - } - }, "outputs": [ { "data": { @@ -146,261 +130,291 @@ "\n", " \n", " Cohort\n", - " \n", "\n", "\n", "\n", - "

genophenocorr cohort analysis

\n", - "

Successfully ingested 127 phenopackets.

\n", + "

GPSEA cohort analysis

\n", + "

Successfully loaded 127 individuals.

\n", " \n", "

No errors encountered.

\n", " \n", - " \n", - "

Top 10 HPO Terms

\n", + "\n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", "
A total of 214 HPO terms were used to annotated the cohort. \n", + "

Top 10 HPO Terms

\n", + " A total of 214 HPO terms were used to annotated the cohort.\n", + "
HPO TermIDAnnotation Count
Seen in n individuals
Ankle contractureHP:003467739
Elbow contractureHP:003439137
Spinal rigidityHP:000330637
Stiff neckHP:002525836
Proximal muscle weakness in upper limbsHP:000899735
Dilated cardiomyopathyHP:000164427
Atrial fibrillationHP:000511025
ArrhythmiaHP:001167523
LipodystrophyHP:000912521
Distal lower limb muscle weaknessHP:0009053
Proximal muscle weakness in lower limbsHP:000899421
\n", "\n", - "

Top 10 Variants

\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + "
\n", - " Variants are shown according to NM_170707.4. A total of 127 unique variants were identified in the cohort.\n", - "
VariantVariant nameVariant Count
1_156138613_156138613_C_Tc.1824C>T15
\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - "
\n", + "

Top 10 Variants

\n", + " Variants are shown according to NM_170707.4. A total of 127 unique variants were identified in the cohort.\n", + "
CountVariant keyVariant NameProtein VariantVariant Class
1_156134474_156134474_C_Ac.585C>A11
151_156138613_156138613_C_Tc.1824C>Tp.Gly608=SYNONYMOUS_VARIANT
1_156137756_156137756_C_Ac.1698+13C>A8
111_156134474_156134474_C_Ac.585C>Ap.Asn195LysMISSENSE_VARIANT
1_156137204_156137204_G_Cc.1580G>C8
81_156134497_156134497_A_Gc.608A>Gp.Glu203GlyMISSENSE_VARIANT
1_156134497_156134497_A_Gc.608A>G8
81_156137756_156137756_C_Ac.1698+13C>ANoneINTRON_VARIANT
1_156115096_156115096_C_Gc.178C>G7
81_156137204_156137204_G_Cc.1580G>Cp.Arg527ProMISSENSE_VARIANT
1_156136984_156136984_C_Tc.1444C>T7
71_156115096_156115096_C_Gc.178C>Gp.Arg60GlyMISSENSE_VARIANT
1_156136036_156136036_G_Ac.1072G>A6
71_156136984_156136984_C_Tc.1444C>Tp.Arg482TrpMISSENSE_VARIANT
1_156136413_156136413_C_Tc.1357C>T6
61_156136413_156136413_C_Tc.1357C>Tp.Arg453TrpMISSENSE_VARIANT
1_156115172_156115172_T_Gc.254T>G5
61_156136036_156136036_G_Ac.1072G>Ap.Glu358LysMISSENSE_VARIANT
\n", - "

Diseases

\n", + " \n", + " \n", + " 5\n", + " 1_156135922_156135923_CT_C\n", + " c.960del\n", + " p.Arg321GlufsTer159\n", + " FRAMESHIFT_VARIANT\n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", "
\n", + "

Diseases

\n", + "
Disease IDDisease NameDisease NameDisease IDAnnotation Count
OMIM:115200Cardiomyopathy, dilated, 1ACardiomyopathy, dilated, 1AOMIM:11520044
OMIM:181350Emery-Dreifuss muscular dystrophy 2, autosomal dominantEmery-Dreifuss muscular dystrophy 2, autosomal dominantOMIM:18135041
OMIM:613205LMNA-related congenital muscular dystrophyHutchinson-Gilford progeriaOMIM:17667015
OMIM:176670Hutchinson-Gilford progeriaLMNA-related congenital muscular dystrophyOMIM:61320515
OMIM:151660Lipodystrophy, familial partial, type 2Lipodystrophy, familial partial, type 2OMIM:15166012
\n", " \n", - "

Variant categories for NM_170707.4

\n", " \n", + " \n", " \n", " \n", " \n", @@ -408,18 +422,18 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -433,18 +447,18 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -464,16 +478,47 @@ } ], "source": [ - "display(HTML(viewer.process(cohort, tx_id)))" + "from gpsea.view import CohortViewable\n", + "from IPython.display import HTML, display\n", + "\n", + "viewer = CohortViewable(hpo)\n", + "display(HTML(viewer.process(cohort=cohort, transcript_id=LMNA_MANE_transcript)))" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "5f2324c7", + "metadata": {}, + "outputs": [], + "source": [ + "from gpsea.preprocessing import configure_protein_metadata_service\n", + "from gpsea.preprocessing import VVMultiCoordinateService\n", + "from gpsea.model.genome import GRCh38\n", + "\n", + "pms = configure_protein_metadata_service()\n", + "protein_meta = pms.annotate(LMNA_protein_id)\n", + "txc_service = VVMultiCoordinateService(genome_build=GRCh38)\n", + "tx_coordinates = txc_service.fetch(LMNA_MANE_transcript)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "3b692a52", + "metadata": {}, + "outputs": [], + "source": [ + "from gpsea.view import ProteinVisualizable, ProteinVisualizer, ProteinViewable\n", + "\n", + "pvis = ProteinVisualizable(tx_coordinates=tx_coordinates, protein_meta=protein_meta, cohort=cohort)" ] }, { "cell_type": "code", "execution_count": 7, - "id": "3fe6dac6", - "metadata": { - "scrolled": true - }, + "id": "a8a1145a", + "metadata": {}, "outputs": [ { "data": { @@ -493,19 +538,25 @@ " }\n", "\n", "\n", + " .table .column-1 {\n", + " text-align: left;\n", + " }\n", " th {\n", - " background-color: #f2f2f2;\n", + " background-color: LightSkyBlue;\n", " border: 1px solid #dddddd;\n", " text-align: left;\n", - " padding: 2px;\n", - " }\n", + " padding: 5px;\n", + " font-weight: bold;\n", + " font-size: 120%;\n", + "}\n", + "\n", "\n", " tr {\n", " border: 1px solid #dddddd;\n", " }\n", "\n", " td {\n", - " padding: 2px;\n", + " padding: 5px;\n", " font-weight: bold;\n", " }\n", "\n", @@ -514,436 +565,151 @@ " }\n", "\n", " .table td,tr {\n", - " text-align: right;\n", + " text-align: left;\n", "}\n", "\n", ".table td:first-child, tr:first-child {\n", " text-align: left;\n", "}​\n", "\n", - " \n", - "\n", - "\n", - "\n", - "

genophenocorr disease analysis

\n", - "\n", - "

Diseases

\n", - "
\n", + "

Variant categories for NM_170707.4

\n", + "
Variant effect
MISSENSE_VARIANT90SYNONYMOUS_VARIANT15
FRAMESHIFT_VARIANT5MISSENSE_VARIANT90
SYNONYMOUS_VARIANT15INTRON_VARIANT8
SPLICE_REGION_VARIANT4FRAMESHIFT_VARIANT5
INTRON_VARIANT8SPLICE_ACCEPTOR_VARIANT1
SPLICE_ACCEPTOR_VARIANT1SPLICE_REGION_VARIANT4
\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Disease IDDisease NameAnnotation Count
OMIM:115200Cardiomyopathy, dilated, 1A44
OMIM:181350Emery-Dreifuss muscular dystrophy 2, autosomal dominant41
OMIM:613205LMNA-related congenital muscular dystrophy15
OMIM:176670Hutchinson-Gilford progeria15
OMIM:151660Lipodystrophy, familial partial, type 212
\n", - "

Variants by Disease

\n", - " \n", - "

OMIM:115200

\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Variant KeyVariant Counts
1_156115096_156115096_C_G7
1_156137756_156137756_C_A8
1_156115172_156115172_T_G5
1_156134497_156134497_A_G8
1_156134474_156134474_C_A11
1_156135922_156135923_CT_C5
\n", - " \n", - "

OMIM:181350

\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Variant KeyVariant Counts
1_156134860_156134860_G_A1
1_156115052_156115052_A_G1
1_156134830_156134830_A_C3
1_156137204_156137204_G_C8
1_156136036_156136036_G_A2
1_156114934_156114934_C_T5
1_156137183_156137183_G_C1
1_156136413_156136413_C_T6
1_156115067_156115067_G_C1
1_156137207_156137207_C_A2
1_156136076_156136076_T_A1
1_156136424_156136424_C_A1
1_156136121_156136121_G_A4
1_156135257_156135257_A_C1
1_156134911_156134911_G_A1
1_156115248_156115251_TGAG_T1
1_156134942_156134945_TAAG_T1
1_156115106_156115106_T_G1
\n", - " \n", - "

OMIM:613205

\n", + "caption {\n", + " caption-side: top;\n", + " text-align: left;\n", + " padding-bottom: 10px;\n", + " font-weight: bold;\n", + "}\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "

GPSEA protein analysis

\n", + "

The UniProt API successfully returned protein information for ID: NP_005563.1

\n", + "

Protein Name: Prelamin-A/C

\n", " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
\n", + "

Protein Features

\n", + "
Variant KeyVariant Counts
Feature NameFeature TypeFeature CoordinatesVariants in Feature
1_156136422_156136422_A_G1
1_156135281_156135281_T_C1
1_156134910_156134910_C_T2
1_156115067_156115067_G_C1
1_156136420_156136420_G_C1
1_156136036_156136036_G_A4
1_156115009_156115012_GAGA_G1
1_156136919_156136919_A_G1
1_156136103_156136103_T_C1
1_156136414_156136414_G_C1
1_156115034_156115034_A_G1
\n", - " \n", - "

OMIM:176670

\n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - "
Variant KeyVariant CountsInteraction with MLIPregion1 - 13026
1_156138613_156138613_C_T15
\n", - " \n", - "

OMIM:151660

\n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Variant KeyVariant CountsHeadregion1 - 337
1_156134853_156134853_G_A1
1_156136984_156136984_C_T7
1_156138507_156138507_C_T1
1_156114947_156114947_C_T1
1_156136251_156136251_C_T1
1_156115093_156115093_C_G1
\n", - " \n", - " \n", - " \n", - "

Variant Effects by Disease

\n", - " \n", - "

OMIM:115200

\n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Variant EffectVariant Effect CountsDisorderedregion1 - 256
MISSENSE_VARIANT31
INTRON_VARIANT8
FRAMESHIFT_VARIANT5
\n", - " \n", - "

OMIM:181350

\n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - "
Variant EffectVariant Effect CountsIF roddomain31 - 38767
MISSENSE_VARIANT34Coil 1Aregion34 - 7013
STOP_GAINED5Linker 1region71 - 800
SPLICE_REGION_VARIANT4Coil 1Bregion81 - 21825
INFRAME_DELETION2Linker 2region219 - 2425
\n", - " \n", - "

OMIM:613205

\n", - " \n", - " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - "
Variant EffectVariant Effect CountsCoil 2region243 - 38319
MISSENSE_VARIANT13Necessary and sufficient for the interaction with IFFO1region259 - 3318
INFRAME_DELETION1Tailregion384 - 66449
SPLICE_ACCEPTOR_VARIANT1Disorderedregion384 - 4425
\n", - " \n", - "

OMIM:176670

\n", - " \n", - " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - "
Variant EffectVariant Effect CountsNuclear localization signalmotif417 - 4220
SYNONYMOUS_VARIANT15LTDdomain428 - 54528
\n", - " \n", - "

OMIM:151660

\n", - " \n", - " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - "
Variant EffectVariant Effect CountsDisorderedregion552 - 5761
MISSENSE_VARIANT12Disorderedregion598 - 61915
\n", - " \n", + " \n", " \n", " \n", "\n", @@ -958,22 +724,30 @@ } ], "source": [ - "from genophenocorr.view import DiseaseViewable\n", - "\n", - "dis_view = DiseaseViewable(hpo, transcript_id=tx_id)\n", - "dis_dict = dis_view._prepare_context(cohort)\n", - "display(HTML(dis_view.process(cohort)))" + "viewer = ProteinViewable()\n", + "html_prot = viewer.process(cohort, pvis)\n", + "display(HTML(html_prot))" ] }, { "cell_type": "code", - "execution_count": 15, - "id": "5f2324c7", + "execution_count": 8, + "id": "804d21d7", "metadata": {}, "outputs": [ { "data": { - "image/png": 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"text/plain": [ "
" ] @@ -983,19 +757,6 @@ } ], "source": [ - "from genophenocorr.preprocessing import UniprotProteinMetadataService\n", - "from genophenocorr.model.genome import GRCh38\n", - "from genophenocorr.preprocessing import VVTranscriptCoordinateService\n", - "pms = UniprotProteinMetadataService()\n", - "protein_metas = pms.annotate('NP_005563.1')\n", - "txc_service = VVTranscriptCoordinateService(genome_build=GRCh38)\n", - "tx_coordinates = txc_service.fetch(tx_id)\n", - "from genophenocorr.preprocessing import UniprotProteinMetadataService\n", - "pms = UniprotProteinMetadataService()\n", - "protein_metas = pms.annotate('NP_005563.1')\n", - "from genophenocorr.view import ProteinVisualizable, ProteinVisualizer\n", - "protein_meta = protein_metas[0]\n", - "pvis = ProteinVisualizable(tx_coordinates=tx_coordinates, protein_meta=protein_meta, cohort=cohort)\n", "drawer = ProteinVisualizer()\n", "drawer.draw_fig(pvis=pvis)" ] @@ -1010,22 +771,25 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 9, "id": "d0ee56e0c371dcd1", "metadata": {}, "outputs": [], "source": [ - "from genophenocorr.analysis import configure_cohort_analysis, CohortAnalysisConfiguration\n", + "from gpsea.analysis import configure_cohort_analysis, CohortAnalysisConfiguration\n", + "from gpsea.analysis.predicate import PatientCategories\n", + "from gpsea.model.genome import Region\n", "\n", "analysis_config = CohortAnalysisConfiguration()\n", - "analysis_config.missing_implies_excluded = True\n", - "analysis_config.pval_correction = 'fdr_bh'\n", + "analysis_config.missing_implies_excluded = False\n", + "analysis_config.hpo_mtc_strategy()\n", + "\n", "analysis = configure_cohort_analysis(cohort, hpo, config=analysis_config)\n" ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 10, "id": "0be03eb2", "metadata": {}, "outputs": [ @@ -1072,166 +836,465 @@ " 15/35\n", " 43%\n", " 0.001167\n", - " 0.079657\n", + " 0.040862\n", " \n", " \n", - " Proptosis [HP:0000520]\n", + " Alopecia [HP:0001596]\n", " 0/3\n", " 0%\n", " 15/15\n", " 100%\n", " 0.001225\n", - " 0.079657\n", + " 0.042892\n", " \n", " \n", - " Alopecia [HP:0001596]\n", + " Prominent superficial blood vessels [HP:0007394]\n", " 0/3\n", " 0%\n", " 15/15\n", " 100%\n", " 0.001225\n", - " 0.079657\n", + " 0.042892\n", " \n", " \n", - " Prominent superficial blood vessels [HP:0007394]\n", + " Reduced subcutaneous adipose tissue [HP:0003758]\n", " 0/3\n", " 0%\n", " 15/15\n", " 100%\n", " 0.001225\n", - " 0.079657\n", + " 0.042892\n", " \n", " \n", - " Reduced subcutaneous adipose tissue [HP:0003758]\n", + " Proptosis [HP:0000520]\n", + " 0/3\n", + " 0%\n", + " 15/15\n", + " 100%\n", + " 0.001225\n", + " 0.042892\n", + " \n", + " \n", + " Failure to thrive [HP:0001508]\n", " 0/3\n", " 0%\n", " 15/15\n", " 100%\n", " 0.001225\n", - " 0.079657\n", + " 0.042892\n", " \n", " \n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", - " ...\n", + " Micrognathia [HP:0000347]\n", + " 0/3\n", + " 0%\n", + " 15/15\n", + " 100%\n", + " 0.001225\n", + " 0.042892\n", + " \n", + " \n", + " Short stature [HP:0004322]\n", + " 1/4\n", + " 25%\n", + " 15/15\n", + " 100%\n", + " 0.004128\n", + " 0.144479\n", " \n", " \n", - " Finger joint contracture [HP:0034681]\n", - " 1/1\n", + " Reduced bone mineral density [HP:0004349]\n", + " 2/5\n", + " 40%\n", + " 15/15\n", " 100%\n", - " 1/1\n", + " 0.008772\n", + " 0.307018\n", + " \n", + " \n", + " Increased circulating creatine kinase MM isoform [HP:0032234]\n", + " 0/4\n", + " 0%\n", + " 3/3\n", " 100%\n", + " 0.028571\n", + " 1.000000\n", + " \n", + " \n", + " Left ventricular systolic dysfunction [HP:0025169]\n", + " 2/25\n", + " 8%\n", + " 3/7\n", + " 43%\n", + " 0.056591\n", + " 1.000000\n", + " \n", + " \n", + " Abnormal left ventricular ejection fraction [HP:0034314]\n", + " 2/25\n", + " 8%\n", + " 3/7\n", + " 43%\n", + " 0.056591\n", + " 1.000000\n", + " \n", + " \n", + " Reduced left ventricular ejection fraction [HP:0012664]\n", + " 2/29\n", + " 7%\n", + " 3/8\n", + " 38%\n", + " 0.056945\n", + " 1.000000\n", + " \n", + " \n", + " Respiratory insufficiency due to muscle weakness [HP:0002747]\n", + " 11/13\n", + " 85%\n", + " 0/2\n", + " 0%\n", + " 0.057143\n", " 1.000000\n", + " \n", + " \n", + " Abnormal left ventricular function [HP:0005162]\n", + " 8/31\n", + " 26%\n", + " 6/10\n", + " 60%\n", + " 0.064478\n", " 1.000000\n", " \n", " \n", - " Abnormality of upper limb joint [HP:0009810]\n", - " 5/5\n", + " Abnormal atrioventricular conduction [HP:0005150]\n", + " 26/26\n", " 100%\n", - " 1/1\n", + " 9/11\n", + " 82%\n", + " 0.082583\n", + " 1.000000\n", + " \n", + " \n", + " Cardiac conduction abnormality [HP:0031546]\n", + " 26/26\n", " 100%\n", + " 10/12\n", + " 83%\n", + " 0.093883\n", " 1.000000\n", + " \n", + " \n", + " Mildly reduced left ventricular ejection fraction [HP:0012663]\n", + " 1/28\n", + " 4%\n", + " 2/7\n", + " 29%\n", + " 0.095187\n", " 1.000000\n", " \n", " \n", - " Abnormality of the hand [HP:0001155]\n", - " 1/1\n", - " 100%\n", - " 16/16\n", + " Ventricular tachycardia [HP:0004756]\n", + " 1/13\n", + " 8%\n", + " 2/4\n", + " 50%\n", + " 0.120588\n", + " 1.000000\n", + " \n", + " \n", + " Sudden cardiac death [HP:0001645]\n", + " 11/21\n", + " 52%\n", + " 1/7\n", + " 14%\n", + " 0.184244\n", + " 1.000000\n", + " \n", + " \n", + " Dilated cardiomyopathy [HP:0001644]\n", + " 23/35\n", + " 66%\n", + " 4/9\n", + " 44%\n", + " 0.274687\n", + " 1.000000\n", + " \n", + " \n", + " Atrial fibrillation [HP:0005110]\n", + " 21/32\n", + " 66%\n", + " 4/9\n", + " 44%\n", + " 0.276263\n", + " 1.000000\n", + " \n", + " \n", + " Tube feeding [HP:0033454]\n", + " 2/13\n", + " 15%\n", + " 1/2\n", + " 50%\n", + " 0.371429\n", + " 1.000000\n", + " \n", + " \n", + " Highly elevated creatine kinase [HP:0030234]\n", + " 15/36\n", + " 42%\n", + " 1/6\n", + " 17%\n", + " 0.380455\n", + " 1.000000\n", + " \n", + " \n", + " Heart block [HP:0012722]\n", + " 8/8\n", " 100%\n", + " 9/11\n", + " 82%\n", + " 0.485380\n", " 1.000000\n", + " \n", + " \n", + " Talipes [HP:0001883]\n", + " 6/16\n", + " 38%\n", + " 0/2\n", + " 0%\n", + " 0.529412\n", " 1.000000\n", " \n", " \n", - " Abnormal circulating lipid concentration [HP:0003119]\n", - " 12/12\n", + " Abnormal circulating creatine kinase concentration [HP:0040081]\n", + " 25/29\n", + " 86%\n", + " 9/9\n", " 100%\n", - " 0/0\n", + " 0.554481\n", + " 1.000000\n", + " \n", + " \n", + " Loss of ambulation [HP:0002505]\n", + " 9/16\n", + " 56%\n", + " 1/3\n", + " 33%\n", + " 0.582043\n", + " 1.000000\n", + " \n", + " \n", + " Second degree atrioventricular block [HP:0011706]\n", + " 3/6\n", + " 50%\n", + " 3/5\n", + " 60%\n", + " 1.000000\n", + " 1.000000\n", + " \n", + " \n", + " Decreased fetal movement [HP:0001558]\n", + " 3/13\n", + " 23%\n", + " 0/2\n", " 0%\n", " 1.000000\n", " 1.000000\n", " \n", " \n", - " Abnormality of the musculoskeletal system [HP:0033127]\n", - " 59/59\n", + " Neck muscle weakness [HP:0000467]\n", + " 10/13\n", + " 77%\n", + " 2/2\n", " 100%\n", - " 26/26\n", + " 1.000000\n", + " 1.000000\n", + " \n", + " \n", + " Third degree atrioventricular block [HP:0001709]\n", + " 2/5\n", + " 40%\n", + " 3/5\n", + " 60%\n", + " 1.000000\n", + " 1.000000\n", + " \n", + " \n", + " Atrioventricular block [HP:0001678]\n", + " 8/11\n", + " 73%\n", + " 8/10\n", + " 80%\n", + " 1.000000\n", + " 1.000000\n", + " \n", + " \n", + " Talipes equinovarus [HP:0001762]\n", + " 3/13\n", + " 23%\n", + " 0/2\n", + " 0%\n", + " 1.000000\n", + " 1.000000\n", + " \n", + " \n", + " Cardiomyopathy [HP:0001638]\n", + " 24/27\n", + " 89%\n", + " 4/4\n", " 100%\n", " 1.000000\n", " 1.000000\n", " \n", " \n", "\n", - "

455 rows × 6 columns

\n", "" ], "text/plain": [ "MISSENSE_VARIANT on NM_170707.4 Yes No \\\n", " Count Percent Count \n", "Lipodystrophy [HP:0009125] 10/75 13% 15/35 \n", - "Proptosis [HP:0000520] 0/3 0% 15/15 \n", "Alopecia [HP:0001596] 0/3 0% 15/15 \n", "Prominent superficial blood vessels [HP:0007394] 0/3 0% 15/15 \n", "Reduced subcutaneous adipose tissue [HP:0003758] 0/3 0% 15/15 \n", - "... ... ... ... \n", - "Finger joint contracture [HP:0034681] 1/1 100% 1/1 \n", - "Abnormality of upper limb joint [HP:0009810] 5/5 100% 1/1 \n", - "Abnormality of the hand [HP:0001155] 1/1 100% 16/16 \n", - "Abnormal circulating lipid concentration [HP:00... 12/12 100% 0/0 \n", - "Abnormality of the musculoskeletal system [HP:0... 59/59 100% 26/26 \n", + "Proptosis [HP:0000520] 0/3 0% 15/15 \n", + "Failure to thrive [HP:0001508] 0/3 0% 15/15 \n", + "Micrognathia [HP:0000347] 0/3 0% 15/15 \n", + "Short stature [HP:0004322] 1/4 25% 15/15 \n", + "Reduced bone mineral density [HP:0004349] 2/5 40% 15/15 \n", + "Increased circulating creatine kinase MM isofor... 0/4 0% 3/3 \n", + "Left ventricular systolic dysfunction [HP:0025169] 2/25 8% 3/7 \n", + "Abnormal left ventricular ejection fraction [HP... 2/25 8% 3/7 \n", + "Reduced left ventricular ejection fraction [HP:... 2/29 7% 3/8 \n", + "Respiratory insufficiency due to muscle weaknes... 11/13 85% 0/2 \n", + "Abnormal left ventricular function [HP:0005162] 8/31 26% 6/10 \n", + "Abnormal atrioventricular conduction [HP:0005150] 26/26 100% 9/11 \n", + "Cardiac conduction abnormality [HP:0031546] 26/26 100% 10/12 \n", + "Mildly reduced left ventricular ejection fracti... 1/28 4% 2/7 \n", + "Ventricular tachycardia [HP:0004756] 1/13 8% 2/4 \n", + "Sudden cardiac death [HP:0001645] 11/21 52% 1/7 \n", + "Dilated cardiomyopathy [HP:0001644] 23/35 66% 4/9 \n", + "Atrial fibrillation [HP:0005110] 21/32 66% 4/9 \n", + "Tube feeding [HP:0033454] 2/13 15% 1/2 \n", + "Highly elevated creatine kinase [HP:0030234] 15/36 42% 1/6 \n", + "Heart block [HP:0012722] 8/8 100% 9/11 \n", + "Talipes [HP:0001883] 6/16 38% 0/2 \n", + "Abnormal circulating creatine kinase concentrat... 25/29 86% 9/9 \n", + "Loss of ambulation [HP:0002505] 9/16 56% 1/3 \n", + "Second degree atrioventricular block [HP:0011706] 3/6 50% 3/5 \n", + "Decreased fetal movement [HP:0001558] 3/13 23% 0/2 \n", + "Neck muscle weakness [HP:0000467] 10/13 77% 2/2 \n", + "Third degree atrioventricular block [HP:0001709] 2/5 40% 3/5 \n", + "Atrioventricular block [HP:0001678] 8/11 73% 8/10 \n", + "Talipes equinovarus [HP:0001762] 3/13 23% 0/2 \n", + "Cardiomyopathy [HP:0001638] 24/27 89% 4/4 \n", "\n", "MISSENSE_VARIANT on NM_170707.4 \\\n", " Percent p value \n", "Lipodystrophy [HP:0009125] 43% 0.001167 \n", - "Proptosis [HP:0000520] 100% 0.001225 \n", "Alopecia [HP:0001596] 100% 0.001225 \n", "Prominent superficial blood vessels [HP:0007394] 100% 0.001225 \n", "Reduced subcutaneous adipose tissue [HP:0003758] 100% 0.001225 \n", - "... ... ... \n", - "Finger joint contracture [HP:0034681] 100% 1.000000 \n", - "Abnormality of upper limb joint [HP:0009810] 100% 1.000000 \n", - "Abnormality of the hand [HP:0001155] 100% 1.000000 \n", - "Abnormal circulating lipid concentration [HP:00... 0% 1.000000 \n", - "Abnormality of the musculoskeletal system [HP:0... 100% 1.000000 \n", + "Proptosis [HP:0000520] 100% 0.001225 \n", + "Failure to thrive [HP:0001508] 100% 0.001225 \n", + "Micrognathia [HP:0000347] 100% 0.001225 \n", + "Short stature [HP:0004322] 100% 0.004128 \n", + "Reduced bone mineral density [HP:0004349] 100% 0.008772 \n", + "Increased circulating creatine kinase MM isofor... 100% 0.028571 \n", + "Left ventricular systolic dysfunction [HP:0025169] 43% 0.056591 \n", + "Abnormal left ventricular ejection fraction [HP... 43% 0.056591 \n", + "Reduced left ventricular ejection fraction [HP:... 38% 0.056945 \n", + "Respiratory insufficiency due to muscle weaknes... 0% 0.057143 \n", + "Abnormal left ventricular function [HP:0005162] 60% 0.064478 \n", + "Abnormal atrioventricular conduction [HP:0005150] 82% 0.082583 \n", + "Cardiac conduction abnormality [HP:0031546] 83% 0.093883 \n", + "Mildly reduced left ventricular ejection fracti... 29% 0.095187 \n", + "Ventricular tachycardia [HP:0004756] 50% 0.120588 \n", + "Sudden cardiac death [HP:0001645] 14% 0.184244 \n", + "Dilated cardiomyopathy [HP:0001644] 44% 0.274687 \n", + "Atrial fibrillation [HP:0005110] 44% 0.276263 \n", + "Tube feeding [HP:0033454] 50% 0.371429 \n", + "Highly elevated creatine kinase [HP:0030234] 17% 0.380455 \n", + "Heart block [HP:0012722] 82% 0.485380 \n", + "Talipes [HP:0001883] 0% 0.529412 \n", + "Abnormal circulating creatine kinase concentrat... 100% 0.554481 \n", + "Loss of ambulation [HP:0002505] 33% 0.582043 \n", + "Second degree atrioventricular block [HP:0011706] 60% 1.000000 \n", + "Decreased fetal movement [HP:0001558] 0% 1.000000 \n", + "Neck muscle weakness [HP:0000467] 100% 1.000000 \n", + "Third degree atrioventricular block [HP:0001709] 60% 1.000000 \n", + "Atrioventricular block [HP:0001678] 80% 1.000000 \n", + "Talipes equinovarus [HP:0001762] 0% 1.000000 \n", + "Cardiomyopathy [HP:0001638] 100% 1.000000 \n", "\n", "MISSENSE_VARIANT on NM_170707.4 \n", " Corrected p value \n", - "Lipodystrophy [HP:0009125] 0.079657 \n", - "Proptosis [HP:0000520] 0.079657 \n", - "Alopecia [HP:0001596] 0.079657 \n", - "Prominent superficial blood vessels [HP:0007394] 0.079657 \n", - "Reduced subcutaneous adipose tissue [HP:0003758] 0.079657 \n", - "... ... \n", - "Finger joint contracture [HP:0034681] 1.000000 \n", - "Abnormality of upper limb joint [HP:0009810] 1.000000 \n", - "Abnormality of the hand [HP:0001155] 1.000000 \n", - "Abnormal circulating lipid concentration [HP:00... 1.000000 \n", - "Abnormality of the musculoskeletal system [HP:0... 1.000000 \n", - "\n", - "[455 rows x 6 columns]" + "Lipodystrophy [HP:0009125] 0.040862 \n", + "Alopecia [HP:0001596] 0.042892 \n", + "Prominent superficial blood vessels [HP:0007394] 0.042892 \n", + "Reduced subcutaneous adipose tissue [HP:0003758] 0.042892 \n", + "Proptosis [HP:0000520] 0.042892 \n", + "Failure to thrive [HP:0001508] 0.042892 \n", + "Micrognathia [HP:0000347] 0.042892 \n", + "Short stature [HP:0004322] 0.144479 \n", + "Reduced bone mineral density [HP:0004349] 0.307018 \n", + "Increased circulating creatine kinase MM isofor... 1.000000 \n", + "Left ventricular systolic dysfunction [HP:0025169] 1.000000 \n", + "Abnormal left ventricular ejection fraction [HP... 1.000000 \n", + "Reduced left ventricular ejection fraction [HP:... 1.000000 \n", + "Respiratory insufficiency due to muscle weaknes... 1.000000 \n", + "Abnormal left ventricular function [HP:0005162] 1.000000 \n", + "Abnormal atrioventricular conduction [HP:0005150] 1.000000 \n", + "Cardiac conduction abnormality [HP:0031546] 1.000000 \n", + "Mildly reduced left ventricular ejection fracti... 1.000000 \n", + "Ventricular tachycardia [HP:0004756] 1.000000 \n", + "Sudden cardiac death [HP:0001645] 1.000000 \n", + "Dilated cardiomyopathy [HP:0001644] 1.000000 \n", + "Atrial fibrillation [HP:0005110] 1.000000 \n", + "Tube feeding [HP:0033454] 1.000000 \n", + "Highly elevated creatine kinase [HP:0030234] 1.000000 \n", + "Heart block [HP:0012722] 1.000000 \n", + "Talipes [HP:0001883] 1.000000 \n", + "Abnormal circulating creatine kinase concentrat... 1.000000 \n", + "Loss of ambulation [HP:0002505] 1.000000 \n", + "Second degree atrioventricular block [HP:0011706] 1.000000 \n", + "Decreased fetal movement [HP:0001558] 1.000000 \n", + "Neck muscle weakness [HP:0000467] 1.000000 \n", + "Third degree atrioventricular block [HP:0001709] 1.000000 \n", + "Atrioventricular block [HP:0001678] 1.000000 \n", + "Talipes equinovarus [HP:0001762] 1.000000 \n", + "Cardiomyopathy [HP:0001638] 1.000000 " ] }, - "execution_count": 20, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "from genophenocorr.model import VariantEffect\n", - "from genophenocorr.analysis.predicate import PatientCategories\n", - "from genophenocorr.analysis.predicate.genotype import VariantEffectPredicate\n", + "from gpsea.model import VariantEffect\n", + "from gpsea.analysis.predicate import PatientCategories\n", + "from gpsea.analysis.predicate.genotype import VariantPredicates\n", "\n", - "by_missense = analysis.compare_by_variant_effect(VariantEffect.MISSENSE_VARIANT, tx_id=tx_id)\n", + "is_missense = VariantPredicates.variant_effect(VariantEffect.MISSENSE_VARIANT, LMNA_MANE_transcript)\n", + "by_missense = analysis.compare_hpo_vs_genotype(is_missense)\n", "by_missense.summarize(hpo, PatientCategories.YES)" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 12, + "id": "9c7b451c", + "metadata": {}, + "outputs": [], + "source": [ + "analysis_config = CohortAnalysisConfiguration()\n", + "analysis_config.missing_implies_excluded = False\n", + "\n", + "analysis_nomtc = configure_cohort_analysis(cohort, hpo, config=analysis_config)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, "id": "1b18c688", "metadata": {}, "outputs": [ @@ -1287,7 +1350,7 @@ " 0/37\n", " 0%\n", " 1.807470e-02\n", - " 4.518674e-02\n", + " 9.037348e-02\n", " \n", " \n", " OMIM:181350\n", @@ -1296,7 +1359,7 @@ " 7/37\n", " 19%\n", " 5.904112e-02\n", - " 9.840187e-02\n", + " 2.952056e-01\n", " \n", " \n", " OMIM:613205\n", @@ -1305,7 +1368,7 @@ " 2/37\n", " 5%\n", " 2.276691e-01\n", - " 2.845863e-01\n", + " 1.000000e+00\n", " \n", " \n", " OMIM:115200\n", @@ -1332,25 +1395,25 @@ "MISSENSE_VARIANT on NM_170707.4 \n", " Corrected p value \n", "OMIM:176670 4.012812e-09 \n", - "OMIM:151660 4.518674e-02 \n", - "OMIM:181350 9.840187e-02 \n", - "OMIM:613205 2.845863e-01 \n", + "OMIM:151660 9.037348e-02 \n", + "OMIM:181350 2.952056e-01 \n", + "OMIM:613205 1.000000e+00 \n", "OMIM:115200 1.000000e+00 " ] }, - "execution_count": 21, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "by_dis = analysis.compare_disease_vs_genotype(VariantEffectPredicate(transcript_id=tx_id, effect=VariantEffect.MISSENSE_VARIANT))\n", + "by_dis = analysis_nomtc.compare_disease_vs_genotype(is_missense)\n", "by_dis.summarize(hpo, PatientCategories.YES)" ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "id": "9285e5dc", "metadata": {}, "outputs": [ @@ -1374,7 +1437,7 @@ "\n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1397,156 +1460,130 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", "
>=1 allele of the variant 1_156138613_156138613_C_Tvariant has ID of 1_156138613_156138613_C_TYesNo10/9511%2.780126e-121.264957e-092.502113e-11
Proptosis [HP:0000520]Prominent superficial blood vessels [HP:0007394]15/15100%0/30%1.225490e-037.965686e-021.102941e-02
Alopecia [HP:0001596]Reduced subcutaneous adipose tissue [HP:0003758]15/15100%0/30%1.225490e-037.965686e-021.102941e-02
Prominent superficial blood vessels [HP:0007394]Proptosis [HP:0000520]15/15100%0/30%1.225490e-037.965686e-021.102941e-02
Reduced subcutaneous adipose tissue [HP:0003758]Micrognathia [HP:0000347]15/15100%0/30%1.225490e-037.965686e-02
.....................1.102941e-02
Finger joint contracture [HP:0034681]0/00%2/2Failure to thrive [HP:0001508]15/15100%1.000000e+001.000000e+00
Abnormality of upper limb joint [HP:0009810]0/00/30%6/6100%1.000000e+001.000000e+001.225490e-031.102941e-02
Abnormality of the hand [HP:0001155]Alopecia [HP:0001596]15/15100%2/2100%1.000000e+001.000000e+000/30%1.225490e-031.102941e-02
Abnormal circulating lipid concentration [HP:0003119]0/00%12/12Short stature [HP:0004322]15/15100%1.000000e+001.000000e+001/425%4.127967e-033.715170e-02
Abnormality of the musculoskeletal system [HP:0033127]Reduced bone mineral density [HP:0004349]15/15100%70/70100%1.000000e+001.000000e+002/540%8.771930e-037.894737e-02
\n", - "

455 rows × 6 columns

\n", "" ], "text/plain": [ - ">=1 allele of the variant 1_156138613_156138613_C_T Yes No \\\n", - " Count Percent Count \n", - "Lipodystrophy [HP:0009125] 15/15 100% 10/95 \n", - "Proptosis [HP:0000520] 15/15 100% 0/3 \n", - "Alopecia [HP:0001596] 15/15 100% 0/3 \n", - "Prominent superficial blood vessels [HP:0007394] 15/15 100% 0/3 \n", - "Reduced subcutaneous adipose tissue [HP:0003758] 15/15 100% 0/3 \n", - "... ... ... ... \n", - "Finger joint contracture [HP:0034681] 0/0 0% 2/2 \n", - "Abnormality of upper limb joint [HP:0009810] 0/0 0% 6/6 \n", - "Abnormality of the hand [HP:0001155] 15/15 100% 2/2 \n", - "Abnormal circulating lipid concentration [HP:00... 0/0 0% 12/12 \n", - "Abnormality of the musculoskeletal system [HP:0... 15/15 100% 70/70 \n", - "\n", - ">=1 allele of the variant 1_156138613_156138613_C_T \\\n", - " Percent p value \n", - "Lipodystrophy [HP:0009125] 11% 2.780126e-12 \n", - "Proptosis [HP:0000520] 0% 1.225490e-03 \n", - "Alopecia [HP:0001596] 0% 1.225490e-03 \n", - "Prominent superficial blood vessels [HP:0007394] 0% 1.225490e-03 \n", - "Reduced subcutaneous adipose tissue [HP:0003758] 0% 1.225490e-03 \n", - "... ... ... \n", - "Finger joint contracture [HP:0034681] 100% 1.000000e+00 \n", - "Abnormality of upper limb joint [HP:0009810] 100% 1.000000e+00 \n", - "Abnormality of the hand [HP:0001155] 100% 1.000000e+00 \n", - "Abnormal circulating lipid concentration [HP:00... 100% 1.000000e+00 \n", - "Abnormality of the musculoskeletal system [HP:0... 100% 1.000000e+00 \n", + "variant has ID of 1_156138613_156138613_C_T Yes No \\\n", + " Count Percent Count \n", + "Lipodystrophy [HP:0009125] 15/15 100% 10/95 \n", + "Prominent superficial blood vessels [HP:0007394] 15/15 100% 0/3 \n", + "Reduced subcutaneous adipose tissue [HP:0003758] 15/15 100% 0/3 \n", + "Proptosis [HP:0000520] 15/15 100% 0/3 \n", + "Micrognathia [HP:0000347] 15/15 100% 0/3 \n", + "Failure to thrive [HP:0001508] 15/15 100% 0/3 \n", + "Alopecia [HP:0001596] 15/15 100% 0/3 \n", + "Short stature [HP:0004322] 15/15 100% 1/4 \n", + "Reduced bone mineral density [HP:0004349] 15/15 100% 2/5 \n", "\n", - ">=1 allele of the variant 1_156138613_156138613_C_T \n", - " Corrected p value \n", - "Lipodystrophy [HP:0009125] 1.264957e-09 \n", - "Proptosis [HP:0000520] 7.965686e-02 \n", - "Alopecia [HP:0001596] 7.965686e-02 \n", - "Prominent superficial blood vessels [HP:0007394] 7.965686e-02 \n", - "Reduced subcutaneous adipose tissue [HP:0003758] 7.965686e-02 \n", - "... ... \n", - "Finger joint contracture [HP:0034681] 1.000000e+00 \n", - "Abnormality of upper limb joint [HP:0009810] 1.000000e+00 \n", - "Abnormality of the hand [HP:0001155] 1.000000e+00 \n", - "Abnormal circulating lipid concentration [HP:00... 1.000000e+00 \n", - "Abnormality of the musculoskeletal system [HP:0... 1.000000e+00 \n", + "variant has ID of 1_156138613_156138613_C_T \\\n", + " Percent p value \n", + "Lipodystrophy [HP:0009125] 11% 2.780126e-12 \n", + "Prominent superficial blood vessels [HP:0007394] 0% 1.225490e-03 \n", + "Reduced subcutaneous adipose tissue [HP:0003758] 0% 1.225490e-03 \n", + "Proptosis [HP:0000520] 0% 1.225490e-03 \n", + "Micrognathia [HP:0000347] 0% 1.225490e-03 \n", + "Failure to thrive [HP:0001508] 0% 1.225490e-03 \n", + "Alopecia [HP:0001596] 0% 1.225490e-03 \n", + "Short stature [HP:0004322] 25% 4.127967e-03 \n", + "Reduced bone mineral density [HP:0004349] 40% 8.771930e-03 \n", "\n", - "[455 rows x 6 columns]" + "variant has ID of 1_156138613_156138613_C_T \n", + " Corrected p value \n", + "Lipodystrophy [HP:0009125] 2.502113e-11 \n", + "Prominent superficial blood vessels [HP:0007394] 1.102941e-02 \n", + "Reduced subcutaneous adipose tissue [HP:0003758] 1.102941e-02 \n", + "Proptosis [HP:0000520] 1.102941e-02 \n", + "Micrognathia [HP:0000347] 1.102941e-02 \n", + "Failure to thrive [HP:0001508] 1.102941e-02 \n", + "Alopecia [HP:0001596] 1.102941e-02 \n", + "Short stature [HP:0004322] 3.715170e-02 \n", + "Reduced bone mineral density [HP:0004349] 7.894737e-02 " ] }, - "execution_count": 22, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "by_var = analysis.compare_by_variant_key(variant_key='1_156138613_156138613_C_T')\n", + "is_var_key = VariantPredicates.variant_key('1_156138613_156138613_C_T')\n", + "by_var = analysis.compare_hpo_vs_genotype(is_var_key)\n", "by_var.summarize(hpo, PatientCategories.YES)" ] }, diff --git a/case_studies/LMNA/input/PMID_10580070_A_III-1.json b/case_studies/LMNA/input/PMID_10580070_A_III-1.json deleted file mode 100644 index f32073f3..00000000 --- a/case_studies/LMNA/input/PMID_10580070_A_III-1.json +++ /dev/null @@ -1,172 +0,0 @@ -{ - "id": "PMID_10580070_A_III-1", - "subject": { - "id": "A_III-1", - "sex": "MALE" - }, - "phenotypicFeatures": [ - { - "type": { - "id": "HP:0001644", - "label": "Dilated cardiomyopathy" - } - }, - { - "type": { - "id": "HP:0001709", - "label": "Third degree atrioventricular block" - } - }, - { - "type": { - "id": "HP:0005110", - "label": "Atrial fibrillation" - }, - "excluded": true - }, - { - "type": { - "id": "HP:0003236", - "label": "Elevated circulating creatine kinase concentration" - }, - "excluded": true - }, - { - "type": { - "id": "HP:0001645", - "label": "Sudden cardiac death" - }, - "excluded": true - }, - { - "type": { - "id": "HP:0005328", - "label": "Progeroid facial appearance" - }, - "excluded": true - }, - { - "type": { - "id": "HP:0009125", - "label": "Lipodystrophy" - }, - "excluded": true - } - ], - "interpretations": [ - { - "id": "A_III-1", - "progressStatus": "SOLVED", - "diagnosis": { - "disease": { - "id": "OMIM:115200", - "label": "Cardiomyopathy, dilated, 1A" - }, - "genomicInterpretations": [ - { - "subjectOrBiosampleId": "A_III-1", - "interpretationStatus": "CAUSATIVE", - "variantInterpretation": { - "variationDescriptor": { - "id": "var_PMmUPzxnCEKuTVWZvdoaaZyzm", - "geneContext": { - "valueId": "HGNC:6636", - "symbol": "LMNA" - }, - "expressions": [ - { - "syntax": "hgvs.c", - "value": "NM_005572.4:c.1711C>A" - }, - { - "syntax": "hgvs.g", - "value": "NC_000001.11:g.156137756C>A" - } - ], - "vcfRecord": { - "genomeAssembly": "hg38", - "chrom": "chr1", - "pos": "156137756", - "ref": "C", - "alt": "A" - }, - "moleculeContext": "genomic", - "allelicState": { - "id": "GENO:0000135", - "label": "heterozygous" - } - } - } - } - ] - } - } - ], - "diseases": [ - { - "term": { - "id": "OMIM:115200", - "label": "Cardiomyopathy, dilated, 1A" - }, - "onset": { - "age": { - "iso8601duration": "P51Y" - } - } - } - ], - "metaData": { - "created": "2024-03-23T17:16:16.745283126Z", - "createdBy": "ORCID:0000-0002-0736-9199", - "resources": [ - { - "id": "geno", - "name": "Genotype Ontology", - "url": "http://purl.obolibrary.org/obo/geno.owl", - "version": "2022-03-05", - "namespacePrefix": "GENO", - "iriPrefix": "http://purl.obolibrary.org/obo/GENO_" - }, - { - "id": "hgnc", - "name": "HUGO Gene Nomenclature Committee", - "url": "https://www.genenames.org", - "version": "06/01/23", - "namespacePrefix": "HGNC", - "iriPrefix": "https://www.genenames.org/data/gene-symbol-report/#!/hgnc_id/" - }, - { - "id": "omim", - "name": "An Online Catalog of Human Genes and Genetic Disorders", - "url": "https://www.omim.org", - "version": "January 4, 2023", - "namespacePrefix": "OMIM", - "iriPrefix": "https://www.omim.org/entry/" - }, - { - "id": "so", - "name": "Sequence types and features ontology", - "url": "http://purl.obolibrary.org/obo/so.obo", - "version": "2021-11-22", - "namespacePrefix": "SO", - "iriPrefix": "http://purl.obolibrary.org/obo/SO_" - }, - { - "id": "hp", - "name": "human phenotype ontology", - "url": "http://purl.obolibrary.org/obo/hp.owl", - "version": "2024-02-27", - "namespacePrefix": "HP", - "iriPrefix": "http://purl.obolibrary.org/obo/HP_" - } - ], - "phenopacketSchemaVersion": "2.0", - "externalReferences": [ - { - "id": "PMID:10580070", - "reference": "https://pubmed.ncbi.nlm.nih.gov/10580070", - "description": "Missense mutations in the rod domain of the lamin A/C gene as causes of dilated cardiomyopathy and conduction-system disease" - } - ] - } -} \ No newline at end of file diff --git a/case_studies/LMNA/input/PMID_10580070_A_III-11.json b/case_studies/LMNA/input/PMID_10580070_A_III-11.json deleted file mode 100644 index 983f1dd2..00000000 --- a/case_studies/LMNA/input/PMID_10580070_A_III-11.json +++ /dev/null @@ -1,171 +0,0 @@ -{ - "id": "PMID_10580070_A_III-11", - "subject": { - "id": "A_III-11", - "sex": "FEMALE" - }, - "phenotypicFeatures": [ - { - "type": { - "id": "HP:0005110", - "label": "Atrial fibrillation" - } - }, - { - "type": { - "id": "HP:0003236", - "label": "Elevated circulating creatine kinase concentration" - } - }, - { - "type": { - "id": "HP:0011706", - "label": "Second degree atrioventricular block" - } - }, - { - "type": { - "id": "HP:0001644", - "label": "Dilated cardiomyopathy" - }, - "excluded": true - }, - { - "type": { - "id": "HP:0001645", - "label": "Sudden cardiac death" - }, - "excluded": true - }, - { - "type": { - "id": "HP:0005328", - "label": "Progeroid facial appearance" - }, - "excluded": true - }, - { - "type": { - "id": "HP:0009125", - "label": "Lipodystrophy" - }, - "excluded": true - } - ], - "interpretations": [ - { - "id": "A_III-11", - "progressStatus": "SOLVED", - "diagnosis": { - "disease": { - "id": "OMIM:115200", - "label": "Cardiomyopathy, dilated, 1A" - }, - "genomicInterpretations": [ - { - "subjectOrBiosampleId": "A_III-11", - "interpretationStatus": "CAUSATIVE", - "variantInterpretation": { - "variationDescriptor": { - "id": "var_PMmUPzxnCEKuTVWZvdoaaZyzm", - "geneContext": { - "valueId": "HGNC:6636", - "symbol": "LMNA" - }, - "expressions": [ - { - "syntax": "hgvs.c", - "value": "NM_005572.4:c.1711C>A" - }, - { - "syntax": "hgvs.g", - "value": "NC_000001.11:g.156137756C>A" - } - ], - "vcfRecord": { - "genomeAssembly": "hg38", - "chrom": "chr1", - "pos": "156137756", - "ref": "C", - "alt": "A" - }, - "moleculeContext": "genomic", - "allelicState": { - "id": "GENO:0000135", - "label": "heterozygous" - } - } - } - } - ] - } - } - ], - "diseases": [ - { - "term": { - "id": "OMIM:115200", - "label": "Cardiomyopathy, dilated, 1A" - }, - "onset": { - "age": { - "iso8601duration": "P46Y" - } - } - } - ], - "metaData": { - "created": "2024-03-23T17:16:16.791958093Z", - "createdBy": "ORCID:0000-0002-0736-9199", - "resources": [ - { - "id": "geno", - "name": "Genotype Ontology", - "url": "http://purl.obolibrary.org/obo/geno.owl", - "version": "2022-03-05", - "namespacePrefix": "GENO", - "iriPrefix": "http://purl.obolibrary.org/obo/GENO_" - }, - { - "id": "hgnc", - "name": "HUGO Gene Nomenclature Committee", - "url": "https://www.genenames.org", - "version": "06/01/23", - 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} - ] - } -} \ No newline at end of file diff --git a/case_studies/LMNA/input/PMID_10580070_A_III-13.json b/case_studies/LMNA/input/PMID_10580070_A_III-13.json deleted file mode 100644 index 31718eaf..00000000 --- a/case_studies/LMNA/input/PMID_10580070_A_III-13.json +++ /dev/null @@ -1,178 +0,0 @@ -{ - "id": "PMID_10580070_A_III-13", - "subject": { - "id": "A_III-13", - "sex": "MALE" - }, - "phenotypicFeatures": [ - { - "type": { - "id": "HP:0005110", - "label": "Atrial fibrillation" - } - }, - { - "type": { - "id": "HP:0001688", - "label": "Sinus bradycardia" - } - }, - { - "type": { - "id": "HP:0011705", - "label": "First degree atrioventricular block" - } - }, - { - "type": { - "id": "HP:0001644", - "label": "Dilated cardiomyopathy" - }, - "excluded": true - }, - { - "type": { - "id": "HP:0003236", - "label": "Elevated circulating creatine kinase concentration" - }, - "excluded": true - }, - { - "type": { - "id": "HP:0001645", - "label": "Sudden cardiac death" - }, - "excluded": true - 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}, - "excluded": true - }, - { - "type": { - "id": "HP:0031956", - "label": "Elevated circulating aspartate aminotransferase concentration" - }, - "excluded": true - }, - { - "type": { - "id": "HP:0012100", - "label": "Abnormal circulating creatinine concentration" - }, - "excluded": true - }, - { - "type": { - "id": "HP:0031888", - "label": "Abnormal HDL cholesterol concentration" - }, - "excluded": true - }, - { - "type": { - "id": "HP:0040081", - "label": "Abnormal circulating creatine kinase concentration" - }, - "excluded": true - }, - { - "type": { - "id": "HP:0012664", - "label": "Reduced left ventricular ejection fraction" - }, - "excluded": true - } - ], - "interpretations": [ - { - "id": "5", - "progressStatus": "SOLVED", - "diagnosis": { - "disease": { - "id": "OMIM:151660", - "label": "Lipodystrophy, familial partial, type 2" - }, - "genomicInterpretations": [ - { - "subjectOrBiosampleId": "5", - "interpretationStatus": "CAUSATIVE", - "variantInterpretation": { - "variationDescriptor": { - "id": "var_aajScqzcZrPLBNmWeMcUytvGH", - "geneContext": { - "valueId": "HGNC:6636", - "symbol": "LMNA" - }, - "expressions": [ - { - "syntax": "hgvs.c", - "value": "NM_170707.4:c.1444C>T" - }, - { - "syntax": "hgvs.g", - "value": "NC_000001.11:g.156136984C>T" - } - ], - "vcfRecord": { - "genomeAssembly": "hg38", - "chrom": "chr1", - "pos": "156136984", - "ref": "C", - "alt": "T" - }, - "moleculeContext": "genomic", - "allelicState": { - "id": "GENO:0000135", - "label": "heterozygous" - } - } - } - } - ] - } - } - ], - "diseases": [ - { - "term": { - "id": "OMIM:151660", - "label": "Lipodystrophy, familial partial, type 2" - }, - "onset": { - "age": { - "iso8601duration": "P60Y" - } - } - } - ], - "metaData": { - "created": "2024-03-23T17:41:42.997889995Z", - "createdBy": "ORCID:0000-0002-0736-9199", - "resources": [ - { - "id": "geno", - "name": "Genotype Ontology", - "url": "http://purl.obolibrary.org/obo/geno.owl", - "version": "2022-03-05", - "namespacePrefix": "GENO", - "iriPrefix": "http://purl.obolibrary.org/obo/GENO_" - }, - { - "id": "hgnc", - "name": "HUGO Gene Nomenclature Committee", - "url": "https://www.genenames.org", - "version": "06/01/23", - "namespacePrefix": "HGNC", - "iriPrefix": "https://www.genenames.org/data/gene-symbol-report/#!/hgnc_id/" - }, - { - "id": "omim", - "name": "An Online Catalog of Human Genes and Genetic Disorders", - "url": "https://www.omim.org", - "version": "January 4, 2023", - "namespacePrefix": "OMIM", - "iriPrefix": "https://www.omim.org/entry/" - }, - { - "id": "so", - "name": "Sequence types and features ontology", - "url": "http://purl.obolibrary.org/obo/so.obo", - "version": "2021-11-22", - "namespacePrefix": "SO", - "iriPrefix": "http://purl.obolibrary.org/obo/SO_" - }, - { - "id": "hp", - "name": "human phenotype ontology", - "url": "http://purl.obolibrary.org/obo/hp.owl", - "version": "2024-02-27", - "namespacePrefix": "HP", - "iriPrefix": "http://purl.obolibrary.org/obo/HP_" - } - ], - "phenopacketSchemaVersion": "2.0", - "externalReferences": [ - { - "id": "PMID:37303127", - "reference": "https://pubmed.ncbi.nlm.nih.gov/37303127", - "description": "The Clinical Characteristics and Potential Molecular Mechanism of LMNA Mutation-Related Lipodystrophy" - } - ] - } -} \ No newline at end of file diff --git a/case_studies/LMNA/input/PMID_37303127_6.json b/case_studies/LMNA/input/PMID_37303127_6.json deleted file mode 100644 index 1a30b80b..00000000 --- a/case_studies/LMNA/input/PMID_37303127_6.json +++ /dev/null @@ -1,302 +0,0 @@ -{ - "id": "PMID_37303127_6", - "subject": { - "id": "6", - "timeAtLastEncounter": { - "age": { - "iso8601duration": "P16Y" - } - }, - "sex": "FEMALE" - }, - "phenotypicFeatures": [ - { - "type": { - "id": "HP:0001397", - "label": "Hepatic steatosis" - } - }, - { - "type": { - "id": "HP:0002155", - "label": "Hypertriglyceridemia" - } - }, - { - "type": { - "id": "HP:0003074", - "label": "Hyperglycemia" - } - }, - { - "type": { - "id": "HP:0001644", - "label": "Dilated cardiomyopathy" - } - }, - { - "type": { - "id": "HP:0002092", - "label": "Pulmonary arterial hypertension" - } - }, - { - "type": { - "id": "HP:0031964", - "label": "Elevated circulating alanine aminotransferase concentration" - } - }, - { - "type": { - "id": "HP:0031956", - "label": "Elevated circulating aspartate aminotransferase concentration" - } - }, - { - "type": { - "id": "HP:0000819", - "label": "Diabetes mellitus" - } - }, - { - "type": { - "id": "HP:0040217", - "label": "Elevated hemoglobin A1c" - } - }, - { - "type": { - "id": "HP:0003560", - "label": "Muscular dystrophy" - } - }, - { - "type": { - "id": "HP:0005110", - "label": "Atrial fibrillation" - } - }, - { - "type": { - "id": "HP:0003236", - "label": "Elevated circulating creatine kinase concentration" - } - }, - { - "type": { - "id": "HP:0002650", - "label": "Scoliosis" - } - }, - { - "type": { - "id": "HP:0001771", - "label": "Achilles tendon contracture" - } - }, - { - "type": { - "id": "HP:0002486", - "label": "Myotonia" - } - }, - { - "type": { - "id": "HP:0001635", - "label": "Congestive heart failure" - } - }, - { - "type": { - "id": "HP:0030718", - "label": "Right atrial enlargement" - } - }, - { - "type": { - "id": "HP:0009064", - "label": "Generalized lipodystrophy" - } - }, - { - "type": { - "id": "HP:0000855", - "label": "Insulin resistance" - } - }, - { - "type": { - "id": "HP:0003233", - "label": "Decreased HDL cholesterol concentration" - } - }, - { - "type": { - "id": "HP:0002149", - "label": "Hyperuricemia" - } - }, - { - "type": { - "id": "HP:0012592", - "label": "Albuminuria" - } - }, - { - "type": { - "id": "HP:0031185", - "label": "Increased circulating NT-proBNP concentration" - } - }, - { - "type": { - "id": "HP:0012663", - "label": "Mildly reduced left ventricular ejection fraction" - } - }, - { - "type": { - "id": "HP:0012100", - "label": "Abnormal circulating creatinine concentration" - }, - "excluded": true - }, - { - "type": { - "id": "HP:0031886", - "label": "Abnormal LDL cholesterol concentration" - }, - "excluded": true - }, - { - "type": { - "id": "HP:0040300", - "label": "Abnormal circulating free fatty acid concentration" - }, - "excluded": true - }, - { - "type": { - "id": "HP:0000842", - "label": "Hyperinsulinemia" - }, - "excluded": true - } - ], - "interpretations": [ - { - "id": "6", - "progressStatus": "SOLVED", - "diagnosis": { - "disease": { - "id": "OMIM:151660", - "label": "Lipodystrophy, familial partial, type 2" - }, - "genomicInterpretations": [ - { - "subjectOrBiosampleId": "6", - "interpretationStatus": "CAUSATIVE", - "variantInterpretation": { - "variationDescriptor": { - "id": "var_aajScqzcZrPLBNmWeMcUytvGH", - "geneContext": { - "valueId": "HGNC:6636", - "symbol": "LMNA" - }, - "expressions": [ - { - "syntax": "hgvs.c", - "value": "NM_170707.4:c.1444C>T" - }, - { - "syntax": "hgvs.g", - "value": "NC_000001.11:g.156136984C>T" - } - ], - "vcfRecord": { - "genomeAssembly": "hg38", - "chrom": "chr1", - "pos": "156136984", - "ref": "C", - "alt": "T" - }, - "moleculeContext": "genomic", - "allelicState": { - "id": "GENO:0000135", - "label": "heterozygous" - } - } - } - } - ] - } - } - ], - "diseases": [ - { - "term": { - "id": "OMIM:151660", - "label": "Lipodystrophy, familial partial, type 2" - }, - "onset": { - "age": { - "iso8601duration": "P1Y" - } - } - } - ], - "metaData": { - "created": "2024-03-23T17:41:42.999521017Z", - "createdBy": "ORCID:0000-0002-0736-9199", - "resources": [ - { - "id": "geno", - "name": "Genotype Ontology", - "url": "http://purl.obolibrary.org/obo/geno.owl", - "version": "2022-03-05", - "namespacePrefix": "GENO", - "iriPrefix": "http://purl.obolibrary.org/obo/GENO_" - }, - { - "id": "hgnc", - "name": "HUGO Gene Nomenclature Committee", - "url": "https://www.genenames.org", - "version": "06/01/23", - "namespacePrefix": "HGNC", - "iriPrefix": "https://www.genenames.org/data/gene-symbol-report/#!/hgnc_id/" - }, - { - "id": "omim", - "name": "An Online Catalog of Human Genes and Genetic Disorders", - "url": "https://www.omim.org", - "version": "January 4, 2023", - "namespacePrefix": "OMIM", - "iriPrefix": "https://www.omim.org/entry/" - }, - { - "id": "so", - "name": "Sequence types and features ontology", - "url": "http://purl.obolibrary.org/obo/so.obo", - "version": "2021-11-22", - "namespacePrefix": "SO", - "iriPrefix": "http://purl.obolibrary.org/obo/SO_" - }, - { - "id": "hp", - "name": "human phenotype ontology", - "url": "http://purl.obolibrary.org/obo/hp.owl", - "version": "2024-02-27", - "namespacePrefix": "HP", - "iriPrefix": "http://purl.obolibrary.org/obo/HP_" - } - ], - "phenopacketSchemaVersion": "2.0", - "externalReferences": [ - { - "id": "PMID:37303127", - "reference": "https://pubmed.ncbi.nlm.nih.gov/37303127", - "description": "The Clinical Characteristics and Potential Molecular Mechanism of LMNA Mutation-Related Lipodystrophy" - } - ] - } -} \ No newline at end of file diff --git a/dev/Statistical tests.ipynb b/dev/Statistical tests.ipynb index aaad9474..077c9228 100644 --- a/dev/Statistical tests.ipynb +++ b/dev/Statistical tests.ipynb @@ -7,7 +7,7 @@ "source": [ "**Statistical tests**\n", "\n", - "This notebook is a playground for showing and testing statistical methods used by the `genophenocorr` library \n", + "This notebook is a playground for showing and testing statistical methods used by the `gpsea` library \n", "to discover genotype-phenotype correlations in patients annotated with HPO terms." ] }, @@ -16,15 +16,15 @@ "id": "ea476a3c-2bcc-496b-88d2-e9cadea3a8f1", "metadata": {}, "source": [ - "# Install the `genophenocorr` library\n", + "# Install the `gpsea` library\n", "\n", - "The notebook needs `genophenocorr` to be installed in the Python environment. `genophenocorr` is still mostly work in progress, so the best way is to install from sources.\n", + "The notebook needs `gpsea` to be installed in the Python environment. `gpsea` is still mostly work in progress, so the best way is to install from sources.\n", "\n", "We assume availability of `git` and Python 3.8 or better in the environment.\n", "\n", "```shell\n", - "git clone git@github.com:monarch-initiative/genophenocorr.git\n", - "cd genophenocorr\n", + "git clone git@github.com:monarch-initiative/gpsea.git\n", + "cd gpsea\n", "git checkout develop && git pull\n", "\n", "python3 -m pip install --editable .[test]\n", @@ -32,9 +32,9 @@ "pytest\n", "```\n", "\n", - "First, we download the source code from the GitHub repository, and we switch to `develop` branch to access the bleeding-edge features. Then, we install `genophenocorr` into the active environment, including the [dependencies](https://github.com/monarch-initiative/genophenocorr/blob/020832e850ef7107aa9de61462bc3490e0deb574/pyproject.toml#L34). As an optional last step, we run the tests to ensure installation went well.\n", + "First, we download the source code from the GitHub repository, and we switch to `develop` branch to access the bleeding-edge features. Then, we install `gpsea` into the active environment, including the [dependencies](https://github.com/monarch-initiative/gpsea/blob/020832e850ef7107aa9de61462bc3490e0deb574/pyproject.toml#L34). As an optional last step, we run the tests to ensure installation went well.\n", "\n", - "With this setup, we are ready to run the rest of the notebook, assuming the notebook kernel corresponds to the Python environment where `genophenocorr` was installed to." + "With this setup, we are ready to run the rest of the notebook, assuming the notebook kernel corresponds to the Python environment where `gpsea` was installed to." ] }, { @@ -73,7 +73,7 @@ "source": [ "import hpotk\n", "\n", - "from genophenocorr.preprocessing import configure_caching_cohort_creator\n", + "from gpsea.preprocessing import configure_caching_cohort_creator\n", "\n", "hpo_url = 'https://github.com/obophenotype/human-phenotype-ontology/releases/download/v2023-10-09/hp.json'\n", "hpo = hpotk.load_minimal_ontology(hpo_url)\n", @@ -94,9 +94,9 @@ "id": "9597e6ed-3016-481a-a2b2-263f87dc349f", "metadata": {}, "source": [ - "Now we can ETL the phenopackets into data format expected by `genophenocorr`. \n", + "Now we can ETL the phenopackets into data format expected by `gpsea`. \n", "\n", - "Assuming the notebook is run from its location within `genophenocorr` repository, we use the pe\u0000 \u0000t\u0000h\u0000e\u0000 \u0000located at [notebooks/SUOX/phenopackets](https://github.com/monarch-initiative/genophenocorr/tree/develop/notebooks/SUOX/phenopackets):" + "Assuming the notebook is run from its location within `gpsea` repository, we use the pe\u0000 \u0000t\u0000h\u0000e\u0000 \u0000located at [notebooks/SUOX/phenopackets](https://github.com/monarch-initiative/gpsea/tree/develop/notebooks/SUOX/phenopackets):" ] }, { @@ -109,7 +109,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Loading phenopackets from /home/ielis/ielis/phenotypes/genophenocorr/notebooks/SUOX/phenopackets\n", + "Loading phenopackets from /home/ielis/ielis/phenotypes/gpsea/notebooks/SUOX/phenopackets\n", "Patients Created: 100%|██████████| 35/35 [00:00<00:00, 620.92it/s]\n", "Validated under none policy\n", "No errors or warnings were found\n" @@ -119,7 +119,7 @@ "source": [ "import os\n", "import sys\n", - "from genophenocorr.preprocessing import load_phenopacket_folder\n", + "from gpsea.preprocessing import load_phenopacket_folder\n", "\n", "fpath_parent = os.path.dirname(os.getcwd())\n", "fpath_phenopacket_dir = os.path.join(fpath_parent, 'notebooks', 'SUOX', 'phenopackets')\n", @@ -160,7 +160,7 @@ "outputs": [], "source": [ "from IPython.display import HTML, display\n", - "from genophenocorr.view import CohortViewer\n", + "from gpsea.view import CohortViewer\n", "\n", "viewer = CohortViewer(hpo)" ] @@ -452,8 +452,8 @@ "metadata": {}, "outputs": [], "source": [ - "from genophenocorr.analysis import configure_cohort_analysis\n", - "from genophenocorr.analysis.predicate import BooleanPredicate\n", + "from gpsea.analysis import configure_cohort_analysis\n", + "from gpsea.analysis.predicate import BooleanPredicate\n", "\n", "analysis = configure_cohort_analysis(cohort, hpo)" ] @@ -479,7 +479,7 @@ "metadata": {}, "outputs": [], "source": [ - "from genophenocorr.model import VariantEffect\n", + "from gpsea.model import VariantEffect\n", "\n", "result = analysis.compare_by_variant_effect(VariantEffect.MISSENSE_VARIANT, tx_id)" ] diff --git a/dev/Visualizers.ipynb b/dev/Visualizers.ipynb index 508630fe..fd7fcb59 100644 --- a/dev/Visualizers.ipynb +++ b/dev/Visualizers.ipynb @@ -117,7 +117,7 @@ "metadata": {}, "outputs": [], "source": [ - "from genophenocorr.preprocessing import configure_caching_patient_creator\n", + "from gpsea.preprocessing import configure_caching_patient_creator\n", "\n", "pc = configure_caching_patient_creator(hpo, validation_runner=validation_runner)" ] @@ -130,7 +130,7 @@ "\n", "Walk the directory, find all JSON files, load them into phenopackets, and transform the phenopackets to patients.\n", "\n", - "> Note: the first run takes longer since we must reach out to VEP REST API. However, the subsequent runs use data that we cache in `.genophenocorr_cache` folder next to this notebook." + "> Note: the first run takes longer since we must reach out to VEP REST API. However, the subsequent runs use data that we cache in `.gpsea_cache` folder next to this notebook." ] }, { @@ -157,7 +157,7 @@ } ], "source": [ - "from genophenocorr.preprocessing import load_phenopacket_folder\n", + "from gpsea.preprocessing import load_phenopacket_folder\n", "\n", "fpath_suox_cohort = os.path.join(os.getcwd(), os.pardir, 'notebooks', 'SUOX', 'phenopackets')\n", "cohort = load_phenopacket_folder(fpath_suox_cohort, pc)\n", @@ -282,8 +282,8 @@ } ], "source": [ - "from genophenocorr.model.genome import GRCh38\n", - "from genophenocorr.preprocessing import VVTranscriptCoordinateService\n", + "from gpsea.model.genome import GRCh38\n", + "from gpsea.preprocessing import VVTranscriptCoordinateService\n", "\n", "txc_service = VVTranscriptCoordinateService(genome_build=GRCh38)\n", "tx_coordinates = txc_service.fetch(tx_id)\n", @@ -429,7 +429,7 @@ } ], "source": [ - "from genophenocorr.preprocessing import UniprotProteinMetadataService\n", + "from gpsea.preprocessing import UniprotProteinMetadataService\n", "\n", "pms = UniprotProteinMetadataService()\n", "\n", @@ -551,7 +551,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We get an instance of `TranscriptCoordinates` that has `exons` property. The property provides a sequence of `genophenocorr.model.genome.GenomicRegion`s that correspond to exon regions.\n", + "We get an instance of `TranscriptCoordinates` that has `exons` property. The property provides a sequence of `gpsea.model.genome.GenomicRegion`s that correspond to exon regions.\n", "\n", "This transcript has 5 exons. Note that the exons are *not* consecutive - they are separated by introns (not in the model). The exons consist of the coding sequence regions (CDS) and UTRs.\n", "\n", @@ -663,7 +663,7 @@ "\n", "Let's discuss the protein features.\n", "\n", - "Genophenocorr knows that `NM_001032386.2` transcript corresponds to `NP_001027558.1` protein and it can fetch the corresponding metadata:" + "gpsea knows that `NM_001032386.2` transcript corresponds to `NP_001027558.1` protein and it can fetch the corresponding metadata:" ] }, { @@ -698,7 +698,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "`genophenocorr.model.ProteinMetadata` knows about the protein anatomy.\n", + "`gpsea.model.ProteinMetadata` knows about the protein anatomy.\n", "\n", "The protein has an identifier and a human-readable label." ] @@ -960,7 +960,7 @@ "\n", "> I recommend adding a check that the features do not overlap and raising an exception if they do.\n", "\n", - "We need to inform the user about the feature type (`genophenocorr.model.FeatureType`). One way of doing this is to use different colors but feel free to do whatever you like:" + "We need to inform the user about the feature type (`gpsea.model.FeatureType`). One way of doing this is to use different colors but feel free to do whatever you like:" ] }, { @@ -1081,7 +1081,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Next, we need to encode the variant effects. Each variant can have `1..m` effects on a transcript. The effects are members of the `genophenocorr.model.VariantEffect` enum. \n", + "Next, we need to encode the variant effects. Each variant can have `1..m` effects on a transcript. The effects are members of the `gpsea.model.VariantEffect` enum. \n", "\n", "For simplicity, let's just choose the first effect and we can update this strategy later:" ] diff --git a/dev/test_draw_variants.ipynb b/dev/test_draw_variants.ipynb index 8e176054..ee90645e 100644 --- a/dev/test_draw_variants.ipynb +++ b/dev/test_draw_variants.ipynb @@ -46,8 +46,8 @@ } ], "source": [ - "from genophenocorr.model.genome import GRCh38\n", - "from genophenocorr.preprocessing import VVTranscriptCoordinateService\n", + "from gpsea.model.genome import GRCh38\n", + "from gpsea.preprocessing import VVTranscriptCoordinateService\n", "\n", "txc_service = VVTranscriptCoordinateService(genome_build=GRCh38)\n", "tx_coordinates = txc_service.fetch(tx_id)" @@ -69,7 +69,7 @@ }, "outputs": [], "source": [ - "from genophenocorr.preprocessing import UniprotProteinMetadataService\n", + "from gpsea.preprocessing import UniprotProteinMetadataService\n", "\n", "pms = UniprotProteinMetadataService()\n", "\n", @@ -258,8 +258,8 @@ "source": [ "import hpotk\n", "import os\n", - "from genophenocorr.preprocessing import load_phenopacket_folder\n", - "from genophenocorr.preprocessing import configure_caching_cohort_creator\n", + "from gpsea.preprocessing import load_phenopacket_folder\n", + "from gpsea.preprocessing import configure_caching_cohort_creator\n", "from hpotk.validate import ValidationRunner\n", "from hpotk.validate import ObsoleteTermIdsValidator, PhenotypicAbnormalityValidator, AnnotationPropagationValidator\n", "\n", @@ -307,7 +307,7 @@ } ], "source": [ - "from genophenocorr.view._draw_variants import VariantsVisualizer\n", + "from gpsea.view._draw_variants import VariantsVisualizer\n", "viz = VariantsVisualizer()\n", "viz.draw_fig(tx_coordinates, protein_meta, cohort)" ] diff --git a/notebooks/ANKRD11/KBG.ipynb b/notebooks/ANKRD11/KBG.ipynb index 7be8c007..79a6020d 100644 --- a/notebooks/ANKRD11/KBG.ipynb +++ b/notebooks/ANKRD11/KBG.ipynb @@ -16,7 +16,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 6, "id": "cd2a5b1b", "metadata": { "ExecuteTime": { @@ -30,18 +30,18 @@ "output_type": "stream", "text": [ "Loaded HPO v2023-10-09\n", - "Using genophenocorr version 0.1.1dev\n" + "Using gpsea version 0.1.1dev\n" ] } ], "source": [ - "import genophenocorr\n", + "import gpsea\n", "import hpotk\n", "\n", "store = hpotk.configure_ontology_store()\n", "hpo = store.load_minimal_hpo(release='v2023-10-09')\n", "print(f'Loaded HPO v{hpo.version}')\n", - "print(f\"Using genophenocorr version {genophenocorr.__version__}\")" + "print(f\"Using gpsea version {gpsea.__version__}\")" ] }, { @@ -65,7 +65,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 7, "id": "86b84c66", "metadata": { "ExecuteTime": { @@ -80,7 +80,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 8, "id": "71cc23b0", "metadata": {}, "outputs": [ @@ -88,21 +88,20 @@ "name": "stdout", "output_type": "stream", "text": [ - "Patients Created: 100%|██████████| 337/337 [01:35<00:00, 3.54it/s]\n", + "Patients Created: 100%|██████████| 337/337 [07:21<00:00, 1.31s/it]\n", "Validated under none policy\n", - "No errors or warnings were found\n", - "\n" + "No errors or warnings were found\n" ] } ], "source": [ "from ppktstore.registry import configure_phenopacket_registry\n", - "from genophenocorr.preprocessing import configure_caching_cohort_creator, load_phenopackets\n", + "from gpsea.preprocessing import configure_caching_cohort_creator, load_phenopackets\n", "\n", "cohort_creator = configure_caching_cohort_creator(hpo)\n", "\n", "cohort_name = 'ANKRD11'\n", - "phenopacket_store_release = '0.1.18'\n", + "phenopacket_store_release = '0.1.19'\n", "\n", "registry = configure_phenopacket_registry()\n", "with registry.open_phenopacket_store(phenopacket_store_release) as ps:\n", @@ -126,7 +125,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 14, "id": "b3c3c71f", "metadata": {}, "outputs": [ @@ -198,8 +197,8 @@ "\n", "\n", "\n", - "

genophenocorr cohort analysis

\n", - "

Successfully ingested 337 phenopackets.

\n", + "

GPSEA cohort analysis

\n", + "

Successfully loaded 337 individuals.

\n", " \n", "

No errors encountered.

\n", " \n", @@ -213,7 +212,7 @@ " \n", " HPO Term\n", " ID\n", - " Annotation Count\n", + " Seen in n individuals\n", " \n", " \n", " \n", @@ -286,92 +285,92 @@ " \n", " \n", " \n", - " Variant\n", + " Count\n", + " Variant key\n", " Variant Name\n", " Protein Variant\n", " Variant Class\n", - " Variant Count\n", " \n", " \n", " \n", + " 74\n", " SO:1000029_HGNC:21316_ANKRD11\n", " 1000029_HGNC\n", " None\n", " TRANSCRIPT_ABLATION\n", - " 74\n", - " \n", + " \n", " \n", " \n", + " 34\n", " 16_89284634_89284639_GTGTTT_G\n", " c.1903_1907del\n", " p.Lys635GlnfsTer26\n", " FRAMESHIFT_VARIANT\n", - " 34\n", - " \n", + " \n", " \n", " \n", + " 10\n", " 16_89284129_89284134_CTTTTT_C\n", " c.2408_2412del\n", " p.Lys803ArgfsTer5\n", " FRAMESHIFT_VARIANT\n", - " 10\n", - " \n", + " \n", " \n", " \n", + " 9\n", " 16_89284140_89284144_TTTTC_T\n", " c.2398_2401del\n", " p.Glu800AsnfsTer62\n", " FRAMESHIFT_VARIANT\n", - " 9\n", - " \n", + " \n", " \n", " \n", + " 8\n", " 16_89285157_89285161_GTTTC_G\n", " c.1381_1384del\n", " p.Glu461GlnfsTer48\n", " FRAMESHIFT_VARIANT\n", - " 8\n", - " \n", + " \n", " \n", " \n", + " 6\n", " SO:1000037_HGNC:21316_ANKRD11\n", " 1000037_HGNC\n", " None\n", " TRANSCRIPT_AMPLIFICATION\n", - " 6\n", - " \n", + " \n", " \n", " \n", - " 16_89275180_89275180_A_AG\n", - " c.7481dup\n", - " p.Pro2495SerfsTer37\n", - " FRAMESHIFT_VARIANT\n", " 5\n", - " \n", - " \n", - " \n", " 16_89279749_89279749_C_CG\n", " c.6792dup\n", " p.Ala2265ArgfsTer8\n", " FRAMESHIFT_VARIANT\n", - " 5\n", - " \n", + " \n", " \n", " \n", - " 16_89283314_89283318_CCTTT_C\n", - " c.3224_3227del\n", - " p.Glu1075GlyfsTer242\n", + " 5\n", + " 16_89275180_89275180_A_AG\n", + " c.7481dup\n", + " p.Pro2495SerfsTer37\n", " FRAMESHIFT_VARIANT\n", + " \n", + " \n", + " \n", " 3\n", - " \n", + " 16_89282710_89282710_T_A\n", + " c.3832A>T\n", + " p.Lys1278Ter\n", + " STOP_GAINED\n", + " \n", " \n", " \n", + " 3\n", " 16_89284358_89284360_GAT_G\n", " c.2182_2183del\n", " p.Ile728GlnfsTer13\n", " FRAMESHIFT_VARIANT\n", - " 3\n", - " \n", + " \n", " \n", " \n", " \n", @@ -406,23 +405,28 @@ " \n", " \n", " \n", + " TRANSCRIPT_ABLATION\n", + " 74\n", + " \n", + " \n", + " \n", " FRAMESHIFT_VARIANT\n", " 175\n", " \n", " \n", " \n", - " INFRAME_DELETION\n", - " 2\n", + " STOP_GAINED\n", + " 67\n", " \n", " \n", " \n", - " TRANSCRIPT_ABLATION\n", - " 74\n", + " SPLICE_DONOR_VARIANT\n", + " 3\n", " \n", " \n", " \n", - " STOP_GAINED\n", - " 67\n", + " SPLICE_ACCEPTOR_VARIANT\n", + " 4\n", " \n", " \n", " \n", @@ -431,18 +435,18 @@ " \n", " \n", " \n", - " TRANSCRIPT_AMPLIFICATION\n", - " 6\n", + " INFRAME_DELETION\n", + " 2\n", " \n", " \n", " \n", - " SPLICE_DONOR_VARIANT\n", - " 3\n", + " TRANSCRIPT_AMPLIFICATION\n", + " 6\n", " \n", " \n", " \n", - " SPLICE_ACCEPTOR_VARIANT\n", - " 4\n", + " SPLICE_REGION_VARIANT\n", + " 2\n", " \n", " \n", " \n", @@ -450,11 +454,6 @@ " 1\n", " \n", " \n", - " \n", - " SPLICE_REGION_VARIANT\n", - " 2\n", - " \n", - " \n", " \n", " \n", " \n", @@ -472,7 +471,7 @@ } ], "source": [ - "from genophenocorr.view import CohortViewable\n", + "from gpsea.view import CohortViewable\n", "from IPython.display import HTML, display\n", "\n", "viewer = CohortViewable(hpo)\n", @@ -481,7 +480,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 15, "id": "7fd85ef9", "metadata": { "ExecuteTime": { @@ -491,9 +490,9 @@ }, "outputs": [], "source": [ - "from genophenocorr.preprocessing import configure_protein_metadata_service\n", - "from genophenocorr.preprocessing import VVMultiCoordinateService\n", - "from genophenocorr.model.genome import GRCh38\n", + "from gpsea.preprocessing import configure_protein_metadata_service\n", + "from gpsea.preprocessing import VVMultiCoordinateService\n", + "from gpsea.model.genome import GRCh38\n", "\n", "pms = configure_protein_metadata_service()\n", "protein_meta = pms.annotate('NP_037407.4')\n", @@ -503,7 +502,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 16, "id": "b136cbdc", "metadata": { "ExecuteTime": { @@ -513,14 +512,14 @@ }, "outputs": [], "source": [ - "from genophenocorr.view import ProteinVisualizable, ProteinVisualizer, ProteinViewable\n", + "from gpsea.view import ProteinVisualizable, ProteinVisualizer, ProteinViewable\n", "\n", "pvis = ProteinVisualizable(tx_coordinates=tx_coordinates, protein_meta=protein_meta, cohort=cohort)" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 17, "id": "e71bd887", "metadata": {}, "outputs": [ @@ -587,7 +586,7 @@ "\n", "\n", "\n", - "

genophenocorr protein analysis

\n", + "

GPSEA protein analysis

\n", "

The UniProt API successfully returned protein information for ID: NP_037407.4

\n", "

Protein Name: Ankyrin repeat domain-containing protein 11

\n", " \n", @@ -735,7 +734,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 18, "id": "d4ae3460", "metadata": { "ExecuteTime": { @@ -750,13 +749,13 @@ "" ] }, - "execution_count": 8, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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" ] @@ -780,7 +779,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 19, "id": "ec6c204f", "metadata": { "ExecuteTime": { @@ -790,9 +789,9 @@ }, "outputs": [], "source": [ - "from genophenocorr.analysis import configure_cohort_analysis, CohortAnalysisConfiguration\n", - "from genophenocorr.analysis.predicate import PatientCategories\n", - "from genophenocorr.model.genome import Region\n", + "from gpsea.analysis import configure_cohort_analysis, CohortAnalysisConfiguration\n", + "from gpsea.analysis.predicate import PatientCategories\n", + "from gpsea.model.genome import Region\n", "\n", "analysis_config = CohortAnalysisConfiguration()\n", "analysis_config.missing_implies_excluded = False\n", @@ -810,7 +809,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 20, "id": "91809a38", "metadata": { "ExecuteTime": { @@ -910,95 +909,109 @@ "
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...
Abnormality of the head [HP:0000234]153/153Abnormality of the cardiovascular system [HP:0001626]16/16100%74/746/6100%1.0000001.0
Abnormality of the mouth [HP:0000153]138/138Increased inflammatory response [HP:0012649]17/17100%66/667/7100%1.0000001.0
Abnormality of body height [HP:0000002]73/73Abnormality of the digestive system [HP:0025031]35/35100%42/4229/29100%1.0000001.0
Azoospermia [HP:0000027]20/20Abnormality of the outer ear [HP:0000356]37/37100%15/1518/18100%1.0000001.0
Abnormal hair quantity [HP:0011362]36/36Abnormal mandible morphology [HP:0000277]21/21100%17/178/8100%1.0000001.0
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189 rows × 6 columns

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196 rows × 6 columns

\n", "" ], "text/plain": [ - "FRAMESHIFT_VARIANT on NM_013275.6 Yes No \\\n", - " Count Percent Count Percent \n", - "Abnormality of the hand [HP:0001155] 95/144 66% 60/71 85% \n", - "EEG abnormality [HP:0002353] 7/33 21% 9/16 56% \n", - "Feeding difficulties [HP:0011968] 33/89 37% 26/45 58% \n", - "Low anterior hairline [HP:0000294] 40/58 69% 15/30 50% \n", - "Intellectual disability [HP:0001249] 99/119 83% 59/64 92% \n", - "... ... ... ... ... \n", - "Abnormality of the head [HP:0000234] 153/153 100% 74/74 100% \n", - "Abnormality of the mouth [HP:0000153] 138/138 100% 66/66 100% \n", - "Abnormality of body height [HP:0000002] 73/73 100% 42/42 100% \n", - "Azoospermia [HP:0000027] 20/20 100% 15/15 100% \n", - "Abnormal hair quantity [HP:0011362] 36/36 100% 17/17 100% \n", + "FRAMESHIFT_VARIANT on NM_013275.6 Yes No \\\n", + " Count Percent Count \n", + "Abnormality of the hand [HP:0001155] 95/144 66% 60/71 \n", + "EEG abnormality [HP:0002353] 7/33 21% 9/16 \n", + "Feeding difficulties [HP:0011968] 33/89 37% 26/45 \n", + "Low anterior hairline [HP:0000294] 40/58 69% 15/30 \n", + "Intellectual disability [HP:0001249] 99/119 83% 59/64 \n", + "... ... ... ... \n", + "Abnormality of the cardiovascular system [HP:00... 16/16 100% 6/6 \n", + "Increased inflammatory response [HP:0012649] 17/17 100% 7/7 \n", + "Abnormality of the digestive system [HP:0025031] 35/35 100% 29/29 \n", + "Abnormality of the outer ear [HP:0000356] 37/37 100% 18/18 \n", + "Abnormal mandible morphology [HP:0000277] 21/21 100% 8/8 \n", "\n", - "FRAMESHIFT_VARIANT on NM_013275.6 \n", - " p value Corrected p value \n", - "Abnormality of the hand [HP:0001155] 0.005661 1.0 \n", - "EEG abnormality [HP:0002353] 0.022884 1.0 \n", - "Feeding difficulties [HP:0011968] 0.027584 1.0 \n", - "Low anterior hairline [HP:0000294] 0.105274 1.0 \n", - "Intellectual disability [HP:0001249] 0.115195 1.0 \n", - "... ... ... \n", - "Abnormality of the head [HP:0000234] 1.000000 1.0 \n", - "Abnormality of the mouth [HP:0000153] 1.000000 1.0 \n", - "Abnormality of body height [HP:0000002] 1.000000 1.0 \n", - "Azoospermia [HP:0000027] 1.000000 1.0 \n", - "Abnormal hair quantity [HP:0011362] 1.000000 1.0 \n", + "FRAMESHIFT_VARIANT on NM_013275.6 \\\n", + " Percent p value \n", + "Abnormality of the hand [HP:0001155] 85% 0.005661 \n", + "EEG abnormality [HP:0002353] 56% 0.022884 \n", + "Feeding difficulties [HP:0011968] 58% 0.027584 \n", + "Low anterior hairline [HP:0000294] 50% 0.105274 \n", + "Intellectual disability [HP:0001249] 92% 0.115195 \n", + "... ... ... \n", + "Abnormality of the cardiovascular system [HP:00... 100% 1.000000 \n", + "Increased inflammatory response [HP:0012649] 100% 1.000000 \n", + "Abnormality of the digestive system [HP:0025031] 100% 1.000000 \n", + "Abnormality of the outer ear [HP:0000356] 100% 1.000000 \n", + "Abnormal mandible morphology [HP:0000277] 100% 1.000000 \n", "\n", - "[189 rows x 6 columns]" + "FRAMESHIFT_VARIANT on NM_013275.6 \n", + " Corrected p value \n", + "Abnormality of the hand [HP:0001155] 1.0 \n", + "EEG abnormality [HP:0002353] 1.0 \n", + "Feeding difficulties [HP:0011968] 1.0 \n", + "Low anterior hairline [HP:0000294] 1.0 \n", + "Intellectual disability [HP:0001249] 1.0 \n", + "... ... \n", + "Abnormality of the cardiovascular system [HP:00... 1.0 \n", + "Increased inflammatory response [HP:0012649] 1.0 \n", + "Abnormality of the digestive system [HP:0025031] 1.0 \n", + "Abnormality of the outer ear [HP:0000356] 1.0 \n", + "Abnormal mandible morphology [HP:0000277] 1.0 \n", + "\n", + "[196 rows x 6 columns]" ] }, - "execution_count": 10, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "from genophenocorr.model import VariantEffect\n", - "from genophenocorr.analysis.predicate.genotype import VariantPredicates\n", + "from gpsea.model import VariantEffect\n", + "from gpsea.analysis.predicate.genotype import VariantPredicates\n", "\n", "frameshift_predicate = VariantPredicates.variant_effect(VariantEffect.FRAMESHIFT_VARIANT, tx_id=tx_id)\n", "frameshift = analysis.compare_hpo_vs_genotype(frameshift_predicate)\n", @@ -1015,7 +1028,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 21, "id": "67266357", "metadata": { "ExecuteTime": { @@ -1025,19 +1038,18 @@ }, "outputs": [], "source": [ - "from genophenocorr.analysis import configure_cohort_analysis, CohortAnalysisConfiguration\n", - "from genophenocorr.analysis.predicate import PatientCategories\n", + "from gpsea.analysis import configure_cohort_analysis, CohortAnalysisConfiguration\n", + "from gpsea.analysis.predicate import PatientCategories\n", "\n", "analysis_config = CohortAnalysisConfiguration()\n", "analysis_config.missing_implies_excluded = False\n", - "analysis_config.min_perc_patients_w_hpo = 0.2\n", - "analysis_config.heuristic_strategy()\n", + "analysis_config.hpo_mtc_strategy()\n", "hpo_analysis = configure_cohort_analysis(cohort, hpo, config=analysis_config)" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 22, "id": "3d3bdfea", "metadata": { "ExecuteTime": { @@ -1089,7 +1101,7 @@ " 9/16\n", " 56%\n", " 0.022884\n", - " 0.800931\n", + " 0.823815\n", " \n", " \n", " Feeding difficulties [HP:0011968]\n", @@ -1098,7 +1110,7 @@ " 26/45\n", " 58%\n", " 0.027584\n", - " 0.965448\n", + " 0.993032\n", " \n", " \n", " Low anterior hairline [HP:0000294]\n", @@ -1128,6 +1140,15 @@ " 1.000000\n", " \n", " \n", + " Inguinal hernia [HP:0000023]\n", + " 2/31\n", + " 6%\n", + " 4/17\n", + " 24%\n", + " 0.166824\n", + " 1.000000\n", + " \n", + " \n", " Intrauterine growth retardation [HP:0001511]\n", " 6/39\n", " 15%\n", @@ -1308,33 +1329,15 @@ " 1.000000\n", " \n", " \n", - " Global developmental delay [HP:0001263]\n", - " 86/91\n", - " 95%\n", - " 46/48\n", - " 96%\n", - " 1.000000\n", - " 1.000000\n", - " \n", - " \n", - " Attention deficit hyperactivity disorder [HP:0007018]\n", - " 34/44\n", - " 77%\n", - " 19/24\n", + " Long philtrum [HP:0000343]\n", + " 66/82\n", + " 80%\n", + " 38/48\n", " 79%\n", " 1.000000\n", " 1.000000\n", " \n", " \n", - " Febrile seizure (within the age range of 3 months to 6 years) [HP:0002373]\n", - " 3/5\n", - " 60%\n", - " 2/3\n", - " 67%\n", - " 1.000000\n", - " 1.000000\n", - " \n", - " \n", " Strabismus [HP:0000486]\n", " 12/51\n", " 24%\n", @@ -1344,15 +1347,6 @@ " 1.000000\n", " \n", " \n", - " Hypotonia [HP:0001252]\n", - " 14/35\n", - " 40%\n", - " 7/18\n", - " 39%\n", - " 1.000000\n", - " 1.000000\n", - " \n", - " \n", " Anteverted nares [HP:0000463]\n", " 41/58\n", " 71%\n", @@ -1362,6 +1356,15 @@ " 1.000000\n", " \n", " \n", + " Global developmental delay [HP:0001263]\n", + " 86/91\n", + " 95%\n", + " 46/48\n", + " 96%\n", + " 1.000000\n", + " 1.000000\n", + " \n", + " \n", " Prominent nose [HP:0000448]\n", " 12/28\n", " 43%\n", @@ -1371,15 +1374,6 @@ " 1.000000\n", " \n", " \n", - " Long philtrum [HP:0000343]\n", - " 66/82\n", - " 80%\n", - " 38/48\n", - " 79%\n", - " 1.000000\n", - " 1.000000\n", - " \n", - " \n", " Generalized non-motor (absence) seizure [HP:0002121]\n", " 1/4\n", " 25%\n", @@ -1389,6 +1383,33 @@ " 1.000000\n", " \n", " \n", + " Attention deficit hyperactivity disorder [HP:0007018]\n", + " 34/44\n", + " 77%\n", + " 19/24\n", + " 79%\n", + " 1.000000\n", + " 1.000000\n", + " \n", + " \n", + " Febrile seizure (within the age range of 3 months to 6 years) [HP:0002373]\n", + " 3/5\n", + " 60%\n", + " 2/3\n", + " 67%\n", + " 1.000000\n", + " 1.000000\n", + " \n", + " \n", + " Hypotonia [HP:0001252]\n", + " 14/35\n", + " 40%\n", + " 7/18\n", + " 39%\n", + " 1.000000\n", + " 1.000000\n", + " \n", + " \n", " Generalized-onset seizure [HP:0002197]\n", " 8/9\n", " 89%\n", @@ -1409,6 +1430,7 @@ "Low anterior hairline [HP:0000294] 40/58 69% 15/30 \n", "Intellectual disability [HP:0001249] 99/119 83% 59/64 \n", "Delayed skeletal maturation [HP:0002750] 37/86 43% 17/28 \n", + "Inguinal hernia [HP:0000023] 2/31 6% 4/17 \n", "Intrauterine growth retardation [HP:0001511] 6/39 15% 7/23 \n", "Bulbous nose [HP:0000414] 45/71 63% 29/39 \n", "Hypertelorism [HP:0000316] 34/63 54% 25/38 \n", @@ -1429,15 +1451,15 @@ "Protruding ear [HP:0000411] 32/44 73% 16/23 \n", "Autistic behavior [HP:0000729] 26/48 54% 14/24 \n", "Macrodontia [HP:0001572] 115/145 79% 55/68 \n", - "Global developmental delay [HP:0001263] 86/91 95% 46/48 \n", - "Attention deficit hyperactivity disorder [HP:00... 34/44 77% 19/24 \n", - "Febrile seizure (within the age range of 3 mont... 3/5 60% 2/3 \n", + "Long philtrum [HP:0000343] 66/82 80% 38/48 \n", "Strabismus [HP:0000486] 12/51 24% 4/19 \n", - "Hypotonia [HP:0001252] 14/35 40% 7/18 \n", "Anteverted nares [HP:0000463] 41/58 71% 22/30 \n", + "Global developmental delay [HP:0001263] 86/91 95% 46/48 \n", "Prominent nose [HP:0000448] 12/28 43% 5/13 \n", - "Long philtrum [HP:0000343] 66/82 80% 38/48 \n", "Generalized non-motor (absence) seizure [HP:000... 1/4 25% 2/4 \n", + "Attention deficit hyperactivity disorder [HP:00... 34/44 77% 19/24 \n", + "Febrile seizure (within the age range of 3 mont... 3/5 60% 2/3 \n", + "Hypotonia [HP:0001252] 14/35 40% 7/18 \n", "Generalized-onset seizure [HP:0002197] 8/9 89% 4/5 \n", "\n", "FRAMESHIFT_VARIANT on NM_013275.6 \\\n", @@ -1447,6 +1469,7 @@ "Low anterior hairline [HP:0000294] 50% 0.105274 \n", "Intellectual disability [HP:0001249] 92% 0.115195 \n", "Delayed skeletal maturation [HP:0002750] 61% 0.128802 \n", + "Inguinal hernia [HP:0000023] 24% 0.166824 \n", "Intrauterine growth retardation [HP:0001511] 30% 0.202568 \n", "Bulbous nose [HP:0000414] 74% 0.291265 \n", "Hypertelorism [HP:0000316] 66% 0.299330 \n", @@ -1467,24 +1490,25 @@ "Protruding ear [HP:0000411] 70% 0.783007 \n", "Autistic behavior [HP:0000729] 58% 0.804713 \n", "Macrodontia [HP:0001572] 81% 0.856244 \n", - "Global developmental delay [HP:0001263] 96% 1.000000 \n", - "Attention deficit hyperactivity disorder [HP:00... 79% 1.000000 \n", - "Febrile seizure (within the age range of 3 mont... 67% 1.000000 \n", + "Long philtrum [HP:0000343] 79% 1.000000 \n", "Strabismus [HP:0000486] 21% 1.000000 \n", - "Hypotonia [HP:0001252] 39% 1.000000 \n", "Anteverted nares [HP:0000463] 73% 1.000000 \n", + "Global developmental delay [HP:0001263] 96% 1.000000 \n", "Prominent nose [HP:0000448] 38% 1.000000 \n", - "Long philtrum [HP:0000343] 79% 1.000000 \n", "Generalized non-motor (absence) seizure [HP:000... 50% 1.000000 \n", + "Attention deficit hyperactivity disorder [HP:00... 79% 1.000000 \n", + "Febrile seizure (within the age range of 3 mont... 67% 1.000000 \n", + "Hypotonia [HP:0001252] 39% 1.000000 \n", "Generalized-onset seizure [HP:0002197] 80% 1.000000 \n", "\n", "FRAMESHIFT_VARIANT on NM_013275.6 \n", " Corrected p value \n", - "EEG abnormality [HP:0002353] 0.800931 \n", - "Feeding difficulties [HP:0011968] 0.965448 \n", + "EEG abnormality [HP:0002353] 0.823815 \n", + "Feeding difficulties [HP:0011968] 0.993032 \n", "Low anterior hairline [HP:0000294] 1.000000 \n", "Intellectual disability [HP:0001249] 1.000000 \n", "Delayed skeletal maturation [HP:0002750] 1.000000 \n", + "Inguinal hernia [HP:0000023] 1.000000 \n", "Intrauterine growth retardation [HP:0001511] 1.000000 \n", "Bulbous nose [HP:0000414] 1.000000 \n", "Hypertelorism [HP:0000316] 1.000000 \n", @@ -1505,19 +1529,19 @@ "Protruding ear [HP:0000411] 1.000000 \n", "Autistic behavior [HP:0000729] 1.000000 \n", "Macrodontia [HP:0001572] 1.000000 \n", - "Global developmental delay [HP:0001263] 1.000000 \n", - "Attention deficit hyperactivity disorder [HP:00... 1.000000 \n", - "Febrile seizure (within the age range of 3 mont... 1.000000 \n", + "Long philtrum [HP:0000343] 1.000000 \n", "Strabismus [HP:0000486] 1.000000 \n", - "Hypotonia [HP:0001252] 1.000000 \n", "Anteverted nares [HP:0000463] 1.000000 \n", + "Global developmental delay [HP:0001263] 1.000000 \n", "Prominent nose [HP:0000448] 1.000000 \n", - "Long philtrum [HP:0000343] 1.000000 \n", "Generalized non-motor (absence) seizure [HP:000... 1.000000 \n", + "Attention deficit hyperactivity disorder [HP:00... 1.000000 \n", + "Febrile seizure (within the age range of 3 mont... 1.000000 \n", + "Hypotonia [HP:0001252] 1.000000 \n", "Generalized-onset seizure [HP:0002197] 1.000000 " ] }, - "execution_count": 12, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -1529,7 +1553,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 26, "id": "ab230116d2781d74", "metadata": { "ExecuteTime": { @@ -1543,13 +1567,13 @@ "analysis_config.missing_implies_excluded = False\n", "analysis_config.min_perc_patients_w_hpo = 0.2\n", "terms_to_test = {\"HP:0001249\", \"HP:0001263\"} # Intellectual disability HP:0001249 and Global developmental delay HP:0001263 \n", - "analysis_config.specify_terms_strategy(specified_term_set=terms_to_test)\n", + "analysis_config.specify_terms_strategy(terms_to_test)\n", "specific_terms_analysis = configure_cohort_analysis(cohort, hpo, config=analysis_config)\n" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 27, "id": "3c68e2aa7d3aa7e8", "metadata": { "ExecuteTime": { @@ -1628,7 +1652,7 @@ "Global developmental delay [HP:0001263] 1.000000 1.000000 " ] }, - "execution_count": 14, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -1638,100 +1662,6 @@ "frameshift.summarize(hpo, PatientCategories.YES)" ] }, - { - "cell_type": "code", - "execution_count": 15, - "id": "eb0dc510", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - " \n", - " Cohort\n", - " \n", - "\n", - "\n", - "\n", - "

Statistical analysis: bonferroni (specified terms filter)

\n", - "

Performed statistical tests for 2 out of the total of 189 HPO terms.

\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Using specified terms filter, 187 term(s) were omitted from statistical analysis.
ReasonCount
Skipping non-specified term187
\n", - "\n", - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from genophenocorr.view import StatsViewer\n", - "\n", - "sv = StatsViewer()\n", - "\n", - "stats_report = sv.process(frameshift.mtc_filter_report)\n", - "\n", - "display(HTML(stats_report))" - ] - }, { "cell_type": "markdown", "id": "7a83a5ca", @@ -1742,7 +1672,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 28, "id": "57dd0e23", "metadata": {}, "outputs": [ @@ -1821,7 +1751,7 @@ "Global developmental delay [HP:0001263] 1.000000 " ] }, - "execution_count": 16, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -1842,7 +1772,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 29, "id": "0dfa0a50", "metadata": {}, "outputs": [ @@ -1916,7 +1846,7 @@ "Global developmental delay [HP:0001263] 0.505131 1.00000 " ] }, - "execution_count": 17, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } @@ -1929,7 +1859,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 30, "id": "1079dcdd", "metadata": {}, "outputs": [ @@ -2023,13 +1953,13 @@ "Global developmental delay [HP:0001263] 1.0 " ] }, - "execution_count": 18, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "from genophenocorr.analysis.predicate.genotype import ProteinPredicates\n", + "from gpsea.analysis.predicate.genotype import ProteinPredicates\n", "\n", "\n", "pp = ProteinPredicates(protein_metadata_service=pms)\n", @@ -2041,7 +1971,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 31, "id": "650371ba", "metadata": {}, "outputs": [ @@ -2135,7 +2065,7 @@ "Intellectual disability [HP:0001249] 1.0 " ] }, - "execution_count": 19, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } @@ -2153,13 +2083,19 @@ "source": [ "TODO - finalize!" ] + }, + { + "cell_type": "markdown", + "id": "3881b458", + "metadata": {}, + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "venv", + "display_name": "enviro", "language": "python", - "name": "python3" + "name": "enviro" }, "language_info": { "codemirror_mode": { @@ -2171,7 +2107,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.3" + "version": "3.10.10" } }, "nbformat": 4, diff --git a/notebooks/ANKRD11/cohort.ipynb b/notebooks/ANKRD11/cohort.ipynb index 29aa36b1..e09d3125 100644 --- a/notebooks/ANKRD11/cohort.ipynb +++ b/notebooks/ANKRD11/cohort.ipynb @@ -20,13 +20,13 @@ } ], "source": [ - "import genophenocorr\n", + "import gpsea\n", "import hpotk\n", "from IPython.display import display, HTML\n", "store = hpotk.configure_ontology_store()\n", "hpo = store.load_minimal_hpo(release='v2023-10-09')\n", "print(f'Loaded HPO v{hpo.version}')\n", - "print(f\"Using genophenocorr version {genophenocorr.__version__}\")" + "print(f\"Using gpsea version {gpsea.__version__}\")" ] }, { @@ -452,7 +452,7 @@ ], "source": [ "ANKRD11_id = 'NM_013275.6' # MANE select\n", - "from genophenocorr.preprocessing import configure_caching_cohort_creator, load_phenopacket_folder\n", + "from gpsea.preprocessing import configure_caching_cohort_creator, load_phenopacket_folder\n", "\n", "fpath_phenopackets = r'phenopackets'\n", "cohort_creator = configure_caching_cohort_creator(hpo, timeout=20)\n", @@ -463,21 +463,21 @@ "cell_type": "code", "execution_count": 3, "metadata": { - "collapsed": false, "ExecuteTime": { "end_time": "2024-05-28T15:20:49.653969Z", "start_time": "2024-05-28T15:20:49.280822200Z" - } + }, + "collapsed": false }, "outputs": [], "source": [ "protein_id = 'NP_001027558.1'\n", - "from genophenocorr.preprocessing import UniprotProteinMetadataService\n", + "from gpsea.preprocessing import UniprotProteinMetadataService\n", "pms = UniprotProteinMetadataService()\n", "protein_metas = pms.annotate(protein_id)\n", "\n", - "from genophenocorr.model.genome import GRCh38\n", - "from genophenocorr.preprocessing import VVTranscriptCoordinateService" + "from gpsea.model.genome import GRCh38\n", + "from gpsea.preprocessing import VVTranscriptCoordinateService" ] }, { @@ -494,7 +494,7 @@ "txc_service = VVTranscriptCoordinateService(genome_build=GRCh38)\n", "tx_coordinates = txc_service.fetch(ANKRD11_id)\n", "protein_id = 'NP_037407.4' \n", - "from genophenocorr.preprocessing import UniprotProteinMetadataService\n", + "from gpsea.preprocessing import UniprotProteinMetadataService\n", "pms = UniprotProteinMetadataService()\n", "protein_metas = pms.annotate(protein_id)" ] @@ -510,7 +510,7 @@ }, "outputs": [], "source": [ - "from genophenocorr.view import ProteinVisualizable\n", + "from gpsea.view import ProteinVisualizable\n", "protein_meta = protein_metas[0]\n", "pvis = ProteinVisualizable(tx_coordinates=tx_coordinates, protein_meta=protein_meta, cohort=cohort)" ] @@ -527,15 +527,17 @@ "outputs": [ { "data": { - "text/plain": "
", - "image/png": 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+ "text/plain": [ + "
" + ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ - "from genophenocorr.view import ProteinVisualizer\n", + "from gpsea.view import ProteinVisualizer\n", "drawer = ProteinVisualizer()\n", "# TODO @peterrobinson: change labeling_method between enumerate and abbreviate\n", "drawer.draw_fig(pvis=pvis, labeling_method='enumerate', legend_x=0.6)\n", @@ -558,10 +560,10 @@ "evalue": "name 'variant_locations' is not defined", "output_type": "error", "traceback": [ - "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m", - "\u001B[1;31mNameError\u001B[0m Traceback (most recent call last)", - "Cell \u001B[1;32mIn[7], line 2\u001B[0m\n\u001B[0;32m 1\u001B[0m \u001B[38;5;28;01mimport\u001B[39;00m \u001B[38;5;21;01mnumpy\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m \u001B[38;5;21;01mnp\u001B[39;00m\n\u001B[1;32m----> 2\u001B[0m variant_locations_counted_absolute, marker_counts \u001B[38;5;241m=\u001B[39m np\u001B[38;5;241m.\u001B[39munique(\u001B[43mvariant_locations\u001B[49m, axis\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m0\u001B[39m, return_counts\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mTrue\u001B[39;00m)\n", - "\u001B[1;31mNameError\u001B[0m: name 'variant_locations' is not defined" + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[7], line 2\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m variant_locations_counted_absolute, marker_counts \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39munique(\u001b[43mvariant_locations\u001b[49m, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m, return_counts\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n", + "\u001b[1;31mNameError\u001b[0m: name 'variant_locations' is not defined" ] } ], @@ -573,9 +575,9 @@ ], "metadata": { "kernelspec": { - "display_name": "gpc_venv", + "display_name": "enviro", "language": "python", - "name": "python3" + "name": "enviro" }, "language_info": { "codemirror_mode": { @@ -587,7 +589,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.6" + "version": "3.10.10" } }, "nbformat": 4, diff --git a/notebooks/FBN1/FBN1_gpc.ipynb b/notebooks/FBN1/FBN1_gpc.ipynb index 90ee5135..e2efef4f 100644 --- a/notebooks/FBN1/FBN1_gpc.ipynb +++ b/notebooks/FBN1/FBN1_gpc.ipynb @@ -25,7 +25,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "id": "aece3db4976b00c4", "metadata": { "is_executing": true @@ -36,18 +36,18 @@ "output_type": "stream", "text": [ "Loaded HPO v2023-10-09\n", - "Using genophenocorr version 0.1.1dev\n" + "Using gpsea version 0.2.1.dev0\n" ] } ], "source": [ - "import genophenocorr\n", + "import gpsea\n", "import hpotk\n", "\n", "store = hpotk.configure_ontology_store()\n", "hpo = store.load_minimal_hpo(release='v2023-10-09')\n", "print(f'Loaded HPO v{hpo.version}')\n", - "print(f\"Using genophenocorr version {genophenocorr.__version__}\")" + "print(f\"Using gpsea version {gpsea.__version__}\")" ] }, { @@ -60,7 +60,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "id": "c3994827", "metadata": {}, "outputs": [], @@ -81,7 +81,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 5, "id": "868726f3", "metadata": {}, "outputs": [ @@ -89,16 +89,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "Patients Created: 100%|██████████| 151/151 [00:00<00:00, 530.83it/s]\n", + "Patients Created: 100%|██████████| 151/151 [00:00<00:00, 468.27it/s]\n", "Validated under none policy\n", - "No errors or warnings were found\n", - "\n" + "No errors or warnings were found\n" ] } ], "source": [ "from ppktstore.registry import configure_phenopacket_registry\n", - "from genophenocorr.preprocessing import configure_caching_cohort_creator, load_phenopackets\n", + "from gpsea.preprocessing import configure_caching_cohort_creator, load_phenopackets\n", "\n", "cohort_creator = configure_caching_cohort_creator(hpo)\n", "\n", @@ -119,7 +118,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 6, "id": "52052bbd", "metadata": {}, "outputs": [ @@ -191,8 +190,8 @@ "\n", "\n", "\n", - "

genophenocorr cohort analysis

\n", - "

Successfully ingested 151 phenopackets.

\n", + "

GPSEA cohort analysis

\n", + "

Successfully loaded 151 individuals.

\n", " \n", "

No errors encountered.

\n", " \n", @@ -206,7 +205,7 @@ " \n", " HPO Term\n", " ID\n", - " Annotation Count\n", + " Seen in n individuals\n", " \n", " \n", " \n", @@ -264,8 +263,8 @@ " \n", " \n", " \n", - " Joint hypermobility\n", - " HP:0001382\n", + " Tall stature\n", + " HP:0000098\n", " 29\n", " \n", " \n", @@ -279,92 +278,92 @@ " \n", " \n", " \n", - " Variant\n", + " Count\n", + " Variant key\n", " Variant Name\n", " Protein Variant\n", " Variant Class\n", - " Variant Count\n", " \n", " \n", " \n", + " 7\n", " 15_48505069_48505069_C_T\n", " c.1916G>A\n", " p.Cys639Tyr\n", " MISSENSE_VARIANT\n", - " 7\n", - " \n", + " \n", " \n", " \n", + " 7\n", " 15_48508660_48508660_A_G\n", " c.1759T>C\n", " p.Cys587Arg\n", " MISSENSE_VARIANT\n", - " 7\n", - " \n", + " \n", " \n", " \n", - " 15_48460258_48460258_C_T\n", - " c.5284G>A\n", - " p.Gly1762Ser\n", - " MISSENSE_VARIANT\n", " 6\n", - " \n", + " 15_48474584_48474584_C_T\n", + " c.4031G>A\n", + " p.Gly1344Glu\n", + " MISSENSE_VARIANT\n", + " \n", " \n", " \n", + " 6\n", " 15_48425483_48425483_C_T\n", " c.7339G>A\n", " p.Glu2447Lys\n", " MISSENSE_VARIANT\n", - " 6\n", - " \n", + " \n", " \n", " \n", - " 15_48474584_48474584_C_T\n", - " c.4031G>A\n", - " p.Gly1344Glu\n", - " MISSENSE_VARIANT\n", " 6\n", - " \n", - " \n", - " \n", " 15_48463210_48463210_T_C\n", " c.5096A>G\n", " p.Tyr1699Cys\n", " MISSENSE_VARIANT\n", + " \n", + " \n", + " \n", " 6\n", - " \n", + " 15_48460258_48460258_C_T\n", + " c.5284G>A\n", + " p.Gly1762Ser\n", + " MISSENSE_VARIANT\n", + " \n", " \n", " \n", + " 4\n", " 15_48412568_48412568_C_A\n", " c.8226+1G>T\n", " None\n", " SPLICE_DONOR_VARIANT\n", - " 4\n", - " \n", + " \n", " \n", " \n", + " 3\n", " 15_48460292_48460292_A_C\n", " c.5250T>G\n", " p.Ser1750Arg\n", " MISSENSE_VARIANT\n", - " 3\n", - " \n", + " \n", " \n", " \n", + " 3\n", " 15_48467994_48467994_C_G\n", " c.4691G>C\n", " p.Cys1564Ser\n", " MISSENSE_VARIANT\n", - " 3\n", - " \n", + " \n", " \n", " \n", + " 3\n", " 15_48613073_48613073_G_A\n", " c.184C>T\n", " p.Arg62Cys\n", " MISSENSE_VARIANT\n", - " 3\n", - " \n", + " \n", " \n", " \n", " \n", @@ -429,11 +428,6 @@ " \n", " \n", " \n", - " STOP_GAINED\n", - " 4\n", - " \n", - " \n", - " \n", " MISSENSE_VARIANT\n", " 119\n", " \n", @@ -444,43 +438,48 @@ " \n", " \n", " \n", + " FRAMESHIFT_VARIANT\n", + " 12\n", + " \n", + " \n", + " \n", " SPLICE_DONOR_VARIANT\n", " 9\n", " \n", " \n", " \n", - " FRAMESHIFT_VARIANT\n", - " 12\n", + " INFRAME_INSERTION\n", + " 1\n", " \n", " \n", " \n", - " SPLICE_DONOR_5TH_BASE_VARIANT\n", - " 1\n", + " STOP_GAINED\n", + " 4\n", " \n", " \n", " \n", - " INTRON_VARIANT\n", - " 2\n", + " INFRAME_DELETION\n", + " 1\n", " \n", " \n", " \n", - " INFRAME_INSERTION\n", + " SPLICE_DONOR_5TH_BASE_VARIANT\n", " 1\n", " \n", " \n", " \n", - " SPLICE_ACCEPTOR_VARIANT\n", + " INTRON_VARIANT\n", " 2\n", " \n", " \n", " \n", - " INFRAME_DELETION\n", + " SPLICE_DONOR_REGION_VARIANT\n", " 1\n", " \n", " \n", " \n", - " SPLICE_DONOR_REGION_VARIANT\n", - " 1\n", + " SPLICE_ACCEPTOR_VARIANT\n", + " 2\n", " \n", " \n", " \n", @@ -505,7 +504,7 @@ } ], "source": [ - "from genophenocorr.view import CohortViewable\n", + "from gpsea.view import CohortViewable\n", "from IPython.display import HTML, display\n", "\n", "viewer = CohortViewable(hpo)\n", @@ -514,7 +513,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 7, "id": "6dfa78ba", "metadata": { "ExecuteTime": { @@ -522,319 +521,658 @@ "start_time": "2024-07-04T12:32:21.276438900Z" } }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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Avr4+tLW10bBhQxw9evSj4gay93M5cOAApkyZgvHjx+PYsWNo166dUuIsPj4e7u7uqFu3LlasWIHWrVsjNTUVrq6u2LRpE/r374/FixejbNmy8PHxEZOG5ubm4nupe/fu4nu0R48eAIDbt2+jSZMmCA8Px9SpU7F06VLo6emhW7du2LNnT4Hxv379Gh07dhT3FKlRowamTJmCQ4cOKdQbNGhQvstPfUgqlWLGjBm4fv26WnGo8vDhQwBQSBrkJ+d9+OH71NnZGRoaGuJxuVyOGzdu5Pl+fvjwoZisU7fN4vDixQsAgJmZmViWnp6uMjGoq6uLjIwMtfcqKehnjIiIiIg+D5qlHQARERHRv1nt2rVRv359bNu2Dd26dVM5A+DevXu4efMmHB0dFcrv37+vMFA3duxY1K9fH8uWLVMayA8PD8edO3dgZ2cHAGjdujXq1KmDbdu2iRuaHzhwAMOGDVPYNH3y5Mni11lZWRgxYgQsLCxw7do1GBkZicc+vFM9PT0dvXv3xoIFC9S6Dg8ePEBERAQqVaoEAOjYsSMaN26MhQsXYtmyZWjatCmqVauGY8eOYcCAAWq1WZCWLVuidevWWLx4MUaNGlXgbAiJRAIvLy9s27YNs2fPBgBs2bIFPXr0gEwmK3T/z58/B/C/2Ri5WVhYICEhAenp6ZDJZHj+/Lm4gfuH9QDg2bNnhW6zKJo0aSLu16Cvr48ZM2ZgyJAh4vEyZcpg165d6NevHzw8PMRyZ2dnnDt3TuE9o4qGhgY6dOiA7t27o1KlSnj06BGWLVsGd3d3/P7770VKUOVISEhAeHi4ONOgfv368PT0xE8//YTx48eL9V68eIG1a9dixIgRYtnKlSsRHh6OzZs3i7OnRo4cCRcXF8yYMQODBw+GgYEBevXqhVGjRqF27dpK79MJEybAysoKYWFh4vUfPXo0WrRogSlTpqB79+75xv/s2TNs3LgRAwcOBAAMGTIE1tbW2LBhA9zd3Yt8XYDs2VRz5szB7Nmz0b17d6X32YdyNox/8+YNduzYgb1796J27dqoXr26Wv09f/4cUqkU5cqVUyjX0tKCqamp+H7Oeb/m9X4Gsq9L9erV1W7zYyUkJODnn39Gy5YtFeKqXr06Lly4gKysLEilUgBARkYGLl68CABqJ3UL+hkjIiIios8DZ5wQERERfSQXFxelpAmguOzR69ev8ebNG7Rs2VJpaS0ge6mmnKQJkJ2wMTQ0VFhqxsjICBcvXsxzgPHq1auIjIzExIkTlQbAVQ20jho1qsDXlqNbt25i0gTIvpu8cePGOHjwoFrnBwcHQxCEQi895e/vLw6Uq6Nfv3548OABwsLCxH+LukxXzkwHVUkMbW1thTqpqalq11O3zaIICgrC4cOHERgYCAcHB6SmpiIrK0uhjrGxMerWrYupU6di7969WLJkCaKiotC7d2+kpaXl276VlRWOHDmCkSNHomvXrpgwYQKuXr0Kc3NzfPPNN0WOG8ieiZF7eaZevXrBwsJC6T0mk8ng6+urUHbw4EFUqFBBnB0GZCeJxo8fj3fv3uH06dP59p2QkICTJ0/C09MTSUlJiIuLQ1xcHOLj4+Hm5oaIiIgCB9b19fUVkjFaWlpo1KiRws8wkL3UV2GX3Mo962Tv3r351k1OToa5uTnMzc1hb2+P6dOno2nTpoWarZKamgotLS2Vx7S1tYv0fla3zY8hl8vRv39/JCYmYtWqVQrHRo8ejfv372PIkCG4c+cObt26hUGDBonJTHX7V+dnjIiIiIj++zjjhIiIiOgj2draqizfv38/5s6di2vXriE9PV0sV5XEyL3fRQ5jY2O8fv1afL5o0SJ4e3ujcuXKcHZ2RqdOnTBo0CBUqVIFwP+W43FyciowZk1NTVhaWhZYL0fVqlWVylTtyVHcWrVqhdatW2PRokUYOXJkgfXr1auHGjVqYOvWrTAyMkKFChXQpk2bIvWdk/jK/b3LkZNgyKmjo6Ojdj112yyKpk2bil97eXmJS0ItWbIEAMTk3bfffquQ6GjQoAFcXV0RFBRUqIQakL0fha+vL77//ns8ffq0UO+r3D58j0kkEtjb2yvty1GpUiWlAfjo6GhUrVoVGhqK94XlvP7o6Oh8+37w4AEEQcDMmTMxc+ZMlXVevXqlkDz8kKWlpdLPtrGxMW7cuJFv3+rq37+/OOukW7duedbT1tbGH3/8ASA7oWFra1vo74mOjk6e+9ekpaUV6f2sbpsfY9y4cTh8+DA2btyIOnXqKBwbOXIknjx5gsWLF4t73zRo0ACTJ0/GvHnzoK+vr1YfBf2MEREREdHngTNOiIiIiD6SqgG/s2fPwsPDA9ra2ggMDMTBgwdx7Ngx9OvXT+Xd5jlLx3wod11PT088evQIq1atQsWKFbF48WLUrFlTaQ8FdchkMqVB5n8rPz8/vHjxAuvWrVOrfr9+/bB9+3Zs3boVffr0KfLrzFnmJ+eO9NyeP38OExMT8U57CwsLvHjxQul7m3NuxYoVC93mxzI2NkabNm2wZcsWsWzXrl14+fKlwjJdQPasKUNDQ/z1119F6qty5coAsmdufGrFMcD+IblcDgCYNGkSjh07pvJhb2+fbxvq/Ax/jJxZJ9euXcO+ffvyrdeuXTu0a9cOLVu2LFIiy8LCAllZWXj16pVCeUZGBuLj48X3c877Na/3M6D43lenzaIKCAhAYGAgvv/+e3G5tA/NmzcPL1++xNmzZ3Hjxg2EhYWJ3/tq1aoVuk9VP2NERERE9Hn4b3xaJiIiIipFBe0noMquXbugra2NI0eOYPDgwXB3d0e7du0+OhYLCwuMHj0ae/fuRWRkJExNTTFv3jwAEJf6UneT48KIiIhQKrt//77C0ltFuU7qcHFxgaurKxYuXKjWcjr9+vXD8+fPcf/+/SIv0wVkz2wwNzfH33//rXTs0qVLqFu3rvi8bt26SElJQXh4uEK9nP0TcuoWps3ikJqaijdv3ojPX758CQBKSwsJgoCsrCxkZmYWqZ+c5ajMzc2LGKnye0wQBDx48ECt5d2sra0REREhDoLnuHv3rngcyPs9mjNrq0yZMmLS4cNH7mXESsuAAQNgb2+PgICAYkvIqJLzPvzwffr3339DLpeLxzU0NFCrVi2V7+eLFy+iSpUq4nVTt82i+PHHH+Hv74+JEydiypQp+dY1NjZGixYtUKtWLQDA8ePHYWlpiRo1ahSp7w9/xoiIiIjo88DECREREVEB9PT0AACJiYlqnyOVSiGRSBQGqKOiogrcnyAvWVlZSoNz5cqVQ8WKFcVlcurXrw9bW1usWLFCKdaPHWTdu3evwh4Ply5dwsWLFxU2vc7vOj1//hx3797F+/fvi9R/zl4n69evL7CunZ0dVqxYgQULFqBRo0ZF6i9Hz549sX//fjx58kQsO3HiBO7fv4/evXuLZV999RXKlCmDwMBAsUwQBKxduxaVKlVCs2bNCt1mYXx4Fz+Q/X47ceIEGjRoIJbl3FX/22+/KdT9/fffkZycjHr16ollb968wd27dxXed7GxsUr9xMTE4JdffkHt2rVVbhKuro0bNyIpKUl8vnPnTjx//lytjdU7deqEFy9eYPv27WJZZmYmVq1aBX19fbi4uAAAdHV1ASi/R8uVKwdXV1esW7dO5ewJVa+7qB4/fiwmdAor96yT33//vdhi+lCbNm1gYmKCNWvWKJSvWbMGurq66Ny5s1jWq1cvhIWFKSRE7t27h5MnTyq8nwvTZmFs374d48ePR//+/bFs2bJCnxsWFoaJEycqzExT9T1S92eMiIiIiD4P3OOEiIiIqADOzs4AgO+++w5eXl4oU6YMunbtKiYKVOncuTOWLVuGjh07ol+/fnj16hV+/PFH2NvbF2nPg6SkJFhaWqJXr16oU6cO9PX1cfz4cYSFhWHp0qUAsu/+XrNmDbp27Yq6devC19cXFhYWuHv3Lm7fvo0jR44U7QIAsLe3R4sWLTBq1Cikp6djxYoVMDU1xeTJk8U6Oddp/PjxcHNzg1QqhZeXFwBg2rRp+PXXXxEZGVnoDeKB7FknLi4uBW7ynWPChAmF7kOV6dOnIyQkBK1bt8aECRPw7t07LF68GLVq1VLYoNzS0hITJ07E4sWL8f79ezRs2BB79+7F2bNnsWXLFoVlnNRtEwA2bdqE6OhopKSkAADOnDmDuXPnAgAGDhwozqSoVasW2rZti7p168LY2BgRERHYsGED3r9/j++//15sr2vXrqhZsyZmz56N6OhoNGnSBA8ePMDq1athYWGBIUOGiHX37NkDX19fBAUFwcfHBwAwefJkPHz4EG3btkXFihURFRWFdevWITk5GStXrlSIPTg4WOn8/JiYmKBFixbw9fXFy5cvsWLFCtjb22PYsGEFnjt8+HCsW7cOPj4+uHz5MmxsbLBz50789ddfWLFihTjrQUdHB46Ojti+fTuqVasGExMTODk5wcnJCT/++KM4E2HYsGGoUqUKXr58ifPnz+Pp06e4fv16gXGoY9CgQTh9+nSRk5k5e51cu3atWOJRRUdHB3PmzMGYMWPQu3dvuLm54ezZs9i8eTPmzZsHExMTse7o0aPx008/oXPnzpg0aRLKlCmDZcuWoXz58gr76BSmzTdv3oibu+csH7d69WoYGRnByMgIY8eOBZCdwB00aBBMTU3Rtm1bpSWzmjVrJs4mOnPmDGbPno0OHTrA1NQUFy5cQFBQEDp27Kj0+0LV90jdnzEiIiIi+kwIRERERFSgOXPmCJUqVRI0NDQEAEJkZKQgCIIAQBgzZozKczZs2CBUrVpVkMlkQo0aNYSgoCDBz89P+PBPsLzasLa2Fry9vQVBEIT09HTh22+/FerUqSMYGBgIenp6Qp06dYTAwECl8/7880+hffv2Yr3atWsLq1atEo97e3sLenp6KmP29vYWrK2txeeRkZECAGHx4sXC0qVLhcqVKwsymUxo2bKlcP36dYVzMzMzhXHjxgnm5uaCRCJReJ3e3t4K1y0/eV2P0NBQAYAAQAgLCxPLc65pbGxskdotyK1bt4QOHToIurq6gpGRkdC/f3/hxYsXSvWysrKE+fPnC9bW1oKWlpZQs2ZNYfPmzR/VpouLi/iaP3yEhoaK9fz8/IQGDRoIxsbGgqamplCxYkXBy8tLuHHjhlKbCQkJwtdffy1Uq1ZNkMlkgpmZmeDl5SU8evRIoV5QUJAAQAgKChLLtm7dKrRq1UowNzcXNDU1BTMzM6F79+7C5cuXlfpZtWqVAEA4fPhwXpdWEIT/fV+3bdsmTJs2TShXrpygo6MjdO7cWYiOjla6HjVr1lTZzsuXLwVfX1/BzMxM0NLSEmrVqqUQe45z584Jzs7OgpaWlgBA8PPzE489fPhQGDRokFChQgWhTJkyQqVKlYQuXboIO3fuVIo39/XPK64Pf55y6qrzMSz3z96Hcr43H77v8/vZLor169cL1atXF7S0tAQ7Ozth+fLlglwuV6r35MkToVevXoKhoaGgr68vdOnSRYiIiChymzmvXdUj9/XMfR1UPXJ//x88eCB06NBBMDMzE38nL1iwQEhPT1eKUdX3qDA/Y0RERET03ycRhE+4OC4RERER/adFRUXB1tYWixcvxqRJk0o7HPoP8fT0RFRUFC5dupRvvVOnTqF169YICQlBr169Sig6IiIiIiKivHGpLiIiIiIiKlaCIODUqVPYvHlzaYdCRERERERUaEycEBEREdEXJzY2FllZWXke19LSUthzgQpHIpGo3EybSldGRgYSEhLyrVO2bFno6OiUUERERERERP9OTJwQERER0RenYcOGiI6OzvO4i4sLTp06VXIBEZWAc+fOoXXr1vnWCQoKgo+PT8kERERERET0L8U9ToiIiIjoi/PXX38hNTU1z+PGxsZwdnYuwYiIPr3Xr1/j8uXL+dapWbMmLCwsSigiIiIiIqJ/JyZOiIiIiIiIiIiIiIiI/qFR2gEQERERERERERERERH9WzBxQkRERERERERERERE9A9uDk/F5vHjx3BwcEBKSkpph0JE/3G6uroIDw+HlZVVaYdCRERERET/ARyTICLKxjGV4sHECRWbuLg4pKSkYPPmzXBwcCjtcIjoPyo8PBwDBgxAXFwc/5MnIiIiIiK1cEyCiIhjKsWJiRMqdg4ODqhfv35ph0FEREREREREXxiOSRARUXHgHidERERERERERERERET/YOKEiIiIiIiIiIiIiIjoH0yc0BdLIpHA39+/tMP44ri6usLV1bW0wyAiIiIiIiKi/5Dg4GBIJBJERUV9sj6ioqIgkUgQHBxcpPNLIsaPderUKUgkEpw6daq0QyH6V+MeJ1Qqnj9/jp9//hmhJ04gKfEN9AwM0LhZU4wcORK2trZqtxMcHAxfX18AwNmzZ9GiRQuF44IgwMrKCk+fPkXnzp2xf//+Yn0d/3VXrlyBs7MzvvvuO8ydO1dlnYiICFSrVg1ff/01li1bVsIRFs3Bgwdx6dIlJsaIiIiIiIgIAPD48WPExcWVdhhFZmZmptZGzw8fPsSiRYtw7NgxPHv2DFpaWqhVqxY8PT0xfPhw6Ojo5HluSkoKFi1axBseiYjAxAmVsKSkJIwZPRrbtm2DliCBm1wfdtDEW2Rh/fmLWLx4Mbp06oSfNmxA+fLl1W5XW1sbW7duVUqcnD59Gk+fPoVMJlM6JzU1FZqaX/aPQP369VGjRg1s27Ytz8TJ1q1bAQADBgwolj6PHj1aLO3k5+DBg/jxxx+ZOCEiIiIiIiI8fvwYNWrUQGpqammHUmQ6Ojq4e/duvsmTAwcOoHfv3pDJZBg0aBCcnJyQkZGBP//8E99++y1u376N9evX53l+SkoKAgICAOBfmTgZOHAgvLy8VI7xkPpatWqF1NRUaGlplXYoRP9qX/aoMZWoxMREtGnlgod3wrEkywLeMIVRrrdgSpYc25CAGYdPoGnDRjhz7i9YWlqq1XanTp0QEhKCH374QSEZsnXrVjg7O6u8q0RbW/vjX9RnoH///pg5cyYuXLiAJk2aKB3ftm0batSogfr1639UPykpKdDV1eV/zERERERERFSi4uLikJqairFjx6JSpUpieVJSEkJDQ3H37l28ffsW+vr6sLCwQPPmzWFvb6/UzuvXr7Fo0SKMGzcOFStWLLH4Y2JisHr1asTFxeWZOImMjISXlxesra1x8uRJWFhYiMfGjBmDBw8e4MCBAyUVsoLk5GTo6el9dDtSqRRSqbQYIvqyaWhocEyMSA3c44RKjFdvT0TduYuzWfaYgPIKSRMA0IUGhsAMF7Lskfk8Fl06uiMzM1Ottvv27Yv4+HgcO3ZMLMvIyMDOnTvRr18/led8uMdJUlISJk6cCBsbG8hkMpQrVw7t27fHlStXxDoRERHo2bMnKlSoAG1tbVhaWsLLywtv3rxRaHvz5s1wdnaGjo4OTExM4OXlhSdPnijUcXV1hZOTE+7cuYPWrVtDV1cXlSpVwqJFi5RiXbVqFWrWrAldXV0YGxujQYMG4kyQHDExMRg8eDDKly8PmUyGmjVr4pdffinw2vXv3x8AlNoDgMuXL+PevXtinX379qFz586oWLEiZDIZ7OzsMGfOHGRlZal8bZcvX0arVq2gq6uL6dOni8dy37mSkZGBWbNmwdnZGWXLloWenh5atmyJ0NBQhTZz1hldsmQJ1q9fDzs7O8hkMjRs2BBhYWFiPR8fH/z4448Asr/HOQ8iIiIiIiL6slWqVAlVqlRBlSpVoK+vjzVr1uDJkyfw9fXFkiVLMHPmTDRs2BCHDh0S6+V+VK5cGQBgaWmp8nhhHlZWVmrXzZ3sycuiRYvw7t07bNiwQSFpksPe3h4TJkzI8/yoqCiYm5sDAAICAsTP0rnHTe7evYtevXrBxMQE2traaNCgAX7//XeFdnL2+Dh9+jRGjx6NcuXKiTfF5owV3LhxAy4uLtDV1YW9vT127twJIHvVkMaNG0NHRwfVq1fH8ePHVbade/8QGxsbdOnSBX/++ScaNWoEbW1tVKlSBRs3blQ4NyEhAZMmTUKtWrWgr68PQ0NDuLu74/r16wVe27zcvn0bbdq0gY6ODiwtLTF37lzI5XKVdQMDA1GzZk3IZDJUrFgRY8aMQWJiokKdj70+0dHRGD16NKpXrw4dHR2Ympqid+/eSvutqNrjpDBjVERfCs44oRIRFhaGI8ePYSeqoDZ0861rDRl2ZFqh6e1bOHDgAL766qsC27exsUHTpk2xbds2uLu7AwAOHTqEN2/ewMvLCz/88EOBbYwcORI7d+7E2LFj4ejoiPj4ePz5558IDw9H/fr1kZGRATc3N6Snp2PcuHGoUKECYmJisH//fiQmJqJs2bIAgHnz5mHmzJnw9PTE0KFDERsbi1WrVqFVq1a4evUqjIyMxD5fv36Njh07okePHvD09MTOnTsxZcoU1KpVS3wdP/30E8aPH49evXphwoQJSEtLw40bN3Dx4kUxKfTy5Us0adIEEokEY8eOhbm5OQ4dOoQhQ4bg7du3mDhxYp6v29bWFs2aNcOOHTuwfPlyhbs3cpIpOf0EBwdDX18f//d//wd9fX2cPHkSs2bNwtu3b7F48WKFduPj4+Hu7g4vLy8MGDAgz6XX3r59i59//hl9+/bFsGHDkJSUhA0bNsDNzQ2XLl1C3bp1Fepv3boVSUlJGDFiBCQSCRYtWoQePXrg0aNHKFOmDEaMGIFnz57h2LFj2LRpU4HfdyIiIiIiIvrybNiwARKJBPPmzVO4+75y5cpo3bq1ynPGjRsHAJgyZQoAwNHREX5+fgCAEydOYP/+/YiNjYW5uTk6duwINzc3AMCrV68wbtw4TJgwAUePHsWDBw8wdOjQYl0O648//kCVKlXQrFmzIp1vbm6ONWvWYNSoUejevTt69OgBAKhduzaA7CRB8+bNUalSJUydOhV6enrYsWMHunXrhl27dqF79+4K7Y0ePRrm5uaYNWsWkpOTxfLXr1+jS5cu8PLyQu/evbFmzRp4eXlhy5YtmDhxIkaOHIl+/fph8eLF6NWrF548eQIDA4N8Y3/w4AF69eqFIUOGwNvbG7/88gt8fHzg7OyMmjVrAgAePXqEvXv3onfv3rC1tcXLly+xbt06uLi44M6dO4WeQfTixQu0bt0amZmZ4vVYv369yj1k/P39ERAQgHbt2mHUqFG4d+8e1qxZg7CwMPz1118oU6ZMsVyfsLAwnDt3Dl5eXrC0tERUVBTWrFkDV1dX3LlzB7q6+Y/HqTNGRfQlYeKESsSawEBYaeqgW6aRWvWbQB+NpQYIXLVarcQJkD24P23aNKSmpkJHRwdbtmyBi4uL2v/5HThwAMOGDcPSpUvFssmTJ4tf37lzB5GRkQgJCUGvXr3E8lmzZolfR0dHw8/PD3PnzhVnWABAjx49UK9ePQQGBiqUP3v2DBs3bsTAgQMBAEOGDIG1tTU2bNgg/qd04MAB1KxZEyEhIXnG/t133yErKws3b96EqakpgOxEUN++feHv748RI0bkuwFc//79MWbMGJw4cQIdOnQAAMjlcmzfvh1NmzZFlSpVAGQnLXK3M3LkSIwcORKBgYGYO3euwjqjL168wNq1azFixIg8+wUAY2NjREVFKSzhNWzYMNSoUQOrVq3Chg0bFOo/fvwYERERMDY2BgBUr14dX331FY4cOYIuXbqgadOmqFatGo4dO1Zs+7IQERERERHR5+Pdu3e4fv06+vTpo3LJoryWlZo3bx6+++47zJgxA5UrVxaXCj979ixCQkLg6+sLW1tbREZGYv369dDW1oaLi4t4/tatWzFw4EDY2toqDJZ/rLdv3yImJkbt8RNV9PT00KtXL4waNQq1a9dW+jw9YcIEWFlZISwsTPzsP3r0aLRo0QJTpkxRSpyYmJjgxIkTSktrPXv2DFu3bkXfvn0BAO3bt0eNGjXQr18/nDt3Do0bNwYAODg4wM3NDbt27YKPj0++sd+7dw9nzpxBy5YtAQCenp6oXLkygoKCsGTJEgBArVq1cP/+fWho/G/xnYEDB6JGjRrYsGEDZs6cWajrtXDhQsTGxuLixYto1KgRAMDb2xtVq1ZVqBcbG4sFCxagQ4cOOHTokNh/jRo1MHbsWGzevBm+vr7Fcn06d+6sMF4FAF27dkXTpk2xa9cucewpL+qMURF9SbhUF5WI3Tt3wSfTCFKov2TS4CxjHD1xHElJSWrV9/T0RGpqKvbv34+kpCTs378/z2W6VDEyMsLFixfx7NkzlcdzZpQcOXIEKSkpKuvs3r0bcrkcnp6eiIuLEx8VKlRA1apVlZaf0tfXV/hjREtLC40aNcKjR48U4nr69KnCclS5CYKAXbt2oWvXrhAEQaFfNzc3vHnzRmG5MVX69OmDMmXKKCzXdfr0acTExIjLdAFQSJokJSUhLi4OLVu2REpKCu7evavQpkwmU/jPPy9SqVRMmsjlciQkJCAzMxMNGjRQGXefPn3EpAkA8Q+j3NeMiIiIiIiIKC8vXryAIAhqLYGVm6GhIQDAwMAARkZG0NfXBwCEhIRg4MCBaNy4McqVK4fGjRujU6dOSkspderUSayT+3Ptx3r79q0Y16eQkJCAkydPwtPTUxwLiIuLQ3x8PNzc3BAREYGYmBiFc4YNG6ZyPxJ9fX14eXmJz6tXrw4jIyM4ODiISQEA4tfqfNZ3dHQUxwaA7Nkz1atXVzhXJpOJSYusrCzEx8dDX18f1atXL3DMRJWDBw+iSZMmYtIkp9/cYygAcPz4cWRkZGDixIkKSZthw4bB0NBQad+Zj7k+ucds3r9/j/j4eNjb28PIyEit16jOGBXRl4QzTuiTy8zMxJt3SbCGSaHOs0H2HQwJCQlq/edvbm6Odu3aYevWrUhJSUFWVpZSpj0/ixYtgre3NypXrgxnZ2d06tQJgwYNEmdb2Nra4v/+7/+wbNkybNmyBS1btoSHhwcGDBggJlUiIiIgCILSHQY5PryjxNLSUmn/DWNjY9y4cUN8PmXKFBw/fhyNGjWCvb09OnTogH79+qF58+YAsu9eSExMxPr167F+/XqV/b569Srf125qago3Nzfs2bMHa9euhba2NrZu3QpNTU14enqK9W7fvo0ZM2bg5MmT4h9mOT7c56VSpUpqbwT/66+/YunSpbh79y7ev38vltva2irV/XAjvJw/Nl+/fq1WX0RERERERPRlEwShwDo//fQTzp49Kz7/cM+MHGlpaXj58iXWrl2LdevWieVyuVxpaaSc8YXilpPQUffG04SEBGRkZIjPdXR0xHENVR48eABBEDBz5sw8Z2a8evVKIRGl6vM8oHocpGzZsuL+MbnLAPU+6384TgBkjxXkPlcul2PlypUIDAxEZGSkwl6tOSt3FEZ0dLRCIiNH9erVleqpKtfS0kKVKlXE4zk+5vqkpqZiwYIFCAoKQkxMjML7/MMxG1XUGaMi+pIwcUKfnFQqhVRDinR5wX+Y5JaO7A21ci//VJB+/fph2LBhePHiBdzd3RX2EymIp6cnWrZsiT179uDo0aNYvHgxFi5ciN27d4tTEpcuXQofHx/s27cPR48exfjx47FgwQJcuHABlpaWkMvlkEgkOHToUJ53VuSmqg6g+Eecg4MD7t27h/379+Pw4cPYtWsXAgMDMWvWLAQEBIgbjw0YMADe3t4q28tZkzQ/AwYMwP79+7F//354eHhg165d6NChg7g5XGJiIlxcXGBoaIjZs2fDzs4O2trauHLlCqZMmaK0AVp+S4PltnnzZvj4+KBbt2749ttvUa5cOUilUixYsAAPHz5Uqq/ONSMiIiIiIiLKi4WFBSQSidIsidw8PT3RtWvXAttKS0sDAAwfPlzpJsrcMwwAqFwWrDgYGhqiYsWKuHXrllr1e/TogdOnT4vPvb29ERwcnGf9nM/7kyZNEvdt+ZC9vb3C87zGBPL6TP8xn/XVOXf+/PmYOXMmBg8ejDlz5sDExAQaGhqYOHFinhu6l4aPuT7jxo1DUFAQJk6ciKZNm6Js2bKQSCTw8vJS6zVyvIVIERMn9MlJJBJUs7PD6QdxGCWYq33eKSTBpGzZQmX+u3fvjhEjRuDChQvYvn17oWO1sLDA6NGjMXr0aLx69Qr169fHvHnzFNZyrFWrFmrVqoUZM2bg3LlzaN68OdauXYu5c+fCzs4OgiDA1tYW1apVK3T/edHT00OfPn3Qp08fZGRkoEePHpg3bx6mTZsGc3NzGBgYICsrC+3atStyHx4eHjAwMMDWrVtRpkwZvH79WmGK6alTpxAfH4/du3ejVatWYnlkZORHvbadO3eiSpUq2L17t8KdDTkb7BXFh3dIEBEREREREeXQ19dHnTp1cPToUbi7uyslNJKTk1G2bFmlWRg5e5rkHoQ2MjKCsbExXr16pbBcVEnr0qUL1q9fj/Pnz6Np06b51l26dKnCTIWcvWHz+iydM1OmTJkyHzXuUJp27tyJ1q1bK+2jmpiYCDMzs0K3Z21tjYiICKXye/fuKdXLKc894ygjIwORkZHFej137twJb29vhb1709LSkJiYWGx9EH1JuMcJlYgRY0ZjFxLxAu8LrgwgBXIESRMxZPjwQm2Ypq+vjzVr1sDf31+tO0NyZGVlKU1bLFeuHCpWrIj09HQA2WuGZmZmKtSpVasWNDQ0xDo9evSAVCpFQECAUkZeEATEx8erHVOOD8/R0tKCo6MjBEHA+/fvIZVK0bNnT+zatUvl3SWxsbFq9aOjo4Pu3bvj4MGDWLNmDfT09BQ2lsu58yD368rIyEBgYGChX1Nuqtq9ePEizp8/X+Q2czby4x8HREREREREpMrgwYMhl8vx3Xff4eLFi3j+/DmePn2KQ4cOYcaMGSrPKVu2LLS0tHDt2jUkJiaK+5/27t0be/fuxaFDh/Ds2TM8fvwYoaGh2L9/f4m9nsmTJ0NPTw9Dhw7Fy5cvlY4/fPgQK1euBAA4OzujXbt24sPR0REAxKXFPvwsXa5cObi6umLdunV4/vy5UtvqjjuUJqlUqjROExISku+so/x06tQJFy5cwKVLl8Sy2NhYbNmyRaFeu3btoKWlhR9++EGh/w0bNuDNmzfo3LlzkfpXRdVrXLVqlcKyZESkPs44oRLh7e2N6VOnYnpaDDbAGpICNon/Hi+QKH+PESNGFKmvwkpKSoKlpSV69eqFOnXqQF9fH8ePH0dYWJiYqT958iTGjh2L3r17o1q1asjMzMSmTZvExAUA2NnZYe7cuZg2bRqioqLQrVs3GBgYIDIyEnv27MHw4cMxadKkQsXWoUMHVKhQAc2bN0f58uURHh6O1atXo3PnzuLeL99//z1CQ0PRuHFjDBs2DI6OjkhISMCVK1dw/PhxJCQkqNXXgAEDsHHjRhw5cgT9+/cXExAA0KxZMxgbG8Pb2xvjx4+HRCLBpk2bPnrKZpcuXbB79250794dnTt3RmRkJNauXQtHR0e8e/euSG06OzsDAMaPHw83NzdIpVKFzdWIiIiIiIjoy1a+fHl8//332LNnDzZt2oTXr1/D0NAQVapUwdChQ1WeI5VK4ePjg127dmHHjh1wcHCAn58f2rZtC5lMhj/++AObN2+GTCaDlZUVOnXqVGKvx87ODlu3bkWfPn3g4OCAQYMGwcnJCRkZGTh37hxCQkLg4+OTbxs6OjpwdHTE9u3bUa1aNZiYmMDJyQlOTk748ccf0aJFC9SqVQvDhg1DlSpV8PLlS5w/fx5Pnz7F9evXS+aFFlGXLl0we/Zs+Pr6olmzZrh58ya2bNlS5H1nJk+ejE2bNqFjx46YMGEC9PT0sH79elhbWyvsCWJubo5p06YhICAAHTt2hIeHB+7du4fAwEA0bNhQYTP2j9WlSxds2rQJZcuWhaOjI86fP4/jx48XaQ8XImLihEqIkZERflyzBr6+vjCAFMtgCamK5IkAAQvxEnPwHPPmzoOdnV2JxKerq4vRo0fj6NGj2L17N+RyOezt7REYGIhRo0YBAOrUqQM3Nzf88ccfiImJga6uLurUqYNDhw6hSZMmYltTp05FtWrVsHz5cgQEBAAAKleujA4dOsDDw6PQsY0YMQJbtmzBsmXL8O7dO1haWmL8+PEKd8CUL18ely5dwuzZs7F7924EBgbC1NQUNWvWxMKFC9Xuq02bNrCwsMDz588VlukCsjdL279/P7755hvMmDEDxsbGGDBgANq2bZvnGqfq8PHxwYsXL7Bu3TocOXIEjo6O2Lx5M0JCQnDq1KkitdmjRw+MGzcOv/32GzZv3gxBEJg4ISIiIiIiIgXGxsYYPHgwBg8erPY5bdu2Rdu2bZXKW7RogRYtWqg8p1y5ckVaTrywPDw8cOPGDSxevBj79u3DmjVrIJPJULt2bSxduhTDhg0rsI2ff/4Z48aNw9dff42MjAz4+fnByckJjo6O+PvvvxEQEIDg4GDEx8ejXLlyqFevHmbNmvXJX9vHmj59OpKTk7F161Zs374d9evXx4EDBzB16tQitWdhYYHQ0FCMGzcO33//PUxNTTFy5EhUrFgRQ4YMUajr7+8Pc3NzrF69Gl9//TVMTEwwfPhwzJ8/v1CrrBRk5cqVkEql2LJlC9LS0tC8eXMcP378o8ZsiL5kEoE7/FAxuXLlCpydnXH58mXUr19fZZ01a9Zg7JixsJJqY2SmMfrAGKbQxFtkYQ8SEaiZgPDMZMycORMBAQHcq4LoC6TO7xIiIiIiIqLc8vsckXNswYIFRZ5hUJoePXqEadOm8TMSERWIYyrFhzNOqESNGjUKjRo1wqpVq+C3dRumvv/fWpJSDQ10/6o71owbBxcXl1KMkoiIiIiIiIg+N0Xdz6K0/VfjJiL6L2PihEqcs7MzgoODsXTpUly4cAFv376Fvr4+6tevj0qVKpV2eERERERERET0GTEzM4OOjg5Wr15d2qEUmY6ODszMzEo7DCKiLwYTJ1RqTE1N0blz59IOg4iIiIiIiIg+Y1ZWVrh79y7i4uJKO5QiMzMzg5WVVWmHQUT0xWDihIiIiIiIiIiIPmtWVlZMPBARkdo0SjsAIiIiIiIiIiIiIiKifwsmToiIiIiIiIiIiIiIiP7BxAmVGkEQEB8fj6ioKMTGxkIul5d2SP9Z/v7+kEgkpdL3qVOnIJFIcOrUqVLpn4iIiIiIiIg+DR8fH9jY2CiUSSQS+Pv7l0o8n4KNjQ26dOlS2mH8a7i6usLV1bW0wyAqddzjhEpcUlIStmzZgsAff8DNW+FiuV0Va4wcNRa+vr4wNTVVu72bN28iICAAYWFhePnyJUxNTeHo6AgPDw+MGzdOrDd//nw4OjqiW7duxflyCu3GjRtYvnw5Tp06hefPn0NTUxP29vbo0KEDRo4ciSpVqpRqfERERERERESfm8ePH38Rm8M/fPgQixYtwrFjx/Ds2TNoaWmhVq1a8PT0xPDhw6Gjo1MC0aovJSUFixYtUjlYf/DgQVy6dOmzStIUtzt37mDHjh0qE1zF5dmzZ1i/fj26deuGunXrfpI+iP6NmDihEnXu3Dl89VUXJCQkwqMVMH0eYFoWeJsM7D0Vje+mT4G//yxs27YdXbt2Vau91q1bw8rKCsOGDUOFChXw5MkTXLhwAStXrlRKnPTq1atUEyc//fQTRo0aBTMzM/Tv3x81atRAZmYmbt26hY0bN2LFihVITU2FVCotVLszZszA1KlTP1HURERERERERP9djx8/hoODA1JSUko7lCLT1dVFeHh4vsmTAwcOoHfv3pDJZBg0aBCcnJyQkZGBP//8E99++y1u376N9evXl2DUBUtJSUFAQAAAqEyc/Pjjj0yc5OPOnTsICAiAq6trsSVOjh49qvD82bNnCAgIgI2NDRMn9EVh4oRKzIULF9CuXRs0dHiPTcECrCooHu/ZFlj6tRwjF6ShW7evsHfvvgKTJ/PmzUPZsmURFhYGIyMjhWOvXr0q5lfwcc6dO4dRo0ahefPm2L9/PwwMDBSOL126FPPmzStS25qamtDU5I8zERERERER0Yfi4uKQkpKC4OAFcHCwBQDExyciKGgP/vrrKmJjE2BsbIhq1Wzg5eWOhg1rqdXuTz+F4PTpv7F580IAwOzZgUhKSsHixZOKNf7w8Ej4+ExDXFxcnomTyMhIeHl5wdraGidPnoSFhYV4bMyYMXjw4AEOHDhQrHHR50lLS6u0QyD6V+AeJ1QiMjIy0LPHV6hfPROHf5ArJU1ylDMBQr4X8JUL0LdvH8THx+fb7sOHD1GzZk2lpAkAlCtXTvxaIpEgOTkZv/76KyQSCSQSCXx8fMTjV69ehbu7OwwNDaGvr4+2bdviwoULCu0FBwdDIpHgzJkzGDFiBExNTWFoaIhBgwbh9evXBV6DgIAASCQSbNmyRSlpAgDa2tqYM2eOwmyTs2fPonfv3rCysoJMJkPlypXx9ddfIzU1VeFcVXucSCQSjB07Fnv37oWTkxNkMhlq1qyJw4cPK/UdExODwYMHo3z58mK9X375Rane06dP0a1bN+jp6aFcuXL4+uuvkZ6eXuBrJyIiIiIiIiptDg62qFfPEcbGZTF06CzcuvUAy5ZNwdWru3HkyM/w8GiDH37Ygnr1HNV6VKhgDh0dbfG5iYkRypY1UPt8dR85yZ78LFq0CO/evcOGDRsUkiY57O3tMWHCBIWyzZs3w9nZGTo6OjAxMYGXlxeePHlSLNc6IyMDs2bNgrOzM8qWLQs9PT20bNkSoaGhYp2oqCiYm5sD+N+YSc7+KT4+Pvjxxx8BQCzPPe6xZMkSNGvWDKamptDR0YGzszN27typMpbNmzejUaNG0NXVhbGxMVq1aqU0qwIA/vzzTzRq1Aja2tqoUqUKNm7cqHA8Z1zozz//xPjx42Fubg4jIyOMGDECGRkZSExMxKBBg2BsbAxjY2NMnjwZgiAotCGXy7FixQrUrFkT2traKF++PEaMGKE0rpSz70p+MQUHB6N3794AgNatW4vXKL89aF+8eAFfX19YWlpCJpPBwsICX331FaKiosQ6uZdNO3XqFBo2bAgA8PX1FfsIDg4W61+8eBEdO3ZE2bJloaurCxcXF/z11195xkD0X8Fb1KlE7N69G8+ev8LRFYCOdv51pVJg7TQBlTunIzg4GN98802eda2trXH+/HncunULTk5OedbbtGkThg4dikaNGmH48OEAADs7OwDA7du30bJlSxgaGmLy5MkoU6YM1q1bB1dXV5w+fRqNGzdWaGvs2LEwMjKCv78/7t27hzVr1iA6OlrcJF2VlJQUnDx5Eq6urrC0tMz/AuQSEhKClJQUjBo1Cqamprh06RJWrVqFp0+fIiQkpMDz//zzT+zevRujR4+GgYEBfvjhB/Ts2ROPHz8W95F5+fIlmjRpIiZazM3NcejQIQwZMgRv377FxIkTAQCpqalo27YtHj9+jPHjx6NixYrYtGkTTp48qfbrISIiIiIiIipt48fPg0QCnDu3BXp6umJ5zZr28PHpJj5PTHyLKVOW4o8/TiE9PQPOzo5YvHgy6tSpXvJBF+CPP/5AlSpV0KxZM7Xqz5s3DzNnzoSnpyeGDh2K2NhYrFq1Cq1atcLVq1dV3qBaGG/fvsXPP/+Mvn37YtiwYUhKSsKGDRvg5uaGS5cuoW7dujA3N8eaNWswatQodO/eHT169AAA1K5dG8nJyXj27BmOHTuGTZs2KbW/cuVKeHh4oH///sjIyMBvv/2G3r17Y//+/ejcubNYLyAgAP7+/mjWrBlmz54NLS0tXLx4ESdPnkSHDh3Eeg8ePECvXr0wZMgQeHt745dffoGPjw+cnZ1Rs2ZNhb7HjRuHChUqICAgABcuXMD69ethZGSEc+fOwcrKCvPnz8fBgwexePFiODk5YdCgQeK5I0aMQHBwMHx9fTF+/HhERkZi9erVuHr1Kv766y+UKVNG7ZhatWqF8ePH44cffsD06dPh4OAAAOK/qvTs2RO3b9/GuHHjYGNjg1evXuHYsWN4/PixyqW+HBwcMHv2bMyaNQvDhw9Hy5YtAUB8n508eRLu7u5wdnaGn58fNDQ0EBQUhDZt2uDs2bNo1KhRnrEQ/esJRMXk8uXLAgDh8uXLSsdatmgmuDaQCsLfUPvR3x2CXRVrISsrK88+jx49KkilUkEqlQpNmzYVJk+eLBw5ckTIyMhQqqunpyd4e3srlXfr1k3Q0tISHj58KJY9e/ZMMDAwEFq1aiWWBQUFCQAEZ2dnhfYXLVokABD27duXZ5zXr18XAAgTJ05UOhYfHy/ExsaKj/T0dPFYSkqKUv0FCxYIEolEiI6OFsv8/PyED3+cAQhaWlrCgwcPlOJYtWqVWDZkyBDBwsJCiIuLUzjfy8tLKFu2rBjDihUrBADCjh07xDrJycmCvb29AEAIDQ3N8/UTFUZ+v0uIiIiIiIhUye9zRM6xixd/E168OCtIJBJhzpzxQkbGjXwfbds2ETp3dhHOn98m3L79h/D114MEU1Mj4cWLs0JGxg1hxoyRQu3a1cX6Awd6CF27ti6w3cI+Ll78Ld/PSG/evBEACF999ZVa1yoqKkqQSqXCvHnzFMpv3rwpaGpqKpR7e3sL1tbWCvUACH5+fvn2kZmZqTC+IQiC8Pr1a6F8+fLC4MGDxbLY2Ng82xszZozSWEeOD8dLMjIyBCcnJ6FNmzZiWUREhKChoSF0795daWxJLpeLX1tbWwsAhDNnzohlr169EmQymfDNN9+IZTnjQm5ubgrnN23aVJBIJMLIkSMVXr+lpaXg4uIilp09e1YAIGzZskUhlsOHDyuVqxtTSEiI2mMyr1+/FgAIixcvzreei4uLQtxhYWECACEoKEihnlwuF6pWrap0PVJSUgRbW1uhffv2BcZExY9jKsWHS3XRJycIAi5eCsNXrbIKdV43F+Dho2gkJCTkWad9+/Y4f/48PDw8cP36dSxatAhubm6oVKkSfv/99wL7yMrKwtGjR9GtWzdUqVJFLLewsEC/fv3w559/4u3btwrnDB8+XOEOgFGjRkFTUxMHDx7Ms5+cNvT19ZWOValSBebm5uIjd9w6Ojri18nJyYiLi0OzZs0gCAKuXr1a4Otr166dOLMGyL5rw9DQEI8ePQKQ/b3ZtWsXunbtCkEQEBcXJz7c3Nzw5s0bXLlyBUD2pmwWFhbo1auX2J6urq44g4eIiIiIiIjo3+7hw8cQBAHVq+e//NVff11BWNgt/PbbUjg710TVqtZYuHASjIwMsHv3sRKKVj05Yw6qlgVXZffu3ZDL5fD09FQYB6hQoQKqVq2qsJxWUUmlUnGvDLlcjoSEBGRmZqJBgwbiOMPHyD1e8vr1a7x58wYtW7ZUaHvv3r2Qy+WYNWsWNDQUh0A/XDHE0dFRnE0BAObm5qhevbo4fpLbkCFDFM5v3LgxBEHAkCFDxDKpVIoGDRoonB8SEoKyZcuiffv2Ctfd2dkZ+vr6Ste9MDGpQ0dHB1paWjh16pRaS84X5Nq1a4iIiEC/fv0QHx8vvp7k5GS0bdsWZ86cgVwu/+h+iEoLl+qiTy4rKwsZGe9hqFe488r+k2N49+4dzMzM8qzXsGFD7N69GxkZGbh+/Tr27NmD5cuXo1evXrh27RocHR3zPDc2NhYpKSmoXl15mq2DgwPkcjmePHmiMC2zatWqCvX09fVhYWGhsB7kh3L+eHn37p3SsX379uH9+/e4fv06Jk1S3EDu8ePHmDVrFn7//Xel/9TevHmTZ385VG0aZ2xsLLYVGxuLxMRErF+/HuvXr1fZxqtXrwAA0dHRsLe3V/rjQtW1IyIiIiIiIvo3Ej7YcyIvN27cx7t3KahQoaVCeWpqOh4+LJ59QIqLoaEhACApKUmt+hERERAEQWl8I0fum0U/xq+//oqlS5fi7t27eP/+vVhua1vwni0F2b9/P+bOnYtr164p7L2ae8zi4cOH0NDQyHdcKEdB4yf51S1btiwAoHLlykrluc+PiIjAmzdvFPbkzS1n/KUoMalDJpNh4cKF+Oabb1C+fHk0adIEXbp0waBBg1ChQh6bEecjIiICAODt7Z1nnTdv3sDY2LhI8RKVNiZO6JPT1NSEtrYW4t9kFOq8uMTsf3P+ACiIlpYWGjZsiIYNG6JatWrw9fVFSEgI/Pz8Chlx8bO3t4empiZu3bqldMzFxQVA9nXKLSsrC+3bt0dCQgKmTJmCGjVqQE9PDzExMfDx8VEra597o/nccv5QzGljwIABef5HV7t27QL7ISIiIiIiIvovsLe3hkQiwb17kfnWe/cuBRYWZjh27BelY0ZG6s3sKCmGhoaoWLGiyjEHVeRyOSQSCQ4dOqRy3EDVahmFtXnzZvj4+KBbt2749ttvUa5cOUilUixYsAAPHz78qLbPnj0LDw8PtGrVCoGBgbCwsECZMmUQFBSErVu3FqnNgsZP1Kmrqjz3+XK5HOXKlcOWLVtUnm9ubl7kmNQ1ceJEdO3aFXv37sWRI0cwc+ZMLFiwACdPnkS9evUK1VbOmNLixYtRt25dlXWK471EVFqYOKES0dq1NXYcP45vB6m/XNf2YxLUruVQpMx0gwYNAADPnz8Xy1Rt3G5ubg5dXV3cu3dP6djdu3ehoaGhdMdAREQEWrduLT5/9+4dnj9/jk6dOuUZj56enrjZfExMDCpVqlTga7h58ybu37+PX3/9VWEjsWPHim9KsLm5OQwMDJCVlYV27drlW9fa2hq3bt2CIAgK11LVtSMiIiIiIiL6NzIxKYsOHZph7drfMHZsP4XN4YHsDeGNjAxRr54DXryIh6amFDY2BX+GL21dunTB+vXrcf78eTRt2jTfunZ2dhAEAba2tqhWrdoniWfnzp2oUqUKdu/erTCG8OHNrarGago6tmvXLmhra+PIkSOQyWRieVBQkEI9Ozs7yOVy3LlzJ8+B/ZJkZ2eH48ePo3nz5gpLjX2M/K5ffnF88803+OabbxAREYG6deti6dKl2Lx5c6H6yFka3tDQsMAxJaL/Iu5xQiVi9Jix+PtOFi6pd/MDHr8A/jgLjB4zPt//BEJDQ1Vm2nP2G8m9jJSenh4SExMV6kmlUnTo0AH79u1TWGrr5cuX2Lp1K1q0aKE042X9+vUKU0zXrFmDzMxMuLu75/uaZs2ahaysLAwYMEDlkl0fvo6cOwtylwuCgJUrV+bbT2FIpVL07NkTu3btUnlnSmxsrPh1p06d8OzZM+zcuVMsS0lJyXOJLyIiIiIiIqJ/o5Urv0NWlhzNmvXH7t3HEBERjfDwR1i9egtathwIAGjbtgmaNKmNXr0m4tixc4iKisH589cwc+YPuHz5dim/AmWTJ0+Gnp4ehg4dipcvXyodf/jwoTie0KNHD0ilUgQEBCiNRQiCgPj4+I+OR9WYxsWLF3H+/HmFerq62YmrD8drgOxxHFXHpFIpJBIJsrL+d3NuVFQU9u7dq1CvW7du0NDQwOzZs5VW7fiYWRtF5enpiaysLMyZM0fpWGZmpsprUJC8rpEqKSkpSEtLUyizs7ODgYGBwnJn6vbh7OwMOzs7LFmyROU4V+4xJaL/Is44oRLh7u6O6tXs4Ds7Gn/+nAnjfFbfSk0DBsyUwsTEEP3798+33XHjxiElJQXdu3dHjRo1kJGRgXPnzmH79u2wsbGBr6+vWNfZ2RnHjx/HsmXLULFiRdja2qJx48aYO3cujh07hhYtWmD06NHQ1NTEunXrkJ6ejkWLFin1mZGRgbZt28LT0xP37t1DYGAgWrRoAQ8Pj3xjbdmyJVavXo1x48ahatWq6N+/vxjz/fv3sWXLFmhpaYnrStaoUQN2dnaYNGkSYmJiYGhoiF27dhXLBl65ff/99wgNDUXjxo0xbNgwODo6IiEhAVeuXMHx48eRkJAAABg2bBhWr16NQYMG4fLly7CwsMCmTZvEP3KIiIiIiIiI/guqVLHExYvb8f33P2HKlKV4/jwW5ubGqFfPEatXzwCQfZf9778HYtasVRg2bBZiYxNQoYIZWrRwRrlypqX8CpTZ2dlh69at6NOnDxwcHDBo0CA4OTmJ4yQhISHw8fER686dOxfTpk1DVFQUunXrBgMDA0RGRmLPnj0YPny40h6shdWlSxfs3r0b3bt3R+fOnREZGYm1a9fC0dFRYZBdR0cHjo6O2L59O6pVqwYTExM4OTnByckJzs7OAIDx48fDzc0NUqkUXl5e6Ny5M5YtW4aOHTuiX79+ePXqFX788UfY29vjxo0bYtv29vb47rvvMGfOHLRs2RI9evSATCZDWFgYKlasiAULFnzUaywsFxcXjBgxAgsWLMC1a9fQoUMHlClTBhEREQgJCcHKlSvRq1evQrVZt25dSKVSLFy4EG/evIFMJkObNm1U7qNy//59cTzL0dERmpqa2LNnD16+fAkvL688+7Czs4ORkRHWrl0LAwMD6OnpoXHjxrC1tcXPP/8Md3d31KxZE76+vqhUqRJiYmIQGhoKQ0ND/PHHH4W+TkT/GgJRMbl8+bIAQLh8+bLK43fv3hVMTY0ExyqawoVgCPIwCMLfio+bv0FoVkcq6OjIhHPnzhXY56FDh4TBgwcLNWrUEPT19QUtLS3B3t5eGDdunPDy5Uul/lu1aiXo6OgIAARvb2/x2JUrVwQ3NzdBX19f0NXVFVq3bq3Uf1BQkABAOH36tDB8+HDB2NhY0NfXF/r37y/Ex8erfZ2uXr0qDBo0SLCyshK0tLQEPT09oXbt2sI333wjPHjwQKHunTt3hHbt2gn6+vqCmZmZMGzYMOH69esCACEoKEis5+fnJ3z44wxAGDNmjFL/1tbWCq9dEATh5cuXwpgxY4TKlSsLZcqUESpUqCC0bdtWWL9+vUK96OhowcPDQ9DV1RXMzMyECRMmCIcPHxYACKGhoWpfA6L8FPS7hIiIiIiI6EP5fY7IOXbx4m9CRsaN/9zj4sXf1P6MdP/+fWHYsGGCjY2NoKWlJRgYGAjNmzcXVq1aJaSlpSnU3bVrl9CiRQtBT09P0NPTE2rUqCGMGTNGuHfvnljH29tbsLa2VjgPgODn55dvHHK5XJg/f75gbW0tyGQyoV69esL+/ftVtnfu3DnB2dlZ0NLSUmg7MzNTGDdunGBubi5IJBKFcY8NGzYIVatWFWQymVCjRg0hKChI5diIIAjCL7/8ItSrV0+QyWSCsbGx4OLiIhw7dkw8bm1tLXTu3FnpPBcXF8HFxUV8njMuFBYWplAvp9/Y2FiFcm9vb0FPT0+p3fXr1wvOzs6Cjo6OYGBgINSqVUuYPHmy8OzZs0LHJAiC8NNPPwlVqlQRpFJpvuMzcXFxwpgxY4QaNWoIenp6QtmyZYXGjRsLO3bsKLCPffv2CY6OjoKmpqbSmNTVq1eFHj16CKampoJMJhOsra0FT09P4cSJEyrjoE+LYyrFRyIIpTA3jT5LV65cgbOzMy5fvoz69eurrHPv3j185dEZ9+4/hLODFH3aZ8G0LPA2GdhzSoozV7JQ0aI8du/Zh8aNG5fwK8hfcHAwfH19ERYWJu6hQkTFT53fJURERERERLnl9zki51hw8AI4ONiWUoRFFx4eCR+fafyMREQF4phK8eFSXVSiqlevjtt37uHw4cMI/HE1/H4KRWpqOmSyMmjcqBF++20cunfvDi0trdIOlYiIiIiIiIg+A2ZmZtDV1YWPz7TSDqXIdHV1YWZmVtphEBF9MZg4oRInlUrRuXNndO7cGUD2BliamnwrEhEREREREVHxs7KyQnh4OOLi4ko7lCIzMzODlZVVaYdBRPTF4Gg1lTomTYiIiIiIiIjoU7KysmLigYiI1KZR2gEQ/Vf4+PhAEATub0JERERERERERET0GWPihIiIiIiIiIiIiIiI6B9MnBB9IDg4GBKJBFFRUWKZq6srXF1dSy2m0mBjYwMfH5/SDuOTOnXqFCQSCU6dOlXaoRARERERERGVGH9/f0gkktIOQ/RviyeHRCKBv79/aYdBRKWAm0tQqbl79y5OnTqFpKQk6OnpoXHjxnB2di50Ozdv3kRAQADCwsLw8uVLmJqawtHRER4eHhg3btwniLxk+Pv7IyAgQHyuqamJSpUqwcPDA7Nnz4aRkVHpBfeJeHh44Pjx43j58iUMDAxU1unfvz9CQkLw/PlzmJqalnCEhZeSkoJFixZ9kck3IiIiIiKif4vHjx9/1pvDBwcHw9fXV3wuk8lgYmKCWrVqoXPnzvD19c3zczYRESlj4oRK3B9//IGly5fidOhpQAOQ6kghT5NDyBJQv2F9fD3+a/Tv31+tOw3OnTuH1q1bw8rKCsOGDUOFChXw5MkTXLhwAStXriy2xMnRo0eLpZ2iWLNmDfT19ZGcnIwTJ05g1apVuHLlCv78889P2u+9e/egoVGyk9L69++PP/74A3v27MGgQYOUjqekpGDfvn3o2LFjsSRNWrVqhdTUVGhpaX10W3lJSUkRE2BMnBAREREREZW8x48fw8GhOlJS0ko7lCLT1dVGePi9Aje4nz17NmxtbfH+/Xu8ePECp06dwsSJE7Fs2TL8/vvvqF27NgBgxowZmDp1akmETkT0n8TECZUYQRDw3XffYcGCBZDaS4EhAOoBWWWygCwAt4Brp69h4MCBOH7iODb8vAFSqTTfNufNm4eyZcsiLCxMaQbGq1evii32TzmwXpBevXrBzMwMADBixAh4eXlh+/btuHTpEho1avTJ+pXJZJ+s7bx4eHjAwMAAW7duVZk42bdvH5KTk9G/f/+P6ictLQ1aWlrQ0NCAtrb2R7VFRERERERE/25xcXFISUnD5s2Ag0N2mZ8fsH9/9tdSKVC2LFC1KuDmBnTtCuTcRxgXBxgaAqU4LIDwcGDAgDTExcUVmDhxd3dHgwYNxOfTpk3DyZMn0aVLF3h4eCA8PBw6OjrQ1NSEpmbJDQtmZmZCLpeX2PhKSkoKdHV1S6QvIvo8cY8TKjELFy7EggULgJ5A1qQsoBGAMv8clAKoA8jHywFfYOPGjZgwYUKBbT58+BA1a9ZUuWxVuXLlFJ5LJBKMHTsWW7ZsQfXq1aGtrQ1nZ2ecOXOmwH5ULbOUlpYGf39/VKtWDdra2rCwsECPHj3w8OFDsY5cLseKFStQs2ZNaGtro3z58hgxYgRev35dYJ95admyJQAo9AMAFy9eRMeOHVG2bFno6urCxcUFf/31l9L5p06dQoMGDaCtrQ07OzusW7dO5VqiqvY4efToEXr37g0TExPo6uqiSZMmOHDggFL7EokEO3bswLx582BpaQltbW20bdsWDx48yPe16ejooEePHjhx4oTKxNfWrVthYGAADw8PJCQkYNKkSahVqxb09fVhaGgId3d3XL9+XWU8v/32G2bMmIFKlSpBV1cXb9++VbnHydmzZ9G7d29YWVlBJpOhcuXK+Prrr5GamqrQro+PD/T19RETE4Nu3bpBX18f5ubmmDRpErKysgAAUVFRMDc3BwAEBARAIpFwfVQiIiIiIqJS4uAA1K+f/TA1BTp2BJ4/B6KjgWPHAA8PYNkyYNYsoHbt7HodOgBNmvzvvOJ8ODmpVy8n2VNUbdq0wcyZMxEdHY3NmzcDUL2nyLFjx9CiRQsYGRlBX18f1atXx/Tp0xXqvHr1CkOGDEH58uWhra2NOnXq4Ndff1WoExUVBYlEgiVLlmDFihWws7ODTCbDnTt3AAB//vknGjZsqDAukZfNmzfD2dkZOjo6MDExgZeXF548eaJQx9XVFU5OTrh8+TJatWoFXV1dMe709HT4+fnB3t5e/Iw/efJkpKenK7SRnp6Or7/+Gubm5uK4w9OnTwtxlYnoc8MZJ1QiXrx4gZmzZgIdkP3ITxNASBHw448/YsSIEahVq1aeVa2trXH+/HncunULTk5OBcZx+vRpbN++HePHj4dMJkNgYCA6duyIS5cuqXV+jqysLHTp0gUnTpyAl5cXJkyYgKSkJBw7dgy3bt2CnZ0dgOwZIjnrjI4fPx6RkZFYvXo1rl69ir/++gtlypQpoCdlOZvWGxsbi2UnT56Eu7s7nJ2d4efnBw0NDQQFBaFNmzY4e/asODPl6tWr6NixIywsLBAQEICsrCzMnj1bHNzPz8uXL9GsWTOkpKRg/PjxMDU1xa+//goPDw/s3LkT3bt3V6j//fffQ0NDA5MmTcKbN2+waNEi9O/fHxcvXsy3n/79++PXX3/Fjh07MHbsWLE8ISEBR44cQd++faGjo4Pbt29j79696N27N2xtbfHy5UusW7cOLi4uuHPnDipWrKjQ7pw5c6ClpYVJkyYhPT09z7tcQkJCkJKSglGjRsHU1BSXLl3CqlWr8PTpU4SEhCjUzcrKgpubGxo3bowlS5bg+PHjWLp0Kezs7DBq1CiYm5tjzZo1GDVqFLp3744ePXoAgDg1moiIiIiIiEqPTAZUqJD9daVK2UmKJk2Atm2B4GBg6FBAIgH27AG6dQMyMoD/+z9g1y7g9WugfHlg5Ehg2rTsNh4/BsaNA06cyJ6x0rEjsGpVdj0A8PcH9u4Fxo4F5s3LTtjI5SXzWgcOHIjp06fj6NGjGDZsmNLx27dvo0uXLqhduzZmz54NmUyGBw8eKNyQmZqaCldXVzx48ABjx46Fra0tQkJC4OPjg8TERKUbYIOCgpCWlobhw4eLe67cvHkTHTp0gLm5Ofz9/ZGZmQk/Pz+Uz7lIucybNw8zZ86Ep6cnhg4ditjYWKxatQqtWrXC1atXFW6ijY+Ph7u7O7y8vDBgwACUL18ecrkcHh4e+PPPPzF8+HA4ODjg5s2bWL58Oe7fv4+9e/eK5w8dOhSbN29Gv3790KxZM5w8eRKdO3f++AtPRP9dAlExuXz5sgBAuHz5stKxuXPnChoyDQHLIGCdGo9ACJpGmsKoUaPy7fPo0aOCVCoVpFKp0LRpU2Hy5MnCkSNHhIyMDKW6AAQAwt9//y2WRUdHC9ra2kL37t3FsqCgIAGAEBkZKZa5uLgILi4u4vNffvlFACAsW7ZMqR+5XC4IgiCcPXtWACBs2bJF4fjhw4dVln/Iz89PACDcu3dPiI2NFaKiooRffvlF0NHREczNzYXk5GSxv6pVqwpubm5i34IgCCkpKYKtra3Qvn17saxr166Crq6uEBMTI5ZFREQImpqawoe/DqytrQVvb2/x+cSJEwUAwtmzZ8WypKQkwdbWVrCxsRGysrIEQRCE0NBQAYDg4OAgpKeni3VXrlwpABBu3ryZ7+vOzMwULCwshKZNmyqUr127VgAgHDlyRBAEQUhLSxP7zBEZGSnIZDJh9uzZYllOPFWqVBFSUlIU6uccCw0NVbhuH1qwYIEgkUiE6Ohosczb21sAoNCXIAhCvXr1BGdnZ/F5bGysAEDw8/PL93XT/+T3u4SIiIiIiEiV/D5H/O8YBEHIfnh7Q/jqq/89z/2oUweCu3v21wCEPXuyv168GELlyhDOnIEQFQXh7FkIW7dmH8vKglC3LoQWLSD8/TeECxcgODtDcHH5X7t+fhD09CB07AjhyhUI16+r7v/Dx+XLKPAzUs5YRlhYWJ51ypYtK9SrV08QhP+NOeRYvny5AECIjY3N8/wVK1YIAITNmzeLZRkZGULTpk0FfX194e3bt4IgZH82ByAYGhoKr169UmijW7dugra2tsLn6zt37ghSqVQhnqioKEEqlQrz5s1TOP/mzZuCpqamQrmLi4sAQFi7dq1C3U2bNgkaGhoK4xiC8L/xhb/++ksQBEG4du2aAEAYPXq0Qr1+/frx8zz953BMpfhwqS4qEet+Wgd5Azmgp+YJUiCzeSaCNwYrTZ/MrX379jh//jw8PDxw/fp1LFq0CG5ubqhUqRJ+//13pfpNmzaFs7Oz+NzKygpfffUVjhw5Ii6vpI5du3bBzMxM5ebzOVNdQ0JCULZsWbRv3x5xcXHiw9nZGfr6+ggNDVWrr+rVq8Pc3Bw2NjYYPHgw7O3tcejQIXGtzmvXriEiIgL9+vVDfHy82E9ycjLatm2LM2fOQC6XIysrC8ePH0e3bt0UZmPY29vD3d29wDgOHjyIRo0aoUWLFmKZvr4+hg8fjqioKHHKbQ5fX1+FWR05S4w9evQo336kUim8vLxw/vx5cXYNkL1MV/ny5dG2bVsA2Xuw5Gxen5WVhfj4eHEq8ZUrV5Ta9fb2ho6OToGvM3ed5ORkxMXFoVmzZhAEAVevXlWqP3LkSIXnLVu2LPA1EhERERER0b9XjRpAro+josePs/dBadECsLbO/rdv3+xjJ04AN28CW7cCzs5A48bAxo3A6dNAWNj/2sjIyC6vVy97ObCSpK+vj6SkJJXHcmZv7Nu3D/I8psEcPHgQFSpUQN+cFw2gTJkyGD9+PN69e4fTp08r1O/Zs6fCChdZWVk4cuQIunXrprBXi4ODA9zc3BTO3b17N+RyOTw9PRXGVCpUqICqVasqjanIZDL4+voqlIWEhMDBwQE1atRQaKNNmzYAILZx8OBBAMD48eMVzp84caLK60BEXwYmTuiTk8vlePr4KWBTyBNtgdTkVMTHx+dbrWHDhti9ezdev36NS5cuYdq0aUhKSkKvXr2UBvOrVq2qdH61atWQkpKC2NhYtUN7+PAhqlevnu9GahEREXjz5g3KlSsHc3Nzhce7d+/U3rx+165dOHbsGLZu3YomTZrg1atXCoP7ERERALITAx/28/PPPyM9PR1v3rzBq1evkJqaCnt7e6U+VJV9KDo6GtWrV1cqd/hnsdXo6GiF8g83rMtZWkyd/V1yNn/funUrAODp06c4e/YsvLy8IJVKAWS/r5YvX46qVatCJpPBzMwM5ubmuHHjBt68eaPUpq2tbYH9AsDjx4/h4+MDExMTcd8SFxcXAFBqV1tbW2mZM2Nj44/aw4aIiIiIiIhKlyBkL9H1IR8f4No1oHp1YPx44OjR/x0LDwcqV85+5HB0BIyMso/lsLYG1Fgt+5N49+4dDAwMVB7r06cPmjdvjqFDh6J8+fLw8vLCjh07FJIo0dHRqFq1qngTY468xgU+/BweGxuL1NRUlWMzH443REREQBAEVK1aVWmsIzw8XGlMpVKlSkpLckdEROD27dtK51erVg0AxDaio6OhoaEhLrueV0xE9GXhHif0yQn/zC0tdJrunz9S1J0JoqWlhYYNG6Jhw4aoVq0afH19ERISAj8/v0J2XDzkcjnKlSuHLVu2qDyuzr4iANCqVSuYmZkBALp27YpatWqhf//+uHz5MjQ0NMQ/YhYvXoy6deuqbENfXx9paWmFfxEfISfB8SFBEAo819nZGTVq1MC2bdswffp0bNu2DYIgiAkVAJg/fz5mzpyJwYMHY86cOTAxMYGGhgYmTpyo8u4YdWabZGVloX379khISMCUKVNQo0YN6OnpISYmBj4+Pkrt5vUaiYiIiIiI6L8rPBxQde9d/fpAZCRw6BBw/Djg6Qm0awfs3Kl+23rqrsRRzJ4+fYo3b97keeOkjo4Ozpw5g9DQUBw4cACHDx/G9u3b0aZNGxw9erRIn3/V+RyeF7lcDolEgkOHDqnsW19fv8C+5HI5atWqhWXLlqnso3LuLBcR0QeYOKFPTiqVwsjUCIkvEwt34itAQ0MDpqamhe6zQYMGAIDnz58rlOfMzsjt/v370NXVVTuRAQB2dna4ePEi3r9/n+cG73Z2djh+/DiaN2/+UX8s5Kavrw8/Pz/4+vpix44d8PLyEu+IMDQ0RLt27fI8t1y5ctDW1saDBw+Ujqkq+5C1tTXu3bunVH737l3xeHHq378/Zs6ciRs3bmDr1q2oWrUqGjZsKB7fuXMnWrdujQ0bNiicl5iYKCaaCuvmzZu4f/8+fv31VwwaNEgsP3bsWNFeBP63dBsRERERERH9+508mb3k1tdfqz5uaAj06ZP96NUrewP4hATAwQF48iT7kTMef+cOkJiYPfOktG3atAkAlJbEyk1DQwNt27ZF27ZtsWzZMsyfPx/fffcdQkND0a5dO1hbW+PGjRuQy+UKs07UHRcwNzeHjo6OyrGZD8cb7OzsIAgCbG1txRkihWVnZ4fr16+jbdu2+X42t7a2hlwuF1cXySsmIvqycKkuKhED+g6A5kVNIFPNEwRA8y9NdPXoKu7loUpoaKjKGQw561N+OK3y/PnzCvtfPHnyBPv27UOHDh0KdfdEz549ERcXh9WrVyuH/k88np6eyMrKwpw5c5TqZGZmIjExUe3+cuvfvz8sLS2xcOFCANmzM+zs7LBkyRK8e/dOqX7OEmRSqRTt2rXD3r178ezZM/H4gwcPcOjQoQL77dSpEy5duoTz58+LZcnJyVi/fj1sbGzgWMx/CebMLpk1axauXbumMNsEyH49H37vQ0JCEBMTU+Q+c94DudsVBAErV64scps579+ifr+JiIiIiIjo00hPB168AGJigCtXgPnzga++Arp0AXLdSydatgzYtg24exe4fx8ICQEqVMhejqtdO6BWLaB//+y2Ll3KbsPFBfjn3s5Sc/LkScyZMwe2trZKn61zJCQkKJXlrGqRs/dsp06d8OLFC2zfvl2sk5mZiVWrVkFfX19c5jovUqkUbm5u2Lt3Lx4/fiyWh4eH48iRIwp1e/ToAalUioCAAKXP/oIgFLisO5A9LhMTE4OffvpJ6VhqaiqSk5MBQNz39YcfflCos2LFigL7IKLPF2ecUIkYPXp0dpLhAoAWBVYHbgOZTzMxLlh58/Xcxo0bh5SUFHTv3h01atRARkYGzp07h+3bt8PGxkZpYzAnJye4ublh/PjxkMlkCAwMBAAEBAQU6vUMGjQIGzduxP/93//h0qVLaNmyJZKTk3H8+HGMHj0aX331FVxcXDBixAgsWLAA165dQ4cOHVCmTBlEREQgJCQEK1euRK9evQrVL5C98dqECRPw7bff4vDhw+jYsSN+/vlnuLu7o2bNmvD19UWlSpUQExOD0NBQGBoa4o8//gAA+Pv74+jRo2jevDlGjRqFrKwsrF69Gk5OTrh27Vq+/U6dOhXbtm2Du7s7xo8fDxMTE/z666+IjIzErl27lNY4/Vi2trZo1qwZ9u3bBwBKf9x16dIFs2fPhq+vL5o1a4abN29iy5YtqFKlSpH7rFGjBuzs7DBp0iTExMTA0NAQu3bt+qg9S3R0dODo6Ijt27ejWrVqMDExgZOTE5ycnIrcJhEREREREX28w4cBCwtAUxMwNgbq1AF++AHw9gZUfcQ1MAAWLQIiIgCpFGjYEDh48H919+0Dxo0DWrXKLuvYEVi1qmRf06FDh3D37l1kZmbi5cuXOHnyJI4dOwZra2v8/vvv0NbWVnne7NmzcebMGXTu3BnW1tZ49eoVAgMDYWlpiRYtsgdyhg8fjnXr1sHHxweXL1+GjY0Ndu7cib/++gsrVqzIc/+U3AICAnD48GG0bNkSo0ePFhMvNWvWxI0bN8R6dnZ2mDt3LqZNm4aoqCh069YNBgYGiIyMxJ49ezB8+HBMmjQp374GDhyIHTt2YOTIkQgNDUXz5s2RlZWFu3fvYseOHThy5AgaNGiAunXrom/fvggMDMSbN2/QrFkznDhxQq3VOYjo88XECZUIBwcH9O3XF9u3b4fcTA7UyKfyE0AaJEVz1+Zo06ZNvu0uWbIEISEhOHjwINavX4+MjAxYWVlh9OjRmDFjBoyMjBTqu7i4oGnTpggICMDjx4/h6OiI4OBg1K5du1CvRyqV4uDBg5g3bx62bt2KXbt2wdTUFC1atECtWrXEemvXroWzszPWrVuH6dOnQ1NTEzY2NhgwYACaN29eqD5zGz58OObOnYvvv/8eHTt2hKurK86fP485c+Zg9erVePfuHSpUqIDGjRtjxIgR4nnOzs44dOgQJk2ahJkzZ6Jy5cqYPXs2wsPDxam1eSlfvjzOnTuHKVOmYNWqVUhLS0Pt2rXxxx9/oHPnzkV+Lfnp378/zp07h0aNGimtwzp9+nQkJydj69at2L59O+rXr48DBw5g6tSpRe6vTJky+OOPPzB+/HgsWLAA2tra6N69O8aOHYs6deoUud2ff/4Z48aNw9dff42MjAz4+fkxcUJERERERFSKgoOzHwXJPdlh2LDsR16srLKTJ3nx989+fEqzZs0CkL0PrImJCWrVqoUVK1bA19c338SGh4cHoqKi8MsvvyAuLg5mZmZwcXFBQEAAypYtCyD7xsBTp05h6tSp+PXXX/H27VtUr14dQUFB8PHxUSu+2rVr48iRI/i///s/zJo1C5aWlggICMDz588VEidA9g2c1apVw/Lly8UbXitXrowOHTrAw8OjwL40NDSwd+9eLF++HBs3bsSePXugq6uLKlWqYMKECQpLgP3yyy8wNzfHli1bsHfvXrRp0wYHDhzgPihEXzCJoM5OzURquHLlCpydnXH58mXUr19f6XhaWhq6enTFidATEFwFwAVAuVwVXgM4C0hPSuHk4ITQ46EwNjYutvgkEgnGjBmjcnmtL123bt1w+/ZtleuMEpW0gn6XEBERERERfSi/zxH/O5a9wft/zZUrgLMz+BmJiArEMZXiwxknVGK0tbVx8MBB+Pv7Y/Wa1Xh7/C2ktlLIdeXQSNOAPFIObR1tDB48GN9//z309fVLO+TPUmpqqsJm9RERETh48CC8vb1LMSoiIiIiIiKiTys8vLQjKJr/atxERP9lTJxQiSpTpgzmzZuHGTNmYMeOHQgNDcXbt2+hr6+Pxl83xsCBA2FoaFjaYX7WqlSpAh8fH1SpUgXR0dFYs2YNtLS0MHny5NIOjYiIiIiIiKjYmZmZQVdXGwMGpJV2KEWmq6sNMzOz0g6DiOiLwcQJlQodHR14e3tzlkMp6NixI7Zt24YXL15AJpOhadOmmD9/PqpWrVraoREREREREREVOysrK4SH30NcXFxph1JkZmZmsLKyKu0wiIi+GEyc0BeD2/lkCwoKKu0QiIiIiIiIiEqUlZUVEw9ERKQ2jdIOgIiIiIiIiIiIiIiI6N+CiRMiIiIiIiIiIiIiIqJ/MHFCX7R3795h6NChqFChAiQSCSZOnFjaIREAHx8f2NjYlHYYRERERERERPSRXF1d4erqKj6PioqCRCJBcHCwWObj4wN9ff2SD46IKA/c44RKxfPnz/Hzzz/jxIlQJCYmwcBAD82aNcbIkSNha2urdjvBwcHw9fVFWFgYGjRoUOg45s+fj+DgYMycORN2dnZwcHAodBv/Rh97XUrCs2fPsH79enTr1g1169Yt7XCIiIiIiIjoM/b48ePPenP4nHGAvJw/fx5NmjQRn6enp2P9+vXYvn07bt++jeTkZJiZmaFBgwbo27cvPD09IZVKAWQnOvIaq2ncuDEuXLhQxFdFRPTvxcQJlaikpCSMHj0G27ZtgyBoQS53A2AH4C3On1+PxYsXo1OnLtiw4SeUL1/+k8dz8uRJNGnSBH5+fp+8L1L07NkzBAQEwMbGRilx8tNPP0Eul5dOYERERERERPRZefz4MWpUd0BqWkpph1JkOtq6uHsvvMAN7mfPnq0yyWFvby9+HRsbC3d3d1y+fBlubm6YMWMGTExM8OLFCxw/fhz9+vXDgwcPMHPmTIU2+vbti06dOimUmZubF/q1WFtbIzU1FWXKlCn0uUREJYWJEyoxiYmJaNWqDe7ceYisrCUAvAEYicezslIAbMPhwzPQsGFTnDt3BpaWlp80plevXsHR0bHAemlpadDS0oKGBle3+1iZmZkFJkX4xxMREREREREVl7i4OKSmpcCr0kqUk2UnEI69WobwdyeU6lrp1Ec3izkAgFfpD3E5MQQxabeQlpUEPU1jmGrZwMnAHba6jSCRSPD2/UsEPxms1E51fVe4lftWZTx3ko7hTPxPGGmzQ634X6U/wG8xExAXF1dg4sTd3b3AlScGDhyIq1evYteuXejRo4fCsWnTpuHvv//GvXv3lM6rX78+BgwYoFbM+ZFIJNDW1v7odoiIPiWOAlOJ6d3bC3fuRCEr6yyACcidNMmmC2AIsrIu4PnzTHTs2AWZmZmF7idnXcyYmBh069YN+vr6MDc3x6RJk5CVlQUAOHXqFCQSCSIjI3HgwAFIJBJIJBJERUWJx3777TfMmDEDlSpVgq6uLt6+fYuEhARMmjQJtWrVgr6+PgwNDeHu7o7r168rxJDTxo4dOxAQEIBKlSrBwMAAvXr1wps3b5Ceno6JEyeiXLly0NfXh6+vL9LT05Vey+bNm+Hs7AwdHR2YmJjAy8sLT548KfQ1Ufe65Pjtt9/g7OwMAwMDGBoaolatWli5cqVCncTEREycOBGVK1eGTCaDvb09Fi5cqJAUyVm3dMmSJVixYgXs7Owgk8kQGBiIhg0bAgB8fX3F65+zvqmqPU6Sk5PxzTffiP1Vr14dS5YsgSAICvUkEgnGjh2LvXv3wsnJCTKZDDVr1sThw4eLdN2IiIiIiIjo81BOZg9LnVqw1KkFXU1jVNd3xcxqfys8BlsHw1KnFt68f46QZ5MglZRBP8tV+LZqKIbb/IYGRr1xOTEEZjJrWOrUQgXt6gCAYdZbFdrpZ7la7OvDh0mZytCANM/jHz5ykj3F4fz58zhy5AiGDx+ulDTJ0aBBA/Tv37/Y+vyQqj1OVLl27RrMzc3h6uqKd+/eAQBiYmIwePBglC9fXvy8/8svv3yyWInoy8UZJ1QiwsLCcPz4EQA7AdQuoLY1MjN34Pbtpjhw4AC++uqrQveXlZUFNzc3NG7cGEuWLMHx48exdOlS2NnZYdSoUXBwcMCmTZvw9ddfw9LSEt988w2A7CmmUVFRAIA5c+ZAS0sLkyZNQnp6OrS0tHDnzh3s3bsXvXv3hq2tLV6+fIl169bBxcUFd+7cQcWKFRXiWLBgAXR0dDB16lQ8ePAAq1atQpkyZaChoYHXr1/D398fFy5cQHBwMGxtbTFr1izx3Hnz5mHmzJnw9PTE0KFDERsbi1WrVqFVq1a4evUqjIyMiv26AMCxY8fQt29ftG3bFgsXLgQAhIeH46+//sKECRMAACkpKXBxcUFMTAxGjBgBKysrnDt3DtOmTcPz58+xYsUKhX6DgoKQlpaG4cOHQyaToXv37khKSsKsWbMwfPhwtGzZEgDQrFkzlXELggAPDw+EhoZiyJAhqFu3Lo4cOYJvv/0WMTExWL58uUL9P//8E7t378bo0aNhYGCAH374AT179sTjx49hampa6OtGREREREREnx9NiRYMypRTKs+QpyDk2bdw0G+DQVbrFY6Vl1VFI2MvpZv49KTGKtsqaW/evFHay0UikYifhf/44w8AKNLMkZSUFKW2y5Yt+0lWjQgLC4ObmxsaNGiAffv2QUdHBy9fvkSTJk3EGybNzc1x6NAhDBkyBG/fvsXEiROLPQ4i+nIxcUIlIjBwDTQ1rZCZ2U3NM5pAKm2MVasCi5Q4SUtLQ58+fcT1OEeOHIn69etjw4YNGDVqFMqXL48BAwaIM0pU/cGQlpaGv//+Gzo6OmJZrVq1cP/+fYUluwYOHIgaNWpgw4YNSut/ZmZm4vTp0+IfEbGxsfjtt9/QsWNHHDx4EAAwevRoPHjwAL/88ouYOImOjoafnx/mzp2L6dOni+316NED9erVQ2BgoEJ5cV0XADhw4AAMDQ1x5MgRcSO4Dy1btgwPHz7E1atXUbVqVQDAiBEjULFiRSxevFicGZLj6dOnePDggcLap+7u7pg1axaaNm1a4B9sv//+O06ePIm5c+fiu+++AwCMGTMGvXv3xsqVKzF27FjY2dmJ9cPDw3Hnzh2xrHXr1qhTpw62bduGsWPHFvayERERERER0Rfk/rszSMl6DRezkXnWkUgkJRiR+tq1a6dUJpPJkJaWBgC4e/cuAMDJyUmhTlpamjirAwA0NTWVbtj08/NT2iM2NDQUrq6uxRD5//z111/o1KkTWrZsiV27dkEmkwEAvvvuO2RlZeHmzZtiImjkyJHo27cv/P39MWLECIUxHCKij8GluqhE7Ny5G5mZPgBUD8SrkpU1GCdOHEVSUlKR+hw5UvEPnJYtW+LRo0dqn+/t7a30H65MJhOTJllZWYiPj4e+vj6qV6+OK1euKLUxaNAghTsvGjduDEEQMHiw4vqnjRs3xpMnT8SlyXbv3g25XA5PT0/ExcWJjwoVKqBq1aoIDQ1V+3V8qKDrYmRkhOTkZBw7dizPNkJCQtCyZUsYGxsrxNeuXTtkZWXhzJkzCvV79uxZpA3jchw8eBBSqRTjx49XKP/mm28gCAIOHTqkUN6uXTuFRErt2rVhaGhYqO8/ERERERERfd7Ck05gRngNhcfJ2NWITc/+7GguqyLWfZJ6XaHenaTjCm39GNld4XhM6q0SfS1iHD/+iGPHjik8cn9mfvv2LQBAX19f4by1a9fC3NxcfLRo0UKp7eHDhyu1XadOnWKNPzQ0FG5ubmjbti12794tJk0EQcCuXbvQtWtXCIKgMBbh5uaGN2/eqByXISIqKs44oU8uMzMT7969AWBdyDNtAAAJCQkwMDAo1Jna2tpKA/XGxsZ4/fq12m3Y2toqlcnlcqxcuRKBgYGIjIxU2BtE1RJQH27aVrZsWQBQmI2RUy6Xy/HmzRuYmpoiIiICgiCIszk+VNRpsOpcl9GjR2PHjh1wd3dHpUqV0KFDB3h6eqJjx45inYiICNy4cSPPZMirV68Unqu6loURHR2NihUrKr0PHBwcxOO5qdosr7DffyIiIiIiIvq82ek1RXeL+QplulIjXHy9VamuhawGJlbJ3jtz0YNWkAuKe4X2r/wjymn97zO8URkLAMDSB23x+n0MAMBWtxGGWG8s1tfwoUaNGuW7OXzO5+p3796JYxRA9g2PObNQvvnmG6W9UAGgatWqKme05LSXe8aKVCot9A2UaWlp6Ny5M5ydnbFjxw5oav5v2DI2NhaJiYlYv3491q9fr/L8D8ciiIg+BhMn9MlJpVJoaEghlytvfp6/7Po5dxcUts+PpWp65/z58zFz5kwMHjwYc+bMgYmJCTQ0NDBx4kSFTdELiiOv8pw1UuVyOSQSCQ4dOqSy7od3hqhLnetSrlw5XLt2DUeOHMGhQ4dw6NAhBAUFYdCgQfj111/F+Nq3b4/JkyerbKNatWoKz0t6qmxB15eIiIiIiIhIS0MXZjIbpXIzWfbNf7Hpj2CtWx8AoKkhU1k3h5FmRZXHB1v9iiwhe3WJMhraHx3zx6pRowYA4NatW2jevLlYXrlyZfEmz5zVJQpjyZIlCAgIEJ9bW1uLe8iqSyaToVOnTti3bx8OHz6MLl26iMdyxlwGDBgAb29vlefXrl3QnrpEROpj4oQ+OYlEAju7anjw4DQEYVQhzjyFsmVN/lWbee/cuROtW7fGhg0bFMoTExNhZmZWbP3Y2dlBEATY2toqJSFKgpaWFrp27YquXbtCLpdj9OjRWLduHWbOnAl7e3vY2dnh3bt3ed5poo7CrAdrbW2N48ePIykpSWHWSc7arNbWhZ3NRERERERERKRaNb1W0JUa4VTcGnhb/fRRbRlrWRZTVMWjS5cu+P7777FlyxaFxMnHGjRokMLyXkW5gVIikWDLli346quv0Lt3bxw6dEjcP8Xc3BwGBgbIysr6qLEIIiJ1cY8TKhFjxowAsAvACzXPSIFUGoThw4cUeVmqT0EqlSrNWggJCUFMTEyx9tOjRw9IpVIEBAQo9ScIAuLj44u1v9w+bFtDQ0O8ayM9PXsWkKenJ86fP48jR44onZ+YmCju1ZIfPT09sX5BOnXqhKysLKxevVqhfPny5ZBIJHB3dy+wDSIiIiIiIqLcMoUMJL1/pfBIzkyATKqHXhUX4e67k/gl2gf33p1GfEY0nqeF41TcGgCAhuS/OaTWvHlztG/fHuvXr8e+fftU1inKag1VqlRBu3btxEdRkzJaWlrYvXs3GjZsiK5du+LSpUsAssdjevbsiV27duHWLeX9Y2JjY4vUHxFRXjjjhEqEt7c3pk6djrS06QA2AChotsH3kMsTMWLEiBKITn1dunTB7Nmz4evri2bNmuHmzZvYsmULqlSpUvDJhWBnZ4e5c+di2rRpiIqKQrdu3WBgYIDIyEjs2bMHw4cPx6RJk4q1zxxDhw5FQkIC2rRpA0tLS0RHR2PVqlWoW7euuKfIt99+i99//x1dunSBj48PnJ2dkZycjJs3b2Lnzp2IiooqcAaOnZ0djIyMsHbtWhgYGEBPTw+NGzdWuR9K165d0bp1a3z33XeIiopCnTp1cPToUezbtw8TJ05U2AieiIiIiIiISB333p3CnPuK+4GYa9nh26qhcDLsiNG2u3Eqbg22P/0aKVmJ0JYawFKnNvpZroaD/r9z1sOhQ4fE1Rlya9asmTh2sXnzZnTs2BHdunWDu7s72rVrB2NjY7x48QLHjx/HmTNnSvUGRR0dHezfvx9t2rSBu7s7Tp8+DScnJ3z//fcIDQ1F48aNMWzYMDg6OiIhIQFXrlzB8ePHkZCQUGoxE9Hnh4kTKhFGRkZYs+ZH+Pr6AjAAsAyAqn0oBAALAczB3Lnz/nUD4tOnT0dycjK2bt2K7du3o379+jhw4ACmTp1a7H1NnToV1apVw/Lly8V1QitXrowOHTrAw8Oj2PvLMWDAAKxfvx6BgYFITExEhQoV0KdPH/j7+0NDI/uOGl1dXZw+fRrz589HSEgINm7cCENDQ1SrVg0BAQEKG8zlpUyZMvj1118xbdo0jBw5EpmZmQgKClKZONHQ0MDvv/+OWbNmYfv27QgKCoKNjQ0WL16Mb775ptivAREREREREX3e+lRahj6VluVbp7JOHQysvDbfOiZalbGo5uNC9d3AuDcaGPcu1DnqmjVrlsryoKAgMXFSrlw5nDt3DuvWrcP27dsREBCAlJQUmJmZoUGDBtiyZQv69OnzSeJTl6GhIY4cOYJWrVqhffv2OHv2LOzt7XHp0iXMnj0bu3fvRmBgIExNTVGzZk0sXLiwVOMlos+PROBuyVRMrly5AmdnZ1y+fBn169dXWWfNmjUYM2YspFIrZGaOBNAHgCmAtwD2QFMzEJmZ4Zg5cyYCAgIKtQ8GEX0e1PldQkRERERElFt+nyNyjo2vcgCWOrVKKcKie5p6Ez886szPSERUII6pFB/OOKESNWrUKDRq1AirVq3C1q1+eP/+fzM1NDSk+Oqr7hg3bg1cXFxKMUoiIiIiIiIi+ty8Sn9Q2iEUyX81biKi/zImTqjEOTs7Izg4GEuXLsWFCxfw9u1b6Ovro379+qhUqVJph0dEREREREREnxEzMzPoaOvit5gJpR1Kkelo6xa4lygRERUfJk6o1JiamqJz586lHQYRERERERERfcasrKxw91444uLiSjuUIjMzM4OVlVVph0FE9MVg4oSIiIiIiIiIiD5rVlZWTDwQEZHaNEo7ACIiIiIiIiIiIiIion8LJk6IiIiIiIiIiIiIiIj+wcQJlRpBEBAfH4+oqCjExsZCLpeXeAzv3r3D0KFDUaFCBUgkEkycOLHEY/ivOXXqFCQSCU6dOlWs7drY2MDHx6dY2yQiIiIiIiKi0uXq6gpXV1fxeVRUFCQSCYKDg8UyHx8f6Ovrl3xwJcDHxwc2NjaFPu/fOE7i6uoKJyenAuup+h5/LFVt+vv7QyKRFFsfRLlxjxMqcUlJSdiyZQtW//ADboeHi+W21tYYPXYsfH19YWpqqlZbwcHB8PX1RVhYGBo0aFDoWObPn4/g4GDMnDkTdnZ2cHBwKHQb/0Y51yWHTCaDiYkJatWqhc6dO8PX1xcGBgYlHte5c+dw9OhRTJw4EUZGRiXePxEREREREX2ZHj9+/FlvDv/hOMCHzp8/jyZNmojP09PTsX79emzfvh23b99GcnIyzMzM0KBBA/Tt2xeenp6QSqUAsgesbW1tVbbbuHFjXLhwoYiviojo34uJEypR586dg0eXLnidmIjaAIYC0AOQBuBadDSmTZkCv1mz8Nv27ejatesnj+fkyZNo0qQJ/Pz8PnlfpWH27NmwtbXF+/f/z959hzV1/WEAf5Owwt4iIFsRBBcKLhRc4Ky7uKq2dVYrtrZV6wC1dbd11NVacde96qjFbd17oAgKorjYyl75/cGP1JiwA1F8P33y1Jx77rnfexMSON97zsnB8+fPceLECQQGBuKnn37Cvn37UL9+/SqN5+zZswgODsbQoUPlEifh4eEQCjkIjoiIiIiIiJQrJiYGLi4uSE9PV3Uo5aatrY27d++WuMB9YT/A25ycnKT/jouLQ6dOnXDlyhX4+flh6tSpMDY2xvPnzxEaGooBAwYgMjIS06ZNk2mjf//+6Ny5s0yZmZlZmc/F1tYWGRkZUFdXL/O+H5L3uZ+kql7jqVOnYtKkSZV6DPpwMXFCVeb8+fNo17YtbHJyMFEigfFb2xsD6JOfj82Zmejx0UfYs3dvpSdPXr58CVdX1xLrZWZmQkND4737wurUqZPMSJzJkyfj2LFj6Nq1K7p37467d+9CLBarMML/aGpqqjoEIiIiIiIiqobi4+ORnp6OBVMWwMHWAQCwNGQpTpw7IVe3oWtDTBtfkDB4GPMQuw/vRlhEGFLTU2GobwhbK1t08O6AJvWbQCAQ4GX8S4z+frRcO609W2P8Z+MVxnPs7DGs3bYWG37ZUKr4Hz56iG9+/Abx8fElJk7e7gdQZPDgwbh27Rp27tyJXr16yWybPHkyLl++jPDwcLn9GjdujEGDBpUq5uIIBAJoaWlVuJ3q7n3uJ6mq11hNTQ1qauzepsrxfvUC03srOzsbvT76CNa5uRiXny+XNCmkD2CERIIGAAI+/hgJCQllPlbhvJixsbHo0aMHdHV1YWZmhokTJyIvLw/Af+t0REVF4cCBAxAIBBAIBIiOjpZu+/PPPzF16lRYWVlBW1sbr169QmJiIiZOnAh3d3fo6upCX18fnTp1wo0bN2RiKGxj27ZtCA4OhpWVFfT09NCnTx+kpKQgKysLgYGBMDc3h66uLoYNG4asrCy5c9m4cSM8PDwgFothbGyMgIAAPH78uMzX5E1t27bFtGnT8OjRI2zcuFFm271799CnTx8YGxtDS0sLTZo0wb59+0rV7oULF+Dv7w8DAwNoa2ujTZs2+Pfff6Xbg4KC8M033wAA7O3tZa45oHjuzocPH6Jv374wNjaGtrY2mjVrhgMHDsjUefNa//DDD7C2toaWlhbatWuHyMjIMl4dIiIiIiIiqq4cbB1Qr0491KtTD4b6hvD29MbpnadlHqvmrkK9OvXw/OVzTJk/Bepq6lg4dSEOrTuEdT+tQ0+/nth9eDdsrGxQr0491HaoDQBYu3CtTDuLpi2SHuvth5WFFURCUZHb334UJnuU4dy5c/j7778xYsQIuaRJoSZNmmDgwIFKO+bbSrv+xfXr12FmZgYfHx+kpqYCAGJjY/Hpp5+iRo0a0NTURL169fDHH3+U6rjx8fG4d+9eqUcebdy4EZ6entDW1oaRkRFat26NI0eOSLfv3bsXXbp0gaWlJTQ1NeHo6IhZs2ZJ+56Kk5+fj8WLF8Pd3R1aWlowMzODv78/Ll++LK3zdj9JUet5hISEyPSvFO7btWtXnDhxAk2aNIFYLIa7u7t0vdpdu3ZJj+3h4YFr166V6poUunLlClq0aAGxWAx7e3usXLlSZntx69gU119XKDk5GUOHDoWBgQEMDQ0xZMgQJCcny8Wh6JoIBAKMHTsWe/bsgZubm/R9cvjwYbn9C6+PlpYWHB0dsWrVKq6bQlJMyVGV2LVrF569fIkZADRKqCsEMEAiweSsLISEhODrr78u8/Hy8vLg5+cHLy8vLFy4EKGhoVi0aBEcHR0xevRouLi4YMOGDZgwYQKsra2lxzAzM5N+0cyaNQsaGhqYOHEisrKyoKGhgbCwMOzZswd9+/aFvb09Xrx4gVWrVqFNmzYICwuDpaWlTBxz5syBWCzGpEmTEBkZiaVLl0JdXR1CoRBJSUkICgrC+fPnERISAnt7e0yfPl267w8//IBp06ahX79++PzzzxEXF4elS5eidevWuHbtWoXWCBk8eDCmTJmCI0eOYPjw4QCAO3fuoGXLlrCyssKkSZOgo6ODbdu2oUePHti5cyd69uxZZHvHjh1Dp06d4OHhgRkzZkAoFGLt2rVo27YtTp8+DU9PT/Tq1Qv379/Hli1b8PPPP8PU1FR6zRV58eIFWrRogfT0dHz55ZcwMTHBunXr0L17d+zYsUMunrlz50IoFGLixIlISUnB/PnzMXDgQFy4cKHc14mIiIiIiIiqLw11DZgZy/9Nmp6Rju8XfA+fZj5YOnOpzDZHW0f06dIHEolEptzQwFBhW1UtJSVFbi0XgUAgXUt2//79AFCukSPp6elybRsYGFTKdEyXLl2Cn58fmjRpgr1790IsFuPFixdo1qyZtGPczMwMhw4dwmeffYZXr14hMDCw2DaXLVuG4OBgHD9+XGaxekWCg4MRFBSEFi1aYObMmdDQ0MCFCxdw7NgxdOzYEUBBwkJXVxdfffUVdHV1cezYMUyfPh2vXr3CggULim3/s88+Q0hICDp16oTPP/8cubm5OH36NM6fP1+uNXwViYyMxIABAzBy5EgMGjQICxcuRLdu3bBy5UpMmTIFY8aMAVDQd9WvX79STw2WlJSEzp07o1+/fujfvz+2bduG0aNHQ0NDA59++mmx+5bUXwcAEokEH330Ec6cOYNRo0bBxcUFu3fvxpAhQ0p97mfOnMGuXbswZswY6OnpYcmSJejduzdiYmKkPwvXrl2Dv78/atasieDgYOTl5WHmzJnlmn6OqicmTqhK/Lp0KeqKRLAsRdYdKBh54pGfj1+XLsWECRPKPEVWZmYmPv74Y+l8nKNGjULjxo2xZs0ajB49GjVq1MCgQYOkI0oU/cKQmZmJy5cvy0xl5e7ujvv378vEM3jwYNStWxdr1qyRm/8zNzcXJ0+elP4SERcXhz///BP+/v44ePAgAGDMmDGIjIzEH3/8IU2cPHr0CDNmzMDs2bMxZcoUaXu9evVCo0aNsHz5cpnysrK2toaBgQEePHggLRs/fjxsbGxw6dIl6XDQMWPGoFWrVvjuu++KTJxIJBKMGjUKvr6+OHTokDQrP3LkSNSrVw9Tp07FkSNHUL9+fTRu3BhbtmxBjx49YGdnV2yMc+fOxYsXL3D69Gm0atUKADB8+HDUr18fX331FT766COZ1yEzMxPXr1+HhkZBas7IyAjjx4/H7du34ebmVu5rRURERERERB+Wfy//i+RXyfgs4LMi67yrd6S3b99erkxTUxOZmZkACmaaACD3d3JmZqZ0VAdQMAXS2zdszpgxQ26N2NIkIcrq33//RefOneHt7Y2dO3dK+yi+//575OXl4datW9LO71GjRqF///4ICgrCyJEjlTIdeWRkJGbOnImePXtix44dMn0PbybMNm/eLHO8UaNGYdSoUVi+fDlmz55d5FRbx48fR0hICL788kssXrxYWv7111/LJeQqIjw8HGfPnkXz5s0BAK6urvDz88Pw4cNx79496bRvRkZGGDlyJE6dOlWq1/Lp06dYtGgRvvrqKwAF/T9eXl6YPHkyBg8eXGwiraT+OgDYt28fTp06hfnz50tnLhk9ejR8fX1Lfe53795FWFgYHB0dAQC+vr5o0KABtmzZgrFjxwIoeD+LRCL8+++/0huh+/XrBxcXl1Ifh6o3TtVFlU4ikeDipUtwL2XSpFADAFGPHiExMbFcxx01apTMc29vbzx8+LDU+w8ZMkTuC1dTU1P6hZmXl4eEhATo6urC2dkZV69elWvjk08+kfnC8PLygkQikcvAe3l54fHjx8jNzQVQMEInPz8f/fr1Q3x8vPRhYWGB2rVr4/jx46U+j6Lo6uri9evXAIDExEQcO3YM/fr1w+vXr6XHS0hIgJ+fHyIiIhAbG6uwnevXryMiIgIDBgxAQkKCdN+0tDS0a9cOp06dQn5+fpnjO3jwIDw9PaVJk8KYR4wYgejoaISFhcnUHzZsmDRpAhS83gDK9JoTERERERHRh+PEuRNo3KmxzGPlxpWIfhINALCv9d8i67fu3ZKpd/yc7N/l/cf2l9keFiH7N2tV+fXXX/HPP//IPA4dOiTd/urVKwAFf1+/aeXKlTAzM5M+3vxbvNCIESPk2m7QoIFS4z9+/Dj8/PzQrl077Nq1S5p8kEgk2LlzJ7p16waJRCLTV+Ln54eUlBSF/TJvCgoKgkQiKTE5sGfPHuTn52P69OlyN/K+mTB7s8+osC/F29sb6enp0gSVIjt37oRAIJBLQr3dfkW5urpKkyZAQd8TUDCF+5tr5RSWl7b/RE1NDSNHjpQ+19DQwMiRI/Hy5UtcuXKlxP1L6q87ePAg1NTUpIkUABCJRBg3blyp4gMKEoiFSRMAqF+/PvT19aXHycvLQ2hoKHr06CEze4yTkxM6depU6uNQ9cYRJ1Tp8vLykJ2Tg7Lm/Avrp6amSqd1Kq3C+SHfZGRkhKSkpFK3YW9vL1dWOAfl8uXLERUVJTMHY+HdDm96e9E2AwMDAECtWrXkyvPz85GSkgITExNERERAIpGgdu3aCmNTxjDY1NRUmJubAyi4m0IikWDatGlyo2YKvXz5ElZWVnLlERERAFDskMmUlBQYGRmVKb5Hjx5Jv7zfVJj5f/TokcwdMm9f68LjleU1JyIiIiIiog+HVyMvzJgg23ltoGeA7Qe2y9V1dnDG7t93AwD8BvnJrcnw0/Sf4Gj7X0dtTbOaAICuQ7vi6YunAACP+h74bd5vSj2Ht3l6ehY71ZOenh6Agj6Bwj4KAOjdu7f0b+yvv/5a4TodtWvXVjiipbC9N0esiESiMk95lJmZiS5dusDDwwPbtm2TWfQ7Li4OycnJWL16NVavXq1w/5cvX5bpeEV58OABhEIhXF1di613584dTJ06FceOHZMmpAqlpKQU276lpSWMjYtaAVg5ytInBfzXf5KRkSEXv4WFhfTflpaW0NHRkdlep04dAAVrmzRr1qzImErTX/fo0SPUrFlTLrnn7OxcZLtve/vc3z7Oy5cvkZGRAScnJ7l6isrow8TECVU6NTU1aGloIDU7u0z7FX7d6uvrl/mYIpGozPu8TdHwzh9//BHTpk3Dp59+ilmzZsHY2BhCoRCBgYEKR1UUFUdR5YVDMvPz8yEQCHDo0CGFdd/+8iirJ0+eICUlRfplUBj7xIkT4efnp3Cfor44CvddsGABGjZsqLBOReMtjZKuKREREREREdGbxFpi2FrZypUXlkU9jkJD14YACu6qV1S3UE3zmgq3r5q7Crl5BbNLaGloKSHqiqlbty4A4Pbt22jZsqW0vFatWtIOdSMjI7m1TEqycOFCBAcHS5/b2trKLFZeGpqamujcuTP27t2Lw4cPo2vXrtJthX0PgwYNKvLGzfr165fpeBWRnJyMNm3aQF9fHzNnzoSjoyO0tLRw9epVfPfdd+WaeaMkRY1GKWox+vL2SW3duhXDhg1TuK2ilNFfV5HjsI+IyoKJE6oSPr6+uBYaCr8yTNd1RSCAu4tLmUcqVKYdO3bA19cXa9askSlPTk4u86iY4jg6OkIikcDe3l6atVemDRs2AIA0SeLg4ACgYCRLUXePFKVw6KO+vn6J+5ZlyKmtrS3Cw8PlyguHu9raFv0LKxEREREREVF5tWzaEgb6Bvh9y+9YNmtZhdqyspCfuUGVunbtirlz52LTpk0yiZOK+uSTT2Sm9yrPWiMCgQCbNm3CRx99hL59++LQoUPSabXMzMygp6eHvLy8MvdblJWjoyPy8/MRFhZW5A2iJ06cQEJCAnbt2oXWrVtLy6OiokrV/t9//43ExMQyjTop7B9LTk6WWX/m0aNHpW6jNPz8/PDPP/8Uuf3p06dIS0uTGXVy//59AChxPdvSsLW1xdGjR5GamipzI66iPqLyMjc3h5aWFiIjI+W2KSqjDxPXOKEq8cXYsYjKy0PJXx8FEgHcBPDFl1++UwuuiUQiuez09u3bi1z/o7x69eoFkUiE4OBgueNJJBIkJCSUu+1jx45h1qxZsLe3x8CBAwEUfGH4+Phg1apVePbsmdw+cXFxRbbn4eEBR0dHLFy4UGZYrqJ9C79Uk5OTS4yzc+fOuHjxIs6dOyctS0tLw+rVq2FnZ1fikFkiIiIiIiKi4mTnZCMuMU7mkZSSBB2xDmZPnI2T509i5KSROH3xNB4/fYzwB+H4fcvvACC39sX7omXLlujQoQNWr16NvXv3KqxTnrvyHRwc0L59e+mjvEkZDQ0N7Nq1C02bNkW3bt1w8eJFAAX9Mb1798bOnTtx+/Ztuf2K67coqx49ekAoFGLmzJlyI0cKr03hiIY3r1V2djaWL19eYvu9e/eGRCKRGaHzdvuKFN64eurUKWlZWloa1q1bV+Ixy6JmzZoyr+Xbiarc3FysWrVK+jw7OxurVq2CmZkZPDw8Knz8zp07Izc3FytWrJCW5eXlYenSpRVuu5BIJEL79u2xZ88ePH36VFoeGRkpsyYQfdg44oSqRKdOnVDb0REbHj3C17m50CmmbjaAtSIRjPT1pR3774quXbti5syZGDZsGFq0aIFbt25h06ZN0hEbyuLo6IjZs2dj8uTJiI6ORo8ePaCnp4eoqCjs3r0bI0aMwMSJE0ts59ChQ7h37x5yc3Px4sULHDt2DP/88w9sbW2xb98+aGn9N0z4119/RatWreDu7o7hw4fDwcEBL168wLlz5/DkyRPcuHFD4TGEQiF+//13dOrUCfXq1cOwYcNgZWWF2NhYHD9+HPr6+ti/fz8ASL9Av//+ewQEBEBdXR3dunWTmxsTACZNmoQtW7agU6dO+PLLL2FsbIx169YhKioKO3fufG9/SSUiIiIiIqJ3w+mLp+Hd21umzL6WPQ6tP4QO3h2wedlm/L7ld0yaOwkpr1Kgq6MLN2c3/DTtJ/g291VR1MUr7Ad4W4sWLaR9Fxs3boS/vz969OiBTp06oX379jAyMsLz588RGhqKU6dOqXSBbLFYjL/++gtt27ZFp06dcPLkSbi5uWHu3Lk4fvw4vLy8MHz4cLi6uiIxMRFXr15FaGgoEhMTi203KCgIwcHBOH78eLELxDs5OeH777/HrFmz4O3tjV69ekFTUxOXLl2CpaUl5syZgxYtWsDIyAhDhgzBl/+/6XfDhg2lSjr5+vpi8ODBWLJkCSIiIuDv74/8/HycPn0avr6+GDt2rML9OnbsCBsbG3z22Wf45ptvIBKJ8Mcff8DMzAwxMTElHldZLC0tMW/ePERHR6NOnTrYunUrrl+/jtWrVytlTd5u3bqhZcuWmDRpEqKjo+Hq6opdu3YVu25MeQQFBeHIkSNo2bIlRo8ejby8PCxbtgxubm64fv26Uo9F7ycmTqhKiEQi7D9wAC2aNcOi1FQMzs2FHYC3x5LEAtgsEuGJmhqOHThQJWtjlMWUKVOQlpaGzZs3Y+vWrWjcuDEOHDiASZMmKf1YkyZNQp06dfDzzz9L70KoVasWOnbsiO7du5eqjenTpwMouGPD2NgY7u7u+OWXXzBs2DDpgnCFXF1dcfnyZQQHByMkJAQJCQkwNzdHo0aNpO0UxcfHB+fOncOsWbOwbNkypKamwsLCAl5eXhg5cqS0XtOmTTFr1iysXLkShw8fRn5+PqKiohQmTmrUqIGzZ8/iu+++w9KlS5GZmYn69etj//796NKlS6nOn4iIiIiIiEiRuZPmYu6kucXWcXd2x+KgxcXWsbawxr3j8omK4vTy74Ve/r3KtE9pFfX3+9q1a6WJE3Nzc5w9exarVq3C1q1bERwcjPT0dJiamqJJkybYtGkTPv7440qJr7T09fXx999/o3Xr1ujQoQNOnz4NJycnXLx4ETNnzsSuXbuwfPlymJiYoF69epg3b16JbaampkIgEMgsdF6UmTNnwt7eHkuXLsX3338PbW1t1K9fH4MHDwYAmJiY4K+//sLXX3+NqVOnwsjICIMGDUK7du2KXDv2TWvXrkX9+vWxZs0afPPNNzAwMECTJk3QokWLIvdRV1fH7t27MWbMGEybNg0WFhYIDAyEkZGR3JoklcnIyAjr1q3DuHHj8Ntvv6FGjRpYtmwZhg8frpT2hUIh9u3bh8DAQGzcuBECgQDdu3fHokWL0KhRI6UcAyi4uffQoUOYOHEipk2bhlq1amHmzJm4e/euwuQjfXgEEq6KQ0py9epVeHh44MqVK2jcuLHCOuHh4ejWpQsiHjyAnUiExnl50AWQAeCGSIT7eXmoWaMGdu/dCy8vryqNn4jeDaX5LCEiIiIiInpTcX9HFG5bMGUBHGyVO2NEVXj46CG++fEb/o1UQZ6enrC1tcX27dtVHQq9w3r06IE7d+4gIiJC1aGUC/tUlIcjTqhKOTs74254OA4fPoxfly3DwePHkZmVBU11dXh6emLmuHHo2bMnNDQ0VB0qEREREREREVUDpqam0NbWxjc/fqPqUMpNW1sbpqamqg7jvfXq1SvcuHFD6euB0PstIyMDYrFY+jwiIgIHDx7EkCFDVBgVvSuYOKEqJxKJ0KVLF+l0S7m5uVBT41uRiIiIiIiIiJTPxsYGd+/eRXx8vKpDKTdTU1PY2NioOoz3lr6+PrKyslQdBr1jHBwcMHToUDg4OODRo0dYsWIFNDQ08O2336o6NHoHsLeaVI5JEyIiIiIiIiKqTDY2Nkw8EJEMf39/bNmyBc+fP4empiaaN2+OH3/8EbVr11Z1aPQOYI81EREREREREREREX1Q1q5dq+oQ6B0mVHUARERERERERERERERE7womTojeMwKBAGPHji2xXkhICAQCAaKjo2XKFyxYAAcHB4hEIjRs2LByglSSEydOQCAQ4MSJE6oOhYiIiIiIiOi9FR0dDYFAgJCQEGnZ0KFDoaurq/R2g4KCIBAIZOrZ2dlh6NCh0ufvy9/7b8dNquPj4wMfH59KPYai925xOnfujOHDh1diROXXsmVLHDx4sEJtFNW3+C6pzM8STtVFKnPv3j2cOHECr1+/ho6ODry8vODh4VGmNkJCQjBs2DCF27777jvMnTtXGaFWG0eOHMG3336LQYMGISgoCKampnj69ClWr16NHj16lJhI6d69O0JDQ/HixQvo6ekprDNw4EBs374dz549g4mJSSWchXKlp6dj/vz5VfIFTERERERERKoRExNTrReHL6l/ZNSoUZUVGqmYj48PTp48ia5du2L//v0y26Kjo2Fvb48FCxZg4sSJxbZz9uxZHDlyBIGBgTA0NKzEiKuHf//9F0eOHMG9e/ekZSdOnICvr6/C+h9//DH+/PNPAAUJOTc3N/z1119y9S5fvoymTZti7dq10qRdUFAQgoODYW5ujqioKGhra8vso6i9Hj16YNq0aejcuXNFT/WDxcQJVbn9+/fj50U/4/jJ4xBCCLFIjMz8TORJ8tC0cVN8OeFLDBw4sEwZ3pkzZ8Le3l6mzM3NTdmhv1cGDx6MgIAAaGpqSsuOHTsGoVCINWvWQENDA0DBB3JwcDDs7OxKTJwMHDgQ+/fvx+7du/HJJ5/IbU9PT8fevXvh7++vlKRJ69atkZGRIY21MqSnpyM4OBgAmDghIiIiIiKqhmJiYlC3rgsyMtJVHUq5icXauHfvbokL3BfVP2Jra4uMjAyoq6srNa7ytlsVf+8rQ3h4OITC92PCnr/++gtXrlwp803Jhc6ePYvg4GAMHTr0nUycHDlyRNUhyFiwYAHatWsHJycnuW1ffvklmjZtKlNmZ2dX4WO+fPkSK1aswNdff11i3T59+qBPnz44duwY2rZtW+Fjv6sq87OEiROqMhKJBN9//z3mzJkDJ5ETPsNnaIRGUM9TRx7ycBu3cfL6SQwePBhHQ4/i9zW/QyQSlartTp06oUmTJqWqm5mZCQ0Njffmi6+8RCKR3PV7+fIlxGJxuT9MunfvDj09PWzevFlh4mTv3r1IS0vDwIEDy9V+oTdfIy0trQq1RURERERERB+2+Ph4ZGSkw2fIShha1AEAXDkwD49v/y1Xt83gX6FrYgt1zYI7uv9e0R+OTXrDqWmfMh0zPy8H2RmvoaljVKYbQxVJfn4fJ9aNQnx8fImJk+L6R5T593Vubi7y8/OhoaFRrnbfl7/337wZ9V1mY2OD169fIzg4GPv27VN1ODLS09PlRkiUx7uUZHv58iUOHDiAlStXKtzu7e2NPn3K9plRGg0bNsSCBQswZswYiMXiYuva29vDzc0NISEh1TpxUpmfJdW755jeKfPmzcOcOXPQG70xMW8iPOEJdRTckSCCCA3QAF/mf4lhGIb169dj/PjxFT5m4Tx3f/75J6ZOnQorKytoa2vj1atXSExMxMSJE+Hu7g5dXV3o6+ujU6dOuHHjhsI2tm3bhuDgYFhZWUFPTw99+vRBSkoKsrKyEBgYCHNzc+jq6mLYsGHIysqSi2Xjxo3w8PCAWCyGsbExAgIC8PjxY5k6ERER6N27NywsLKClpQVra2sEBAQgJSVFrr09e/bAzc0NmpqaqFevHg4fPiyz/e15CAUCAdauXYu0tDQIBALpHKSFGfBhw4bJlCsiFovRq1cvHD16FC9fvpTbvnnzZujp6aF79+5lvr6KXiNF8xSePn0affv2hY2NDTQ1NVGrVi1MmDABGRkZMu0WztUaGxuLHj16QFdXF2ZmZpg4cSLy8vIAFAxZNTMzAwAEBwdLzz8oKEjh+RMREREREdH7y9CiDkxtGsDUpgG0dIxg7doOA34Mk3k4evZFzdrNpfVEahrQNbKSPi/Nw9jKFeb2TWDt6gsz24Zl2lfRozDZUxGK1iIp9PDhQ/j5+UFHRweWlpaYOXMmJBKJ3L4LFy7EL7/8AkdHR2hqaiIsLKzYdouj6O99Hx8fuLm5ISwsDL6+vtDW1oaVlRXmz59fofN7++/8wnUsIiMjpaMrDAwMMGzYMKSny45KUrTGyZ07d9C2bVuIxWJYW1tj9uzZ+OOPP+TWgiiqf0FRm8nJyQgMDEStWrWgqakJJycnzJs3D/n5+SWeOwDo6elhwoQJ2L9/P65evVqqfd4UFBSEb775BkBBh3th/8ib51Oafq3C1/DKlSto3bo1tLW1MWXKFJn30K+//goHBwdoa2ujY8eOePz4MSQSCWbNmgVra2uIxWJ89NFHSExMlGv7zZlC3uyv++GHH2BtbQ0tLS20a9cOkZGRMvuWti+ptA4cOIDc3Fy0b9++XPuX1/Tp0/HixQusWLGiVPU7dOiA/fv3y/w8F0XR+7qo99/y5ctRr149aGpqwtLSEl988QWSk5Nl6hS+F27evIk2bdpAW1sbTk5O2LFjBwDg5MmT8PLyglgshrOzM0JDQ2X2f/ToEcaMGQNnZ2eIxWKYmJigb9++cuutKPuz5E0ccUJV4vnz55g+bTo6/v+/4jRDM6RL0vHrr79i5MiRcHd3L7H9lJQUublKTU1Npf+eNWsWNDQ0MHHiRGRlZUFDQwNhYWHYs2cP+vbtC3t7e7x48QKrVq1CmzZtEBYWBktLS5n25syZA7FYjEmTJiEyMhJLly6Furo6hEIhkpKSEBQUhPPnzyMkJAT29vaYPn26dN8ffvgB06ZNQ79+/fD5558jLi4OS5cuRevWrXHt2jUYGhoiOzsbfn5+yMrKwrhx42BhYYHY2Fj89ddfSE5OhoGBgbS9M2fOYNeuXRgzZgz09PSwZMkS9O7dGzExMUVOkbVhwwasXr0aFy9exO+//w4AqF27NmbOnInp06djxIgR8Pb2BgC0aNGiyGs9cOBArFu3Dtu2bZNZpD4xMRF///03+vfvD7FYjDt37pTp+ip6jRTZvn070tPTMXr0aJiYmODixYtYunQpnjx5gu3bt8vUzcvLg5+fH7y8vLBw4UKEhoZi0aJFcHR0xOjRo2FmZoYVK1Zg9OjR6NmzJ3r16gUAqF+/fpHnT0RERERERNWDSE0D2gY1ZMr++qU7TKzd0LzPj9KynKxUHPtjOGJuHYaGWB8N/SbAtc3n0u2/f2GCFh8vwJOwo3gafgru7ceiZu2WOLj4Iwxe8BCa2gaoKiX1j7wtLy8P/v7+aNasGebPn4/Dhw9jxowZyM3NxcyZM2Xqrl27FpmZmRgxYgQ0NTVhbGxc6o790kpKSoK/vz969eqFfv36YceOHfjuu+/g7u6OTp06KfVY/fr1g729PebMmYOrV6/i999/h7m5OebNm1fkPs+fP4evry9yc3MxadIk6OjoYPXq1SXe/V+c9PR0tGnTBrGxsRg5ciRsbGxw9uxZTJ48Gc+ePcMvv/xSqnbGjx+Pn3/+GUFBQWUeddKrVy/cv38fW7Zswc8//yx9zxTebFqafq1CCQkJ6NSpEwICAjBo0CDUqPHfz9imTZuQnZ2NcePGITExEfPnz0e/fv3Qtm1bnDhxAt999520z23ixIn4448/Sox97ty5EAqFmDhxIlJSUjB//nwMHDgQFy5ckNYpS19SaZw9exYmJiawtbVVuP3169dyP4fGxsYVnv3G29sbbdu2xfz58zF69OgS33ceHh74+eefcefOnWKXNCjL+7pwvZX27dtj9OjRCA8Px4oVK3Dp0iX8+++/MtP2JSUloWvXrggICEDfvn2xYsUKBAQEYNOmTQgMDMSoUaMwYMAALFiwAH369MHjx4+laypfunQJZ8+eRUBAAKytrREdHY0VK1bAx8cHYWFhJY5iUsZnCRMnVCXWrFkDYb4Q/vAvVf02aIO/1f7GihUrsHz58hLrK8rwvplNzczMxOXLl2V+4N3d3XH//n2ZD63Bgwejbt26WLNmDaZNmybTXm5uLk6ePCn9AIiLi8Off/4Jf39/HDx4EAAwZswYREZG4o8//pAmTh49eoQZM2Zg9uzZmDJlirS9Xr16oVGjRli+fDmmTJmCsLAwREVFYfv27TLD+d5MwBS6e/cuwsLC4OjoCADw9fVFgwYNsGXLFplkxpsGDRqE0NBQXL16FYMGDZKWi0QiTJ8+Hc2bN5cpL0rbtm1Rs2ZNbN68WeZY27dvR05OjnSarrJeX0WvkSLz5s2TqTNixAg4OTlhypQpiImJkRm2nJmZiY8//lh6rFGjRqFx48ZYs2YNRo8eDR0dHfTp0wejR49G/fr1S3X+RERERERE9GG5GboUDf0mwKPLd3hy9xjO7ZgCfXNHWLv8twj01YPz4fnRNDTr8wOEQjW8io9WSawl9Y+8LTMzE/7+/liyZAmAgn6Nbt26Yd68efjyyy9lki5PnjxBZGSktDMdgNzd3xX19OlTrF+/HoMHDwYAfPbZZ7C1tcWaNWuUnjhp1KgR1qxZI32ekJCANWvWFJs4mTdvHuLi4nDhwgV4enoCAIYMGYLatWuXO46ffvoJDx48wLVr16TtjBw5EpaWlliwYAG+/vpr1KpVq8R29PX1ERgYiBkzZuDq1ato3LhxqWOoX78+GjdujC1btqBHjx4y63GUtl+r0PPnz7Fy5UqMHDlSWlb4PomNjUVERIT05uC8vDzMmTMHGRkZuHz5MtTUCrqq4+LisGnTJqxYsaLE6dIyMzNx/fp16Q24RkZGGD9+PG7fvi1NFpSlL6k07t27V+yaJZ9++qlcWVRUlFLWOZkxYwbatGmDlStXYsKECcXWdXBwAACEhYUVmzgp7fs6Li4Oc+bMQceOHXHo0CFpn1/dunUxduxYbNy4EcOGDZPWf/r0KTZv3oz+/fsDKBgBU7duXQwYMABnz56Fl5cXAMDFxQV+fn7YuXOndDRWly5d5KY769atG5o3b46dO3dKPyOKoozPEk7VRVXit1W/oUl+E+hAp1T1RRChZW5LrA9Zr3Daq7f9+uuv+Oeff2QebxoyZIhch7ympqb0BzwvLw8JCQnQ1dWFs7OzwmGNn3zyiUzW1MvLCxKJRO7D0MvLC48fP0Zubi4AYNeuXcjPz0e/fv0QHx8vfVhYWKB27do4fvw4AEi/NP7++2+5oaFva9++vTRpAhR8wenr6+Phw4fF7qcMIpEIAQEBOHfunMwvSJs3b0aNGjXQrl07AGW/vopeI0XerJOWlob4+Hi0aNECEokE165dk6s/atQomefe3t5Vcp2IiIiIiIjo3RZz+whCJthIH0d/H6awXg0HLzToGAiDGk6o5zMC9o264/Yx2bUNnJr0Rp3mA6FvagddY+uqCF+hkvpHFHnzpkiBQICxY8ciOztbbuqc3r17yyRNKoOurq7MTY0aGhrw9PSslL/jFfUXJCQk4NWrV0Xuc/DgQTRr1kzauQwUjMqoyFqv27dvh7e3N4yMjGT6jdq3b4+8vDycOnWq1G2NHz8eRkZGCA4OLnc8byttv1YhTU1Nmc7zN/Xt21dmRpXCjvNBgwZJkyaF5dnZ2YiNjS0xvmHDhsnMWlI4m8qb75my9iWVJCEhAUZGRkVunz59utzPoYWFRZmPo0jr1q3h6+uL+fPnlzjVWGGMb49+eVtp39ehoaHIzs5GYGCgzI3Sw4cPh76+Pg4cOCBTX1dXFwEBAdLnzs7OMDQ0hIuLi/S1B/57HxT1muXk5CAhIQFOTk4wNDQs1XR0yvgs4YgTqnT5+fmIeRKDVmhVpv3sYY8DGQeQkJAgN63T2zw9PYtdHN7e3l5hXIsXL8by5csRFRUlXfcCgMLprt7OPhd+0L+d9TcwMEB+fj5SUlJgYmKCiIgISCSSIu8+KEzG2Nvb46uvvsJPP/2ETZs2wdvbG927d8egQYNkvlQUxQIUfBgmJSUpPIayDRw4ED///DM2b96MKVOm4MmTJzh9+jS+/PJL6YL0Zb2+il4jRWJiYjB9+nTs27dP7nzfXgtGS0tL7pe6qrxORERERERE9O6qWacVWn68UPpcXVMbx9aOkKtnbt9U7vnt47KJE1ObhpUSY1mV1D/yNqFQKL0rvVCdOgVrqrw9mqS0f7dXhLW1NQQCgUyZkZERbt68qfRjvd23UtjJnJSUBH19fYX7PHr0SKbDt5Czs3O544iIiMDNmzeLTEopWmO2KAYGBtJRJ9euXZPr3M/Ly0NcXJxMmbGxcbELr5e2X6uQlZVVke2VpW8NQKn6b4p7HQuVpS+ptIobyeXu7l7h9U/e/jl4U1BQUKlGnRTGWFxbQOnf148ePVJYrqGhAQcHB+n2Qop+ng0MDEr1emdkZGDOnDlYu3YtYmNjZa53aV4zZXyWMHFClU4ikUAikUBYxgFOAhS8ud/scC8vRSMZfvzxR0ybNg2ffvopZs2aJZ1rMDAwUOEcnYUJgdKWF/5A5+fnQyAQ4NChQwrr6urqSv+9aNEiDB06FHv37sWRI0fw5ZdfYs6cOTh//jysrf+7Y6WkY1Y2Dw8P1K1bF1u2bMGUKVOwZcsWSCQSmUx0Wa9vaUab5OXloUOHDkhMTMR3332HunXrQkdHB7GxsRg6dKhcu0VdJyIiIiIiIiJ1DW0YmDuUXLEU1DSLn2+/OqjIOh6lVd7+jqI6hovrU1JV38rbMeXn56NDhw749ttvFdYvTGSVVuFaJ8HBwXLrozx+/FguAXb8+HGZRdffVpZ+LaD490l5+9aKU9K+Ze1LKg0TE5MK3ZSrpaVV5GiRwllotLS0ity/devW8PHxwfz58+VGTr2pMMbi1jmqTBV5vceNG4e1a9ciMDAQzZs3h4GBAQQCAQICAkr1minj55uJE6p0IpEIJoYmeJH8okz7vcRLCIXCIhc7r6gdO3bA19dXZj5LAEhOTlbqB4qjoyMkEgns7e1L9WXn7u4Od3d3TJ06FWfPnkXLli2xcuVKzJ49W2kxvamkrHNRBg4ciGnTpuHmzZvYvHkzateujaZN/7sLpzKu761bt3D//n2sW7cOn3zyibS8NEOPi1Le8yciIiIiIqIPw8uoy3LPDWuUrTP7XZWfn4+HDx/K9Ffcv38fAJSyHkNVKRxlkJycLFP+9h3wFWVra4uIiAi58vDwcIUxvR1PdnY2nj17JlPm6OiI1NTUCo9QKFQ46iQoKAhDhgyR2WZhYSHXh9KgQQMARfePlLVf611TGX1JdevWxc6dO8u9v62tLcLCwhRuK3wvFbXwfKGgoCD4+Phg1apVRdaJiooCULCGSEnxlOZ9XRhTeHi4zEi17OxsREVFKe09DBT0Kw4ZMgSLFi2SlmVmZsr9TFUmrnFCVaL/oP64oHYBucgtVX0JJPhX7V9079Yd2tqVc9eGSCSSyzJu3769VPMnlkWvXr0gEokQHBwsdzyJRIKEhAQAwKtXr6TrohRyd3eHUCgs1Tov5aWjU7DuTFk/eApHl0yfPh3Xr1+Xm/ewMq5vYbb4zXYlEgkWL15c7jYL319V+cFLRERERERE748XDy/gxj9LkPIiEmEnf0fUtb1w8x1Z8o7viWXLlkn/LZFIsGzZMqirq0vXMH0f6Ovrw9TUVG49kOXLlyv1OJ07d8b58+dx8eJFaVnhQuZvc3R0lItn9erVciNO+vXrh3PnzuHvv/+WayM5OVmur6g0AgMDYWhoiJkzZ8qUa2lpoX379jKPwqRTUf1Dpe3XeldVRl9S8+bNkZSUVO61dzp37ownT55gz549MuVZWVn4/fffYW5ujsaNGxfbRps2beDj44N58+YhMzNTYZ0rV67AwMAA9erVKzGe0ryv27dvDw0NDSxZskTmeq5ZswYpKSno0qVLsccpC0X9ikuXLlXKzESlxREnVCXGjBmDZcuW4TzOl2qtkzu4gye5TxAyLqTSYuratStmzpyJYcOGoUWLFrh16xY2bdokN7dnRTk6OmL27NmYPHkyoqOj0aNHD+jp6SEqKgq7d+/GiBEjMHHiRBw7dgxjx45F3759UadOHeTm5mLDhg0QiUTo3bu3UmN6Oz5DQ0OsXLkSenp60NHRgZeXV4lzl9rb26NFixbYu3cvAMglTirj+tatWxeOjo6YOHEiYmNjoa+vj507d1ZoeKRYLIarqyu2bt2KOnXqwNjYGG5ubnBzcyt3m0RERERERFR9uLf7AvGPruPawQVQ19KDV69ZsHZtq+qwlEJLSwuHDx/GkCFD4OXlhUOHDuHAgQOYMmVKpS8Er2yff/455s6di88//xxNmjTBqVOnpKNnlOXbb7/Fhg0b4O/vj/Hjx0NHRwerV6+Gra2t3NoJn3/+OUaNGoXevXujQ4cOuHHjBv7++2+5WTi++eYb7Nu3D127dsXQoUPh4eGBtLQ03Lp1Czt27EB0dHSZZ+4wMDDA+PHjy7RIvIeHBwDg+++/R0BAANTV1dGtW7dS92u9qyqjL6lLly5QU1NDaGgoRoyQXxepJCNGjMAff/yBvn374tNPP0WjRo2QkJCArVu34vbt21i/fn2x684UmjFjBnx9fYvc/s8//6Bbt24lzrZS2ve1mZkZJk+ejODgYPj7+6N79+4IDw/H8uXL0bRpU5nF2Cuqa9eu2LBhAwwMDODq6opz584hNDS00mYmUoSJE6oSLi4uGNB/ALZu3QrTfFPURd0i6z7GY6wVrYVPSx+0bVt5v4hMmTIFaWlp2Lx5M7Zu3YrGjRvjwIEDmDRpktKPNWnSJNSpU0c6xyRQsPBVx44d0b17dwAFQyP9/Pywf/9+xMbGQltbGw0aNMChQ4fQrFkzpcdUSF1dHevWrcPkyZMxatQo5ObmYu3ataVa9G3gwIE4e/YsPD094eTkJLOtMq6vuro69u/fL137RUtLCz179sTYsWOlQ0vL4/fff8e4ceMwYcIEZGdnY8aMGUycEBERERERVWNtPvlVYXnXwH0yzwNmXS+xrc9/lb/j3rJOK4Xl7xqRSITDhw9j9OjR+Oabb6Cnp4cZM2Zg+vTpqg6tzKZPn464uDjs2LED27ZtQ6dOnXDo0CGYm5sr7Rg1a9bE8ePHMW7cOMydOxcmJiYYNWoULC0t8dlnn8nUHT58OKKiorBmzRocPnwY3t7e+Oeff+RG8mhra+PkyZP48ccfsX37dqxfvx76+vqoU6cOgoODpQtnl1VgYCB++eWXUi9+3rRpU8yaNQsrV67E4cOHkZ+fj6ioKOjo6JSqX+tdVRl9STVq1EDnzp2xbdu2ciVOxGIxTp48iZkzZ2LPnj1Yu3YtxGIxPDw8cPDgQfj7+5eqHR8fH7Rp0wYnT56U2xYVFYXbt2/LrXOjSFne10FBQTAzM8OyZcswYcIEGBsbY8SIEfjxxx+hrq5eqrhLY/HixRCJRNi0aRMyMzPRsmVLhIaGws/PT2nHKIlAUlWrSVO1d/XqVXh4eODKlSsKh5NlZmaie7fuOH70OHwkPmiDNjDHf19eSUjCaZzGMdExuLi5IPR4qHS4IBF9OEr6LCEiIiIiInpbcX9HFG7r8d0xmNqU/6Y7VYmPuYE989ryb6R3WEhICIYNG4aoqKj3am0YKr/Tp0/Dx8cH9+7dQ+3atVUdjlTh513//v1x7949XLlyhev7lhNHnFCV0dLSwoGDBxAUFIQVy1Yg9FUo7EX20M7XRqYwE1H5URBrifHpp59i7ty50NXVVXXIRERERERERFRNJD9X7rRNVeV9jZuoOvP29kbHjh0xf/58/Pbbb6oOR86ePXuwY8cOJk0qgIkTqlLq6ur44YcfMHXqVGzbtg3Hjx/Hq1evoKuriwleEzB48GDo6+urOkwiIiIiIiIiqiZMTU0hFmvjxLpRqg6l3MRi7TKvc0FElevQoUOqDqFIZ86c4Qi1CmLihFRCLBZjyJAhGDJkiKpDISIiIiIiIqJqzMbGBvfu3UV8fLyqQyk3U1NT2NjYqDoMIqIPBhMnRERERERERERUrdnY2DDxQJVm6NChGDp0qKrDICIlEqo6ACIiIiIiIiIiIiIioncFEydERERERERERERERET/x8QJERERERERERERyYmOjoZAIMDChQsr1M7QoUNhZ2dX6uOFhISU6zghISEQCASIjo4u1/5V4cSJExAIBDhx4oSqQyGiYnCNE1K6u3fvqjoEInqP8TOEiIiIiIiULSYmplovDh8SEoJhw4bh0qVLaNKkidx2Hx8fxMfH4/bt25UZJhFRtcHECSmNqakptLW1MWjQIFWHQkTvOW1tbZiamqo6DCIiIiIiqgZiYmLg7OKMzPRMVYdSblraWgi/G/7eLnD/22+/IT8/X9VhvBNat26NjIwMaGhoqDoUIioGEyekNDY2Nrh79+57fQcHEb0bSrqbioiIiIiIqLTi4+ORmZ4Ju1l20LLXAgA8XfUU+en5sJ5gLVM3LSwNj398jNqrakOkIyq23ey4bDyc8BB2P9hBy1arTDHlZ+ZDkiOBSK/4YwBAZlQmoqdFIz4+/r37OyktLQ06OjpQV1dXdSjvDKFQCC2tsr1fiKjqMXFCSmVjY/PefYkTERERERERUfWnZa8FbRdtAICagRryhHnS54Xy0vIAAGJnMdT0iu82ExmI/mvXWbvYuoUkEgmQBwjUBGUNX6natGmD5ORk3LhxQ26bs7Mz7Ozs8Pfff8uU//zzz/jll1/w8uVLeHp64tdff4Wbm5t0+9ChQ7Fjxw7cuHED48aNw+nTp9GuXTvs2bMHQ4cOxYkTJ2TWHklOTkZgYCB2794NgUCAjz76CBMmTCj1Ody5cwfjxo3DuXPnYGJiglGjRsHS0lJh3eXLl+PXX39FZGQkTExM0LNnT/zwww8wNDSU1imczmzz5s0YN24cLl26BEtLS8ydOxd9+vTByZMn8e233+LmzZuwsbHBr7/+ivbt20v3f/ToEebNm4ejR48iJiYG2traaNu2LRYsWCCzvsuJEyfg6+uL48ePw8fHR+bY27ZtwxdffIELFy7AyMgI48ePx7ffflvqa0JEysPF4YmIiIiIiIiIiN6Qn5GP662vIyk0SaY8+Xgyrre8Lk2wAEBmdCbCh4XjWvNrCOsXhtdXXku3vb78Glc9riLl3xTcHXgX15tdR+r1VDxd9RR3+yt/fceUlBTEx8fLPXJycmTqDR48GDdv3pRb8+TSpUu4f/++3DTs69evx5IlS/DFF19g8uTJuH37Ntq2bYsXL17I1MvNzYWfnx/Mzc2xcOFC9O7dW2GcEokEH330ETZs2IBBgwZh9uzZePLkCYYMGVKq83z+/Dl8fX1x/fp1TJo0CYGBgVi/fj0WL14sVzcoKAhffPEFLC0tsWjRIvTu3RurVq1Cx44d5a5LUlISunbtCi8vL8yfPx+ampoICAjA1q1bERAQgM6dO2Pu3LlIS0tDnz598Pr1f6/1pUuXcPbsWQQEBGDJkiUYNWoUjh49Ch8fH6Snp5d4TklJSfD390eDBg2waNEi1K1bF9999x0OHTpUqmtCRMrFESdERERERERERERvEIqFMOpohIR9CTBqbyQtT9ifAMN2hhDpiJCbkgsAiF0cC+uvraHloIWXG1/iwYQHcNvnBjXD/7rdni59CqtAK2haaUKkL5JJrijTmyMg3lavXj3pv/v27Ytx48Zh48aNmDt3rrR848aN0NHRQa9evWT2jYyMREREBKysrAAA/v7+8PLywrx58/DTTz9J62VlZaFv376YM2dOsXHu27cPp06dwvz58/HNN98AAEaPHg1fX99Snee8efMQFxeHCxcuwNPTEwAwZMgQ1K5dW6ZeXFwc5syZg44dO+LQoUMQCgvuIa9bty7Gjh2LjRs3YtiwYdL6T58+xebNm9G/f38AQIcOHVC3bl0MGDAAZ8+ehZeXFwDAxcUFfn5+2LlzJ4YOHQoA6NKlC/r06SNz/G7duqF58+bYuXMnBg8eXOw5PX36FOvXr5fW++yzz2Bra4s1a9agU6dOpbouRKQ8HHFCREREREREREQfnJQzKbje6rrMI/LLSOl20x6meHX+FXLiCkYl5CTmIOVMCkw+MpFpx6yfGYzaGUFsL4bNZBuIdEWI3yu7/mvNUTWh30wfmrU0oWZQefcx//rrr/jnn3/kHvXr15epZ2BggI8++ghbtmwpmD4MQF5eHrZu3YoePXpAR0dHpn6PHj2kSRMA8PT0hJeXFw4ePCgXw+jRo0uM8+DBg1BTU5OpKxKJMG7cuFKd58GDB9GsWTNp0gQAzMzMMHDgQJl6oaGhyM7ORmBgoDRpAgDDhw+Hvr4+Dhw4IFNfV1cXAQEB0ufOzs4wNDSEi4uLNGkCQPrvhw8fSsvEYrH03zk5OUhISICTkxMMDQ1x9erVEs9JV1dXZqSPhoYGPD09ZY5BRFWHI06IiIiIiIiIiOiDo9dED7Um15IpS7+Vjuhp0QAAHTcdiB3ESPgrARbDLJB4MBGaNTWh21hXZh+d+v8lGQRqAmi7aCMzKlOmjrZr6dZAqShPT080adJErtzIyAjx8bLJnE8++QRbt27F6dOn0bp1a4SGhuLFixcKR0a8PZIDAOrUqYNt27bJlKmpqcHa2rrEOB89eoSaNWtCV1f2Wjo7O5e4b+H+byYyitr/0aNHCss1NDTg4OAg3V7I2toaAoHs+jMGBgaoVauWXBlQML1WoYyMDMyZMwdr165FbGysNCEFFEyhVhJFxzYyMsLNmzdL3JeIlI+JEyIiIiIiIiIi+uAItYTQqqUlU5bzQnbNC5MeJojbFgeLYRZI2JcA4+7Gcp3bpTqW+N2b9MXPzw81atTAxo0b0bp1a2zcuBEWFhbFTvdVEk1NTZmRHe8bkUhUpvI3kyPjxo3D2rVrERgYiObNm8PAwAACgQABAQHIz88v97HfPAYRVZ3395OMiIiIiIiIiIioEhl3Nkb282y83PISmVGZMOlqIlcn7Vaa9N+SXAnS76ZDy15Lrt67RiQSYcCAAdixYweSkpKwZ88e9O/fX2EHfkREhFzZ/fv3YWdnV65j29ra4tmzZ0hNTZUpDw8PL/X+imJ6e39bW1uF5dnZ2YiKipJuV4YdO3ZgyJAhWLRoEfr06YMOHTqgVatWSE5OVtoxiKjqMHFCRERERERERESkgJq+Ggx9DRG7OBb6zfShUUNDrk78tngkH0tGZlQmHs97jLzXeTDtbqqCaMtu8ODBSEpKwsiRI5Gamiqzxsab9uzZg9jYWOnzixcv4sKFC+VetLxz587Izc3FihUrpGV5eXlYunRpqfc/f/48Ll68KC2Li4vDpk2bZOq1b98eGhoaWLJkiczIjTVr1iAlJQVdunQpV/yKiEQiudEhS5cuRV5entKOQURVh1N1ERERERERERERFcH0I1MkHU6CSXf50SYAYDnOEs9DniPjfgY0a2nC8SdHqBm9H11ujRo1gpubG7Zv3w4XFxc0btxYYT0nJye0atUKo0ePRlZWFn755ReYmJjg22+/Lddxu3XrhpYtW2LSpEmIjo6Gq6srdu3aVaq1QADg22+/xYYNG+Dv74/x48dDR0cHq1evhq2trcyaIGZmZpg8eTKCg4Ph7++P7t27Izw8HMuXL0fTpk2LTBSVR9euXbFhwwYYGBjA1dUV586dQ2hoKExMFL9viOjd9n58ihMRERERERERESmJXbCdwnK9JnpofEU2eZAdlw2RgQgGPgYy5ZqWmtK6xv7GpW4PACxHWsJypGU5Ile+Tz75BN9++63CReHfrCMUCvHLL7/g5cuX8PT0xLJly1CzZs1yHVMoFGLfvn0IDAzExo0bIRAI0L17dyxatAiNGjUqcf+aNWvi+PHjGDduHObOnQsTExOMGjUKlpaW+Oyzz2TqBgUFwczMDMuWLcOECRNgbGyMESNG4Mcff4S6unq54ldk8eLFEIlE2LRpEzIzM9GyZUuEhobCz89PaccgoqojkHCFISIiIiIiIiIieo9dvXoVHh4euHLlityoicJtdTfWhbaLdqnbzM/IR058Dh589QAGPgaw+sJK2WGXSvrddNwbdE/huSnD4sWLMWHCBERHR8PGxkbp7RNR1Snus5DKhiNOiIiIiIiIiIio2suMyixT/bidcUjYlwBtZ20YtDBA+t30SoqseGWNuywkEgnWrFmDNm3aMGlCRPQGJk6IiIiIiIiIiKjaMjU1hZa2FqKnRZdr//SwdNz//L5ygyojLW0tmJoqb8H5tLQ07Nu3D8ePH8etW7ewd+9epbVNRFQdMHFCRERERERERETVlo2NDcLvhiM+Pl7VoZSbqampUkeExMXFYcCAATA0NMSUKVPQvXt3pbVNRFQdMHFCRERERERERETVmo2NDaeieoOdnR247DERUdGEqg6AiIiIiIiIiIiIiIjoXcHECRERERERERERERER0f8xcUJERERERERERETvtaFDh8LOzk7VYZRaUFAQBAJBmeq+z+v0EL1vuMYJERERERERERFVazExMe91p3NJi8OHhIRg2LBhuHTpEpo0aVKmttPT0zF//nz4+PjAx8engpFWrqdPn2L16tXo0aMHGjZsqOpwlO7HH3+Eq6srevToodR2hw4dinXr1kFPTw8vXryAWCyW2R4REYE6deoAABYsWICJEycCAE6cOAFfX1+FbX788cf4888/pc8PHjyILl26oGbNmnjy5AmEQvn79e3s7PDo0SPpczMzMzg7O+Orr75Cz549K3yeRMrExAkREREREREREVVbMTExcHF2RnpmpqpDKTdtLS3cDQ+vlAXu09PTERwcDADvReIkODgYdnZ2comT3377Dfn5+aoJrBymTp2KSZMmyZT9+OOP6NOnj9ITJwCgpqaG9PR07N+/H/369ZPZtmnTJmhpaSGziJ+RL7/8Ek2bNpUpe3t0z6ZNm2BnZ4fo6GgcO3YM7du3V9hWw4YN8fXXXwMoeD1XrVqFXr16YcWKFRg1alQ5z45I+Zg4ISIiIiIiIiKiais+Ph7pmZnY6Aa46BSUzXgAvM4DfqpTujY8LgALawO+xpUXZ1HupgGDbmciPj6+UhInlSUtLQ06OjpVdjx1dfUqO5YyqKmpQU2t6rpmNTU10bJlS2zZskUucbJ582Z06dIFO3fuVLivt7c3+vTpU2TbaWlp2Lt3L+bMmYO1a9di06ZNRSZOrKysMGjQIOnzTz75BE5OTvj555+ZOKF3Ctc4ISIiIiIiIiKias9FB2isX/AwUQcM1f57XtIDABy1S1+/qIe7btn3cSln7mHo0KHQ1dVFbGwsevToAV1dXZiZmWHixInIy8sDAERHR8PMzAwAEBwcDIFAAIFAgKCgIGk79+7dQ58+fWBsbAwtLS00adIE+/btkzlWSEgIBAIBTp48iTFjxsDc3BzW1tYAgEePHmHMmDFwdnaGWCyGiYkJ+vbti+joaLmYk5OTMWHCBNjZ2UFTUxPW1tb45JNPEB8fjxMnTkhHPQwbNkwaa0hIiPR83x4FkZaWhq+//hq1atWCpqYmnJ2dsXDhQkgkEpl6AoEAY8eOxZ49e+Dm5gZNTU3Uq1cPhw8fLvYaSyQSmJqa4quvvpKW5efnw9DQECKRCMnJydLyefPmQU1NDampqQDk1zgRCARIS0vDunXrpOc2dOhQueszdOhQGBoawsDAAMOGDUN6enqxMb5pwIABOHTokExcly5dQkREBAYMGFDqdt62e/duZGRkoG/fvggICMCuXbuKHL3yNgsLC7i4uCAqKqrcxyeqDEycEBERERERERHRB8vnMvDlPeDb+4DxccDiJBD04L/tdqcL/t/zBiD457/nALD3JdD4PKB1FHA4AwQ/AHLfmC1K8A+w4jHQ/RqgcxT4oYr7hvPy8uDn5wcTExMsXLgQbdq0waJFi7B69WoABWtMrFixAgDQs2dPbNiwARs2bECvXr0AAHfu3EGzZs1w9+5dTJo0CYsWLYKOjg569OiB3bt3yx1vzJgxCAsLw/Tp06XTUF26dAlnz55FQEAAlixZglGjRuHo0aPw8fGR6fRPTU2Ft7c3li5dio4dO2Lx4sUYNWoU7t27hydPnsDFxQUzZ84EAIwYMUIaa+vWrRWeu0QiQffu3fHzzz/D398fP/30E5ydnfHNN9/IJDoKnTlzBmPGjEFAQADmz5+PzMxM9O7dGwkJCUVeX4FAgJYtW+LUqVPSsps3byIlJQUA8O+//0rLT58+jUaNGkFXV1dhWxs2bICmpia8vb2l5zZy5EiZOv369cPr168xZ84c9OvXDyEhIdJp1kqjV69eEAgE2LVrl7Rs8+bNqFu3Lho3blzkfq9fv0Z8fLzM481p0TZt2gRfX19YWFggICAAr1+/xv79+0sVU05ODh4/fgwTE5NSnwdRVeBUXURERERERERE9EFb9wz4yga44AmcSwGG3gFaGgIdTIBLXoD5SWBtPcDfBBD9f5DA6STgkzvAEmfA2xB4kAGMCCvYNsPxv7aDHgJznYBfnAE1wdtHrlyZmZn4+OOPMW3aNADAqFGj0LhxY6xZswajR4+Gjo4O+vTpg9GjR6N+/foyUygBwPjx42FjY4NLly5BU1MTQEFypFWrVvjuu+/kFvQ2NjbG0aNHIRKJpGVdunSRm+apW7duaN68OXbu3InBgwcDKFiU/Pbt29i1a5dMu1OnToVEIoFAIECnTp0wffp0NG/eXC7Wt+3btw/Hjh3D7Nmz8f333wMAvvjiC/Tt2xeLFy/G2LFj4ej43wt19+5dhIWFSct8fX3RoEEDbNmyBWPHji3yON7e3pg0aRJev34NPT09nD59Gra2tqhRowZOnz6NLl26ID8/H//++y+GDRtWZDuDBg3CqFGj4ODgUOS5NWrUCGvWrJE+T0hIwJo1azBv3rxir0UhPT09dO3aFZs3b8ann36K/Px8/Pnnnxg9enSx+3366adyZVFRUbCzs8PLly8RGhoqTcDZ2NigefPm2LRpE/r27Su3X05ODuLj4wEUrHEyZ84cvHjxAuPGjSvVORBVFY44ISIiIiIiIiKiD1p93YJkR20d4BNLoIk+cDSxYJuZRsH/DdUAC83/ngc/BCbZAUMsAQftgiTLLCdg1RPZtgdYAMOsCurYiKvslKTeXjfC29sbDx8+LHG/xMREHDt2TDrKoXCkQUJCAvz8/BAREYHY2FiZfYYPHy6TNAEAsfi/k87JyUFCQgKcnJxgaGiIq1evSrft3LkTDRo0kEvGAJCZ0qq0Dh48CJFIhC+//FKm/Ouvv4ZEIsGhQ4dkytu3by+TSKlfvz709fVLvFbe3t7Iy8vD2bNnARSMLPH29oa3tzdOny4YnnT79m0kJyfD29u7zOfxJkWvZUJCAl69elXqNgYMGIATJ07g+fPnOHbsGJ4/f17iNF3Tp0/HP//8I/OwsLAAAPz5558QCoXo3bu3tH7//v1x6NAhJCUlybV15MgRmJmZwczMDA0aNMD27dsxePDgUid/iKoKR5wQEREREREREdEHrf5bsyfV1AReZhe/z43XwL/JstNv5UmAzHwgPQ/Q/n/+oIm+UkMtEy0tLekaJoWMjIwUdmi/LTIyEhKJBNOmTZOOWHnby5cvYWVlJX1ub28vVycjI0O6aHhsbKzM+iKFU1oBwIMHD2Q63yvq0aNHsLS0hJ6enky5i4uLdPubbGxs5NoozbVq3LgxtLW1cfr0afj5+eH06dMIDg6GhYUFli5diszMTGkCpVWrVhU5JbkYjYyMAABJSUnQ1y/dG61z587Q09PD1q1bcf36dTRt2hROTk4K15wp5O7uXuRi7xs3boSnpycSEhKk05o1atQI2dnZ2L59O0aMGCFT38vLC7Nnz4ZAIIC2tjZcXFxgaGhYqtiJqhITJ0RERERERERE9EFTf2tOFgGAfInCqlKpeUCwI9DLXH6b1hvt6Yjkt1eVt0d/lEXhGhYTJ06En5+fwjpOTk4yz98cXVJo3LhxWLt2LQIDA9G8eXMYGBhAIBAgICBAZp0MVSvqWr29kPzb1NXV4eXlhVOnTiEyMhLPnz+Ht7c3atSogZycHFy4cAGnT59G3bp15ZJYVRXjmzQ1NdGrVy+sW7cODx8+RFBQULnjiYiIwKVLlwAAtWvXltu+adMmucSJqalpkUkYoncJEydERERERERERETFUBcUjCZ5U2M9IDwNcNJWTUzKUtQ0WA4ODgAKEgMV6ejesWMHhgwZgkWLFknLMjMzkZycLFPP0dERt2/fLlesitja2iI0NFS69kihe/fuSbcri7e3N+bNm4fQ0FCYmpqibt26EAgEqFevHk6fPo3Tp0+ja9euJbZTninJymPAgAH4448/IBQKERAQUO52Nm3aBHV1dWzYsEEuqXPmzBksWbIEMTExCkfzEL3ruMYJERERERERERFRMezEBWuePM8CknIKyqY7AOufAcEPgDupwN1U4M/nwNRI1cZaVtraBZmftxMZ5ubm8PHxwapVq/Ds2TO5/eLi4krVvkgkkhsRsXTpUuTl5cmU9e7dGzdu3MDu3bvl2ijcX0dHR2GsinTu3Bl5eXlYtmyZTPnPP/8sXWheWby9vZGVlYVffvkFrVq1kiZAvL29sWHDBjx9+rRU65vo6OiU6twqytfXF7NmzcKyZcuka5WUx6ZNm+Dt7Y2PP/4Yffr0kXl88803AIAtW7YoK2yiKsURJ0RERERERERERMVYVAf4Khz4LRaw0gSivQE/U+CvhsDMh8C86ILpvupqA59bldTau0UsFsPV1RVbt25FnTp1YGxsDDc3N7i5ueHXX39Fq1at4O7ujuHDh8PBwQEvXrzAuXPn8OTJE9y4caPE9rt27YoNGzbAwMAArq6uOHfuHEJDQ2FiYiJT75tvvsGOHTvQt29ffPrpp/Dw8EBiYiL27duHlStXokGDBnB0dIShoSFWrlwJPT096OjowMvLS+HaKt26dYOvry++//57REdHo0GDBjhy5Aj27t2LwMBAmYXgK6p58+ZQU1NDeHi4zNRUrVu3xooVKwCgVIkTDw8PhIaG4qeffoKlpSXs7e3h5eWltDgLCYVCTJ06tUJtXLhwAZGRkRg7dqzC7VZWVmjcuDE2bdqE7777rkLHIlIFJk6IiIiIiIiIiOiDEuL2379PNJHfvqeh7PNuZgWPt/mZFjyKIulQrvCq3O+//45x48ZhwoQJyM7OxowZM+Dm5gZXV1dcvnwZwcHBCAkJQUJCAszNzdGoUSNMnz69VG0vXrwYIpEImzZtQmZmJlq2bInQ0FC5dVN0dXVx+vRpzJgxA7t378a6detgbm6Odu3awdraGkDBtGHr1q3D5MmTMWrUKOTm5mLt2rUKEydCoRD79u3D9OnTsXXrVqxduxZ2dnZYsGABvv7664pftDfo6OigUaNGuHTpkswC8IXJklq1apVqarCffvoJI0aMwNSpU5GRkYEhQ4ZUSuJEGTZt2gSgIEFVlG7duiEoKAg3b95E/fr1qyo0IqUQSMqyehAREREREREREdE75urVq/Dw8MCVK1fQuHFjhds2ugEuOioKsALupgGDbkPhuRERvam4z0IqG444ISIiIiIiIiKiasvU1BTaWloYdDtT1aGUm7aWFkxNixnaQkRESsXECRERERERERERVVs2Nja4Gx6O+Ph4VYdSbqamprCxsVF1GEREHwwmToiIiIiIiIiIqFqzsbFh4oGIiEpNqOoAiIiIiIiIiIiIiIiI3hVMnBAREREREREREREREf0fEydERERERERERET0zgsJCYFAIEB0dHSlHSM6OhoCgQAhISHl2r8qYqyoEydOQCAQ4MSJE6oOheidxTVOiIiIiIiIiIioWouJianWi8OHhIRg2LBh0NTUxIMHD2BlZSWz3cfHB/Hx8bh9+3axx0lPT8f8+fPh4+MDHx8fZYRORPReYuKEiIiIiIiIiIiqrZiYGDi71EVmeoaqQyk3LW0xwu/eK3GB+6ysLMydOxdLly4t13HS09MRHBwMAO9k4mTw4MEICAiApqamqkN5r7Vu3RoZGRnQ0NBQdShE7ywmToiIiIiIiIiIqNqKj48vSJqEfAa4WBQUztwPHLgJjPEFhrT4r/LJcODbHcCF7xU39tupgjobh1d+4IXuPkfm0DWIj48vMXHSsGFD/Pbbb5g8eTIsLS2rKMCSpaWlQUdHp8LtiEQiiEQiJUT0YRMKhdDS0lJ1GETvNK5xQkRERERERERE1Z+LBdDItuBhrANoqQObLwB2pv+V25sV1C18/vbDwgAQaxS9vbSPelalr1uY7CmFKVOmIC8vD3Pnzi3z5YmOjoaZWcH5BwcHQyAQQCAQICgoSFrn3r176NOnD4yNjaGlpYUmTZpg3759Mu0UrvFx8uRJjBkzBubm5rC2tgZQMIrFzc0NN2/eRJs2baCtrQ0nJyfs2LEDAHDy5El4eXlBLBbD2dkZoaGhCtt+c/0QOzs7dO3aFWfOnIGnpye0tLTg4OCA9evXy+ybmJiIiRMnwt3dHbq6utDX10enTp1w48aNMl+rQnfu3EHbtm0hFothbW2N2bNnIz8/X2Hd5cuXo169etDU1ISlpSW++OILJCcny9Sp6PV59OgRxowZA2dnZ4jFYpiYmKBv375y660oWuOk8NhhYWHw9fWFtrY2rKysMH/+/HJfH6L3GRMnRERERERERET04WnrAtTQB+YdKl399WeB2X8BN58AGiMKHuvPFmxLTgdGrgcsvwJMvgQ6LgJuPP5v35n7gCYzgT9OA3UmA3pfKP98ANjb2+OTTz7Bb7/9hqdPn5ZpXzMzM6xYsQIA0LNnT2zYsAEbNmxAr169ABQkCZo1a4a7d+9i0qRJWLRoEXR0dNCjRw/s3r1brr0xY8YgLCwM06dPx6RJk6TlSUlJ6Nq1K7y8vDB//nxoamoiICAAW7duRUBAADp37oy5c+ciLS0Nffr0wevXr0uMPTIyEn369EGHDh2waNEiGBkZYejQobhz5460zsOHD7Fnzx507doVP/30E7755hvcunULbdq0KfO1AoDnz5/D19cX169fx6RJkxAYGIj169dj8eLFcnWDgoLwxRdfwNLSEosWLULv3r2xatUqdOzYETk5OTJ1K3J9Ll26hLNnzyIgIABLlizBqFGjcPToUfj4+CA9Pb3Ec0pKSoK/vz8aNGiARYsWoW7duvjuu+9w6FApf0aIqhFO1UVERERERERERB8ekQCY1RP45HdgbDvA2qj4+n2bAHdigb/vAIcnFJQZiAv+339VwQiW/V8C+mLg91OA/0/AndkFo1sA4EEcsPsqsHU0IKq8e5m///57rF+/HvPmzVPYiV8UHR0d9OnTB6NHj0b9+vUxaNAgme3jx4+HjY0NLl26JF1jZMyYMWjVqhW+++479OzZU6a+sbExjh49Kje11tOnT7F582b0798fANChQwfUrVsXAwYMwNmzZ+Hl5QUAcHFxgZ+fH3bu3ImhQ4cWG3t4eDhOnToFb29vAEC/fv1Qq1YtrF27FgsXLgQAuLu74/79+xAK/7v2gwcPRt26dbFmzRpMmzat1NcKAObNm4e4uDhcuHABnp6eAIAhQ4agdu3aMvXi4uIwZ84cdOzYEYcOHZIev27duhg7diw2btyIYcOGKeX6dOnSBX369JE5frdu3dC8eXPs3LkTgwcPLvacnj59ivXr10vrffbZZ7C1tcWaNWvQqVOnMl0fovcdR5wQEREREREREdGHqUcjoEGtghEhJRFrADqagJqwYMquwmm7/o0ALkUBf44EPOyA2jWAeX0BQ21g15X/9s/OBf74FGhkA9S3rrRTcnBwwODBg7F69Wo8e/ZMKW0mJibi2LFj6NevH16/fo34+HjEx8cjISEBfn5+iIiIQGxsrMw+w4cPV7geia6uLgICAqTPnZ2dYWhoCBcXF2lSAID03w8fPiwxPldXV2nSBCgYPePs7Cyzr6ampjRpkZeXh4SEBOjq6sLZ2RlXr14t5ZX4z8GDB9GsWTNp0qTwuAMHDpSpFxoaiuzsbAQGBsokbYYPHw59fX0cOHBApn5Fro9YLJb+OycnBwkJCXBycoKhoWGpzlFXV1cmYaahoQFPT89SvQZE1Q0TJ0RERERERERE9OH6sRew4Rxw960kg9G4/x5fbCx6/5tPgNQswOIr2X2i4gtGmRSyNQHM9CrnHN4ydepU5ObmFrnWSWJiIp4/fy59pKSkFNteZGQkJBIJpk2bBjMzM5nHjBkzAAAvX76U2cfe3l5hW9bW1hAIBDJlBgYGqFWrllwZUDB9VElsbGzkyoyMjGT2zc/Px88//4zatWtDU1MTpqamMDMzw82bN0s8f0UePXokN7oEKEh0vF1PUbmGhgYcHByk2wtV5PpkZGRg+vTpqFWrlsw5Jicnl+ocFR377etI9KHgVF1ERERERERERPTh8q4DdHQFpu4CPmnxX/mlN6Zu0tcqev/ULKCmAfDPRPlthv+NAIC2RsVjLSUHBwcMGjQIq1evlllfpFCvXr1w8uRJ6fMhQ4YgJCSkyPYKFzyfOHEi/Pz8FNZxcnKSef7m6Ic3KRqFUly5RCIpMq6y7Pvjjz9i2rRp+PTTTzFr1iwYGxtDKBQiMDCwyAXdVaEi12fcuHFYu3YtAgMD0bx5cxgYGEAgECAgIKBU51iR14CoumHihIiIiIiIiIiIPmyzewFNZwF1LP4rczKXr6ehBuS91QHdyAZ4/qpgCi8708qNswymTp2KjRs3Yt68eXLbFi1aJDOKwNLSEgDkRhsUcnBwAACoq6ujffv2lRBt5duxYwd8fX2xZs0amfLk5GSYmpb9dbO1tUVERIRceXh4uFy9wvLC6wgA2dnZiIqKUur13LFjB4YMGYJFixZJyzIzM5GcnKy0YxB9KDhVFxERERERERERfdjcrYH+XsCvx4qvZ2sCRCcA1x8D8a+BrBygnQvQzAHosxz45w4QHQ+cewBM2w1cia6S8BVxdHTEoEGDsGrVKjx//lxmm4eHB9q3by99uLq6AgC0tbUBQK6j3dzcHD4+Pli1apXCdVPi4uLkyt41IpFIbuTE9u3b5dZmKa3OnTvj/PnzuHjxorQsLi4OmzZtkqnXvn17aGhoYMmSJTLHX7NmDVJSUtClS5dyHV8RRee4dOlS5OXlKe0YRB8KjjghIiIiIiIiIiKa0R3Yfrn4Or0aA3uuAR0XAcnpwO9DC6b32vclMH0PMHwdEPcasNAHWtUBzPWrIvIiff/999iwYQPCw8NRr169EuuLxWK4urpi69atqFOnDoyNjeHm5gY3Nzf8+uuvaNWqFdzd3TF8+HA4ODjgxYsXOHfuHJ48eYIbN25UwRmVX9euXTFz5kwMGzYMLVq0wK1bt7Bp0yaZUSBl8e2332LDhg3w9/fH+PHjoaOjg9WrV8PW1hY3b96U1jMzM8PkyZMRHBwMf39/dO/eHeHh4Vi+fDmaNm0qsxh7RXXt2hUbNmyAgYEBXF1dce7cOYSGhsLExERpxyD6UDBxQkREREREREREH5Y1w+TL7EyB1OXF76epDmwdJV+upwX8HFDwUGR694JHFXNycsKgQYOwbt26Uu/z+++/Y9y4cZgwYQKys7MxY8YMuLm5wdXVFZcvX0ZwcDBCQkKQkJAAc3NzNGrUCNOnT6/Es1COKVOmIC0tDZs3b8bWrVvRuHFjHDhwQOEaMKVRs2ZNHD9+HOPGjcPcuXNhYmKCUaNGwdLSEp999plM3aCgIJiZmWHZsmWYMGECjI2NMWLECPz4449QV1dXxukBABYvXgyRSIRNmzYhMzMTLVu2RGhoaJHr0hBR0QQSru5DRERERERERETvsatXr8LDwwNXrlxB48aNFW7Dhe+BRrYqirACrj0CvH5QeG5ERG8q7rOQyoYjToiIiIiIiIiIqPq7+7zkOu+i9zVuIqL3GBMnRERERERERERUbZmamkJLW4zMoWtUHUq5aWmLYWpqquowiIg+GEycEBERERERERFRtWVjY4Pwu/cQHx+v6lDKzdTUFDY2NqoOg4jog8HECRERERERERERVWs2NjZMPBARUakJVR0AERERERERERERERHRu4KJEyIiIiIiIiIiIiIiov9j4oSIiIiIiIiIiIiIiOj/uMYJERERERERERFVazExMdV2cXiBQFCqNo4fPw47OzvY29vLlOvp6cHBwQGff/45Ro8eDZFIVOF4iYjed0ycEBERERERERFRtRUTEwOXus5Iz8hUdSjlpi3Wwt174QqTJxs2bJB5vn79evzzzz9y5S4uLsjIyAAA9O/fH507dwYApKSk4ODBgxg3bhwePXqEBQsWVNJZEBG9P5g4ISIiIiIiIiKiais+Ph7pGZmY1bcupm2/V2zdnk0s0LquCSZsvCMtEwkFMNHVQOu6xvjIw6LUIzyUJSouHdO230N8fLzCxMmgQYNknp8/fx7//POPXDkAREdHAwAaN24ss33MmDHw8vLC5s2bmTghIgITJ0RERERERERE9AGwN9PG35OaS58fufUSK0OjsWuCp7RMW0OE5PQcAMCKT+vDwVwHObn5uPYoBbN334eLlR56NKlZ5bFXNoFAgBo1auDFixeqDoWI6J3AxAkREREREREREX0QTPU0pP/W1VKDQCBbBkCaODEQq0u31TTSwv6rz3HvaWrVBVuJ0tPTpWu+vHr1CocOHcLhw4cxefJkFUdGRPRuYOKEiIiIiIiIiIioGGFPXuNubCq6NKqh6lCUYsaMGZgxY4ZM2ejRoxEcHKyiiIiI3i1MnBAREREREREREb1l2OprEAqAnDwJcvMk6NW0Jro2slB1WEoxYsQI9O3bF0DBiJNjx45hxYoV0NTUxM8//6zi6IiIVI+JEyIiIiIiIiIiorfM/dgF9uY6yM3LR+SLdCz4KwJ6YjV86eeg6tAqrHbt2mjfvr30ea9evSAQCPDLL7/g008/hbu7uwqjIyJSPaGqAyAiIiIiIiIiInrX1DDQQi0TMezNddDB3Qz9W1hj45knyMrJV3VolaJdu3YAgFOnTqk4EiIi1WPihIiIiIiIiIiIqAQioQB5+RLk5FXPxElubi4AIDU1VcWREBGpHqfqIiIiIiIiIiIiektKRg7iX2cjL1+CyOep2HL2CZo4GEJXq3p2p+3fvx8A0KBBAxVHQkSketXzk56IiIiIiIiIiKgCRv9xEwAgEgKmeppoWccEX3S0U21QSnL16lVs3LgRAPD69WscPXoUO3fuRIsWLdCxY0cVR0dEpHpMnBARERERERER0Qene2MLdG9sIVduaaSFKz+0UUFEVWfLli3YsmULAEBNTQ02Njb45ptvMH36dAiFnNmfiIiJEyIiIiIiIiIiompi2bJlWLZsmcJtdnZ2kEgkVRwREdH7h4kTIiIiIiIiIiKq9qLi0lUdQrm8r3ETEb3PmDghIiIiIiIiIqJqy9TUFNpiLUzbfk/VoZSbtlgLpqamqg6DiOiDwcQJERERERERERFVWzY2Nrh7Lxzx8fGqDqXcTE1NYWNjo+owiIg+GEycEBERERERERFRtWZjY8PEAxERlZpQ1QEQERERERERERERERG9K5g4ISIiIiIiIiIiIiIi+j8mToiIiIiIiIiIiIiIiP6Pa5wQEREREREREVG1cPfuXVWHQESkMvwMVB4mToiIiIiIiIiI6L1mamoKbW1tDBo0SNWhEBGplLa2NkxNTVUdxntPIJFIJKoOgoiIiIiIiIiIqCJiYmIQHx+v6jCIiFTK1NQUNjY2qg7jvcfECRERERERERERERER0f9xcXgiIiIiIiIiIiIiIqL/Y+KEiIiIiIiIiIiIiIjo/5g4ISIiIiIiIiIiIiIi+j81VQdARETVU05ODh5HRCIhMhoZSckQZGRBkC+BRF0NAj0dGNQ0h5W7K4yNjVUdKhERERERERERkRQXhyciIqXKzMzEnZP/Ij0iGja7/oX59YcQJ6XK1MlTEyHFxgwx3ZshxaMOHJs1gbWTg4oiJiIiIiIiIiIi+g8TJ0REpDSPIyLx8Ni/cF+wHcYPnpdqn1x1Ndwb2h6pXZqjoV87aGlpVXKURERERERERERERWPihIiIKkwikeD26XPI33YEbisPQpSXX+Y2kuxq4Mb0AfDo0x16enqVECUREREREREREVHJmDghIqIKu/PveYhW7kLdjccq1E6GkS4uLPgcnv17QVtbW0nRERERERERERERlZ5Q1QEQFWfixIkQCoUQCATSh1gsxvXr11UdGhH938tnz5Bx4FSFkyYAIE5KhcfUdbh64AiY1yciIiIiIiIiIlVQU3UAREVZsGABFi1aBADQ1taGWCxGYmIiMjMz0ahRI3aqEr0DcnNzEfb3cbT8ZY/S2tR7ngTz7SfxsI4jHBu4IyMjA08iHiApKgZZya+A3FxAIIBAWwwDC3PUcKkNM3NzCAQCpcVARETycnJyEBsVjcTIKKQlJAPZOYAAgKYG9MxMYFrHETVrWUMkEqk6VCIiIiIiogrhVF30ztLW1kZGRgY+//xz/Pbbb9JyoVAIiUSCv/76C126dFFhhEQUefMWNMb/BJsTt5TarkQAHP09EDoGBkDkY9jsPA2T+7HQSkmX1slTEyGllimetW2AuFbuqNXIDQ713ZhAISJSsszMTISd/BdpEdGotecsTG9FQ+dlCgo/bfMFAqRaGuFlU2fEdmoK47qOcGnhBTU13qNFRERERETvJ/41Q++s3NxcAED79u1lytXU1JCTkwNzc3NVhEVE/yeRSBB7/Q5anb6j1HbzBQKEd/eERmQs6v/2B3TiXymsJ8rNg3HUCxivOYL8tf/gYXcvnAloi4adO3BxeSIiJXkc+QAPjp6B+8IdMIl8prCOUCKBfmwi9GPPwXHPOTxvUhtnvniEev5tYWZhUcURExERERERVRxHnNA7q1GjRtK1TPr3749GjRph2bJliImJgUgkkiZWiEg1UlJSEDXzVzT8aZfS2swXCXFxTGeY3YmBw7GbKOvYkTQzfVwOHgz3Pl1hbGamtLiIiD5EYecuImvr36i/bD9Eefll2jdHrIErkz9GzT5+sHWpW0kREhERERERVQ4mTuidJhaLkZmZKVOmoaGBrKwsFUVERIWiwu9D7dNZqHX2rlLakwC4NMofllcfwPpiRLnbyRZr4NzCz9FoUB/o6+srJTYiog9NxJVryPxtF9xXHSp3G/kCAS5P6w/rIT1h6WCvxOiIiIiIiIgql1DVARAVx9jYGABgYmICR0dHCAQCZGdnw9DQULWBERGSHsbA8IHiaVvK43ELF+i8TKlQ0gQANDKy0XTqelz/6wjy88t2hzQRERWMKIwLPQu3CiRNgIIpvDx++BMRR07K3QhDRERERET0LuMaJ/TOsrW1xdOnT7Fo0SJ89dVX0nKhUIiUlBTs3LkTvXv3ltvv+vXr+OGHHxS2mZycjLCwMCxatAgBAQGVFjvRhyA7IwOar9JLrlgKWTpaiPJxg/e8nUppTzspFTYb/kFEbXs4N/VQSptEVDZ5eXl49eoVcnNzIRQKoaenBw0NDVWHRSWQSCS4ceAIms7dVubpEhUR5eWj/rztuGFpDq/unYs9bnp6OjIyMgAUjDDW09ODQKCMKIiovLKyspCamor8/Hyoq6tDT08PIpFI1WERERERVTpO1UXvrMI/lN9+i9auXRuRkZFo2rQpLl68KLdf3759sWPHjmLbtrS0RGxsrPKCJfoAXdi1H40GzYFGRnaF27rXtSn0nybC8uoDJURWQALg5O+BaD1sAIRCDrAkqgrZ2dmIunUHL+7chygxBfoPnkEtJR35Wup47VAT2WaG0Le1gqOXB/T09FQdLinw8uVLvJy2BG6rDyu13QuzP4Fb4GfQ0dGRlkkkEsTFxSHq34vIfpkAcWwCdB7HARIJMs0NkWprDqGJIWw8G8LKzo6f5URVJCUlBQ/OXULqk2fQfJEMvajnEOTkIsdQB68cakJiYoCa9V1h6+IMdXV1VYdLREREVCk44oTeO4WLwufk5Cjc/v333xe579mzZ/H06VO4uLhUSmxEHxINbW1kGupCIyOxQu1IADxvaI86By4rJ7D/EwCw/PsynrbzhrWdrVLbJiJZEokEEVev4/m5q3DcfBy1L4RDmK/43pxEBwvc/aQdhJ5uqN+uDUehvGMe/nsR9befUXq7jhuO4kHLxqjv0woA8OrVK1w/8A+MT16H+/bT0E5MVbhfjlgD0Z2a4FTXZnDp6IMaVpZKj42ICmRmZuL6kWNQP38bThuPweBxvMJ6eSIhYlu54myAD2q18oR9PReODiMiIqJqh4mTamLAgAE4c+YMYmJipGWNGzfGtWvXAAD6+vpISUlRVXgVMmHCBPz888/S59HR0QCAzz//XGH9hg0bYvv27Qq3FY5GMTIyUnqcRB8aIydbJNeuCf1nFUucpNUwhH5sIoSVMADS8sQthN+5x8QJUSXKzs7Gxb0HUXNTKLz3ni9xeifjh8/hGbQJca42OPv1CzTo2YXfy+8IiUSCnBfx0E5SnMSoCJPwWNyNKRjtG33nLp4cPokmc7ZCO+F1sfupZ2Sj9q6zsDtwCbe+jMHzTi1Q38ebnbREShb3/Dnu7DuCBnO3wijqRbF1RXn5sDl5G9an7uB+/zs437cNmnb1h5oauxeIiIio+uB492piy5YtePz4sfR5UFCQNGkCFNzVZ2pqqorQyq1169YAgF9++QVisRhGRkbSP5IFAgG++OILVYZH9MEzNjdHfHPXCreTZF8DRg+fKyEieTpxKUhPSK6UtomoYPTnuZ374DplDRxLkTR5k1lYDJp/uRI3N+9GUlJSpcVIpZeWlgbtx3GV0rYAgCD5NR7cvI2EdXvR8pvfS0yavEk9KweNF+yA7uJtuBZ6Qm4qVyIqv/gXL3Bvy160/HJFiUmTNwklEtTdfByOM0Jwbs9fyMvLq8QoiYiIiKoWEyfVyJtzRs+bNw8A8Oeff0IikUAgECAhIUFVoZXLyZMn0bVrVwAFw8aTk5MBAGKxGC9fvlRhZEQEFIxkS3WzR656xe4ufG1hBL0KjlopigCAIC2jUtomIuDa30fhOnszjCOflWt/zbRMNJv0B27sPSSdipNUJzU1FXoRlbcGnCjpNZ7vP4bGC3dAUM68h+Pe89Da9Dcehd1TbnBEH6isrCzc3vc3mk0OgXqW4qmQS2J+MxqOC7fj5vHTSo6OiIiISHWYOKlG3kycZGZmAgA+/vhjAICJiYlKYqqo/fv3QyKRyDzS09Pfu9EzRNVVLY/6iGnXsEJtSNSEEObmKycgRfIrsW2iD9jTqGhoHT4Ps7CYkisXQzM1Ey5L9uIWO9xUTiKRQFjOjtOS5IuEeKWnCY/55U+aFKq74ShiTpxDRgYT40QVdePIcdSfv73cSZNClhfCkXf8EuKeV84oYiIiIqKqxsRJNVI4zcWKFSsAQGa+8KysLJXERETVm02d2ogZ4IssPXG52xBl5iBXS12JUcmScL5tIqWTSCSIOHMBrqsPK6W9GtceIPP6PXaEq5hIJEKunnaltB3btDZsT9yG1qv0CrclzJeg3uLduH/ukhIiI/pwvXr1CoJLYeUeNfi2+ov34P7Js0ppi4iIiEjVmDipJkQiEXJycqCmpoYxY8YAABYuXCjdnpqq/EU+iYhEIhHqd26Pa9/2QXlvIDZ4Eo9kGzOlxlUoXygA9CunE5DoQ5aYmAjDy/ehlqO86bXsNx3Hw2s3ldYelZ2BgQFSXG0qpe1H3vXgGHpdae0ZRzxDckQU11QgqoAHF67AaeNRpbWnkZYFjbBovH5d+vWLiIiIiN5VTJxUE2fOnIFAIJD+8Whvb49PP/0UAHD8+HFIJBKoq1feHd1E9OEyNjGBSec2uDm2W7mSJ0YPXyDJsabS4wKAlFpm0DevnKQM0Yfs2Z1wWB+8qNQ2a9x8iMSHj5TaJpWNpqYmssyNSq5YRjma6hDm5kI9I1tpbQoAmJ2/i8TEylkji+hD8PrxszItBl8a1vvO4dmDKKW2SURERKQKTJxUE82aNUN+fr50HZCHDx9Kt3l5eeHIkSN4+vSpCiMkouqsduOGEA/pjstTPkaOZtmStFqv0pGtq4UcLQ2lxxXTozmsG7opvV2iD13Ks+cwfPRSqW0KJACSU5HPdYlUSt/WCon2NZTaZrKdOfQfxyu1TQAwvhqJpCf8/ZaoPHJycqCekKL0do0evkBS9GOlt0tERERU1Zg4qSaEQiG6dOmicJu2tjaWLFkCc3PzKo6KiD4kdZo0gs2Ygfh3+Vg8b+RQ6tEnWbpakOhq42GHhkqNJ1dDDckedWBsbKzUdokIyE/LhChH+VMkiV8mIzMzU+ntUuk5NmuCB4PbKbXN1BqGMHiSoNQ2AUD3WRJSnyk3gUf0oUhLS4Pu4zilt6v1Kh3ZqWlKb5eIiIioqnHF3GpCIpHg5s2i5wW/efMmJJLyrkBARFQ6NawsYTy4H8Jr2+PevQewPHIZ5hfvQ/9JPIT5/30GZeqJkeRogcc9WiLHxRZ1Wnrint0Z2J64Ba3Xylkc+vaoLqjduhkEAoFS2iOi/wgq6XcKYW4ef19RMT09PUiauiKhjhVM7scqpc0El1qwuPpAKW29SZiXBwlHKBGVi0QigUCJ61S91XjltEtERERUhZg4+UAkJSWpOgQi+kCoq6vDzbsF8ls2wzN/H8Tcf4hXL+OBjExI8iWAugga+vowsreGq5MjdHV1AQCanTvg2ncv0GzqelQ01RHnaoOcjl6wtLWt+AkRkRyJuhokQIV/Vt+Wra/NNdneAQ06+OLcxDi0Grccalk5FWorw1AHCd7uMI5W/siQbF0x1LXFSm+X6EOgrq6ObENdpbebLxJCoM5uBiIiInr/8Tea91jdunVx//596fMnT55AKJSffY13bhKRKgiFQljVqgWrWrWkZVeuXIGHh4fC+kbGxjDr4oMbcSlosHhvuTtkk23NEfZ9f7To6FvOFoioJGIjA2QY60I7MVWp7eaYGkBDQ/nrHVHZaGpqol63Djj/Kg3NpoRALbt8d6Vn6olxce6naNCuDZ7efgT8fVWpcSbVsYKhg41S2yT6UOjo6CCtlpnS231lbQJ9c+W3S0RERFTVuMbJeywmJka6GHyhwudvPgoFBgaqIMqK69u3LwQCgczDwMBA1WERUTlkZBQ/DZdTw/rQHdIdF4MGIltHs8ztP27jjptzP0Oz3t151zpRJTJ2skN8fXultpmprw11Y36/vyvMLCxQJ6A7/v1pBF5bGJV5/4Taljj/80g0DOiBGjVq4LWDRanXviqt+Jb1YGLGDlqi8hAIBBAY6SNHU7m/L8U1doKJk51S2yQiIiJSBY44eY+lp6dL/y0QCGBtbY3Hjx+rMCLl8/DwwNWrBXcn6unpQUNDA69evSqx85WI3l9OjRrAyNoSZ+1rwm5DKGqduAVRbvGLUKfUMkXYqC7Qa90ELZt7QiQSVVG0RB8ma0cHXOzVEjYnbimtzaiezWHn1Vhp7b1PsrOzEXMvHKnP4pCTlQU1DXWITYxhU68utLW1VRaXuZUldIZ+jGvmxjDZdxaOu/+FRlpWsftkGOkifHBbZHfwRPMOvtDULEiCGzraItHRAiYPnisltmyxBrKcbaCjo6OU9og+RLWaNEBMx0Zw3H9RKe1JADz1b4pWVlZKae9Dkpubi6iIKCQ8TkBWZhbU1NSgY6QDRzdH6OnpqTo8IiKiDxITJ9VEdZyO6/jx49KkSVpamko7DoioapmYmcF7YF9EN3bHmWu3oXcnGiYX7sEw8inU07MgEQmRamGExIZOiPesA21HG9Rr6QV9fX1Vh070QdDQ0IB2HXulLSCeI9bAi7aNUNfSUgnRvT+SkpLw4OxFZEY+hu32U6jx8BnUMrKRp6mO1JrGuPlxa0hq28ChpSfMa9SAQKDsVWVKpqOjg5Z9PsLTpo1wsWszaNx/ApPzYTC6+xhar9IhAZBhrIckN1vEN3cFHKzg2NIT5hYWMu04enrg1ued0HzyWqXEFdnXG3bNPsxEG5GyWDvY4/RHLWB36EqJN6mUxvMmtWHi4sQbWMrg9evXuH3+Nl5EvMCrPa+QfScbknQJBOoCCEwFeNDvAXRddFGveT1YWlsqnJqbiIiIKgcTJ9XQ+fPncfr0aeTlyf/yO2nSJBVEVD69e/cGAIwaNQra2tq4dOkS6tWrxwQK0QdCJBLB0a0eHOq54vXr10iKi0d0VAxyM7MQl50BextbmNhYwcnIiNNyEalAPZ9WODcxFt5f/ApRTsU63G6O7wHXDm1UkhhQBYlEgrvnLyH10GnUXfM39J8mytXRfZEMi+sPkWGki4iANojp1goefu1U0mkmEAhgZWcLKztbpKenIzEhAU8fPkLW6zTEZWegVg0LGDvYopaREbS0tBS2oaurC71WjfG4zWXUOlmxkUqvrIyR/FFLuNjZVagdog+dSCRCbd+WuDM8AvVXHKhQW9liDYR/0Q2tmnsqKbrqL/xWOG4fuo3klcnIi5L9HpVAAsQBCcEJSNRNRMJHCTDvZY423drw914iIqIqwsRJNREeHg53d3fk5OQUW+99SpwkJSUBKFhM+s2OFKFQiLCwMDg7O6sqNCKqQgKBAPr6+tDX14etowMA4MyZM3Bu0kjFkRF92DQ1NeHcoQ2uTIpD09lbICjn4Neozk2h1qEZzGvWVG6A77CbJ05D67d9aLrlBEpKFYmTUlF/xQHE3orG+awsNOveWaV3HGtra0NbWxvWtWoBKPg8rteyWan2dWnuiX9HPYVe9AsYPnpZruNn6Wrh6oxB8PRv/8Ek2ogqk5WDPZ52bYnYW9GwOnOnXG3ki4S4PH0A3Dq1g5oauxhK486VO7i94TZSlqSgpAWgJKkSpG5KRea1TBzNOYr2vdvzOhMREVUBfttWEy4uLtLpuvT09KrVqIxLly5BJBLBzs4O0dHRyMvLg4uLC/Lz81UdGhER0Qetpp0tsj7ujItCITzmboNaVvE3cLxJAiCyd0skf9YVTXy9Ky/Id8yD6zch2nIEzltOlGk/qzN3kKephhs6OmjUwbdygqtkIpEIXj274nx+Plx/2AKzsJgy7Z9qZoDLsz5Bwz7dqtXvukSq5uHfHhdycpGjswe2f18tMaH7pmxtTVyeNgC2fTvDtEaNSouxOol5GIOwXWFIWZxSpv1yw3Lx7MdnOKV5Cm17tK2k6IiIiKgQEyfVhEQigVAoVDg91/tOKBQiNzdX+lwkEiE/Px8TJ07EwoULVRgZERER2dVzgdhQH2dszOGyZA/Mb0SV2OmWam6AG9/2hWkbTzTxaPTBjBzIy8vDk4vX0fq3w+Xa3+boDTxt1wjpLb3e28SBpqYmmvf5CDcMDRB75ALqrT4E9YzsYvfJEwnx8KNmeP6xL5p07gBdXd0qipbowyAUCuHVzR93TY1xvk19NFi4A9qJqcXuIwHwtFldRIzqArdO7WBqbl41wb7nJBIJbp65iaT5SeXaP+dmDuJPxiOxdSKMjY2VHB0RERG9iYmTasTQ0FDVIVSKVq1ayT0/deoUNm3apDBxcv36dfzwww8K2zp79iyA/6YBIyIiooqrYWUF48H9cNfBFvfuP0TNvy/D9PpDGMTEQZSbB4kASLUwQmJtKzzp3gxqTrVQv21r6OnpqTr0KhUbFQ2rgxfLdDf32xw2HsXDVo3h5t1CaXFVNQ0NDTTt3BHP3V1w3ssF4tvRsDx8CUYPn0OcnAagYL2EZAcLvPB2Q0IzF9g0ro9Wbq4fTJKNqKoJhULUa9kMya7OuFHHFoiMgfW+8zCKfAqdlykQAMhVV0OKnRniPGrjebtGMHOpjVZeTThtVBnEx8cj7UIakFty3aK82vIKN31vwqe7j9LiIiIiInn8DaeaEAgESEyUX1i0OmjYsKHMc3d3d5w6dQrp6ekK6//www/YsWNHsW3evXtXWeERERERAHV1ddT3aYU87+Z40aUdnkZG4e6LOEhyc5EsAqwMjGHkZAsPa+siFxCv7h5dvIZmf1+tUBtmYY8Rdu8BUCNh0QAAl/BJREFU8lp4QSQSKSky1bCoVQsW/Wvh1atXePmxP2KjYpCVlo5kYT5M1cUwtLFETXtbuJmYMGFCVEUMjYzQvHc3ZGRk4HmPDrj/4BHSU14hWZAHY6EGDGrWgLGTHerUqKHS9ZbeV7f/vY1X215VqI38F/lIepCErKwsaGpqKikyIiIiehsTJ9XEd999h7lz50IoFKJXr17o2bMndHR05Or16NGj6oMrJ3V1deTk5ODixYsy5VeuXAEA6OvrK9zv+++/L7LNs2fP4unTp3BxcVFeoERERCQlEolgaWUFSysradmZM2fQ6K0RpB+a7OxsqD1+AfXM4qelKokAgNmFe0jqmQRTU1PlBKdi+vr60K/vBtR3A1Dwfmn2gb9fiFRNLBbD3rkO7J3rACj4uWzBn8sKe/XsFSTxJawGX5p2/nqFF71fwMbGRglRERERkSJMnFQTc+fOBVAwZ+rOnTuxc+dOhfUKF5B/H/Tq1Qtbt27FhQsXZMoLn0+aNEnhfg0bNsT27dsVbuvbty927NgBIyMj5QZLREREVIzs7GxolbBmQGlpPU9CdnbFEjBERFS18vLykJ+Rr5y2EvOQkZKhlLaIiIhIMSZOqglNTc1qN4XBn3/+iW3btkkXvjcwMEBKSgokEgnU1dXxxRdfqDrEKpGTk4OYe/fx/F4E8rNzAKEAmtrasPVsBHMLi2r3uhMREVVH+fn5EOTlKaUtQXYO8vOV0/lWWTIzMxF14zYSomMgyc2DQCiE2EAfDs2bwMDAAM+ePMHjyzeQk5kFQAKRhgas3F1h7eSg6tCJiCqFRCIBlPM1AOQBebnKaoyIiIgUYeKkmsjMzFR1CJUiNjYWDg4OyMzMRHJyMgBAT08Pr15VbF7Y90FWVhbunDiDtMhHsNl1Bl6nw6CWkwsJgAxjXUT1bIm7rdxQq5EbHOq7MYFCRET0DtPQ0ECOvrZS2soxNYCuhoZS2lK21NRU3Dl6CnmRMbDfchzO1x5CmC+BBECKlQlujO6MDD0t2J69h4YHL0PrdcEd0zlaGnjcrgHOdGuGtBqGyMnJgbq6umpPhohIiUQiEQRayvmbTaAjgNhArJS2iIiISDEmTuidVrNmTWRkfHhDkFNTU3F55364/7AFJhFPZbYJAGgnpqLemr+Rv/YIIvt64/In/vDwb88FGomIiN5RmpqaSKtlhnyBAMIKTp0a37QO7AwMlBSZ8iTExeH2jgPwCN4I3RfJMtvyNNQQ1q8VbP++AtszYRC8dQnUM7PhcOAS7A9cwtHZg3A2Yy+8enaFlpZW1Z0AEVElEggE0DTRBLQAVPC+R732ejAxNVFKXERERKQYe1mrmfbt28PAwABisVjhg959mZmZuLxzPzy/Xi2XNHmbMF+COltPocairbgWevy9WsOGiIjoQyIQCKBva41njSs2FVVqDUOoO9aCpqamkiJTjlevXuH2tv1o8fVquaRJvkCAS6M7wzH0OuxOyydN3iQAoJWSjobf/oYLu/9Cbm5upcZNRFSV6nrWhXbHio0+FGgLoFdPD/r6+kqKioiIiBRh4qSa2LNnDwQCAY4ePYpXr14hMzNT4YPefbeOnkSD4I3QTnhd6n1sjt2A+p5TePH8eSVGRkREROWVn5+P5JhYRLdxr1A7kf194NTKS0lRKc+NA0fg9X0I1DPkF62P8XaFafgT1Lj1qNTtGTxJQJ2fduLu2YvKDJOISKVs7G2g10WvQm1od9GGi5eLkiIiIiKiojBxUk306tULAGBjY4Pg4GAAgIeHB7799lvp9E1r1qxRWXxUOllZWciKiIFR1Isy71t78wlE/cvOBSIionfR89hYWB69Bq3kVDxvYFeuNlKsTPC6eT0YGxsrN7gKSk5Ohs61SGilpCvcHtPSBfbHbpa5XYvLEUgMf4C8PC6ATETVg0gkgq2bLbS7lG/UicBIAIOuBrC2tVZyZERERPQ2Jk6qCYlEApFIhEePHqFevXoACn4pmzdvnvSPzc8//1yVIVIpRN8Kg/3WE+XaV5ySBkHEY6SnK+60ICIiItWJOncZ9nvPo+GG47jfyQMJTjXLtH+amT6uzh6Cpt07VVKE5Rd59hKcNh9TuC3R0QL6TxKgll32KbcEAKwOXkRsVHTFAiQieoc0aN4AViOsoOGlUab9BHoCmP1ghjY923BtSyIioirAb9tq5H/s3Xd8FGX+B/DP7G62pJLeKxASAgFC700RRUSl6AmWO9t5Kp71p4end556epZTj7MXVDwL2MCGDaTX0EkIhARISCW97WZ35/fHJkuS3STbd7P5vF+vfWV39nnm+T5lZjb77My0//pw5MiRAIDa2lrje8HBwbz/RR9QnpeP6N15NueP+2o7Sk6fcWBEREREZC+9Xg996Xko6pshbdVhwsvrkXPVRJyZlA5R6DmvCKA8PR5bHlmMYfMu9sibpTcVlyKwuMrseyWjUhC/I9fmdcf/cgAlh3Nszk9E5GkEQcC0edOQ9GgS/Bb6AdLe80gTpYh4MQLTr52OgAD7LvVFRERElpG5OwBynIaGBgDAwIEDAQCnT1+4jnRjY6NbYiIraVoh0ds+waWobUR9bZ0DAyIiIiJ7tba2QlF34YxQebMGE1/8CifmjcFvK5Yg6mAhkjcdhqK+2ZhGq/DBmcnpODthCAYUliPmZCmkUgu+XXMDQdPa7XsaPyUU3VzCyxLyhma0qtU25yci8kRSqRTT503H8YTjODn/JBq2N6B+bT3Eqg7/C0oBxVQFghYGIXxQOEZNGwVfX/tuLE9ERESW48SJlxAEAc3NF/7ZlkqlaGlpga+vL0RRhEZjeqNO8jyC0MvPTnshSiQQJJ75pQoREVF/JQgCRGnnE72lOj3S1u1G6vo9KB2ZjH03XwydXAah/TszUUT8zuOY8twXkLbqcOSOeXZ/TnCWnqIS9CJEie1xixAAD603EZE9BEFAWmYahgwfgopLK3Bk7hE01zRDr9VDkAiQKqVIHp6MgekD4ePj4+5wiYiI+h1OnHiJG264AZ9//jny8/MxcOBAfP3117j88ss7Taa8+uqrbozQMXx9fY118sZLjwlKBbQKH8jU3f9ysydN0SFQhgQ5OCoiIiKyh4+PD9RBfmbfk4giYvafQsz+U8Zl+26+GKPf+alTuqboEI+8TBcA6FUKiDA/gaKsbURTeCACympsWndzWACU/IU1EXkxQRAQERGBWVfNMi7bt28fRo8e7caoiIiIiPc48RKrVq1CfX298TJd8+bNgyiK+O6777B582aIoog77rjDzVHa58UXX+w0EeSN4kYNw+mLRtqcv2jBRMQkJDguICIiIrKbIAhQxkWhPnKARembQzpfv75VJYd6UCxUKpUTorNfyOBkVKbHm30vbsdxFE4bZvO6C66chISxI23OT0TUF3n7/71ERER9ASdOvNyll16KqVOnujsMh3jggQfcHYLTxQ1MQdH8CbDlXJq62BAoByZALpc7PC4iIiKyz6Ap45H/uxk25S2cOwZJ47IcG5ADpYweifxls82+519RC63SBy0B1k/66KUSVE7OQHhkpL0hEhERERERWYWX6uqjoqOjAQBnzpyBj4+P8XVvSkpKnBmW00yfPh2iKOLGG2/E+++/7+5wnEYqlSIkfRCKp2YgbstRi/OJAI79cR7SJo9zXnBELtTY2IhTe/ej+tQZCOpWQCqBLCQIKZPGIiIqyqp16fV6nDtzFqd37YO+rhHQ6yEqFQhPG4jkEcM99tI3/Z0oijh//jzyt+6CpqIK0OkhymUIiI/GoAljERAQ0PtKiDxISEgIjoxLR0P4RvhX1FqcT+OnQNH8CZiWlOjE6CyMRaNB4ZEcnD56DLqaBkjVGjSEBWLHsXyIMSGoSolCyKlSk3wDfz6InKsnYtT7v1pVXv6VExE3YpjH3tuF+hZRFFFRVoZT2/eg9XyN4biikCEoMR4Dx2XB39/f3SESoampCcf2HkNZURm+fv1riHIROq0OEr0EUkghkUsQGh+KjPEZ8PPzw9nCs8jdnYvW+laIogipSoqEoQlIHZ4KhULh7uoQERH1aZw46aNKSw3/lJaUlCAhIcH42hvV1tZi8+bNkEgkWLVqlVdPnADA0MkTsGN5BRTV9Qg/cqbX9CKAQ3fOR9jlMzFgwACnx0fkTHq9Hvt/2gjt3qMY+OGvGHa8yHjN/OYBfii4ajJyZ42APjbCovVVlpfjyLc/Ieb7PRj77R7Im9QAAFEASkcNxL7rZmLA+BEYOnkCv5jzIE1NTdi37nsEbT2MYZ9thl9FnfG96uRIHF86C7qx6Rh92RzIZPwoQ33H6CvmYk9jE8Y/+DZUNY29pm9VyrHrqZsw4opLIJVKXRBh907uP4TTO/dCrK5H3O48JG88DGVdE7Y+eDUmP/cFisYOxo575mPaM2sRUFLdKW/UoUJUpMfh+GVjMOS7vRaVVzRlKKpuuhTjRth+mS+idg0NDdj39fcI/e0gMtduhW91AwDD5+iqQdE4dv1sSMYMxahLZrt9W6P+Sa/XY+fPO1G6txTVq6uhvEoJ6ADdGR00P2ugr9Ab05YlleHsrWeBQEDziwZNG5qA9it7CUDF2Aqc/N1JpExMQea4TH7GJSIishG/beijnnzySQBAQtv9LNpfe6OkpCQAwAcffODeQFxEKpVi/IJ52C2RoubDH5H83R7INFqzaRvDA3HkT/MRcsUsDBqZ6eJIiRxLr9dj1/ofEP/CZ4jbcsTkfVVNI4a+9yOav9yGDf+5HXV1dQgMDOx2fRWlpcj931eYvOID+LRoOr0niEB0dj6is/NxYtEUHGhRY+Ts6fzH0gM0Nzdj16dfYcwj75q9mXRwQRnGPPkxyjOTsKNFjUkLr+CXXNRn+Pn5IWvJAuySSTHkla8RdSAfgpnrc4oAKgfH4Oh9VyPjqksREhrq8lg7yt21F3WfbYBMKcP4/3wDZX3na++LEgFnpg7F0M+3I/sPFyPll4OI3XMCEt2FL/qGfboVRxdPQfZNs5H+5U6oas1PHGl8FaiPD8O5Gcsw5pLZ3C+T3RoaGrD3ky8x7uF34Xu+vtN7AoDQkyUIfXw1zk0Ygp0taky8ch4kEl7RmlxHFEVs/nYzzvz7DFo2tkAIFiBNlqLh0Qboy/Qm6YUAAZrzGjT9vQnQdF0ZoNmtQcXuCrRc34JWdSvGTBvjmooQERF5GU6c9FErVqwwPi8pKcHx48cxcuRI3HfffW6MyvE+//xz1NTUwN/fH0uXLnV3OC7j4+ODiVfOw5mhQ7B90QEE7D+JuG93Q17fDFEmQWNUCE4vngrpoHgMmjIBoW7+QoXIEU7s3Y/It9abnTTpSFXTiOCcs8gWv8f0ZUvMfqmm1Wpx5JufMOUv78NH3drj+gav3YojA/xQnJyAuIEpdtWB7Ldv/Q8Ys+I9s5MmHUUcKoTu+TU4HBqCkbOnuyY4IgcIDAzExN8tRP6QQTiecwLRP2cjbPdx+DRroFX4oDojEeczk3Hu0qkYO340fH193RpvZWUl6tZtRKOfHJNeWgdFvekNi3OvnIC43SeQuPUY4nfl4dTM4dj8yCKE5xYh6mABfJrU0Ml9EFBShbxLsrDxyWUIrm1G0udboaqsgyCKUAf54cxVk9GSkQRZSADGXnwxJ03IbqIoInvd92YnTbqK2Xkc2v9+hZzIcGRMnuCiCImAY9nHUPR2EVo2tgAAVH9QQXdGZ37SxE+AYoECjc82Aj1/xEX9h/XID85HzMAYxMTGOCN0IiIir8aJEy/g6+uLDz/8EF9//bXXTZwsWbIEAHD0qOX3+zhw4ACeeuops+9t374dAFBdXW32fU8ikUiQlJaKpLRUVF1ahbJFF0FT34Sy5gakJCUhK3UQ781AXkOv16PkSC6mf7fHovTSVh2CNx1A+UXTEGnmHk9njp9A8qe/9Tpp0m7I6o3YOXMUJ07crLa2Fqr9J0wu89Od6D15OHH8FLTTJ/OSXdSnKBQKDJ04Dmnjx6D0omkoPVMEbVMzytXNGJKehtCSc8icOtXdYQIATm7ZifDtRxHkpzQ7aSIKQPnQeEz/YgcAQKZuReoP2Rj8QzbKMxJQNiwRrb5KlGfEY9CP+zHjqc+Q+/uLMeCRW1B/2XRUVNeitL4WCdExSE0bjMDAQGzdupWTJuQQ5ysrMWDb0V4nTdrF/3oQv117AvqJ43jWCbmEKIo4dfgUmtY3AQAk8RKINSIEifl9oHy6HJoNml4nTdrVvVeHI5OPIGYJJ06IiIisxW8ZvEBQUBAAQKfTuTkSx3rggQeg1+sRGBiIVatWmbz/xBNPIDMzE1deeWWn5U899RTWrl3b47pzcnIcGKnzhYSEIGSC4cbvW7duxaBMXu+bvEtpcTGiftlv9pI13Un5YjsOXDoBkYsWmLx3NvsQpvx22OJ1+bRo4HsoH7Xzao37VHK9/B17MOgj624enfjFNpyeNh4Dhw11UlREziORSBCTEI+YhHgAhmN8wqCBOFNa4ubIDFpaWqA/eRalowdhzOs/mE8zwA9Jm4+i61d8AoDIo2cQedRwv7atD16N5E2GMwpT1mzBkYvHYvxVlwMA6rZuRdq40c6qBvVj+dt2Y9hnmy1OLwCI/WEPimdPRnwKf0xBzldSUoKGTQ2GazQCUFykgPpHNVTXqsyml2XJ0Ph07/fIaifWi6jPqUdDQwP8/f0dETIREVG/wYkTLxEYGIi6ujp89dVXJhMJfdXOnTsBAHV1dXj88cdN3n/88cchlUqh1Xa+/0fHy5h1tX37dpw7dw7p6emODZaI7FJdeBbRu3KtyqOqaYS2us5kuV6vh7SqDtJW6yaTI347hOpbKzlx4kaNFecRdLbSqjzh+08ip/AswIkTIoerra1F2N48lCWFQ9HYYjZNq68C4W2TI5byq6xDa1WtI0Ik6pGmqgZ+laafFXoSsScPpwvOcuKEXKK8sBxNO5qMryXhEujPml6iCwCgBMQ6Eejm7e40bG1Azc01nDghIiKyEidOvIReb/j0dNVVV0EQBCiVSpOb5QqCgLo66/5xcKe7774bFRUVJsvz8vIAAKmpqZg4caLJ+yNHjsSaNWvMrnPx4sVYu3YtgoODHRssEdlFp2mFVKPtPaFJRtP/HPV6PSSt1q9LqtFC06K2PgZyHDP92RupRgudDf1NRL3T6XSQNva8XxQlEpv236Le+u2dyFqCrccVjYXXQSKyk1aj7XyD9x7OvhZ8BIgaK07PbqNv0kPLz0pERERW48SJl2hoaDA+F0URzc2m16Dua6655hpcc801Jsvbr3l9/PhxV4dERE4iD/RHS5Avgs5ankcEICp8TJZLpVJofa2//09LaADkAfwlnjsJch/opRJIrPiiSx3kC7mf+ctZEJF95HI5qqODIfZwrweJVgf1AD8ElNVYvF4RgCg33X8TOZpe7gNRgFWXAm0J9IU8wM95QRF1oApUQQjqcrFDqfm0YpMISYD1997xifaBQqmwIToiIqL+jRMnXuLJJ590dwhERDaLTUtF7tVTEHnkfxbnOT8kDgOSE0yWC4IARXQ4GsMCrbo8x7nLJ2BcVJTF6cnxItNTcW5CGuK2HbM4z+krJiJ2JO/7ROQMISEhODI+DYFbD6M6KQLBheUmaZQ1jTg9OR1hx4stXm/58CSEDkpyYKRE5oUOTkb58CREHiq0OM+ZqycjZRgv60uukZiaiNyFuTh/8DwAoPVwK3zGdDOxrANErQghUDBcsstCAbMDEB4e7ohwiYiI+hVOnHiJnu7r4W1E0frTk4nIswUEBEA9NAlqP2W319HvKn/ZLAwbO8rseylTxiN/yTRkvvqNReuqiwmBcmAC5HK5xTGT4yVmpGH3kmkWT5zopBJUTUjHMH4ZQOQUEokE4emD4bv6V+RfPApj3tpgksanWYOm8CC0KuXwadGYWYupU8tmYWTWCEeHS2QiZVQmDiydhchD71qUvlUpR2NmCgYMGODcwIja+Pv7I2BIAKoCqiDWi9D8poHfXd2f8aT5VQP5LDnUX1l2eVlpshSRgyMhk/GrHyIiImtZf54nERGRE6RNn4zsR66BXhB6Tdsc4g/ZmKHw8zP/j2VYWBjqZ2Xh/JC4Xtellctw4OElGDLN9J5J5FpyuRzBozKQf2XvfSECOHDf1Rg4aazxEo5E5HgDR4/E6WWzoJXLUDIi2Wya1G/2IPvmiyBasCmemT0CqqyhUKl4iT1yPpVKBVXWUJy5aGSvaUUByP6/RUidys8D5FqjZoxCyF9CDN/ONAPaU1oI4eZ3qNpDWshSZZAmdnM9r46UQOiDoRg+cbhjAyYiIuonOHHiRW644QZIJBIIgtDtg4jIU4VFRSJ24SXY9Y8boPEzfx1mUQAKLh2D5oGxGDVnVrfrEgQB4664FEf/fj3OjU3t9j6bTcH+2P7czUi7+jIEBAQ4oBZkr6FTJqD69gU4/rvp0EvNf0xpVfhg3/8tRuCSSxA3aKCLIyTqX5RKJUYuvBzNmSk4celonJ6cbjJBEnn0DCIPn8auO+dBozJ/5p4IIP+KCTh3zyJkzprm/MCJ2mTOmoaS5QtxcsGEbn+coVHJsfuxpYhYeAki43v/0QWRI4VHhCPr6iyEPhkKwV+A+nM1BJUAxeUK0/udiEDTq01QXq+ELLP7s0iEMAERz0dg8sLJ3f7QiIiIiHrG8zW9xJQpU7Bt27Ye03DihIg8XUJaKlR/CMTugbFQ5BQi4YutUNY2QSeXoXzcEJRelIWojCEI1jRD0sPNigHAx8cHkxYuQE5UJE4cP4XYb3ch5HAhJFodmsODcHrxVIiDEzBi9jQE8ZIcHkMQBIy+ZDZORUdh85wxCN92BFGbDkHWooEmQIWz8yegIXMgBk8eh+ikRHeHS9QvDAgOxrili3Dkp03Iiw7BiblZiN95HOE5RWhVyVE6PBHFUzLQkpGE7eOGwj/vLBK+3A5FbSN0Ch+UTcrA+ZEpiFxyKcaPHsnPpORSEokE4y6fixMxUdi8YBIiNx1E5LajkKpboQ7yw5krJ6E5Ixmp0yYiMjbG3eFSP5Wcmgz/P/hjf8p+1OfWQ12jhqgQ4f+YP7S5WrTuaYXYIkIIEBA4PxB+aX5QjlGivqQedevroMnRQNSKkEZJMWDxAAQNDMKYi8YgMDDQ3VUjIiLqszhx4iXaJ01+/fVXlJeX49prr8WECROwY8cOzJs3D9999x2GD+cpukTk+cKjoxB+7dWor69H8bzp0NQ3oKy5ARmZmUiNi4VEIkHl1q0WrUsmk2H49CnQTZmI4tlTcK6oBOdqqpAUH4/MtFT4+vo6uTZkC0EQMHDEMKRkZqB8bhlKTxag+Hwl4iMjkZKWymvPE7mBn58fxl85D2q1Gmdy81AxMg15DfXQSICqy2ZgREYa/P39AQB1dXUovnwGWusbUNbSiGEjRyC0oACpY8zfl4rI2QRBQOroURicNRKlF09DScEZFFedR3xUNFLTU/nlMnmE8MhwzLlmDurr67Hlty0InRiKqqYqxF8ZD3WtGmVVZYiJiEFSepLxs5BWq8WZWWdQVVKFipoKJCYmInlIMi+HSERE5AC8VJcX8ff3x8yZM43/tLa2tgIAvv32W/j4+ODQoUPuDI+IyCoBAQFIGzcambOnQzUgCDEJ8b2eZdIdqVSKhIEpGDZ9MnxDg5GaNZKTJn2AIAiIjIpCxpSJ8A0NRvqEcZw0IXIzhUKBwSOGY9LllyI4LgYyhQJDx48xfv4EgMDAQKSPH4PMi2ZANSAI0XFxPMuEPIIgCIiOi0PG1EmG48p4/iKfPE9AQAACBwRi/OzxCAgOwLDMYRg9dTQCQwMxcuLITp+FZDIZUlJTMGb6GASEBmDoSN5DioiIyFE4ceJFFArDPQHmzJkDACgtLTW+x38IiIiIiIiIiIiIiIh6x4kTL1JbWwvAcF1/ACguLja+d/78ebfERERERERERERERETUl3DixEvI5XJotVpUVlYCACIjIwEYTkdvvzQCL0tDRERERERERERERNQzTpz0YX/4wx+MzwsLC7F27Vrj5EhpaSliYmKM70dGRqKxsdHlMdpjwYIFkEqlxskfQRAgkUhw7733ujs0IiIiIiIiIiIiIvJSnDjpw9577z0IggB/f398/PHHWLhwYaezSoqLiyGKIkRR7HS/k75i3bp10Ov1kEqlCAsLg1KphCiKeOmllzpNGhEREREREREREREROQonTvqw9nuZNDY24v7774cgCIiIiMBPP/3k5sgc4+KLL8bp06eh1WpRUVGB5uZm3H///QCAVatWuTc4IiIiIiIiIiIiIvJKnDjpwzQaDURRxJw5cyCVSgEAFRUVmDNnDgRBwKBBg3DmzBk3R2m7H3/8EQkJCZ2WPf/88wAAURTdERIREREREREREREReTlOnHiBDRs2QKvVorGxEaNGjTLeDD4/Px+JiYmQSCSYOHGim6N0jNbWVneHQERERERERERERERejBMnXsTX1xfZ2dnQ6/U4efIkkpOTARjOzti5c6dxQqUvS0pKAgBERES4NxAiIiIiIiIiIiIi8kqcOPFSAwcOxP79+5Genu7uUBzmrrvuwrlz5wAAeXl5bo6GiIiIiIiIiIiIiLyRzN0BkONNnz4dW7duhV6vNy4TBAHDhw93Y1T2eeutt/Df//4XALBy5UoEBQV1m/bAgQN46qmnzL63fft2AEB1dbXjgyQiIiIiIiIiIiKiPo8TJ17iuuuuw9q1a03uARIbG4svv/wSY8eOdVNk9vvpp59w2223AQBuvfVW3HnnnT2mf+qpp7B27doe0+Tk5DgsPiIiIiIiIiIiIiLyHpw46cMeffRRvPDCC2hpaem0PDAwEK+++iqWLl3qpsgc5/jx45gzZw4AYM6cOXjzzTd7zbNixYpu39u+fTvOnTvnVZcwIyIiIiIiIiIiIiLH4cRJH9bxclRKpRLLly/Hs88+68aIHKu2thZpaWkAgGHDhmHDhg0W5Rs5ciTWrFlj9r3Fixdj7dq1CA4OdlicREREREREREREROQ9OHHSh0mlUlx66aVYv369u0NxitDQUOPzqqoqxMbGdnq/uLjY1SERERERERERERERkZfjxEkfptVq3R2CU+l0OuPzc+fOuTES71F+rgQntuyEpqYOTaIOErUGMo0OGOCP9DkzEJeY2GN+jUaD3O27UJN/BoJOB2V0BNJnToG/v79T4tRX10H0VSBmRAYGZg6DIAgOLYes09raitwdu1F9shCCVgdFZBjSZk5BYGCgxesQRRHH92Sj7NhxCC0aSEMHYMj0SQgND7cppurz53H8t+1QV1ShBSIkOj0UUhlUMeFImzEFAQEBqKurQ/b3P6Gl/Dyk6lY0RAejIPQ4ktOH9LjupqYmHNu0FVVni6FXawCJBJBKodIDQoAvooenQxAEnDt0DEKTGpLgQAyeOgERMdEmMeZu3IaG81XQaVoh14vwCQ/BwEljEJOU1GsddTod8vZkoyL3BDSaVuh0OihFQAjwQ3xWJpKGpnXaNvR6PfLzDqLk7CEIaIbUJwSDh05DeESURW3a2tqK3O27UJV/GhKtDsrocAQlxqPscA5Q3wQE+CJ5fBbiBg20aH0d4zq5/yBKj+Qa+j4kCKnTJyEsIsKi/BqNBjs+Xw9tZRX0Ch9EpKcidcwoSKVSq+KwVfGZMzj64yaIdQ0QIEARHY4xV1wKPz+/bvN0bUtFVDjSZxrGpTO0tLQgZ/N21J85B0CEf1w00qdPhkqlsnmddXV1yN24FeqySogyKUIGJ2PIhLHw8fFBQ0MDNu7biIaqYvhDDaVMhG9ALNIzZyEkJLTH9ba0tCBnyw7Uny4GRBF+8dFInzYJvr6+3eYRRRGFObk4sW03xIZmCKIIeUw4AiPC0XC6GEKrFvKIUKTNmIygAQNsrrMjabVa5B7dgfNlJyCBFnLfSKRnzuxxv9nY2IiasnJsfutDQCIgKCkeaVMmQKFQOCwuR+zPAUOf6LRabH7/Y+jVGmgkAgS9HjpNK6Q6PfQKHzT5KfFDzipUK32xcve3EAUJ4pMCMeeSUcb+7rp9N4UGQqfTmd2+i07mo2BXtnF/lDRuFAZERSJ301a0lFRAlEoxYGAC0iaNh1wud0h7OUpDQwP2bdyI2pISCFIpogYOxIhJk+yOUxRFFB7LxdnsQ5A0tgAD/DFw0jhEJ8Q7KPILmpqakP1bNqqKqiAIAsKTwjFyykgolUqHl+WpzuSdwOk9ByDUN0EM9EXy+DGIG5hs93rLS0qQu2UHmiuqAU0rZHIfBA9KQvq0SXbtx/uCsyfzUdhhu44cMRQ1tflQN5ZCDxlCwgchbdhE+Pj4ODWO6qoqHN+0Da0V1dArZIhIG4TUsaNt+qxRcuYs8rfvBmoaoPdTmv3M1he1tLRg/5b9qDxdCVEUERIfgqxpWT0ev4mIiPoqTpyQxxJF0d0heJWik/koXr0Ow9/4DtsevApDfshGyi8HIdGLUAeosOueMuhvWIiEwea/DNVqtdjx+Tqk/fNjDD98GgKAuugQ7P1bGcZct9BhkyfF+QU4u/orjHn+cygaWqCXSpB/xQTsv30Bsi6Z7ZAyyHo6nQ47vliH1Gc/xbD9pyAAqI8cgOzHyzBq6UKLvmwTRRE1ZeWIf/MHTPn5ACSiiOYgP2SvKMbgpVeZTDj05nxFBY6t/gIj//kZsm+dgxHrdyOybWzWRwdj32NlSL1iDo58+R3Cdx3HlM+2QqZuxeb/W4jqjYfRcsvVSJ8w1uy6m5ubsfuTL+BztACDsk9BXt+MoklpGPXez8ZxueUvSxB1sABTvt0DiU4Ptb8S+x9YiNZlVyK27QuU8+XlOLb6SwRk5yGwpRVp63ZB2qpDq1KOQ3ddjpbrr0BK5rAe22zX198h/uXPoRL1qEyLR+bqjZA3a6CTSZF37XQcvvkKZM6YYsyTvWsDgtWvYUrwt5AIeqh1Acg+8BC0GcsQHZvUY5vqdDps/3wdhjzzKYYdNPTzoWunosFXiYmrN0Km0UKr8MGRW+ei6Yb5SB2bZXF/7f3uJ4S9+iWm/LAPElFES6Avsh8ugm7ZlYiMj+sx7/nyctSfPYcJ974N3+oGiAJwdvpw7Fq+EBOvnOf0LyHO5p/CkW9+xLj/fIPQ/FIAQHVSBLZU12LGH5aa/cJQp9Nhx+frkNqhLeujgrHv8TKMXrrQ4ZMnarUaOz/7CsOfWN0pxl2PlWPC76626UvNuro67F+9FqP//hH8y2shAigdPRA7HroGI+dehE9+/QT16nzMD9+GwYHbIQgimrXB2Lt7BdJHL0NYeKTZ9Wo0Guxc8xWGP/ERQk+WGGJNjMCux8sw8XcLu4312I7dKNq+F5mrNyHqYAFEiYAtDy9CxE9rMWrPSQgAGsMDsfexUoxYthAD3Dx5otfrsWPzFxjo8zwyQvZAEICG1nDs2/44Rk5YanZyR6fTYc/HX8DvbBmmPfEJAKAiLQ47VlyHyddc5ZAvDR2xP29XU1YONDRhzL1vY9fy+UjalYeq1BhkfLYV+267BJkf/Ybt9y1AcX0QitRh+NvfZgEAEhOrseIv23D9DVPRWFdnsn3//MyN2KX93mT7ztu7H03vr8PEt36ATN0KrVyGA3+6HMdTIjH+n58hoKQaIoDyzGTsePgaTF58JWQyz/h3p6GhAb989BEq/v536EsM475i+HBUPPII5ixebFechzdthc876zD5082QanXQqOQ4eM+V0NxwBRLT0xxVBTQ3N+PnT35G+d/LoT+jBwCUDylH5YpKzLlmjsdNVDlDzo490K5ah4mrfoJMo0WrwgdHb78UzTdegcFZI21e77lTBSj4eD10rRqM/WAjQgrKAABVyZHY9Xg5Jl57tUMnTz3J8d17oV61HhPf2QCZRovqxHDsfWYmJiS9iIDQMogiUNYwCjs2P4TJMxY77QcThs+VX2L0P/5n3BcVTR2GnfcsxKSrLrfqs8bpY7ko/3Adxr381YXPbNdMM3xmmznVKfG7gkajwc9rfkbZE2XQnTT8yLEisQJVj1dhzu/m9KsJVCIi6h8k7g6AiJxPFEWc3LoLY5/8GHmXjUboiXMY9NMBSPSGySlFfTMmP/s5cjdu6XbC6kzeCSSu+tH4xTQABJZUYdQT/0Pu5u0Oi/PElp0Y94+PoWhoAQBIdHoM/nI7tDsOob6+3iHlkPWK8gsQ+9GviGr7kg0AAspqMPrvHyF301aL1lFVVQVJZS2SftoPSds4U9U2Ytxjq3HcwnV0lPPrFoz764eoHBqP+B25iOowNgNKqpH1xP9w+NufoDh3HiM/3AiZuhUAINGLGPXOTyjbmY3W1laz687bsRuJ7/8E3/P1SN50GCcvzcLYV78zjsvG8CD4ltdgyLpdkOgMXx4pGlow7h8f48TWncbtKOfXrRjx3Fo0RgUj4/PtkLYa/sn0adEg64UvcHZndqez67oqLy1F0Hc7EbvtGAqnD8PotzdA3qwBAEi1OqSv/hUNew6hubkZgOHeUGjcgoEB6yER2uKS1mNc8BPIO9r99t3u7ImTiP/wJ0S1fdHfqpKjNjECo941fEEEADJ1K0asXI/SPQctPvOxuroaPr9lI+X7vca+V9Y1YdzfVuP4b73vP3J+3Yrg3CL4VjcAAAQRSNh0GIE/7EJFeblFMdhKFEUc27gVI1b9YpyQAIDgwnKM+u83OLp1h9l8Z0/kI65DWwJAQGk1Rj/xEXJ/2+bwOPOzD2LIS1+axDj0hbU4uSfbpnXm/LoFo/9mmDQBAAFA9L58RH+yCT9t/xH/q/4IYwJOIzVoGwShbZuWVWN8yGPIPfxr97HuP4TU/3xtnDQBgODT5Rj23Bqc2LXXbB6NRoOS/Ucw8Mf9iD5YAAHAudGDEHWgAHFtkyYA4FdRh7GPf4jcX7fYVGdHKi46jUjJGsT4GiZNAMDfpwJjBjyOnMMbzeZpqKpG5tOfwKdtOweA8NwiDHzjWxQeyXFIXI7YnwMX9ulSjRb5l2Qh47OtODd2EMa++h3OTE5H2lc74dOkRrNShTfWTERj44Uv1U+fDsa/XxqBXbvyzG7fqsp6k+1bq9WidPcBjFi53rg/l2m0EBubMe7ZNQgoqTbkBxB5qAAJH/yEsydO2tdYDrT/t99Q8Y9/GCdNAEB7+DDOv/8+CvLybF5vU1MTGncfQvpHGyHVGo4n8mYNRj+7BgXb90Kv19sde7vDOw6j4pkK46QJAOiO61D5WiXyDtleh75Co9Ggcs9BDHvze+Mx0UfdihGvrEPJrgM2Xw1AFEXkbdmJoAMnMfSLHcZJEwAIKShD2r+/wMl9BxxRBY/T2tqK8t2HMPy1b41teuL6EZiY9CICfAztIAhAlO9+xEr/h6Kz+U6LJefXLRj32Aed9kXxm49gwPc7UV5a2kvuC/R6PQp27MXoZ9d0/sz20UY07j5s/MzWF+Xuz0XFfyqMkyYAoDutQ8VzFTiy64gbIyMiInIOTpwQ9QONjY0IOFEMiU6PqkExSNiea5JG2qqDb0EpWlpazK6j7FgeYjebfiAOKj6P5nOO+eKyqakJASeLjV9EdxS3fifKTp9xSDlkvdKjxxG76ZDJcv+yGqhLKi1bR04eVGXVJst9WjSQnqu06gsHURQhllVB0diCsswkxO0y/cImsKQKumZ1p8m+dgKAqN8O4/z582bXX3fmHFqC/RG3Ow9NoQHwL62BtMO4LBueiNg9J0zySXR6BJwoRlNTE/R6PcTy86hLCEd0tuk/+oIIhO47gZqamm7rWXr0OGJ/3Ie6uFCE5JdCMDPvEfvNblSUGb5cKDuXjzjFtyZppJJWqCQF3W7fxvKO5SHut8PG1+dTYxB5qNBs+0VsPdJt+5ms9/hJxH6722S5TKOF/Gw5NBqNmVwGer0eKDtvfr+wYS9Kjx63KAZbqdVqoL4R0QcLTN6LOHYW1adOm81Xeux4p7ZsF1BSjZYSx0/2nD91GlEHTMdZ+NEzqC48a9M6NWWV8K+oNVke9+tBlNacQ4YiGpkDTPtVLm0CtKXdTgpW5hciaq/pF9phOUWoOV1kPk9lJWQV1Z229bLMJMTtNt32fasa0Fpm2dh0prLiI4hTmU4g+fmcR2tzmZkcgLa5pdOXpu1id+SgLM8xkwCO2J8DQGnuCajKawAANUkR8KuohX+ZYV9ZNSgGkUdOo2x4IrSQYe/+WJP8OTnhKCiosnj7rqqqQsS2oyb7o5YQfwQWV5nm/+0wSo95zpf5NSUl0Ju5P1/zb7/h9LFjNq+3oqwMMd/sMlkuEUUEHy5AXV2dzes2KetsBXQnTLdrzS4Nik6Y33a9yfnz5xG18aDZY2L4jhxUV5t+xrFEc3Mz/E+VoDolGhGHTY8pkYcKUNXNsaavO3/+PCK2HO7Upi1xCvj7mO4H43w3obToqFPiEEURYnmV8QcyncrdsA+lRyz/rFFXV4fgwwXGH4p0FPONdZMwnqY4vxite01/dKTL0aHstPnjGhERUV/GiROifkAqlULrazi9XxBFtCrNX0pBq5J3e/q71McHWpVpPlEARJljTpnvGGdXrQEqyOTOva4xdU8ml5nvfwB6H8v6X6pQQC81f9jRyX2svtxSe7lSjbbb2ESp0O141wT4dn+5B5kUEo0WrUq5Yf3KzmPPsMz8elt9lZBKpRAEAaJMBqmm1Wx8AKANUPV4eRaZQmHYLtU9lBfkB2nb5XukMjm0evP33NDpVb1e3kLq49OpvaRq820LANpAP4svLSNTKrrdtnVKOSSS7j+OCIIAvY/5clpVCsiUzr10iUQiASQS6Mzsf/QyKQSp+di6tmU7EYDeCZcOksikPcRo2z5aL5PC3DlKWl85ZIIMjXo1WvXmL8uhF+XdbtOGWE3bQC813EfIHKlUCgiSTuOxu22/PXZ3k0rl0OrN35dA393VcgUBejPtplX6QOqgceOI/TkASBVy6Nu3XVGEoNMb91OCXg+dTNZ2ZogIudz0y3apVA+ZzPLtWyqVQhtoeg19USKYHaetKjlkTr4fgjWEbvpPUKnsilMqk0EbZH6/r/Xv+RhjLYlUApgbIgpA6gHbnLN1NwYBQBvY+zG2x/Uq5Ybtxsz2oJPJbN6PezqpVAptQOc2FfVSmDtBtlWvgkzmvMvBdXdsblXJIe3mM5g5MpkMWj/z+/6On9n6IqlMCphrCmnb/oGIiMjL8OhG1A+oVCq0DoxF8wA/JGzLwclLRkHs8r1MY2gA9Ekx3V6fOmHMCJxcbHpN3nPjUhGamuKQOJVKpTHOjvSCgNOLplp0I21yjoSxo3Dymukmy8tGpiBkYKJF64gfOgRNsWEmX3DVRwfDJyHaqi8cBEGAb3wMahLDkbDtGE7OGWWSpmTMIPgGB6F8eCJaFZ3/SdXKZaiYNhyhoeZvYB2VkQZBJsOp2SOgqG+GVilHc/CF+/jE7j2BwmkZJttR8wA/aAfGQKlUQhAEqBKioahtwrnRg6Dr8qWSRiVH7YiBPd5PIH5EBvKXzoJ/aTUaogZA7df5S2q9VIKiy8chMtJwL4nY+EEoaFkCsUtgja2h0MuTer3+fMLYkci/ZprxdejxYpRnJEDb5UturcIHlRPSEBIS0uP62sUNGYxTv5th0vcNEUGQJEb1+MVeezu2dvmiVwSQv3QW4ocPtSgGW8nlcsjCgnFq5nCT907NykRslulywLQt25WOHojQQUmODhOxI4eh4DLTe/YUzslCdKZtbRSckojy4Ukmy09eMx2pCWko07Vg+/mLTb7gqtXEQekf3+2EWNyo4SiYP95k+ZmLRiF6eLrZPOHh4dAkRHTa1hO255jd9ivS4xGY7PibYlsrYeAYnGy81mR5eXMGgkLM30haEeCPommm9z06efVkxI8e4Zi4HLA/B4D49CFoijPsQ6P3n0LpyBRoFT5oDvZH7J4TKJw5DDH7TkIu0WL+bNMzKmbNKkBmZqzF23dISAgqx6eZ7M+DT5ageMwgk/XnL56G+LEjLa6Ps8WmpkI+bpzJ8oDFi5E6ZozN642KjsbZ+eNNfpig9leiMT3RYfegA4DEjEQoZ5hOlvot8MPgrMEOK8dThYWFoWz6cGi7TG60KnxwfuwQBAcH27RehUIBbUoMwo6dReEM0+2/8NLRiBmRYdO6PV1oaCgqJg/t9DkjZH8FShtM93cnG65BfIr5e9PZSxAE+CZEozoxwuS9/KWzkDCi+/vRdeXv74/GoQlQ+5v5zDZ/AqKiouyO110GjxoM3/mmk4eqi1RIHm7+uEZERNSXceLES+zZswcSiQR33XWXu0MhD5V52cXY9dwt8K2uh14qxbb7r0J1YgQ0KjnOjkvFlr8txZgrL+s2f2RMDDQLZ+LQHfPQGB6IlgAVTl45EafuW4wh40Y7Ls5LL8Ku529F8fhUaFRyVCdHYufTNyJx1uR+cdNRTxUWHg4smI4Dd1+BhoggqANUyL98HPL+7xqkTTL9AtQcPz8/yMNDsPvx61AbHwaNrwJnpg3Dvn/+AZlzZlod0/CLpuPAP25Cc0QwNH4KHFg6Aw3hhthOXjEeJx9YgvFXXg4xOQZb/rIYZRkJaFXJoQ5Q4renbsCwS2d3+8VuSmYGKm66BFIR2HX35UjeeAg7774cRWMGQaOSozFiAFrDgrD1ietRnRwJjUqO4vGp2PX8rci89CLjeobNno5Df78BwYUV2Hb/lahMjYFGJUfpiGTseOE2DLt0do9n2gQGBiLw4knY98gSpPx0ANvvW4CSEUloVclxfnA0tj/7BwyePc048aBUKhE7cDZ2Vv0D1epEaHQqFDVOxJ7af2HEmEt7bdOIyEjorpyBg3fOR0N4EFr9lQg6V4VN/1iG8qHxaFXJUT4sEdufvwUZl8yy+CwhlUqFqFmTsOvvy1CTEG7Y70wZir3P3ozMS2b3mn/Y7OmoTYvH6VmZ0PgpUBcbgr0rrsGAiyY6/Cbr5oydPxf5Cybg6KJJaAr2R/MAPxxdNAlnbrgYqSPMT5x0bUt1gAr588fjxIPXYMgEx3/xkzB4EM7fdCmO3TDbGGPudTNQdus8JA+17ebQaZPHI/cvv0P+vLFQ+yvREDkAB+5ZAGH+VEwZMxV/iPoDjjT74IeSP6JWEwuNzheF9dORXfcUhmdd3O164wemoPqGS3DsxovQFOKP5iA/5F47HSW3X47kDPMTJxKJBMNnT0fZuFRk3zgLDRFB8CurQVOIP3bdNQ/1UcFQ+ylRMHc0jj16HTKmTbKpzo4UGhoKWcg87K++F/WtkVDr/HGq/jLkNK/A0EzzNwf2CwrEmbuvRENMCFqCfNEYGoAjt1yCxmsvQmxigkPicsT+HAB8fX0hDwtBq78SISfO4dzoQfAvrcaO5ZdD1qRG+dAEnJwzCn6Vdbhi5lHER9cgJKQJQUHNWLLwCG699RwyMpLNbt+1qTEm27cgCMi4ZBa2v3AryoclolUlR0V6PM5PHY68P1+NkwsmoCVAhYbwIBz60zy0Xj0DkR70BeXwceMQff/98L3ySggBAZCEhyPwjjsQv3AhomNibF6vTCbDoNlTsf3ZP+D8IMMkVEnWQOx44VYMv+yi3ldghSGZQxDzpxj4XeMHIUiAECIg4PcBiFsah4Rkx4xPTyaRSJB+8Qxsf/4WVKTHoVUlR9nwRGx/4VYMm2v5MdGcEZddjJKrp6BowhDkLBiP5mB/NAf74+h101Hxh8uQlJbqwJp4DolEgqFzZmL787caP2eE7i3D/rLbcLLucqh1AWhojcDBmruBAQsQHh7utFiGz56Og/+4EadnjzTsi2JCsPeRJQi8eFKPP3Ixu67LLsaOF25FSdZAw2e2QYbPbINmT3HoWWCuljgwEXE3xCHgxgAIIQKEIAH+1/oj+vZoDM7w/slTIiLqfwSxtzvFUp/w+eefY9GiRZgwYQJ27NiBjRs3YtasWbjmmmvwySefuDs8j7F48WKsXbsWixYtwpo1a9wdjk22bt2KKVOm2JRXrVaj8PBRVJ0uQiW0UFY3otZPhqTIGAyfPAEKRc+XvBFFEeVlZTi79wDKW5owbOQIxCYn2Xxpgt7irD5TjFopMHHmdKv/YSHHE0URlRUVOL17P8pbGpGRORxxA1Os6v+tW7di2LBhKNiVjZK6agxOTUXS0DT49HLZgq7jvv21VqvF6dzjKD+ej/PQIkTwQY2+FcNGZCI2JRlSqRQ6nQ75x3Jwdt8h1EsNl4aae9ml8PMzf2mTdnq9HufOnEFh9mGcb26ARCpFqF8ABHUr6nwETJxp+MV2wZ5sFFedx8DkZCQNzzDZjtpjLDl+ElWaFkgkEqQkJCJ55HCoVOYv5dBVVVUVCndno6S+FsFKXwjqVtQrJJg0c4bZXxPX1tai4MQenCutxqCBKUgaONziiUdRFFFRVoYzew+ivKURw0ZkIjgqEqcPHMaZ0hIkRscgOWsEfH3NX66kJzU1NSjcnY1ztdUYPGgwEjPSLI5r8+bNiA0ORcWJU6gR9Bg3farFZ7w4glqtxqEdu1Gdl4+6ACVGjByJgUNSe7zMmCiKqCgvx5k9BwzbzIhMxLWNS2cQRRElRUUo3n8ElZpmZI4bg+i4uB5j7I1Op0PRyVMoPXYc1aIWo6dMQlh4uOESano9Cs4U4GDeQaChCT5SGdLThyIxOd1km+66DYuiiNLiYhRlHzbEOmY0ohO6P0ulXUNDA47u2I2S0lIIEgFDR2QiKDwMZ/cdRFlTA4YOy0D84EFOa2NbVFZU4HT+bpRVNmPo0GGITxzcbXxbt27FpEmT8MvPPyOoqRXndRqMmjgekdHRdn0p25Uj9uft8VZWViLRNxDnaqsRER6O1qpaVKqbEQQB9XIZ9DodJIKAVlFEs1oCiVSKMeNGICUlwdjfXbdveVAAZs2aZbbMpqYmFGQfRH1ZJeoVEkyeNRMKhQLFBQUoOZyLKlGLrEkTEBEZ6dA2cwSdToczBQUoPHwYDaKIcZMmOSzOhoYGnNqzH0WV5UhOSETyiGFQKs1fSs8eer0eRWeKkH8gHw26BoyeMBrRMY4dn65i62fqxsZGFGQfNBwTY2KRkjXC4uN5TzQaDU4dOoKSU4Woa1VDJpdj1PhxiIqLtWs/7ij2/A/Sm6amJpzad8D4OSNhxDBUnS9BadFRVNWKGDN2ivHY40yGz2x5qMzLR7WdnzVaWlpQcPAIaotKUOcjYNIs85/ZutPe3h3bvbc+cGYftRNFEeeKz+Fk9knUaeowYswIxCXY91mDiIjIY4nUZwEQBUEQAwMDxWHDhokAxEmTJomiKIpr164VAYgTJkxwc5SeZdGiRSIAcdGiRe4OxWZbtmxx6Hq++uort8bhKeWQdWztl475rFlH17Tm8rYv62m9W7ZssWnMb9mypcfYLa1L1/XYEoc1Zdq7/dhaT2vXa20ed+8XbB1DruSM8nrbtnp735b3rCnP3eOiN9Zss5bszxzF3n1S+/Zgbhtt76euj55isGY/2du6PJmz4nRl/ftKW3fH0cdIR7L384IzuGt/5K52cGS59nzusebzjzd81iAiIvIkffc8UUJAQAAaGhpQV1eHI0eOAAC2b98OlUqFpLZ7QfCXH0REREREREREREREluO36n1YXV0d9Ho9RFHELbfcYlze0tKC3NxcAIaJFLlcjoyMDHz++efuCtVm+fn5kMvlEATB+Bhjxw00iYiIiIiIiIiIiIh6womTPuyBBx4wPp87dy4AYMKECRBFEX/+85+N77W2tuLYsWNYtGiRq0O02+DBg9Ha2gqZTIbQ0FAAwL59+7q97jURERERERERERERkT04cdKHvfDCC8azMH73u98BMEySADDeFK59IiU3NxcXX3yx22K1xfz58yGKIvz8/NDa2orKykrjmTQbN250c3RERERERERERERE5I04cdKHXX/99QgNDYUgCMYJk3379kEQBFx77bUALkykDBkyBD/++KPbYrXFhg0bAADr1683LhsyZAhUKhUA4LnnnnNLXERERERERERERETkvThx0od98MEHqKyshF6vxyeffAIAkMvlkMlk0Gq1AC5MpEilUiQkJLgzXKu1T/rMnDmz0/Lhw4cDAN577z2Xx0RERERERERERERE3o0TJ15CJpMBALKystDa2mqcSFEqlVAoFNDr9Th79qw7Q3SYESNGAADKysrcHAkREREREREREREReRtOnHgJX19fAEBgYCCACxMpI0eOREtLC0RRRGNjo9vic6SIiAgAF85IISIiIiIiIiIiIiJyFJm7AyDHuPTSSyGKovF1SEgIACAxMdG4rH1ypa8rLy8HAPj4+Jh9/8CBA3jqqafMvrd9+3YAQHV1tXOCIyIiIiIiIiIiIqI+jRMnXmrmzJmdJlK8ycGDBwEAkZGRZt9/6qmnsHbt2h7XkZOT4/C4iIiIiIiIiIiIiKjv48QJeaz2m9xv3Lix0w3iDx8+DAD4/e9/bzbfihUrul1nTU0Njh07hhdeeMGxwRIRERERERERERGRV+DECXmsOXPm4LvvvsP8+fPR0NAAAMjPz0dzczMA4MEHHzSbb+TIkVizZo3L4iQiIiIiIiIiIiIi78GJE/JY3377LSQSCRobGyGXyxEYGIjz588DAKZNm+bm6JzvzJkzqKysNFleU1OD7Oxsu9ffvh6tVmvT+hwVh6eU0xeFhYUhISHBqjzdjaueqNVqKBSKTsss6ZewsDAA6FRex3zdrcOS8szlbZ9UbW5u7ja2mpoai8Z817Ztbm6GWq3uNnZL61JTUwMANrVd13LMldk1n63bT8f6dxwz1qyvfR3mxpy1cbXXyxxbxjRgfpxZms+W/WZVVZXNfQGYjgVnlddTu/TUb+3bX0/bXnfbiC3jtKqqChKJxKLtsGt5tva7tfls2R7b03T960z2lNFxn2pu/9S+z+uqa3ld85pLY2nsfeVzg7PidGX9+0pbd8fe+J1Zf2u2A1dx1/7IXePMkeXasq6ux4GePgsRERGRk4hEHiw3N1eUyWQiAONj1KhR7g7L6U6fPi0qfVWd6t3+ePDBB80ut/bRvp433njDrvzOfriqnL74UPqqxNOnT1s1rnx9fa0uRyKR2NQvSqVSVKlU3ebrbh2WlGcu78MPPyxu375dfPjhh3scT5aMeV+V0ti2p0+fFh9++OEeY7e0Lg8++KDNbWdJ+ymVSlGlVFjVTz3V/8cff+w0ZqxZn6+vr7h9+3bRV6W0afx0jefHH380juUtW7bYNaa7G2eW5rNlv/nQQw/ZVF53Y8FZ5fXULj3128MPP9zrttddebaM04ceesji7dBR/W5L3ymVF8a/JfG1p+n615kPe8rouE81t39q3+d1ffQUg6X7ye5i7yufG5wVpyvr31fa2lnxO7P+1mwHfaW9bC3DXe3gyHJtWVfX44BKper0Wcic9s9HruLq8oiIiFxNEEUvvYM4UR+WnZ2N0aNHA6tuBtKjOr33YFkSnosstLuM9vW8cSYdtyfk2Jzf2VxVTp+TUwrc9A727duHrKwsi7K0j6vn/vIcUhJTLMqzeddmvPzuyyZ5dH46SBul3eY7dfoUHnzacDm9jnk75jO3DkvL6/r61OlTKG8tx4gRI3Dw4EEEFq5HcrivSVzSjKshBERDu/O/3cZeUNGEv67JNbZtdnY2PvnkEwwdPBSBYqBF8XRXF52fzhCHDW3XtRxzbdCe7x+L05Ac7gtpxtXQHf2i27J6q/+5c+cwf/58Yxy99XvXWFavXo1ly5YZ42lnTVzt8axfvx6XX345AGDr1q2YMmWKTWMa6H6cWZrvo9UfQdmotCqfPFSOCJ8Iq8rraSw4o7ze2qW7/m/f/gB0W2ZP28h7b75n3LYsjVMeKu+0TfYUn6X1c2S+jn131113ITY2FpGRkSgrK+sxX3uarn+dyZ4yIiMjoVAocObMmU7r6Ri/OV3L65rXXBpLY3dFmzmCs+J0Zf37Slt3x974nVl/a7YDV3HX/shd48yR5dqyro770ezsbKxcubLTZyFz2j8fuYqryyMiInI1XqqLyJOlRwGjEjsv26EyXWaL9vWUSG1bn6Pi8JRy+pGUxBRkpGZYlPbUmVNm8xTUFCA5Ntnq8jrmM7cOS8szl7f8ZLnxeXK4L9JjA0xiOaeSQS0VMNjMe73x8/VDRqxpPaypS0FNgSE+G9quazk99UF7/c+pZIixoa7dxWFNv5uLp52j4uoan6W6G2eW5pNJZVbnK9eUW11eR9Zut7aU11u79NT/7dufNXnby+u4bVkaZ7mm3CRfb+PT3n63tf9iY2ORkmKYcGn/25OuaS3JYy97yzAXa0/rNPde12WWxmTJujyVs+J0Zf37Slt3x1Fj31k8rX3dtT9yVzs4slxb1tWeJzY21mFxEBERkeUk7g6AiIiIiIiIiIiIiIjIU3DihIiIiIiIiIiIiIiIqA0nToiIiIiIiIiIiIiIiNpw4oSIiIiIiIiIiIiIiKgNbw5PRNTPNDU3oaGxodv3/f38e83T2NSIBp+GHvOYy9sxX2NTIzDAshi7lucOTU3m69H+2pK6NDY1AoBNbWeuXFdpj6O78nuqgyvYMqbtytfk2vJ6y+vw+nWTr73/u8unVqt7zNvtNmJrezbZtl9y9XghIiIiIiKivocTJ0RO9NNPP6GlpcXqfPn5+U6IhrzR5s2bUVxcbFHa9nF1w703QBAEiKJokkYQBBz75ZjJclvytLvx3hshwrp8vZX37effms2XnZ0NqVTabSzWaG/b9nb7/e2/t6kNnNV23bWBo2zevBkhISEAbK9Ddna2w+KpqqrC+vXrARi+oF+/fr1dY9qefH+45Q8eNRYcXZ6t+V555RVIBInVcdq6bbl6m7Sn/4iIiOzV8bOQOe2fj1zFmvKUSiUuvvhiJ0dERETkWJw4IXKilpYWNDc325SvO1UqvT0hmaynXm76BZA1+Z3NVeX0VdaMsfZxtfb1tchIzbCqnK55iuqLEBcQZ1HeNa+vMebtmK+ovsjm8szlraqqgkqlMrSHwvx6ZdpGtMosm8xsb9uWlhZUVVXh0/c/RWZ8psXxmKtLezpb2q5ruT2V2U6mbbSoHHNaWlqg0WgAXKiDNf0OwJjfEXFpNJpOY729bzrGZy1b833y0ScYET3CqjxVVVU2lweYjgVnltddvp76v6qqCjfffDMuG3dZt3m703HbslTXbbK3+Dpy9XghIiJyhK6fhcyx5X9Pe7i6PCIiIlfiPU6I+ph3Rlp/BktP63ngItsu++OoODylnP7uyx++tCp9XECc1Xm65rPmC/iu5ZnL+84773T6a05ExWYknv3U4nI7rlvafOFMlvbyra1LXECczW0HAHu27TGJoScRFZttKqddZWVlp9cdy7S1DoBtcXWNpTe2xmdJPlm96e9OesvX3bh0VpzOKK+nMffOO++YLdOSbaTjttU1X0/ldc1nz7bl6nxERETWsvazEBEREdmHEydERIQPv/jQJXnckc+Z+nMbeFIs5vSVvmG+/pmPiIiIiIiIPBsnToiIyOw1+52Rxx35nKk/t4EnxWJOX+kb5uuf+YiIiIiIiMizceKEiIhw0+KbXJLHHfmcqT+3gSfFYk5f6Rvm65/5iIiIiIiIyLNx4oSIqJ/Ze2iv8Xn7r6UXzFng8DxdWZPP3vKKq5pRXqsGAGzOPY9/rT+BT3cUQ6e3/dfhtsbk6rYDHF9/e+tQ1aBxWCyOjK+v5OuIcfa9fERERERERNT3cOKEiKif+WTdJ8bnC29f6LQ8XVmTz97ynl53AstezcbrPxfi9Z8LofSRYsPhcrzw7Umr12VvTK5uO8Dx9be1Dnv3Gr5o/uvaXIf2haPi6yv5OmKcfS8fERERERER9T0ydwdARETu4633Nnn+ugzEh6qw4IXd2PDwRASoZNBo9Vj63302xeCImFyZz5n1tyaW48ePAwCeXJyGBz465pS+sCc+5mM+3tuEiIiIiIiIzOHECRFd8MF24JZVgEIG5D4FxAZ3fv+i54HKBuDA37pfR5MaeH4DMH2I4UEeTRAEl+RxdT6VXIrwQAXiQ1UIUBkOdXKZBD5Sx5xo6elt4Mz6WxOLVCoFAAT7yZ3WF/bEx3zM5+p8ZL/S0lKsW7cOhw8fRnV1NWQyGeLj4zFx4kRcdNFFkMvlZvOp1WqsW7cOQ4cORUZGhoujJiIiIiKivoYTJ0RkSq0FnvsBeOl31udt0gBPfmN4zokTj1RcWoyrb7saAHDqzCnj83ZfvPmFQ/K0e+DJB6BSqqzKZ095HUkljvty09aYXN12HTmq/rbWoeOXy47sC0fF11fyAa7djrw9Tne0CzlGdnY2/v3vf8PHxwfTpk1DfHw8tFotcnNzsXr1ahQVFeG2224zm1etVmPt2rVYtGgRJ06IiIiIiKhXnDghIlMj4oF3tgAPXQrEDHB3NAaNasBP4e4ovMKjyx9FUlyS0/O0+/2S37u0PAC4/Z2DkEoENKq1mPnkNgCAKAJNGp3N67Q1Jle3HeD4+ttah8rKSmM8La06h/WFo+LrK/kA125H3h6nO9qF7FdeXo6XX34ZYWFheOyxxxAcfOGs2EsuuQSlpaXIzs52eVwtLS1QKpUuL5eIiIiIiJyLEydEZOr/LgVueNtw1sm/r7U8X2ElkPoXw/Mnv7lw5smjlwOPXWF4nlsCPP41sCnXcHZKRiywYh4wf+SF9bRfMuzn+4E1e4EvsoFWHVD+kgMqRxmpGchIte7XtrbkcVd5APDUkjQMivS3Ob85tsbk6rYDHF9/W+O4/vrr8cEHHzilPzpydd/0lbHAOD0jHznGunXr0NLSgj/+8Y+dJk3aRUVF4bLLLjObt7y8HHfffTcAYO3atVi7di0AYNGiRVi8eDEAoLi4GJ9++imOHDkCjUaD+Ph4LFy4EGPGjDGuZ9OmTXjttdfw+OOPY/v27di1axd0Oh3effddR1eXiIiIiIjcjBMnRGQqOQxYNtFw1smDcy0/6yQ8AFi5FLjrI2DBKODKUYblw+MMf4+eA2Y8a1jfg3MNZ5Cs3Qsseg349I8X0re7+3+Gda6YBzRqHFW7fq+wqBBnz53F3BlzAQAr/rUCdQ11AIBbrr0FI4aOcEiedtv3bTd+2WhpPnvKA4CwAAWigx37C2BbY3J12wGOr7+tddixY4dT4nFUfH0lH+Da7cjb43RHu5D99u3bh8jISAwZYv1lQAMDA3HLLbfg7bffxtixYzF+/HgAQEJCAgDg7NmzeOyxxxASEoIFCxZAqVRix44deP7553Hfffdh3Lhxndb3zjvvIDAwEAsXLoRarba/ckRERERE5HGcc2dWIur7Hr4M0OqA53+wPI+fArg6y/B8eCywdILhkdk2cXL/J0B8CLD7UeCBucAdM4GfHwAmpAB/+dx0fSF+wIb7gD/NMky0kEN8tv4zDAgcYHydfSQbsybNwsSsiXj7k7cdlqddgF+A1fnsKc9ZbI3J1W3nDLbWobq62gXRub5v+spYYJyekY/s19TUhKqqKsTHx9uUX6lUGidLEhMTMXXqVEydOhWJiYkAgFWrViEsLAzPPPMMFixYgEsuuQSPP/44Bg8ejP/9738m6/P398df//pXzJ07FwsWLLC9YkRERERE5LF4xgkRmZcSbpj0eLvtrJPoAfatr6oR2HgcePwKoL7F8Gh3cQbwxDqguBqI7XD5jT9MBaSc33W0iqoKTMiaYHytVChx1dyrAADfbfzOYXnaDU8bbnU+e8pzFltjcnXbOYOtdeh4c3hncnXf9JWxwDg9Ix/Zr7m5GQCgUqkcvu6GhgYcPXoUixcvRnNzs7EsABgxYgTWrFmDqqoqhISEGJfPmjULEgk/nxAREREReTNOnBBR9x6ZB3y003Cvkxe73OukqhHQaC+8VvkAQb7dryu/3HBH6L99bXiYU17feeIkOcz22KlbWq220+tX/v6K8Xltfa3D8phjaT57y2u/OXpHoggIArDx0cmWhuuQmFzddoDj629rHTreHL5jPPb2haPi6yv5umKcfSsf2a99wqTjpEZ3GhoaOvWVXC6Hr2/3n09KS0shiiI+++wzfPbZZ2bT1NbWdpo4iYiIsDR0IiIiIiLqozhxQkTdSwkHrutw1klHS14DNuddeH39ROCd33e/Lr1o+HvfHODioebTDOryRYTKx/qYqVc6vQ4NjQ3w9zPcrDs+xnDpk/qGepMvBu3J066pucn43NJ89pQHOOfm8LbG5Oq2Axxff1vrEBwcjKqqKqffHN7VfdNXxgLj9Ix8ZD9fX18EBwfj7NmzvaZ94YUXcOzYMePr6dOn409/+lO36fV6PQBg/vz5yMzMNJsmKiqq02u5XG5J2ERERERE1IfxHHMi6tkj7fc62dB5+b8WA9//+cLj/ksMy7u7NE/72SMyKTB7qPlHgPNuHk0XTBkzBY88+wjqG+qNy+ob6rHiuRW4bNZlDsvTbuWqlVbns6c84MLNyM09bGVrTK5uO8Dx9be1DlKptMd4HMXVfdNXxgLj9Ix85BhZWVkoKytDXl5ej+muv/56rFixwvi44oorAHR/6cDIyEgAhv1VZmam2YczLhFGRERERESejWecEFHPBkYYzjp5azOQEGKY+ACArETz6X3bfoVZ2+VyGhGBwPRU4O3NwJ0zTe+ZUlEPhAeAnG/hZQvx4RcfYvqS6UiMNfTj6eLTmD15Nu64/g6H5WmnkCuszmdPec5ia0yubjtnsLUOoih6dHx9JR/g2u3I2+N0R7uQ/a644gps3boVb7zxBv76179iwIABnd4vLS1FdnY2LrvM/CSWQqEAADQ2NnZaHhQUhKFDh+Lnn3/G3LlzERwc3On9uro6BAYGOq4iRERERETUJ3DihIh69/Blhnud5JUBQ2N6TquSA+nRwJo9wOBIINgXyIgFhsUCL18HzPwXkPV3w43fU8KBsjpgVz5QVAPse8wl1envisuK8dyK51BYVIgDRw9AqVAifXA6EmMTsfvAbowbOc4hedrdc/M98PP1syqfPeU5i60xubrtnMHWOlx77bVYuXKlx8bXV/IBrt2OvD1Od7QL2S8qKgrLly/HSy+9hPvuuw/Tpk1DfHw8tFot8vLysHPnTkyfPr3b/HK5HHFxcdixYweio6Ph7++P+Ph4JCQk4Oabb8Zjjz2GBx98ELNmzUJkZCRqampw4sQJnD9/Hs8995wLa0pERERERJ6Al+oiot4NigCuG295+jduAGKCgQc+A65/G/hin2H50Bhgxwrg0kzgw+3A8v8Bb/0GCBJgxTznxE4mXnnXcEPjpLgkfPD5B5g7Y67x19PPvPqMw/J0ZG0+e8tzBltjcnXbOYMn9kdHru6bvjIWGKdn5CPHGTNmDJ577jlMmDABe/fuxbvvvouPP/4YFRUVuP766/H73/dwrzUAt99+O0JCQvDBBx/glVdewa5duwAAcXFx+Oc//4lRo0bht99+wzvvvIOff/4ZgiBg0aJFrqgaERERERF5GJ5xQkQX3DDJ8DDnnd/3fPP3jiYMBHauMP9eSjjwbi/r6SkOcqiul1Ky5NJKtuRxRz5n6s9t4EmxmNNX+ob5+mc+sl90dDRuu+02m/Kmpqbin//8p9n3IiMjceedd/aYf8aMGZgxY4ZNZRMRERERUd/CM06IiPqxrjfL7e7mufbmcUc+Z+rPbeBJsZjTV/qG+fpnPiIiIiIiIuobeMYJEVE/o2nV4Pip44AItKhbjM8Bw2tH5Wl3uvg0ZFKZVfnsKc9ZbI3J1W3nDJ7YHx25um/6ylhgnJ6Rj4iIiIiIiPoeTpwQEfUzmlYN/rTiT8bXHZ9396tpW/K0e+a/z8DHx8eqfPaU5yy2xuTqtnMGT+yPjlzdN31lLDBOz8hHREREREREfQ8nToiI+pnXn34dGakZTs/T7rWnX3Npec5ia0yubjtn8MT+6MjVfdNXxgLj9Ix8RERERERE1PfwHidERERERERERERERERtOHFCRERERERERERERETUhhMnREREREREREREREREbThxQkRERERERERERERE1IYTJ0RERERERERERERERG1k7g6AiHrw/REgt8Ty9AN8gagg58VDniOnFABQXFxscZb2tKdOn7I4T1FJkdV5uqZ3dXkAUFDRZFV+S/JaG5Or265j2o51qKzXoL5F222+AKUMYQFys3lticNcemf0R0/l9cbWvnF1PldvR94epyvzdUxrzX6aiIioo5qaGhQUFAAwPZ4EBgYiLCzMHWERERH1C4IoiqK7gyDyVuvXr0dzc7PV+fLy8vDYY49DFPVW5RMEidV5qA+TCIDeul24RCKBXm/dGLElDwDI5XIIEKDWqF1SnkKhwKOPPoon//EPqDUaq/N3Wpdcjhf//W+EhYWhsrIS9913H9Rq6+oBuL7t5HI5ABEaTWuHIAD0FIKZ99vrD8DmujurPzpyR9+4Op+rtyNvj9OV+eRyOURRRGtra++JiYiIzJBIgO4OP0qlD1544aU+MXmiUqkwf/58d4dBRERkFU6cEDmRrRMnp06dwiOPPIIZN76OAVGpFuWpKc3Dpvf/iLvuuguxsbE9pi0uLsbKlSsxJ/wBhMjju01XpTmLHyueBwCL0y5ZsgSfffYZ7rgoCTHBSotiN+dcdQte+7kQd911FwBg5cqVuOcP9yAuOs7idWQfzsbH6z62Ol9RSRFefvdlp9Slt76xRHv/zY98HMl+4yzOV64+iU+K77E6Dq1WC5nM+hMUAwMDAQB1dXVW5bOnvPaJDmvL7G5d7Wxdp6vbrmu+9rGS9I8kKJNNx3BLQQsK/2o6NjvW39a6O7M/OnJ137g6n6u3I2+P05X5AgMDcebMGTz77LN2H0eA7o8l7du5q4+RALymXh3r5Ip8tra9K+vWNa8zPgvZ0seubANb+8mevI78DOqoNndl+9kzXm0pzx3bojWfw9v7a/VqID2983s5OcCyZcA///lPpKSkWBy7u3DihIiI+iJeqovIgw2ISkVYwgir8sTGxlr84TktYCbiVMO7fb+o+bBx4sTStBEREQCAyakhSI8NsCLyznKK6/Haz4Wd/rGYNn4aMlIzrFrPx+s+tjrf0byjePndl51SF0f+Y5PsN67HPumOo+Pojat/BRcWFubwMp2xTkvLdUQ+ZbISvum+3abvaUzYW3dnt527+sbV+kod+0qcrtI+kWTvcQTo/Vji6mMk4D316lgnV+SzJUZb89kToyP7uqd+dnbbuaOfbM3ryM+gjmpzV7afPWXZUp6teWzJ1143Wz6Hp6cDWVlWZSEiIiIH4M3hiYiIiIiIiIiIiIiI2nDihIiIiIiIiIiIiIiIqA0nToiIiIiIiIiIiIiIiNpw4oSIiIiIiIiIiIiIiKgNbw5P1I+p9U1o0dXbnVYpte/GsJZqam5CQ2NDt+/7+/k7NJ+n663/XNUvRETkft56jHR1vXrK11Nb2FKeJ9Wtp3zO5ilt54x264ttbmt9uC1aF6e1dDrgppuADz902CqJiIioF5w4IerH3ihcAgECANHMuwLuTllvfPVm4TXdpns2o9A5AXZxw703QBAEiKJpHIIg4Ngvxxyaz9P11n+u6hfyfGUflSH5yWTk358PCBeW6+p17guKiBzKW4+Rrq7XjffeCNHMcbW3trClPFfXzVP72pVtZ0v/2tNuntrmto5zV7afN2+LtpBKgbw8h62OiIiILMCJE6J+bHnKN4hTDe/2/aLmw8bnd6es7zGtK6x9fS0yUjNcls/T9dZ/RO18030BAANmDOi0XHNOg4a93f9Skoj6Dm89Rrq6XmteX+Oy8lxdN0/ta1e2nS39a0+7eWqb2zrOXdl+3rwt2mrmTOC22wxnnpw9a1hWUlKClJQUl8VARETUn/AeJ0TUyd6aNQ5N52xf/vClS/N5Ok/pF/IsAVkBEHUiWgpaEDo/1PgImhbk7tCIyIm89RjpynrZ0xauLM/b+tpVbeeOdvPENu8L487bt0VLfPop8NNPwNKlwH33GZZ9yGt3EREROQ0nTog8WNGxXwAARza+YfbhDFvPv+fQdM724Re2/bNgaz5P5yn9Qp5HkAqo32vZPY2IyDt46zHSlfWypy1cWZ639bWr2s4d7eaJbd4Xxp23b4uWKCi48FjfdkXlhx56yGnlERER9Xe8VBeRB6s/bzgH+3zRYZP3hI43KnAoc/fLsCedc5m7prAz83k+b60XOULg5ECUvFOC0CtCIVVJoWviPU6IvJm3HiNdWS972sKV5XlbX7uq7dzRbp7Y5n1h3Hn7tmip3buBn38Gzp1zajFEREQETpwQebT0qTdBr9chYdgcJI+6wiVlTg29xaHpnO2mxTe5NJ+n85R+Ic9U+lYpAKDktRI3R0JEruCtx0hX1suetnBled7W165qO3e0mye2eV8Yd96+LVrizTeBJ58Err4aqK42LNuzZw/vcUJEROQkvFQXkYeTSKQ4sOHfTll3QeNu4/P2X0eNHrCwxzyWpnOGvYf2msSxYM4Cp+XzdLb0H/VvWXuzOj3SPkxzd0hE5CDeeox0V72szWNLea6um6f2tSvbzpY89pTlqW3eztaYuC3al89WK1cC+/YBL70E3H+/Ydn27dudVh4REVF/x4kToj4gND4TpSd3Ony9O6tXG5+/fGqeRXksTVdc1YzyWjUAYHPuefxr/Ql8uqMYOr3tp69/su4T4/OFt1s+QWBrvnZVDRoAjquHo9jSf0SaEg3Of3ceVd9VofV8q7vDIeoXnHFM7Modx0hvrZcteWwpz9V189S+dmXb2ZLHnrLsjdPZ25gtMVmbj9uiY4WHu6QYIiIiAi/VRdQnVBTsxYmdHyMgLAk+Cj/j8qse3mjXesVO98Nw7L1Nnl53AjKJBFePjcbm3POYMCgYGw6X43RlEx6aP9jqWE2icMF1h/fuNfyK7K9rc7FkfKxT6mEP2/qP+rOq76tw9rmz8B/lDwBo2Nfg5oiI+gdnHxO7ctW1+b21XvaU5eryvK2veW+Tzrzh87S9+bx9W7TG4MHAihXA7bcDJW1XXQ0NDXV6uURERP0VJ06I+oBJ1/zLKevtfIN5S282b1m656/LQHyoCgte2I0ND09EgEoGjVaPpf/dZ3WcZqMQLI3X9nzHjx8HADy5OA0PfHTMKfWwh239R/1ZyVslSPswDYpYBQCgZnMNTt17ys1REXk/Zx8Tu3LFMRLw3nrZU5ary/O2vnZV27m6vW3N6w2fp+3N5+3bojVefx24+24gKwvQag3LrrzySqeXS0RE1F9x4oSoD4gePBkA0FhdDAgC/AbEOGS91a1FeCn/MgBAhfqk8Xm7RTHPGp9/XLQcPhKV2XR/HvidybpVcinCAxWID1UhQGXY1chlEvhIbb9CYHFpMa6+7WoAwKkzp4zP233x5hcOzSeVSgEAwX5yh9bDUXrrP3P9Qv2bRCkxTpoAgDxc7sZoiPoPZxwTu3L1MRLwzno98OQDUClVVreFLeW5um6e2teubDtb+teedrMnL+C8drd1nLuy/bx5W7RVeDjwSdvVwbKzgdGjAX9/f4eWQURERBdw4oSoDzhfdAS/vnsLmuvKIQgCVIERmPmHtxEam2HXeq+I+jvCFSkWpZ0aeqvFaTuSShz366tHlz+KpLgkl+Xr+MsxR9bDUazpP+rfdE066Bp0CJoShHNvnEPYlWGACFR+Wenu0Ij6FWceS1x9jOzIm+r1+yW/d1l5rq6bp/a1K9vOlv61p90c0eaA49vd1nHuyvbz5m3RVm+8AVx7LRAUBDzb9vu2goICpKTw/wEiIiJn4MQJUR+w5aN7MPryR5CStQAAUJC9DltWL8eV//eLXeuNUw1HnGp4t+8XNR+2OG1Xt79zEFKJgEa1FjOf3AYAEEWgSaOzOd6M1AxkpFo/WWRrvspKw5fKt79zEC2tOofVw1Gs7RPqv07cfsJwNbe2y2+XvlXq1niI+htnHBO7cvUxEvDOerkyX1+IsZ0z+9rT286edrMnL+C8du8L7efNMdrqv/813N9k2zbg5EnDsu+++w6zZ892WQxERET9CSdOiPoAnVZtnDQBgOSsK5D9/XN2r7dSXYAqzWlkBl0OAFhT/CCadbUAgBlhd0AiXNhFnGjYYvySvmu6BN9RJut+akkaBkU69tTxwqJCnD13FnNnzAUArPjXCtQ11AEAbrn2FowYOsKh+a6//np88MEHTqmLI/TWf+b6hfqntA/T4Jvua7K8KacJucty3RARUf/iiuOIq4+RgHfWa/u+7cYvQq1pC1vKc3XdPLWvXdl2tvSvPe1mT17Aee1u6zh3Zft587ZoK1nbv2a//grMm2e4XJder3doGURERHSB+y/ST0S9CovPxLm8rcbXJSe2ISzB/g/iu6o/gq802Pi6sGkvhgZejEH+U7Cp8vVOaZXSQIvSGWMOUCA6WGn2YavP1n+GAYEDjK+zj2Rj1qRZmJg1EW9/8rbD8+3YsaPHuribNf1HBAAVn1dA38J/sIlczRnHxK5cfYwEvLNeAX4BVuextTxX181T+9qVbWdL/9rTbvbkBZzX7raOc1e2nzdvi7aSSIBPPzU8xo83LNPp3H8WPBERkbfiGSdEHmzje7dBpvCFXteKE7s/Q0BoIgCg/vxpBEen273+Om05BvlPNr72kSgxZsBiAMDB2nWd0sarRliUzpkqqiowIWuC8bVSocRVc68CAHy3sfsboduar7q62t6Qncqa/iMCgIbsBpS8UYKQS0IQtiTM3eEQkQO5+hjpKq6u1/C0C5fAtKYtbCnP1XXz1L52ZdvZ0r/2tJuntrmt49yV7efN26KtVq4EnnkGuPVWIDrasIz3NyEiInIeTpwQebDhs+9EUORAp61fL2o7vb4+/sJZCu2XfDLH0nSOptV2jveVv79ifF5b330ctubreHN4T2Rr/1H/lfxUMlrPt6Lyy0qc/ONJ+ET4uDskInIQVx8jXcWd9bImjy3lubpuntrXrmw7W/LYU5antnlHtsbEbdH1/TthAvDVV4bn2dmGv1dccYXDyyEiIiIDTpwQebCwhBEOuSRXd/TQoUVXD6XUcCp8qNxwRkuzrg66Ll/Kq/VNxuc9pWvXfjPLjkQREARg46OTzebpjU6vQ0NjA/z9DNd6jo+JBwDUN9Sb/OPiiHwdbw7fsS721sNRrOk/onY+oT6IviUa/iP9UfCXAgDAiy++iNtvvx3Dhw/vJTcR2cIZx8SuXH2MBLyzXk3NFz7vWNMWtpTn6rp5al+7su1s6V972s2evIDz2t3Wce7K9vPmbdFaH34IZGUB995r6HsAKC83/P3mm2+wfPlyh5VFREREF/AeJ0Qe7PCvrwEAfnrzevz05g0mD3ul+k/HZ8X3o1lXZ1zWrKvD2nMPYkTQ/E5pfy5/0aJ07Z5akob/3Tm60+Pjuwx/bTVlzBQ88uwjqG+oNy6rb6jHiudW4LJZlzk8X3BwsNm62FsPR7Gm/4gAQK/Wo/KLSuT8LgfnXj2HiN9FAACWLFmCN954w83REXkvZxwTu3L1MRLwznqtXLXSprawpTxX181T+9qVbWdL/9rTbvbkBZzX7raOc1e2nzdvi9by9TX8HTDgwsPfMFcDpdL9910kIiLyVjzjhMiDlZ7YBgBIzLwMmqZayH2DHLr+sQOuwbaq9/BU3niEyZMAAJWaQmQEzMHs8OUoackxppVJFN2mM6f9ZpaOtPCyhfjwiw8xfcl0JMYazq44XXwasyfPxh3X3+HwfFKpFIBz6uIIvfUfUVdH5h9B4PhAJDyaAL8MPzTlNOHcynOIi4vj2SZETuSK44irj5GAd9ZLIVfY1Ba2lOfqunlqX7uy7WzpX3vazZ68gPPa3dZx7sr28+Zt0VoLFwJ79gBHjxoeggDExhreu+iiixxWDhEREXXGiRMiDyaTG35elDrhd/jymZm46uGNDl1/dWsRfhf3MirUBTjTvA8+ghIxymEIUyQhv3EnFBI/Y9pLIh6EQuJvNt1Avwk9lOI4xWXFeG7FcygsKsSBowegVCiRPjgdibGJ2H1gN8aNHOfQfKIoOrM6duut/1zVL+T56vfVQ1OuQdx9cZCoJGitbEXNbzVQn1Ub09x+++1ujJCI7OXqY6SruLpe99x8D/x8/axuC1vKc3XdPLWvXdl2tvSvPe3mqW1u6zh3Zft587ZorUOHDJfpuuMO4LrrDJdrW7/e8N6ZM2d4g3giIiIn4aW6iDxZx0saO+FL/B/LXwAAhCuSseX8u8gMuhxhiiQAwPrSJ0zSW5rOWV5513DDxaS4JHzw+QeYO2Ou8dddz7z6jMPzXXvttY4K3Sms7T/qv6p/qEb5R+Wo/KIShX8tRPlH5Sj/qBzVP1S7OzQichBXHyNdxR31siWPLeW5um6e2teubDtb8thTlqe2uT0xcVt0TN2s8f77wLvvAk8/DVx5JXDVVcBddxne++233xxWDhEREXXGM06IPJiuVYOq4mMQIULb2mJ83i40NsOu9XdcF9B1Yqa7iRpL0zlX17NBLD07xNZ8nsi2/qP+KGFFAnzTDWew5VyXg9Q3UwEATTlNyF2W687QiMgJvPUY6cp62dMWrizP2/raVW3njnbzxDbvC+PO27dFS5w6ZZgsMaesrMxh5RAREVFnnDgh8mA6rRo/vrHU+LrjcwECrnki2671C51OaRFM3u0ul2XpnEsQhB5fOzqfJ7Kt/4iIyNt56zHSlfWypy1cWZ639bWr2s4d7eaJbd4Xxp23b4uW6On+73K53GHlEBERUWecOCHyYJfc8THCEkY4bf1aUY2SllwAIrT6FuNzANDqWzqlrdQUQirIek3nTJpWDY6fOg6IQIu6xfgcMLx2dD5PZ03/ERGRd/PWY6Sr63W6+DRkUpnVbWFLea6um6f2tSvbzpb+tafdPLXNbR3nrmw/b94WraXRAIcPd75y84kThr9ardZh5RAREVFnnDgh6se0ogarztxsfN3xedffSH1T+gSkgrzXdM6kadXgTyv+ZHzd8XlPv+qyNZ+ns6b/qH9rOdNiPOLr1Xo0n2iGCNGwnIi8grceI11dr2f++wx8fHysymNrea6um6f2tSvbzpb+tafdPLXNbR3nrmw/b94WraVWA1dc0aVsjeEvJ06IiIichxMnRP3Y7xPeQ5xqeLfvFzUfNj6/KeHdHtO6wutPv46MVOvv62JrPk/XW/8RtSv+dzEEnwv/wOfflw8AEFvdf311InIMbz1Gurperz39msvKc3XdPLWvXdl2tvSvPe3mqW1u6zh3Zft587ZorW++AbKyOi/LzgZGjwYeeughp5dPRETUX3HihIiIyMsN/PdA483hO+LN4YmIiIiIiIiITEncHQAREREREREREREREZGn4MQJERERERERERERERFRG06cEBERERERERERERERteHECRERERERERERERERURveHJ7Ig9WU5lmdtri4uPe0NTUAgILG3ShXn+w2XZXmrPF5bv1Gi9KWl5cDALblVaGgoqnXWLpzrroFQOf6bN61GafOnLJ4HdmHs23KV1RS1Om1PfVwRP7u9NQfjkhP3qOloMWq5UTkWO3Hgcp6DQAgLEBu8zq6c+q05cc54MKxztp8HdM74vjmCfXqmNYV+Wxte1fWrWt6Z34Wcnbbubqf7MnbNb097e6oNndl+9kzXm0pz13borVycixbRkRERI4liKIoujsIIm+1fv16NDc3W52vsrIS9957HzQatVX5BEECUdT3ms5HZvjCRqfVoPfU/ZMEwL3334+V//kP1BqN3etTyOV48d//RlhYmN3rqqysxL1/vg+aVuvGBwDIfRT490svOiQO8nyVlZX4831/Rqu6tds0PgofvPTiSxwTRE5QWVmJ++6913gckUgMJ3vr9bYdfc0dSyorK3HfffdBrbb+mCCRSGyKRS6XAxCh0XS/b7GGJ9RLLpdDgAC1lZ+9bM1na9u7sm7teR3V11372dY+dmUb2NpP9uRVKBR49NFH8eQ//mH3Z1BHtbkr28+e8WpLea7cFhUKBV580fLP4ZWVlbj//j+jpcX89qdU+uCFF/rGZziVSoX58+e7OwwiIiKrcOKEyIlsnTgBDB+U6+rqLE5fXFyMlStX4trYlxGhGNRtunL1SXxSfA9uvPFGvP/++1g9DEj3sylEr5XTCCw7Avzzn/9EYGCgVf3QncDAQIf+U2Pt+HBWHOT5ehsrHBNEztW+DbYfpwHgrrvuQmxsrNXr6m57tfWYoNVqIZNZfwJ6YGAgADjk+Ni+PnfXy9Y62ZrP1rZ3Zd3szWtuXV372ZY+dmUb2NpP9uRtbydbx7+5dXVky3pd2X72jDlbynP1tmjtZ66e+qsvfYbjxAkREfVFvFQXkYcKCwuz6YNwhGIQ4lTDe00XEBAAwDBpkhVodTH9hq394GyeGhd5Ho4VIvcytw3GxsYiJSXFqWW4grPLdEe9bC2vL+xn7YnRWfVzdR/3hX5q56y2sWe97CvX4+c4IiIi9+HN4YmIiIiIiIiIiIiIiNpw4oSIiIiIiIiIiIiIiKgNJ06IiIiIiIiIiIiIiIjacOKEiIiIiIiIiIiIiIioDSdOiIiIiIiIiIiIiIiI2sjcHQAROZZa34QWXb3Z95TSABdHQ0RERERERERERNS3cOKEyMu8WXgNANHMOwKezSh0cTREREREREREREREfQsnToi8zN0p6xGnGu7uMIiIiIiIiIiIiIj6JN7jhMjL7a1Z4+4QiIiIiIiIiIiIiPoMnnFC5OW2nn8PYwYsdncYRERE/dorr7xifP6f//wHcrm80/vPPvusq0MiIiIiIiKibnDihMjrmbvfCREREbnS5ZdfjrfeegsAMG/ePMTExLg5IiIiIiIiIuoOJ06IvNzU0FvcHQIREVG/l5KS0ul5x9dERERERETkWThxQuSlRFGEIAgYPWChu0MhIiLq9z788EPj89WrV8PX17fT+w888ICrQyIiIiIiIqJucOKEyEu9fGoe/jzwO3eHQURERACGDh2KY8eOAQDS09MRERHh5oiIiIiIiIioO5w4IfJazr+3yapzwO+PAnvGAWOCLM/XpAP+VQjMCAZmhDgtPCIiIo8xevRorF271vg8Pj4ePj4+bo6KiIiIiIiIzJG4OwAichbB3QF0q0kH/P0UsKna3ZEQERG53ksvvYTly5cDAE6dOoXVq1e7OSIiIiIiIiLqiGecEHmZj4uWw0eiQoX6JF7Kv6zTe95+6a5GHeAndXcUREREPbvyyiuxYcMGAEBycjL++9//YtmyZW6OioiIiIiIiNpx4oTIy0wNvRXhihS3lH3TEWBtOXB8EnBnLvBzFaCSADfGAM8OBqQCUNgMJG81pP/7KcMDAB5PAf420PA8txF49CTwaxXQpAeG+QGPpQBXdLgcfPtlwjaNBj4tA9aWAa0iUD3TtXUmIiKyVlJSkvG5IAiQyfiRnIiIiIiIyJPwvzQiLxOnGo441XC3la8TgUuygfFBwPODDZMnL5wGBqqAO+KBcDnwWhpwRy5wVQRwddtkSKa/4e/RBmDyHiBWATycbDiD5LNS4MqDwOcjDHk6+lOuYZ2PpRjOOCEiIvJ0Ot2FA1ZlZSUkEl49l4iIiIiIyJNw4oTIy5xo2GKcOFlT/CCadbUAgBlhdyDBd5TTy2/RA9dEAX9tO+nlj/FA1k7gnWLDxImfFFgUaZg4yfQHlkV3zn/PcSBBCewZDyjavkf6UxwwZQ/wfydMJ05CfIBfRhvOZiEiIuoLPvzwQ9TX1+OTTz7B1q1bsXTpUneHRERERERERB3w521EXkYpDTQ+L2zai6GBF2OQ/xRsqnzdZTH8Ma7z66kDgFPNveerajVcnmtJJFCvBSo1hsf5VuCSUOBEE1Dc0jnPrbGcNCEior5l5syZmDx5MrRaLe666y5MnDjR3SERERERERFRBzzjhMjLxKtGGJ/7SJQYM2AxAOBg7TqXlK+UGC6d1VGwD1Ct7T3vySZABPDXfMPDnHINEKu88DpZZXOoRERELtPScmHmPzExETNnGm7KVV5ejqamJvj6+rorNCIiIiIiIuqCEydEXuz6+AtnmbRfssvZ7Dn7Qy8a/j6QaDjDxJxBXb5XUvG8OSIi6gO+//57s8sLCgpw+PBh3HLLLS6OiIiIiIiIiLrDiRMiL6PWNxmfh8oTAQDNujroRAtO+XCR7uZWUtomRXwE4KJuJk6IiIj6oqKiIrPLx48fj88++8zF0RAREREREVFP+FttIi/zc/mLaNbVGV836+qw9tyDGBE0341RdeYrNfyt6TKXEyEHZgQDbxQDJWrTfBUa58dGRETkDHq9vtv3BIE36yIiIiIiIvIkPOOEyMvIJAo8lTceYfIkAEClphAZAXMwO3y5ewPrQCUFhvoBn5YCqb5AiA8wzN/w+G8aMGUPMHyH4cbvKSqgTAPsqAWKWoCDvH8uERH1QTqdzuzypqambt8jIiIiIiIi9+DECZGXuSTiQSgk/jjTvA8+ghIxymEIUyQhv3EnBvpNcHd4Rm8PBe7OBe49DmhE4PEUw8TJUH9g73jg76eAVeeA862GM1FGBQCPpbg7aiIiIttkZmbil19+6bSsoaEBr7/+OiZNmuSmqIiIiIiIiMgcTpwQeaFwRTI+KroTfx74nXHZ+tInOr12hJtiDI92q4YBq8yk+9tAw6OjiQOAvd3M46T4Au8Ps65sIiIiTzZr1izjxMkrr7wChUKBkpISjB8/HgsXLnRzdERERERERNQRJ06IvJbYy2siIiJyFYnkwq0FZ86ciYiICCQnJyMqKsqNUREREREREZE5nDgh8lpdbzTLG88SERF5guHDhyMlhdefJCIiIiIi8lScOCHyMpWaQkgFGbT6FpS05KL9TBOtvsW9gRERERERERERERH1AZw4IfIy35Q+AakgBwCsOnOzcTnPNyEiIiIiIiIiIiLqHSdOiLzMTQnvIk413N1hEBEREREREREREfVJkt6TEBERERERERERERER9Q+cOCEiIiIiIiIiIiIiImrDiRMiIiIiIiIiIiIiIqI2nDghIiIiIiIiIiIiIiJqw4kTIiIiIiIiIiIiIiKiNjJ3B0BEjlWuPmnV+zmNzozGoEQN1Gh7TjNABkQrnB+LJVzRJkREREREREREROSZOHFC5CUCAwMh91Hgk+J7ek0r91EgMjISSrkPlh1pdX5wEgB6B6RxIaXcB4GBge4Og4iIvERgYCCUSoXxOREREREREXkuQRRF0d1BEHmr9evXo7m52WXlVVZWoq6urtd0gYGBCAsLszi9PYqLi7Fy5Uok/SMJymSl2TQtBS0o/Gsh7rrrLsTGxjo1Hku1txEREZGjVFZWAgCPL0RE1K+oVCrMnz/f3WEQERFZhWecEHmRsLAwq76MsTa9PZTJSvim+/aYJjY2FikpKS6Jh4iIyNU4YUJERERERNQ38ObwREREREREREREREREbThxQkRERERERERERERE1IYTJ0RERERERERERERERG04cUJERERERERERERERNSGN4cnIpfJvz8fEEyXR98c7fpgiIiIiIiIiIiIiMzgxAkRucyAGQPcHQIRERERERERERFRjzhxQkQuIepFtBS0IHZ5rMl7TTlNboiIiIiIiIiIiIiIyBTvcUJELiFIBNTvrXd3GEREREREREREREQ94sQJEblM4ORAlLxTAk2FBroGnfFBRERERERERERE5Cl4qS4icpnSt0oBACWvlVxYKABpH6S5KSIiIiIiIiIiIiKizjhxQkQuk7U3y+xy3uOEiIiIiIiIiIiIPAUnTojIpTQlGtTvr4cAAf5Z/pBHyd0dEhEREREREREREZERJ06IyGWqvq/C2efOwn+UPwCg6IUixD0UB2WC0s2RERERERERERERERlw4oSIXKbkrRKkfZgGRawCAKA+p8bJu04i+alkN0dGREREREREREREZCBxdwBE1H9IlBLjpAkAKGIUkCi5GyIiIiIiIiIiIiLPwW8sicgldE06BE0Jwrk3zkFTpoGmVIOSN0sQNC3I3aERERERERERERERGfFSXUTkEiduP2F8XvpW6YU3BGDA9AGuD4iIiIiIiIiIiIjIDE6cEJFLpH2YBt90X7PvNeU0uTgaIiIiIiIiIiIiIvN4qS4icpmKzyugb9G7OwwiIiIiIiIiIiKibnHihIhcpiG7AUeuOIKiF4rQcrbF3eEQERERERERERERmeCluojIZZKfSkbr+VZUflmJk388CWWKEuHXhMMn1MfdoREREREREREREREB4MQJEbmYT6gPom+Jhv9IfxQ+VojCvxZC6id1d1hEREREREREREREADhxQkQupFfrUfVtFSrWVECikiD2z7EInh2Mqh+qcPqx0+4Oj4iIiIiIiIiIiIgTJ0TkOkfmH0Hg+EAkPJoAvww/43JVisqNURERERERERERERFdwIkTInKJ+n31iLsvDhKVBK2Vraj5rcb4njxC7r7AiIiIiIiIiIiIiDrgxAkRuUT1D9WQ+ErQlNsE3zTfC28IQNyf49wXGBEREREREREREVEHnDghIpdIWJEA33Rf5FyXg9Q3Uzu915TT5KaoiIiIiIiIiIiIiDqTuDsAIiIiIiIiIiIiIiIiT8GJEyIiIiIiIiIiIiIioja8VBeREymVSneH4HbtbdBypgWQAXq1Hs0nmiFCNJtWpVK5OkQiIiIiIiJyEv5fTEREfZEgiqLpt5dERA6SnZ2N0aNHwyfcB4KPYJpAAFKeTUHuslzs27cPWVlZrg+SiIiIiIiIiIiIqA3POCEilxj474HwTfc1+x5vDk9ERERERERERESegvc4ISIiIiIiIiIiIiIiasOJEyIiIiIiIiIiIiIiojacOCEiIiIiIiIiIiIiImrDiRMiIiIiIiIiIiIiIqI2nDghIiIiIiIiIiIiIiJqI3N3AETUP7QUtNj0HhEREREREREREZErceKEiJwqLCwMSl8lCv9a2GM6pa8SYWFhrgmKiIiIiIiIiIiIqBuCKIqiu4MgIu925swZVFZW9pgmLCwMCQkJLoqIiIiIiIiIiIiIyDxOnBAREREREREREREREbXhzeGJiIiIiIiIiIiIiIjacOKEiIiIiIiIiIiIiIioDSdOiIiIiIiIiIiIiIiI2nDihIiIiIiIiIiIiIiIqA0nToiIiIiIiIiIiIiIiNpw4oSIiIiIiIiIiIiIiKgNJ06IiIiIiIiIiIiIiIjacOKEiIiIiIiIiIiIiIioDSdOiIiIiIiIiIiIiIiI2nDihIiIiIiIiIiIiIiIqA0nToiIiIiIiIiIiIiIiNpw4oSIiIiIiIiIiIiIiKgNJ06IiIiIiIiIiIiIiIjacOKEiIiIiIiIiIiIiIiojczdARB5s8rKSmzYsAFJSUlQqVTuDofIqLm5GYWFhRyb5JE4PsmTcXySJ+P4JE/G8dn/tPf5JZdcgrCwMHeHQ0REZBVOnBA50YYNG7Bs2TJ3h0FERERERETkFqtXr8bSpUvdHQYREZFVOHFC5ERJSUkADB8U09PT3RsMUQc5OTlYtmwZxyZ5JI5P8mQcn+TJOD7Jk3F89j/tfd7+fzEREVFfwokTIidqPwU9PT0dWVlZbo6GyBTHJnkyjk/yZByf5Mk4PsmTcXz2P7w0GxER9UW8OTwREREREREREREREVEbTpwQERERERERERERERG14cQJERERERERERERERFRG06cEBERERERERERERERteHECRERERERERERERERURtOnBAR9UNpaWnYt28f0tLS3B0KkQmOT/JkHJ/kyTg+yZNxfBIREVFfInN3AERE5Hq+vr7IyspydxhEZnF8kifj+CRPxvFJnozjk4iIiPoSnnFCRERERERERERERETUhhMnREREREREREREREREbThxQkRERERERERERERE1IYTJ0RERERERERERERERG04cUJE1Mdt2rQJgiCYfezcubNT2u3bt2PKlCnw9fVFVFQUli9fjoaGBpN1qtVq/N///R9iYmKgUqkwfvx4/PTTT66qEvVRDQ0NePzxxzF37lyEhIRAEASsWrXKbNqcnBzMnTsX/v7+CAkJwfXXX4+KigqTdHq9Hv/617+QnJwMpVKJzMxMfPzxx3atk/onS8fnTTfdZHZ/mpaWZpKW45McZc+ePbjrrruQkZEBPz8/JCQkYMmSJcjLyzNJy/0nuZql45P7TyIiIvImMncHQEREjrF8+XKMHTu207JBgwYZnx84cACzZ89Geno6XnzxRRQVFeH555/HiRMn8P3333fKd9NNN2Ht2rX485//jMGDB2PVqlW47LLLsHHjRkyZMsUl9aG+p7KyEk888QQSEhIwYsQIbNq0yWy6oqIiTJs2DUFBQXj66afR0NCA559/HocPH8bu3bshl8uNaVesWIFnnnkGt956K8aOHYuvv/4a1113HQRBwLXXXmvTOql/snR8AoBCocDbb7/daVlQUJBJOo5PcpRnn30W27Ztw+LFi5GZmYnS0lKsXLkSWVlZ2LlzJ4YNGwaA+09yD0vHJ8D9JxEREXkRkYicZt++fSIAcd++fe4OhbzYxo0bRQDimjVrekx36aWXitHR0WJtba1x2VtvvSUCEDds2GBctmvXLhGA+NxzzxmXNTc3iwMHDhQnTpzo+AqQ12hpaRFLSkpEURTFPXv2iADE9957zyTdHXfcIapUKvH06dPGZT/99JMIQHzjjTeMy4qKikQfHx/xzjvvNC7T6/Xi1KlTxbi4OFGr1Vq9Tuq/LB2fN954o+jn59fr+jg+yZG2bdsmqtXqTsvy8vJEhUIhLl261LiM+09yB0vHJ/ef1BX/HyYior6Ml+oiIvIi9fX10Gq1Jsvr6urw008/YdmyZQgMDDQuv+GGG+Dv74/PPvvMuGzt2rWQSqW47bbbjMuUSiVuvvlm7NixA2fPnnVuJajPUigUiIqK6jXd559/jssvvxwJCQnGZRdddBFSU1M7jcWvv/4ara2t+NOf/mRcJggC7rjjDhQVFWHHjh1Wr5P6L0vHZzudToe6urpu3+f4JEeaNGmSyS/nBw8ejIyMDOTk5BiXcf9J7mDp+GzH/ScRERF5A06cEBF5id///vcIDAyEUqnEzJkzsXfvXuN7hw8fhlarxZgxYzrlkcvlGDlyJPbv329ctn//fqSmpnaaYAGAcePGATBc8ovIVsXFxSgvLzcZi4BhjHUdi35+fkhPTzdJ1/6+teskskRTUxMCAwMRFBSEkJAQ3HnnnSb3g+L4JGcTRRFlZWUICwsDwP0neZau47Md959ERETkLXiPEyKiPk4ul2PhwoW47LLLEBYWhmPHjuH555/H1KlTsX37dowaNQolJSUAgOjoaJP80dHR2LJli/F1SUlJt+kA4Ny5c06qCfUHvY3FqqoqqNVqKBQKlJSUIDIyEoIgmKQDLoxFa9ZJ1Jvo6Gg89NBDyMrKgl6vxw8//IBXX30VBw8exKZNmyCTGT4+c3ySs3300UcoLi7GE088AYD7T/IsXccnwP0nEREReRdOnBAR9XGTJk3CpEmTjK+vuOIKLFq0CJmZmXjkkUfwww8/oLm5GQDM/mOpVCqN7wNAc3Nzt+na3yeyVW9jsT2NQqGweCxas06i3vzzn//s9Praa69FamoqVqxYgbVr1xpvWszxSc6Um5uLO++8ExMnTsSNN94IgPtP8hzmxifA/ScRERF5F16qi4jICw0aNAgLFizAxo0bodPpoFKpAABqtdokbUtLi/F9AFCpVN2ma3+fyFa9jcWOaSwdi9ask8gW9957LyQSCX7++WfjMo5PcpbS0lLMmzcPQUFBxvuOAdx/kmfobnx2h/tPIiIi6qs4cULkRGlpadi3bx/S0tLcHQr1Q/Hx8dBoNGhsbDRe4qD9kgcdlZSUICYmxvg6Ojq623QAOqUlslZvYzEkJMT4y9Ho6GiUlpZCFEWTdMCFsWjNOolsoVKpEBoaiqqqKuMyjk9yhtraWlx66aWoqanBDz/8YHJ8Brj/JPfpaXx2h/vP/o3/DxMRUV/GiRMiJ/L19UVWVhZ8fX3dHQr1Q6dOnYJSqYS/vz+GDRsGmUzW6YbxAKDRaHDgwAGMHDnSuGzkyJHIy8tDXV1dp7S7du0yvk9kq9jYWISHh5uMRQDYvXu3yVhsampCTk5Op3Rdx6I16ySyRX19PSorKxEeHm5cxvFJjtbS0oL58+cjLy8P33zzDYYOHdrpfe4/yZ16G5/d4f6zf+P/w0RE1Jdx4oSIqI+rqKgwWXbw4EGsW7cOc+bMgUQiQVBQEC666CKsXr0a9fX1xnQffvghGhoasHjxYuOyRYsWQafT4c033zQuU6vVeO+99zB+/HjEx8c7t0Lk9RYuXIhvvvkGZ8+eNS775ZdfkJeX12ksLliwAD4+Pnj11VeNy0RRxOuvv47Y2NhO9/axdJ1EPWlpaem0j2z3j3/8A6IoYu7cucZlHJ/kSDqdDtdccw127NiBNWvWYOLEiWbTcf9J7mDJ+OT+k4iIiLyNIHY9P5aIiPqUWbNmQaVSYdKkSYiIiMCxY8fw5ptvwsfHBzt27EB6ejoAIDs7G5MmTcLQoUNx2223oaioCC+88AKmTZuGDRs2dFrnkiVL8OWXX+Lee+/FoEGD8P7772P37t345ZdfMG3aNHdUk/qIlStXoqamBufOncNrr72Gq6++GqNGjQIA3H333QgKCsLZs2cxatQoDBgwAPfccw8aGhrw3HPPIS4uDnv27Ol02Y2HHnoIzz33HG677TaMHTsWX331Fb799lt89NFHuO6664zprFkn9V+9jc/q6mqMGjUKv/vd74yXFdmwYQO+++47zJ07F99++y0kkgu/O+L4JEf585//jJdffhnz58/HkiVLTN5ftmwZAOvGEscnOYol47OwsJD7TyIiIvIuIhER9Wkvv/yyOG7cODEkJESUyWRidHS0uGzZMvHEiRMmabds2SJOmjRJVCqVYnh4uHjnnXeKdXV1Jumam5vFBx54QIyKihIVCoU4duxY8YcffnBFdaiPS0xMFAGYfRQUFBjTHTlyRJwzZ47o6+srDhgwQFy6dKlYWlpqsj6dTic+/fTTYmJioiiXy8WMjAxx9erVZsu2dJ3Uf/U2Pqurq8Vly5aJgwYNEn19fUWFQiFmZGSITz/9tKjRaEzWx/FJjjJ9+vRux2bXf9m4/yRXs2R8cv9JRERE3oZnnBA5WPuvoHbt2oXdu3ejuroa7733Hm666SZ3h0ZERERERETUrT179uD999/Hxo0bUVhYiNDQUEyYMAFPPvkkUlNTO6X97LPP8OKLLyI3NxdSqRTDhg3DQw89hHnz5hnT/O1vf8Pf//73bsvbunUrJk+eDMBw75pVq1Zh165dOHToELRaLfiVFRERuQsnTogcrLCwEMnJyUhISEBKSgo2bdrEiRMiIiIiIiLyeIsWLcK2bduwePFiZGZmorS0FCtXrkRDQwN27tyJYcOGAQD+85//YPny5Zg3bx4uv/xytLS0YNWqVTh48CA+//xzXH311QCAQ4cO4dChQybl/OUvf0FDQwNKS0shl8sBGCZZnn76aWRmZqK+vh55eXmcOCEiIrfhxAmRg6nValRXVyMqKgp79+7F2LF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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "from genophenocorr.model.genome import GRCh38\n", - "\n", - "from genophenocorr.preprocessing import VVMultiCoordinateService\n", - "from genophenocorr.preprocessing import configure_protein_metadata_service\n", - "\n", - "from genophenocorr.view import ProteinVisualizable, ProteinVisualizer\n", - "\n", - "txc_service = VVMultiCoordinateService(genome_build=GRCh38)\n", - "tx_coordinates = txc_service.fetch(FBN1_MANE_transcript)\n", + "from gpsea.preprocessing import configure_protein_metadata_service\n", + "from gpsea.preprocessing import VVMultiCoordinateService\n", + "from gpsea.model.genome import GRCh38\n", "\n", "pms = configure_protein_metadata_service()\n", - "\n", "protein_meta = pms.annotate(FBN1_protein_id)\n", - "\n", - "pvis = ProteinVisualizable(tx_coordinates=tx_coordinates, protein_meta=protein_meta, cohort=cohort)\n", - "drawer = ProteinVisualizer()\n", - "drawer.draw_fig(pvis=pvis)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "62037174", - "metadata": { - "ExecuteTime": { - "end_time": "2024-07-04T12:32:26.276827900Z", - "start_time": "2024-07-04T12:32:25.949485300Z" - } - }, - "outputs": [], - "source": [ - "from genophenocorr.analysis import configure_cohort_analysis, CohortAnalysisConfiguration\n", - "from genophenocorr.analysis.predicate import PatientCategories\n", - "\n", - "config = CohortAnalysisConfiguration()\n", - "config.pval_correction = 'fdr_bh'\n", - "config.heuristic_strategy(threshold_HPO_observed_frequency=0.4)\n", - "hpo_analysis = configure_cohort_analysis(cohort, hpo, config=config)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "bead7f0a", - "metadata": { - "ExecuteTime": { - "start_time": "2024-07-04T12:32:26.009014300Z" - }, - "is_executing": true - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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Variant that affects TB 5 feature on the protein encoded by transcript NM_000138.5YesNo
CountPercentCountPercentp valueCorrected p value
Arachnodactyly [HP:0001166]0/10%54/8663%0.3793101.0
Tall stature [HP:0000098]0/10%38/6261%0.3968251.0
Joint hypermobility [HP:0001382]0/10%38/6757%0.4411761.0
High palate [HP:0000218]0/10%33/6749%1.0000001.0
Aortic root aneurysm [HP:0002616]0/10%34/8241%1.0000001.0
Scoliosis [HP:0002650]0/10%30/6050%1.0000001.0
Ectopia lentis [HP:0001083]1/1100%81/9090%1.0000001.0
Mitral valve prolapse [HP:0001634]0/10%25/6141%1.0000001.0
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" - ], - "text/plain": [ - "Variant that affects TB 5 feature on the protein encoded by transcript NM_000138.5 Yes \\\n", - " Count \n", - "Arachnodactyly [HP:0001166] 0/1 \n", - "Tall stature [HP:0000098] 0/1 \n", - "Joint hypermobility [HP:0001382] 0/1 \n", - "High palate [HP:0000218] 0/1 \n", - "Aortic root aneurysm [HP:0002616] 0/1 \n", - "Scoliosis [HP:0002650] 0/1 \n", - "Ectopia lentis [HP:0001083] 1/1 \n", - "Mitral valve prolapse [HP:0001634] 0/1 \n", - "\n", - "Variant that affects TB 5 feature on the protein encoded by transcript NM_000138.5 \\\n", - " Percent \n", - "Arachnodactyly [HP:0001166] 0% \n", - "Tall stature [HP:0000098] 0% \n", - "Joint hypermobility [HP:0001382] 0% \n", - "High palate [HP:0000218] 0% \n", - "Aortic root aneurysm [HP:0002616] 0% \n", - "Scoliosis [HP:0002650] 0% \n", - "Ectopia lentis [HP:0001083] 100% \n", - "Mitral valve prolapse [HP:0001634] 0% \n", - "\n", - "Variant that affects TB 5 feature on the protein encoded by transcript NM_000138.5 No \\\n", - " Count \n", - "Arachnodactyly [HP:0001166] 54/86 \n", - "Tall stature [HP:0000098] 38/62 \n", - "Joint hypermobility [HP:0001382] 38/67 \n", - "High palate [HP:0000218] 33/67 \n", - "Aortic root aneurysm [HP:0002616] 34/82 \n", - "Scoliosis [HP:0002650] 30/60 \n", - "Ectopia lentis [HP:0001083] 81/90 \n", - "Mitral valve prolapse [HP:0001634] 25/61 \n", - "\n", - "Variant that affects TB 5 feature on the protein encoded by transcript NM_000138.5 \\\n", - " Percent \n", - "Arachnodactyly [HP:0001166] 63% \n", - "Tall stature [HP:0000098] 61% \n", - "Joint hypermobility [HP:0001382] 57% \n", - "High palate [HP:0000218] 49% \n", - "Aortic root aneurysm [HP:0002616] 41% \n", - "Scoliosis [HP:0002650] 50% \n", - "Ectopia lentis [HP:0001083] 90% \n", - "Mitral valve prolapse [HP:0001634] 41% \n", - "\n", - "Variant that affects TB 5 feature on the protein encoded by transcript NM_000138.5 \\\n", - " p value \n", - "Arachnodactyly [HP:0001166] 0.379310 \n", - "Tall stature [HP:0000098] 0.396825 \n", - "Joint hypermobility [HP:0001382] 0.441176 \n", - "High palate [HP:0000218] 1.000000 \n", - "Aortic root aneurysm [HP:0002616] 1.000000 \n", - "Scoliosis [HP:0002650] 1.000000 \n", - "Ectopia lentis [HP:0001083] 1.000000 \n", - "Mitral valve prolapse [HP:0001634] 1.000000 \n", - "\n", - "Variant that affects TB 5 feature on the protein encoded by transcript NM_000138.5 \n", - " Corrected p value \n", - "Arachnodactyly [HP:0001166] 1.0 \n", - "Tall stature [HP:0000098] 1.0 \n", - "Joint hypermobility [HP:0001382] 1.0 \n", - "High palate [HP:0000218] 1.0 \n", - "Aortic root aneurysm [HP:0002616] 1.0 \n", - "Scoliosis [HP:0002650] 1.0 \n", - "Ectopia lentis [HP:0001083] 1.0 \n", - "Mitral valve prolapse [HP:0001634] 1.0 " - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from genophenocorr.analysis.predicate.genotype import ProteinPredicates\n", - "\n", - "pp = ProteinPredicates(protein_metadata_service=pms)\n", - "affects_tb5 = pp.protein_feature(feature_id='TB 5',tx_id=FBN1_MANE_transcript)\n", - "\n", - "tb4 = hpo_analysis.compare_hpo_vs_genotype(affects_tb5)\n", - "tb4.summarize(hpo, category=PatientCategories.YES)" + "txc_service = VVMultiCoordinateService(genome_build=GRCh38)\n", + "tx_coordinates = txc_service.fetch(FBN1_MANE_transcript)" ] }, { "cell_type": "code", "execution_count": 8, - "id": "0fc8c921", - "metadata": { - "is_executing": true - }, + "id": "b20a75ce", + "metadata": {}, "outputs": [], "source": [ - "from genophenocorr.analysis.predicate.phenotype import DiseasePresencePredicate\n", + "from gpsea.view import ProteinVisualizable, ProteinVisualizer, ProteinViewable\n", "\n", - "dp_pred = DiseasePresencePredicate(hpotk.TermId.from_curie(\"OMIM:614185\")) # Geleophysic dysplasia 2 )(FBN1)" + "pvis = ProteinVisualizable(tx_coordinates=tx_coordinates, protein_meta=protein_meta, cohort=cohort)" ] }, { "cell_type": "code", "execution_count": 9, - "id": "3baa3613", + "id": "5ac8536b", "metadata": {}, - "outputs": [], - "source": [ - "analysis = configure_cohort_analysis(cohort, hpo)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "a1fe3a01", - "metadata": { - "is_executing": true - }, "outputs": [ { "data": { "text/html": [ - "
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GPSEA protein analysis

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The UniProt API successfully returned protein information for ID: NP_000129.3

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Protein Name: Fibrillin-1

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\n", + "

Protein Features

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Feature NameFeature TypeFeature CoordinatesVariants in Feature
N-terminal domainregion45 - 4508
Fibrillin unique N-terminal (FUN) domainregion45 - 813
EGF-like 1domain81 - 1120
EGF-like 2domain115 - 1462
Interaction with MFAP4region119 - 3294
EGF-like 3domain147 - 1781
TB 1domain184 - 2360
Hybrid domain 1region195 - 2210
EGF-like 4; calcium-bindingdomain246 - 2870
EGF-like 5; calcium-bindingdomain288 - 3290
TB 2domain334 - 3890
EGF-like 6domain449 - 4890
EGF-like 7; calcium-bindingdomain490 - 5290
EGF-like 8; calcium-bindingdomain530 - 5711
EGF-like 9; calcium-bindingdomain572 - 6129
EGF-like 10; calcium-bindingdomain613 - 6539
TB 3domain659 - 7115
EGF-like 11; calcium-bindingdomain723 - 7643
EGF-like 12; calcium-bindingdomain765 - 8062
EGF-like 13; calcium-bindingdomain807 - 8462
TB 4domain851 - 9021
Hybrid domain 2region862 - 8870
EGF-like 14; calcium-bindingdomain910 - 9511
TB 5domain956 - 10081
EGF-like 15; calcium-bindingdomain1028 - 10690
EGF-like 16; calcium-bindingdomain1070 - 11120
EGF-like 17; calcium-bindingdomain1113 - 11542
EGF-like 18; calcium-bindingdomain1155 - 11960
EGF-like 19; calcium-bindingdomain1197 - 12370
EGF-like 20; calcium-bindingdomain1238 - 12790
EGF-like 21; calcium-bindingdomain1280 - 13212
EGF-like 22; calcium-bindingdomain1322 - 13627
EGF-like 23; calcium-bindingdomain1363 - 14031
EGF-like 24; calcium-bindingdomain1404 - 14453
EGF-like 25; calcium-bindingdomain1446 - 14860
EGF-like 26; calcium-bindingdomain1487 - 15270
C-terminal domainregion1528 - 273168
TB 6domain1532 - 15899
Cell attachment sitemotif1541 - 15430
EGF-like 27; calcium-bindingdomain1606 - 16470
EGF-like 28; calcium-bindingdomain1648 - 16881
TB 7domain1693 - 174822
EGF-like 29; calcium-bindingdomain1766 - 18072
EGF-like 30; calcium-bindingdomain1808 - 18480
EGF-like 31; calcium-bindingdomain1849 - 18900
EGF-like 32; calcium-bindingdomain1891 - 19290
EGF-like 33; calcium-bindingdomain1930 - 19721
EGF-like 34; calcium-bindingdomain1973 - 20121
EGF-like 35; calcium-bindingdomain2013 - 20540
TB 8domain2059 - 21111
EGF-like 36; calcium-bindingdomain2127 - 21650
EGF-like 37; calcium-bindingdomain2166 - 22050
EGF-like 38; calcium-bindingdomain2206 - 22462
EGF-like 39; calcium-bindingdomain2247 - 22901
EGF-like 40; calcium-bindingdomain2291 - 23320
TB 9domain2337 - 23901
EGF-like 41; calcium-bindingdomain2402 - 24430
EGF-like 42; calcium-bindingdomain2444 - 24846
EGF-like 43; calcium-bindingdomain2485 - 25231
EGF-like 44; calcium-bindingdomain2524 - 25661
EGF-like 45; calcium-bindingdomain2567 - 26060
EGF-like 46; calcium-bindingdomain2607 - 26471
EGF-like 47; calcium-bindingdomain2648 - 26871
Disorderedregion2726 - 27464
\n", + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "viewer = ProteinViewable()\n", + "html_prot = viewer.process(cohort, pvis)\n", + "display(HTML(html_prot))" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "714f475d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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MISSENSE_VARIANT on NM_000138.5YesNo
CountPercentCountPercentp valueCorrected p value
OMIM:61418519/11916%0/320%0.0136130.013613
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" + ], + "text/plain": [ + "MISSENSE_VARIANT on NM_000138.5 Yes ... \n", + " Count Percent ... p value Corrected p value\n", + "OMIM:614185 19/119 16% ... 0.013613 0.013613\n", "\n", - "Variant that affects TB 5 feature on the protein encoded by transcript NM_000138.5 \n", - " Corrected p value \n", - "OMIM:614185 1.0 " + "[1 rows x 6 columns]" ] }, - "execution_count": 10, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "results = analysis.compare_disease_vs_genotype(\n", - " predicate=affects_tb5, \n", + " predicate=missense_predicate, \n", " disease_ids=(\"OMIM:614185\",),\n", ")\n", "\n", "results.summarize(hpo, category=PatientCategories.YES)" ] }, + { + "cell_type": "code", + "execution_count": 18, + "id": "1951e18d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Variant that affects TB 7 feature on the protein encoded by transcript NM_000138.5YesNo
CountPercentCountPercentp valueCorrected p value
Mitral stenosis [HP:0001718]4/1331%0/40%0.5193281.0
Tricuspid stenosis [HP:0010446]3/1225%0/40%0.5285711.0
Abnormal blood vessel morphology [HP:0033353]0/00%39/39100%1.0000001.0
Abnormal jaw morphology [HP:0030791]0/00%7/7100%1.0000001.0
Abnormality of the vasculature [HP:0002597]0/00%43/43100%1.0000001.0
.....................
Abnormal renal morphology [HP:0012210]0/00%2/2100%1.0000001.0
Abnormality of prenatal development or birth [HP:0001197]0/00%2/2100%1.0000001.0
Abnormality of the cardiovascular system [HP:0001626]9/9100%54/54100%1.0000001.0
Strabismus [HP:0000486]0/00%3/3010%1.0000001.0
Abnormality of the respiratory system [HP:0002086]7/7100%2/2100%1.0000001.0
\n", + "

300 rows × 6 columns

\n", + "
" + ], + "text/plain": [ + "Variant that affects TB 7 feature on the protein encoded by transcript NM_000138.5 Yes ... \n", + " Count ... Corrected p value\n", + "Mitral stenosis [HP:0001718] 4/13 ... 1.0\n", + "Tricuspid stenosis [HP:0010446] 3/12 ... 1.0\n", + "Abnormal blood vessel morphology [HP:0033353] 0/0 ... 1.0\n", + "Abnormal jaw morphology [HP:0030791] 0/0 ... 1.0\n", + "Abnormality of the vasculature [HP:0002597] 0/0 ... 1.0\n", + "... ... ... ...\n", + "Abnormal renal morphology [HP:0012210] 0/0 ... 1.0\n", + "Abnormality of prenatal development or birth [H... 0/0 ... 1.0\n", + "Abnormality of the cardiovascular system [HP:00... 9/9 ... 1.0\n", + "Strabismus [HP:0000486] 0/0 ... 1.0\n", + "Abnormality of the respiratory system [HP:0002086] 7/7 ... 1.0\n", + "\n", + "[300 rows x 6 columns]" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from gpsea.analysis.predicate.genotype import ProteinPredicates\n", + "\n", + "prot_pred = ProteinPredicates(pms)\n", + "prot_feat_pred = prot_pred.protein_feature(\"TB 7\", tx_id=FBN1_MANE_transcript)\n", + "\n", + "prot_results = analysis.compare_hpo_vs_genotype(prot_feat_pred)\n", + "prot_results.summarize(hpo, PatientCategories.YES)" + ] + }, { "cell_type": "markdown", "id": "51241ddb", @@ -925,9 +1604,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "enviro", "language": "python", - "name": "python3" + "name": "enviro" }, "language_info": { "codemirror_mode": { @@ -939,7 +1618,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.3" + "version": "3.10.10" } }, "nbformat": 4, diff --git a/notebooks/MAPK8IP3/MAPK8IP3.ipynb b/notebooks/MAPK8IP3/MAPK8IP3.ipynb index c6221dff..c62b045a 100644 --- a/notebooks/MAPK8IP3/MAPK8IP3.ipynb +++ b/notebooks/MAPK8IP3/MAPK8IP3.ipynb @@ -20,19 +20,19 @@ "text": [ "Loaded HPO v2023-10-09\n", "hpotk version 0.5.2\n", - "Using genophenocorr version 0.1.1dev\n" + "Using gpsea version 0.2.1.dev0\n" ] } ], "source": [ "import hpotk\n", - "import genophenocorr\n", + "import gpsea\n", "\n", "store = hpotk.configure_ontology_store()\n", "hpo = store.load_minimal_hpo(release='v2023-10-09')\n", "print(f'Loaded HPO v{hpo.version}')\n", "print(f'hpotk version {hpotk.__version__}')\n", - "print(f\"Using genophenocorr version {genophenocorr.__version__}\")" + "print(f\"Using gpsea version {gpsea.__version__}\")" ] }, { @@ -52,7 +52,6 @@ "metadata": {}, "outputs": [], "source": [ - "fpath_phenopackets = 'phenopackets'\n", "MAPK8IP3_id = 'NM_001318852.2'" ] }, @@ -75,16 +74,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "Patients Created: 100%|██████████| 20/20 [00:00<00:00, 261.06it/s]\n", + "Patients Created: 100%|██████████| 20/20 [00:00<00:00, 299.27it/s]\n", "Validated under none policy\n", - "No errors or warnings were found\n", - "\n" + "No errors or warnings were found\n" ] } ], "source": [ "from ppktstore.registry import configure_phenopacket_registry\n", - "from genophenocorr.preprocessing import configure_caching_cohort_creator, load_phenopackets\n", + "from gpsea.preprocessing import configure_caching_cohort_creator, load_phenopackets\n", "\n", "cohort_name = 'MAPK8IP3'\n", "phenopacket_store_release = '0.1.18'\n", @@ -176,8 +174,8 @@ "\n", "\n", "\n", - "

genophenocorr cohort analysis

\n", - "

Successfully ingested 20 phenopackets.

\n", + "

GPSEA cohort analysis

\n", + "

Successfully loaded 20 individuals.

\n", " \n", "

No errors encountered.

\n", " \n", @@ -191,7 +189,7 @@ " \n", " HPO Term\n", " ID\n", - " Annotation Count\n", + " Seen in n individuals\n", " \n", " \n", " \n", @@ -219,26 +217,26 @@ " \n", " \n", " \n", - " Seizure\n", - " HP:0001250\n", + " Intellectual disability\n", + " HP:0001249\n", " 6\n", " \n", " \n", " \n", - " Delayed ability to walk\n", - " HP:0031936\n", + " Spastic diplegia\n", + " HP:0001264\n", " 6\n", " \n", " \n", " \n", - " Spastic diplegia\n", - " HP:0001264\n", + " Delayed ability to walk\n", + " HP:0031936\n", " 6\n", " \n", " \n", " \n", - " Intellectual disability\n", - " HP:0001249\n", + " Seizure\n", + " HP:0001250\n", " 6\n", " \n", " \n", @@ -264,92 +262,92 @@ " \n", " \n", " \n", - " Variant\n", + " Count\n", + " Variant key\n", " Variant Name\n", " Protein Variant\n", " Variant Class\n", - " Variant Count\n", " \n", " \n", " \n", + " 6\n", " 16_1762843_1762843_C_T\n", " c.1735C>T\n", " p.Arg579Cys\n", " MISSENSE_VARIANT\n", - " 6\n", - " \n", + " \n", " \n", " \n", + " 5\n", " 16_1767834_1767834_C_T\n", " c.3439C>T\n", " p.Arg1147Cys\n", " MISSENSE_VARIANT\n", - " 5\n", - " \n", + " \n", " \n", " \n", + " 2\n", " 16_1760409_1760409_T_C\n", " c.1334T>C\n", " p.Leu445Pro\n", " MISSENSE_VARIANT\n", - " 2\n", - " \n", + " \n", " \n", " \n", - " 16_1748705_1748705_G_A\n", - " c.1201G>A\n", - " p.Gly401Arg\n", - " MISSENSE_VARIANT\n", " 1\n", - " \n", + " 16_1706384_1706384_C_G\n", + " c.45C>G\n", + " p.Tyr15Ter\n", + " STOP_GAINED\n", + " \n", " \n", " \n", - " 16_1766768_1766768_C_G\n", - " c.2985C>G\n", - " p.His995Gln\n", - " MISSENSE_VARIANT\n", " 1\n", - " \n", + " 16_1706450_1706450_C_G\n", + " c.111C>G\n", + " p.Tyr37Ter\n", + " STOP_GAINED\n", + " \n", " \n", " \n", - " 16_1706384_1706384_C_G\n", - " c.45C>G\n", - " p.Tyr15Ter\n", - " STOP_GAINED\n", " 1\n", - " \n", + " 16_1706418_1706418_G_T\n", + " c.79G>T\n", + " p.Glu27Ter\n", + " STOP_GAINED\n", + " \n", " \n", " \n", - " 16_1706402_1706403_CG_C\n", - " c.65del\n", - " p.Gly22AlafsTer3\n", - " FRAMESHIFT_VARIANT\n", " 1\n", - " \n", + " 16_1748705_1748705_G_A\n", + " c.1201G>A\n", + " p.Gly401Arg\n", + " MISSENSE_VARIANT\n", + " \n", " \n", " \n", + " 1\n", " 16_1762388_1762388_G_A\n", " c.1577G>A\n", " p.Arg526Gln\n", " MISSENSE_VARIANT\n", - " 1\n", - " \n", + " \n", " \n", " \n", - " 16_1706450_1706450_C_G\n", - " c.111C>G\n", - " p.Tyr37Ter\n", - " STOP_GAINED\n", " 1\n", - " \n", + " 16_1766768_1766768_C_G\n", + " c.2985C>G\n", + " p.His995Gln\n", + " MISSENSE_VARIANT\n", + " \n", " \n", " \n", - " 16_1706418_1706418_G_T\n", - " c.79G>T\n", - " p.Glu27Ter\n", - " STOP_GAINED\n", " 1\n", - " \n", + " 16_1706402_1706403_CG_C\n", + " c.65del\n", + " p.Gly22AlafsTer3\n", + " FRAMESHIFT_VARIANT\n", + " \n", " \n", " \n", " \n", @@ -389,13 +387,13 @@ " \n", " \n", " \n", - " STOP_GAINED\n", - " 3\n", + " FRAMESHIFT_VARIANT\n", + " 1\n", " \n", " \n", " \n", - " FRAMESHIFT_VARIANT\n", - " 1\n", + " STOP_GAINED\n", + " 3\n", " \n", " \n", " \n", @@ -416,7 +414,7 @@ ], "source": [ "from IPython.display import display, HTML\n", - "from genophenocorr.view import CohortViewable\n", + "from gpsea.view import CohortViewable\n", "\n", "viewer = CohortViewable(hpo)\n", "display(HTML(viewer.process(cohort=cohort, transcript_id=MAPK8IP3_id)))" @@ -440,7 +438,7 @@ }, { "data": { - "image/png": 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" ] @@ -450,10 +448,10 @@ } ], "source": [ - "from genophenocorr.preprocessing import configure_protein_metadata_service\n", - "from genophenocorr.model.genome import GRCh38\n", - "from genophenocorr.preprocessing import VVMultiCoordinateService\n", - "from genophenocorr.view import ProteinVisualizable, ProteinVisualizer\n", + "from gpsea.preprocessing import configure_protein_metadata_service\n", + "from gpsea.model.genome import GRCh38\n", + "from gpsea.preprocessing import VVMultiCoordinateService\n", + "from gpsea.view import ProteinVisualizable, ProteinVisualizer\n", "\n", "pms = configure_protein_metadata_service()\n", "protein_meta = pms.annotate('NP_055948.2')\n", @@ -481,12 +479,12 @@ "metadata": {}, "outputs": [], "source": [ - "from genophenocorr.analysis import configure_cohort_analysis, CohortAnalysisConfiguration\n", + "from gpsea.analysis import configure_cohort_analysis, CohortAnalysisConfiguration\n", "\n", "analysis_config = CohortAnalysisConfiguration()\n", "analysis_config.missing_implies_excluded = True\n", "analysis_config.pval_correction = 'fdr_bh'\n", - "analysis_config.heuristic_strategy(threshold_HPO_observed_frequency=0.25)\n", + "analysis_config.all_terms_strategy()\n", "hpo_analysis = configure_cohort_analysis(cohort, hpo, config=analysis_config)" ] }, @@ -533,81 +531,139 @@ " \n", " \n", " \n", - " Hypotonia [HP:0001252]\n", + " Autism [HP:0000717]\n", " 1/1\n", " 100%\n", - " 10/17\n", - " 59%\n", - " 1.0\n", + " 1/14\n", + " 7%\n", + " 0.133333\n", " 1.0\n", " \n", " \n", - " Cerebral visual impairment [HP:0100704]\n", + " Abnormality of movement [HP:0100022]\n", " 0/1\n", " 0%\n", - " 2/6\n", - " 33%\n", - " 1.0\n", + " 8/13\n", + " 62%\n", + " 0.428571\n", " 1.0\n", " \n", " \n", - " Inability to walk [HP:0002540]\n", - " 0/1\n", + " Upslanted palpebral fissure [HP:0000582]\n", + " 0/0\n", " 0%\n", - " 4/10\n", - " 40%\n", + " 1/6\n", + " 17%\n", + " 1.000000\n", " 1.0\n", + " \n", + " \n", + " Abnormal eye morphology [HP:0012372]\n", + " 0/0\n", + " 0%\n", + " 3/3\n", + " 100%\n", + " 1.000000\n", " 1.0\n", " \n", " \n", - " Autistic behavior [HP:0000729]\n", - " 1/1\n", + " Abnormality of the integument [HP:0001574]\n", + " 0/0\n", + " 0%\n", + " 3/3\n", " 100%\n", - " 3/6\n", - " 50%\n", + " 1.000000\n", " 1.0\n", + " \n", + " \n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " \n", + " \n", + " Abnormal joint morphology [HP:0001367]\n", + " 0/0\n", + " 0%\n", + " 2/2\n", + " 100%\n", + " 1.000000\n", " 1.0\n", " \n", " \n", - " Seizure [HP:0001250]\n", - " 0/1\n", + " Abnormal eyelid morphology [HP:0000492]\n", + " 0/0\n", " 0%\n", - " 6/17\n", - " 35%\n", + " 2/2\n", + " 100%\n", + " 1.000000\n", " 1.0\n", + " \n", + " \n", + " Limb joint contracture [HP:0003121]\n", + " 0/0\n", + " 0%\n", + " 1/1\n", + " 100%\n", + " 1.000000\n", " 1.0\n", " \n", " \n", - " Gait disturbance [HP:0001288]\n", - " 0/1\n", + " Abnormality of the head [HP:0000234]\n", + " 0/0\n", " 0%\n", - " 5/10\n", - " 50%\n", + " 10/10\n", + " 100%\n", + " 1.000000\n", " 1.0\n", + " \n", + " \n", + " Persistent head lag [HP:0032988]\n", + " 0/0\n", + " 0%\n", + " 2/5\n", + " 40%\n", + " 1.000000\n", " 1.0\n", " \n", " \n", "\n", + "

175 rows × 6 columns

\n", "
" ], "text/plain": [ - "FRAMESHIFT_VARIANT on NM_001318852.2 Yes No \\\n", - " Count Percent Count Percent p value \n", - "Hypotonia [HP:0001252] 1/1 100% 10/17 59% 1.0 \n", - "Cerebral visual impairment [HP:0100704] 0/1 0% 2/6 33% 1.0 \n", - "Inability to walk [HP:0002540] 0/1 0% 4/10 40% 1.0 \n", - "Autistic behavior [HP:0000729] 1/1 100% 3/6 50% 1.0 \n", - "Seizure [HP:0001250] 0/1 0% 6/17 35% 1.0 \n", - "Gait disturbance [HP:0001288] 0/1 0% 5/10 50% 1.0 \n", + "FRAMESHIFT_VARIANT on NM_001318852.2 Yes No \\\n", + " Count Percent Count Percent \n", + "Autism [HP:0000717] 1/1 100% 1/14 7% \n", + "Abnormality of movement [HP:0100022] 0/1 0% 8/13 62% \n", + "Upslanted palpebral fissure [HP:0000582] 0/0 0% 1/6 17% \n", + "Abnormal eye morphology [HP:0012372] 0/0 0% 3/3 100% \n", + "Abnormality of the integument [HP:0001574] 0/0 0% 3/3 100% \n", + "... ... ... ... ... \n", + "Abnormal joint morphology [HP:0001367] 0/0 0% 2/2 100% \n", + "Abnormal eyelid morphology [HP:0000492] 0/0 0% 2/2 100% \n", + "Limb joint contracture [HP:0003121] 0/0 0% 1/1 100% \n", + "Abnormality of the head [HP:0000234] 0/0 0% 10/10 100% \n", + "Persistent head lag [HP:0032988] 0/0 0% 2/5 40% \n", "\n", - "FRAMESHIFT_VARIANT on NM_001318852.2 \n", - " Corrected p value \n", - "Hypotonia [HP:0001252] 1.0 \n", - "Cerebral visual impairment [HP:0100704] 1.0 \n", - "Inability to walk [HP:0002540] 1.0 \n", - "Autistic behavior [HP:0000729] 1.0 \n", - "Seizure [HP:0001250] 1.0 \n", - "Gait disturbance [HP:0001288] 1.0 " + "FRAMESHIFT_VARIANT on NM_001318852.2 \n", + " p value Corrected p value \n", + "Autism [HP:0000717] 0.133333 1.0 \n", + "Abnormality of movement [HP:0100022] 0.428571 1.0 \n", + "Upslanted palpebral fissure [HP:0000582] 1.000000 1.0 \n", + "Abnormal eye morphology [HP:0012372] 1.000000 1.0 \n", + "Abnormality of the integument [HP:0001574] 1.000000 1.0 \n", + "... ... ... \n", + "Abnormal joint morphology [HP:0001367] 1.000000 1.0 \n", + "Abnormal eyelid morphology [HP:0000492] 1.000000 1.0 \n", + "Limb joint contracture [HP:0003121] 1.000000 1.0 \n", + "Abnormality of the head [HP:0000234] 1.000000 1.0 \n", + "Persistent head lag [HP:0032988] 1.000000 1.0 \n", + "\n", + "[175 rows x 6 columns]" ] }, "execution_count": 7, @@ -616,9 +672,9 @@ } ], "source": [ - "from genophenocorr.model import VariantEffect\n", - "from genophenocorr.analysis.predicate import PatientCategories\n", - "from genophenocorr.analysis.predicate.genotype import VariantPredicates\n", + "from gpsea.model import VariantEffect\n", + "from gpsea.analysis.predicate import PatientCategories\n", + "from gpsea.analysis.predicate.genotype import VariantPredicates\n", "\n", "is_frameshift = VariantPredicates.variant_effect(\n", " VariantEffect.FRAMESHIFT_VARIANT, tx_id=MAPK8IP3_id,\n", @@ -628,138 +684,6 @@ "frameshift.summarize(hpo, PatientCategories.YES)" ] }, - { - "cell_type": "code", - "execution_count": 8, - "id": "cdf2c9e1", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - " \n", - " Cohort\n", - " \n", - "\n", - "\n", - "\n", - "

Statistical analysis: fdr_bh (heuristic sampler)

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Performed statistical tests for 8 out of the total of 310 HPO terms.

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Using heuristic sampler, 302 term(s) were omitted from statistical analysis.
ReasonCount
Skipping term with only 1 observations (not powered for 2x2)130
Skipping general term60
Skipping term with only 2 observations (not powered for 2x2)28
Skipping term with only 3 observations (not powered for 2x2)23
Skipping term because one genotype had zero observations20
Skipping term with only 6 observations (not powered for 2x2)12
Skipping term with only 4 observations (not powered for 2x2)11
Skipping term with only 5 observations (not powered for 2x2)10
Skipping term because all genotypes have same HPO observed proportions8
\n", - "\n", - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from genophenocorr.view import StatsViewer\n", - "\n", - "sv = StatsViewer()\n", - "\n", - "display(HTML(sv.process(hpo_mtc_report=frameshift.mtc_filter_report)))" - ] - }, { "cell_type": "markdown", "id": "9ac309bf", @@ -774,8 +698,8 @@ }, { "cell_type": "code", - "execution_count": 9, - "id": "8e46c391", + "execution_count": 11, + "id": "095a0476", "metadata": {}, "outputs": [ { @@ -821,7 +745,7 @@ " 2/13\n", " 15%\n", " 0.021709\n", - " 0.095238\n", + " 1.0\n", " \n", " \n", " Inability to walk [HP:0002540]\n", @@ -830,7 +754,7 @@ " 1/8\n", " 12%\n", " 0.024242\n", - " 0.095238\n", + " 1.0\n", " \n", " \n", " Autistic behavior [HP:0000729]\n", @@ -839,7 +763,7 @@ " 4/4\n", " 100%\n", " 0.028571\n", - " 0.095238\n", + " 1.0\n", " \n", " \n", " Gait disturbance [HP:0001288]\n", @@ -848,95 +772,109 @@ " 2/8\n", " 25%\n", " 0.060606\n", - " 0.151515\n", + " 1.0\n", " \n", " \n", - " Hypotonia [HP:0001252]\n", - " 2/5\n", + " Abnormality of movement [HP:0100022]\n", + " 4/4\n", + " 100%\n", + " 4/10\n", " 40%\n", - " 9/13\n", - " 69%\n", - " 0.325980\n", - " 0.476190\n", + " 0.084915\n", + " 1.0\n", " \n", " \n", - " Thin corpus callosum [HP:0033725]\n", - " 6/6\n", - " 100%\n", - " 3/4\n", - " 75%\n", - " 0.400000\n", - " 0.476190\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", " \n", " \n", - " Spastic diplegia [HP:0001264]\n", + " Abnormal upper lip morphology [HP:0000177]\n", " 4/4\n", " 100%\n", - " 2/3\n", - " 67%\n", - " 0.428571\n", - " 0.476190\n", + " 3/3\n", + " 100%\n", + " 1.000000\n", + " 1.0\n", " \n", " \n", - " Short philtrum [HP:0000322]\n", - " 0/3\n", + " Abnormal joint morphology [HP:0001367]\n", + " 1/1\n", + " 100%\n", + " 1/1\n", + " 100%\n", + " 1.000000\n", + " 1.0\n", + " \n", + " \n", + " Abnormal eyelid morphology [HP:0000492]\n", + " 0/0\n", " 0%\n", - " 2/4\n", - " 50%\n", - " 0.428571\n", - " 0.476190\n", + " 2/2\n", + " 100%\n", + " 1.000000\n", + " 1.0\n", " \n", " \n", - " Anteverted nares [HP:0000463]\n", - " 0/3\n", + " Limb joint contracture [HP:0003121]\n", + " 1/1\n", + " 100%\n", + " 0/0\n", " 0%\n", - " 2/4\n", - " 50%\n", - " 0.428571\n", - " 0.476190\n", + " 1.000000\n", + " 1.0\n", " \n", " \n", - " Prominent nasal bridge [HP:0000426]\n", - " 1/3\n", - " 33%\n", - " 3/4\n", - " 75%\n", - " 0.485714\n", - " 0.485714\n", + " Abnormality of the head [HP:0000234]\n", + " 5/5\n", + " 100%\n", + " 5/5\n", + " 100%\n", + " 1.000000\n", + " 1.0\n", " \n", " \n", "\n", + "

175 rows × 6 columns

\n", "" ], "text/plain": [ - "variant has ID of 16_1762843_1762843_C_T Yes No \\\n", - " Count Percent Count Percent \n", - "Seizure [HP:0001250] 4/5 80% 2/13 15% \n", - "Inability to walk [HP:0002540] 3/3 100% 1/8 12% \n", - "Autistic behavior [HP:0000729] 0/3 0% 4/4 100% \n", - "Gait disturbance [HP:0001288] 3/3 100% 2/8 25% \n", - "Hypotonia [HP:0001252] 2/5 40% 9/13 69% \n", - "Thin corpus callosum [HP:0033725] 6/6 100% 3/4 75% \n", - "Spastic diplegia [HP:0001264] 4/4 100% 2/3 67% \n", - "Short philtrum [HP:0000322] 0/3 0% 2/4 50% \n", - "Anteverted nares [HP:0000463] 0/3 0% 2/4 50% \n", - "Prominent nasal bridge [HP:0000426] 1/3 33% 3/4 75% \n", + "variant has ID of 16_1762843_1762843_C_T Yes No \\\n", + " Count Percent Count Percent \n", + "Seizure [HP:0001250] 4/5 80% 2/13 15% \n", + "Inability to walk [HP:0002540] 3/3 100% 1/8 12% \n", + "Autistic behavior [HP:0000729] 0/3 0% 4/4 100% \n", + "Gait disturbance [HP:0001288] 3/3 100% 2/8 25% \n", + "Abnormality of movement [HP:0100022] 4/4 100% 4/10 40% \n", + "... ... ... ... ... \n", + "Abnormal upper lip morphology [HP:0000177] 4/4 100% 3/3 100% \n", + "Abnormal joint morphology [HP:0001367] 1/1 100% 1/1 100% \n", + "Abnormal eyelid morphology [HP:0000492] 0/0 0% 2/2 100% \n", + "Limb joint contracture [HP:0003121] 1/1 100% 0/0 0% \n", + "Abnormality of the head [HP:0000234] 5/5 100% 5/5 100% \n", + "\n", + "variant has ID of 16_1762843_1762843_C_T \n", + " p value Corrected p value \n", + "Seizure [HP:0001250] 0.021709 1.0 \n", + "Inability to walk [HP:0002540] 0.024242 1.0 \n", + "Autistic behavior [HP:0000729] 0.028571 1.0 \n", + "Gait disturbance [HP:0001288] 0.060606 1.0 \n", + "Abnormality of movement [HP:0100022] 0.084915 1.0 \n", + "... ... ... \n", + "Abnormal upper lip morphology [HP:0000177] 1.000000 1.0 \n", + "Abnormal joint morphology [HP:0001367] 1.000000 1.0 \n", + "Abnormal eyelid morphology [HP:0000492] 1.000000 1.0 \n", + "Limb joint contracture [HP:0003121] 1.000000 1.0 \n", + "Abnormality of the head [HP:0000234] 1.000000 1.0 \n", "\n", - "variant has ID of 16_1762843_1762843_C_T \n", - " p value Corrected p value \n", - "Seizure [HP:0001250] 0.021709 0.095238 \n", - "Inability to walk [HP:0002540] 0.024242 0.095238 \n", - "Autistic behavior [HP:0000729] 0.028571 0.095238 \n", - "Gait disturbance [HP:0001288] 0.060606 0.151515 \n", - "Hypotonia [HP:0001252] 0.325980 0.476190 \n", - "Thin corpus callosum [HP:0033725] 0.400000 0.476190 \n", - "Spastic diplegia [HP:0001264] 0.428571 0.476190 \n", - "Short philtrum [HP:0000322] 0.428571 0.476190 \n", - "Anteverted nares [HP:0000463] 0.428571 0.476190 \n", - "Prominent nasal bridge [HP:0000426] 0.485714 0.485714 " + "[175 rows x 6 columns]" ] }, - "execution_count": 9, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -963,6 +901,12 @@ "source": [ "*-* EOF *-*" ] + }, + { + "cell_type": "markdown", + "id": "518311d0", + "metadata": {}, + "source": [] } ], "metadata": { @@ -981,7 +925,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.3" + "version": "3.10.10" } }, "nbformat": 4, diff --git a/notebooks/PPP2R1A/PPP2R1A.ipynb b/notebooks/PPP2R1A/PPP2R1A.ipynb index 57a94caf..05ea9d6b 100644 --- a/notebooks/PPP2R1A/PPP2R1A.ipynb +++ b/notebooks/PPP2R1A/PPP2R1A.ipynb @@ -19,17 +19,17 @@ "output_type": "stream", "text": [ "Loaded HPO v2024-08-13\n", - "Using genophenocorr version 0.1.1dev\n" + "Using gpsea version 0.2.1.dev0\n" ] } ], "source": [ - "import genophenocorr\n", + "import gpsea\n", "import hpotk\n", "store = hpotk.configure_ontology_store()\n", "hpo = store.load_minimal_hpo()\n", "print(f'Loaded HPO v{hpo.version}')\n", - "print(f\"Using genophenocorr version {genophenocorr.__version__}\")" + "print(f\"Using gpsea version {gpsea.__version__}\")" ] }, { @@ -71,16 +71,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "Patients Created: 100%|██████████| 60/60 [00:00<00:00, 925.44it/s]\n", + "Patients Created: 100%|██████████| 60/60 [00:37<00:00, 1.61it/s]\n", "Validated under none policy\n", - "No errors or warnings were found\n", - "\n" + "No errors or warnings were found\n" ] } ], "source": [ "from ppktstore.registry import configure_phenopacket_registry\n", - "from genophenocorr.preprocessing import configure_caching_cohort_creator, load_phenopackets\n", + "from gpsea.preprocessing import configure_caching_cohort_creator, load_phenopackets\n", "\n", "cohort_name = 'PPP2R1A'\n", "phenopacket_store_release = '0.1.18'\n", @@ -172,8 +171,8 @@ "\n", "\n", "\n", - "

genophenocorr cohort analysis

\n", - "

Successfully ingested 60 phenopackets.

\n", + "

GPSEA cohort analysis

\n", + "

Successfully loaded 60 individuals.

\n", " \n", "

No errors encountered.

\n", " \n", @@ -187,7 +186,7 @@ " \n", " HPO Term\n", " ID\n", - " Annotation Count\n", + " Seen in n individuals\n", " \n", " \n", " \n", @@ -260,92 +259,92 @@ " \n", " \n", " \n", - " Variant\n", + " Count\n", + " Variant key\n", " Variant Name\n", " Protein Variant\n", " Variant Class\n", - " Variant Count\n", " \n", " \n", " \n", + " 11\n", " 19_52212726_52212726_C_T\n", " c.544C>T\n", " p.Arg182Trp\n", " MISSENSE_VARIANT\n", - " 11\n", - " \n", + " \n", " \n", " \n", + " 7\n", " 19_52213076_52213076_G_A\n", " c.773G>A\n", " p.Arg258His\n", " MISSENSE_VARIANT\n", - " 7\n", - " \n", + " \n", " \n", " \n", + " 6\n", " 19_52212961_52212961_G_A\n", " c.658G>A\n", " p.Val220Met\n", " MISSENSE_VARIANT\n", - " 6\n", - " \n", + " \n", " \n", " \n", + " 5\n", " 19_52212721_52212721_T_C\n", " c.539T>C\n", " p.Met180Thr\n", " MISSENSE_VARIANT\n", - " 5\n", - " \n", + " \n", " \n", " \n", + " 5\n", " 19_52212959_52212959_C_T\n", " c.656C>T\n", " p.Ser219Leu\n", " MISSENSE_VARIANT\n", - " 5\n", - " \n", + " \n", " \n", " \n", + " 3\n", " 19_52212730_52212730_G_A\n", " c.548G>A\n", " p.Arg183Gln\n", " MISSENSE_VARIANT\n", - " 3\n", - " \n", + " \n", " \n", " \n", - " 19_52212720_52212720_A_G\n", - " c.538A>G\n", - " p.Met180Val\n", - " MISSENSE_VARIANT\n", " 3\n", - " \n", - " \n", - " \n", " 19_52212718_52212718_C_T\n", " c.536C>T\n", " p.Pro179Leu\n", " MISSENSE_VARIANT\n", + " \n", + " \n", + " \n", " 3\n", - " \n", + " 19_52212720_52212720_A_G\n", + " c.538A>G\n", + " p.Met180Val\n", + " MISSENSE_VARIANT\n", + " \n", " \n", " \n", + " 2\n", " 19_52212715_52212715_C_A\n", " c.533C>A\n", " p.Thr178Asn\n", " MISSENSE_VARIANT\n", - " 2\n", - " \n", + " \n", " \n", " \n", - " 19_52190098_52190098_T_C\n", - " c.2T>C\n", - " p.Met1?\n", - " START_LOST\n", " 2\n", - " \n", + " 19_52212729_52212729_C_T\n", + " c.547C>T\n", + " p.Arg183Trp\n", + " MISSENSE_VARIANT\n", + " \n", " \n", " \n", " \n", @@ -380,13 +379,13 @@ " \n", " \n", " \n", - " MISSENSE_VARIANT\n", - " 58\n", + " START_LOST\n", + " 2\n", " \n", " \n", " \n", - " START_LOST\n", - " 2\n", + " MISSENSE_VARIANT\n", + " 58\n", " \n", " \n", " \n", @@ -406,7 +405,7 @@ } ], "source": [ - "from genophenocorr.view import CohortViewable\n", + "from gpsea.view import CohortViewable\n", "from IPython.display import HTML, display\n", "\n", "viewer = CohortViewable(hpo)\n", @@ -415,13 +414,13 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "id": "b8619776", "metadata": {}, "outputs": [ { "data": { - "image/png": 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hQwcsWrQIa9asQY8ePaCnp4fly5fD3NwcM2bMKLcDjhBCCCHkizFCCCHkP+DYsWNMRkaGycjIsE6dOrFZs2axo0ePsvz8fJF8KSkpTEZGhi1evFgk/fbt20xWVlYk3d7engFgmzZtEmvP3t6e2dvbc+8TEhIYANa0aVOWl5fHpa9du5YBYLdv32aMMVZcXMwsLCyYs7MzKy4u5vJlZ2czExMT1qNHD5F2tmzZwgQCAcvOzubSrK2tRdoWCg8PZwCYk5OTSN0//PADk5GRYRkZGVyakZERA8COHDkiUscvv/zCFBUV2cOHD0XSZ8+ezWRkZNizZ88YY4zt27ePAWCLFi0Syefh4cF4PB5LSkri0gAwOTk5kbRbt24xAGz9+vVi8ScnJ4vFeebMGS7t7du3jM/ns+nTp1c57vIIPz89PT326dMnLn337t0MAFu7di2XVt55sWbNGgaAbdu2jUvLz89nnTp1YkpKSly97969YwBYQECAWBzdu3dnzZs3Z7m5uVxacXEx69y5M7OwsBCLNyEhQSyuP//8k0vLy8tjOjo6zN3dvcL9Z6zkcwLArl69yqU9ffqUycvLswEDBnBpAQEBDAAbOXKkSPkBAwYwdXV17v3NmzcZADZ69GiRfDNmzGAA2MmTJxljjMXGxjIA7MqVK+XGlpyczAAwgUDAXrx4waVfunSJAWA//PADl+bj48MAsIULF4rU0bp1a2ZjYyOSVvq6Yqzk82rWrBnr1q2bSLqioiLz8fERi6tsecYYu3Dhgtjn0LJlS+bi4lLu/jEm/WcvrfT0dKalpcVsbW3Ftl29epUBYPHx8Vw7+vr6bOrUqZXWe/36dQaAqaurM21tbRYaGsqioqJY+/btGY/HY4cPH+byPn36lPXs2ZOFhYWxv/76i61Zs4YZGhqyevXqsYMHD1balo+PD1NUVGSMMXb27FmmoqLCXFxcRI4RYyXnvrW1dbn1CM+x0texpM/O2dmZmZqaShUXADZ79myR9L///psBYFFRUSLpR44cEUl//fo1k5WVZW5ubiL5AgMDGQCRc014vZUl6fuy7H2psLBQ5H7EGGMfPnxg2traItdvRd9JrVq1YlpaWiw9PZ1Lu3XrFqtXrx4bMWKEWJze3t5idXh7ezNdXV1WVFTEpQnPo/DwcLH8pamqqrKJEydWmMfIyEji9VnefVqa7/mq1lnZvZ+xkvPGyMiIe1/RcS/PmzdvmKysLPvtt9+4tM6dO7P+/ftLVV7Seb906VLG4/HY06dPuTRJ511N3yPKi0fa67BsjKtXr2YA2Lt378otI+19mrGSc6Ds97a05YX3LBUVFfb27VuJcY8dO5ZLKywsZPr6+ozH47Fly5Zx6R8+fGACgUDiuUgIIYQQUhNo5BAhhJD/hB49euDChQtwdXXFrVu3sGLFCjg7O0NPT09kWqaYmBgUFxfD09MTaWlp3EtHRwcWFhYi0+0AAJ/Ph5+fn9Rx+Pn5icwnb2trCwDctE43b97Eo0ePMGTIEKSnp3PtZ2VloXv37jhz5ozINGhxcXFwdHSs0lpAY8eOFZlqxdbWFkVFRXj69KlIPhMTEzg7O4ukRUdHw9bWFg0aNBA5Pk5OTigqKuJ+vRoXFwcZGRlMmTJFpPz06dPBGBMbqeLk5AQzMzPufYsWLaCioiJxuquyrKysuOMIlPziukmTJiJlpY27MiNGjICysjL33sPDA40aNUJcXJxIPknnRVxcHHR0dODt7c2l1a9fH1OmTMHnz59x+vTpCtt+//49Tp48yf3aWrgP6enpcHZ2xqNHjyqcfgkAlJSURNZdkZOTQ/v27aU6zgDQqVMn2NjYcO8NDQ3Rv39/HD16VGxKNn9/f5H3tra2SE9Px6dPnwCAO2ZlR25Nnz4dALjROcKpIA8ePCgyskISNzc3kVEG7du3R4cOHcQ+n/LiK3scSl9XHz58wMePH2FraysyoqcipcsXFBQgPT0d5ubmUFNTE6lDTU0Nd+/exaNHjyTWUxOffWnFxcUYOnQoMjIysH79erHtUVFR0NbW5kZC8ng8DB48GDt37hT7nMsSTuWWnp6O/fv3Y/z48RgyZAhOnDgBdXV1LFq0iMtraGiIo0ePwt/fH/369cPUqVNx48YNaGpqcueBNBISEuDs7Izu3bsjJiYGfD5f6rIAuBFGmZmZXFrpz+7jx49IS0uDvb09njx5IjL9XEXGjx8v8j46Ohqqqqro0aOHyPeQjY0NlJSUuPvLiRMnUFhYiAkTJoiUnzx5cpX2qzIyMjLc/ai4uBjv379HYWEh2rZtK9U5/urVK9y8eRO+vr4iIx5atGiBHj16SHXdASXfq6mpqSL316ioKAgEAm7EVXnU1NRw6dIlpKamVhqvtKT9nq+Kyu79NWXnzp2oV6+eyHHz9vbG4cOHxaYulKT0eZ+VlYW0tDR07twZjDGJU/+VVZP3iLLxVPc6FBLeS/bv31/udLJfep+uanl3d3du+sCyRo8ezf2/jIwM2rZtC8YYRo0aJbJPZf/eIYQQQgipSdQ5RAgh5D+jXbt2iImJwYcPH3D58mXMmTMHmZmZ8PDwwL179wAAjx49AmMMFhYW0NTUFHklJibi7du3InXq6elVafFgQ0NDkfcNGjQAAO6hjfDhsI+Pj1j7v//+O/Ly8rgHIgUFBYiPj+fWG6qpGIRMTEzEyj569AhHjhwRi0242LXw+Dx9+hS6uroiD9iAkulYhNsrikkYlzQPs6QpK23c7969w+vXr7lX2TVLLCwsRN7zeDyYm5uLTZ8k6bx4+vQpLCwsUK+e6J9X5R2TspKSksAYw/z588X2IyAgQGQ/yqOvry+2BoO0xxkQ338AaNy4MbKzs8XWrarsPHv69Cnq1asHc3NzkXw6OjpQU1Pjjoe9vT3c3d0RFBQEDQ0N9O/fH+Hh4WLrElUUX9nPR15eXuyBnKTjcPDgQXTs2BHy8vJo2LAhN9WTtA8lc3JysGDBAm6dKw0NDWhqaiIjI0OkjoULFyIjIwONGzdG8+bNMXPmTPzzzz/c9pr47EubPHkyjhw5gt9//x0tW7YU2VZUVISdO3fC0dERycnJSEpKQlJSEjp06IA3b97gxIkTFdYtfJhrYmKCDh06cOlKSkro168fLl++jMLCwnLLN2zYEH5+fnjw4AFevHhR6b7k5ubCxcUFrVu3xu7du6u1mLvwOi/9fXXu3Dk4OTlx6+hoampya5NI8/nLyspCX19fJO3Ro0f4+PEjtLS0xD7Hz58/i3x/AhC7Nho2bMhdRzVl69ataNGiBbfWlaamJg4dOiTVPgrjbNKkidi2pk2bcj9sKE3SfaVHjx5o1KgRN7VccXExduzYgf79+4vdQ8pasWIF7ty5AwMDA7Rv3x6BgYFf/KBc2u/5qpD2vvultm3bhvbt2yM9PZ27dlu3bo38/HxER0dXWv7Zs2dcZ59wPTZ7e3sA0p33NXmPAL78Oixt8ODB6NKlC0aPHg1tbW14eXlh9+7dIh1FX3qfrmp5SdeDUNnjo6qqCnl5ebFpQFVVVWv8PCKEEEIIEZKt6wAIIYSQmiYnJ4d27dqhXbt2aNy4Mfz8/BAdHY2AgAAUFxeDx+Ph8OHDkJGREStbdg2LqozYASCxTqBkjR4A3EOK4OBgtGrVSmJeYQxnz57Fp0+fuPWGaioGIUn7VlxcjB49emDWrFkS62jcuHGVYqlqTNUtK23c7dq1E3l4ExAQINUC4GVV9byQhvDcmDFjhtiILqGyD5PL+pLjXFXStlW2s6osHo+HPXv24OLFizhw4ACOHj2KkSNHIiQkBBcvXpRqXRlpYyvt77//hqurK+zs7BAaGopGjRqhfv36CA8Px/bt26VqZ/LkyQgPD8e0adPQqVMnqKqqgsfjwcvLS+SBpJ2dHR4/foz9+/fj2LFj+P3337F69Wps2rQJo0ePrpHPXigoKAihoaFYtmwZhg8fLrb95MmTePXqFXbu3ImdO3eKbY+KikLPnj3LrV9XVxcAoK2tLbZNS0sLBQUFyMrKgqqqarl1GBgYACgZMVW2g6UsPp+PPn36YP/+/Thy5Aj69u1bYX5JhOuhCI/h48eP0b17d1haWmLVqlUwMDCAnJwc4uLisHr16nJHHZSNq+wD4uLiYmhpaUlcXwdAuSMIKlLe9VPZCC+gpCPB19cXbm5umDlzJrS0tCAjI4OlS5fi8ePHVY5FGpK+G2VkZDBkyBD89ttvCA0Nxblz55CamioyyrE8np6esLW1RWxsLI4dO4bg4GAsX74cMTEx6N27N4CKj5E03wWSVLXO2vjuLb0umKROmqioKIwdO7bc8kVFRejRowfev3+Pn376CZaWllBUVMTLly/h6+sr1XlfFZUdk5q4DksTCAQ4c+YMEhIScOjQIRw5cgS7du1Ct27dcOzYsWqfC1+ior8V6uo8IoQQQggpjTqHCCGE/Ke1bdsWQMn0OABgZmYGxhhMTEyq3dHxJYRTq6moqHCjWspz6NAhWFlZwdjYWCS9softX8LMzAyfP3+uNDYjIyMcP34cmZmZIr/8vn//Pre9Nkkbd1RUlMii46ampiLby077xRhDUlISWrRoUWkMRkZG+Oeff1BcXCzy0LjsMSnv8xPGUr9+/Ur342uRNO3Zw4cPoaCgUOUH20ZGRiguLsajR4+4X1UDwJs3b5CRkSF2jnTs2BEdO3bE4sWLsX37dgwdOhQ7d+4UmXqnvPjKXiPS2Lt3L+Tl5XH06FGRacrCw8PF8pb3me3Zswc+Pj4ICQnh0nJzc5GRkSGWVzhixs/PD58/f4adnR0CAwMxevToGvvsN27ciMDAQEybNg0//fSTxDxRUVHQ0tKSuOB7TEwMYmNjsWnTpnIfaurq6kJHR0fiNHepqamQl5evdDSIcOSHNOcUj8dDVFQU+vfvj0GDBuHw4cNwcHCotJxQUVERtm/fDgUFBXTt2hUAcODAAeTl5eGvv/4S+fV+2WlFq8rMzAzHjx9Hly5dKnwoLDz3k5KSREYWpKeni40QEI62yMjI4KbNAiof4QCUnJ+mpqaIiYkROYeFo9GEyju/hXE+ePBAbNv9+/ehoaEBRUXFSuMASqZyCwkJwYEDB3D48GFoamqW2xFaVqNGjTBhwgRMmDABb9++RZs2bbB48WKuc6hBgwYSr7mnT5+KfccD0n3PV7XO6qjqvTwqKgr169dHZGSkWCfC2bNnsW7dOjx79kziaFsAuH37Nh4+fIitW7dixIgRXHp8fLzUMdTkPeJrXIf16tVD9+7d0b17d6xatQpLlizB3LlzkZCQACcnJ6nv0+X50vKEEEIIId8amlaOEELIf0JCQoLEX1YK1xAQToszcOBAyMjIICgoSCw/Ywzp6elfNU4bGxuYmZlh5cqVYlOaARCZliUuLk7ilHKKiooSH1rVBE9PT1y4cAFHjx4V25aRkcFNF9WnTx8UFRVhw4YNInlWr14NHo/HPbSrLdLG3aVLFzg5OXGvsg/5/vzzT5F1Sfbs2YNXr15JtT99+vTB69evsWvXLi6tsLAQ69evh5KSEjd1j4KCAhdXaVpaWnBwcMCvv/7KdWaWVnbKnq/hwoULImuRPH/+HPv370fPnj2r/Ktr4Yi3NWvWiKSvWrUKALhz+8OHD2LXonBUXdmp5fbt2yfSKXH58mVcunSpWuebjIwMeDyeyAiMlJQU7Nu3TyxvedecjIyMWOzr168XG9VR9ntFSUkJ5ubm3P7VxGe/a9cuTJkyBUOHDuWOcVk5OTmIiYlB37594eHhIfaaNGkSMjMzRdZpk2Tw4MF4/vy5yEPltLQ07N+/H926deMemkqK++XLl/jjjz/QokULNGrUqNL9AkpGg8bExKBdu3bc1HXSKCoqwpQpU5CYmIgpU6ZARUUFwP9+nV/6s/v48aPEjsGq8PT0RFFREX755RexbYWFhdw51L17d8jKyiIsLEwkT9nvU+B/PygovW5aVlYWtm7dWmk8kvbz0qVLuHDhgki+8r6TGjVqhFatWmHr1q0i2+7cuYNjx45VaVRrixYt0KJFC/z+++/Yu3cvvLy8ICtb8e8Ui4qKxKYW09LSgq6ursh3g5mZGS5evIj8/Hwu7eDBg3j+/LnEeqX5nq9qndVR3nEvT1RUFGxtbTF48GCxa3fmzJkAgB07dpRbXtL5wBjD2rVrpY65Ju8RNX0dvn//Xiyt7L1E2vt0eb60PCGEEELIt4ZGDhFCCPlPmDx5MrKzszFgwABYWloiPz8f58+fx65du2BsbAw/Pz8AJQ98Fi1ahDlz5iAlJQVubm5QVlZGcnIyYmNjMXbsWMyYMeOrxVmvXj38/vvv6N27N6ytreHn5wc9PT28fPkSCQkJUFFRwYEDB5CcnIzExESxh4dASQdTWFgYFi1aBHNzc2hpaaFbt241Et/MmTPx119/oW/fvvD19YWNjQ2ysrJw+/Zt7NmzBykpKdDQ0EC/fv3g6OiIuXPnIiUlBS1btsSxY8ewf/9+TJs2jXugWVukjbsyDRs2RNeuXeHn54c3b95gzZo1MDc3x5gxYyotO3bsWPz666/w9fXFtWvXYGxsjD179uDcuXNYs2YNN5pCIBDAysoKu3btQuPGjdGwYUM0a9YMzZo1w8aNG9G1a1c0b94cY8aMgampKd68eYMLFy7gxYsXuHXr1hcfq4o0a9YMzs7OmDJlCvh8PkJDQwGUTFVWVS1btoSPjw82b96MjIwM2Nvb4/Lly9i6dSvc3Nzg6OgIoGRNlNDQUAwYMABmZmbIzMzEb7/9BhUVFbGHz+bm5ujatSvGjx+PvLw8rFmzBurq6uVOJ1gRFxcXrFq1Cr169cKQIUPw9u1bbNy4Eebm5iLrAQEl19zx48exatUq6Orqcuvt9O3bF5GRkVBVVYWVlRUuXLiA48ePQ11dXaS8lZUVHBwcYGNjg4YNG+Lq1avYs2cPJk2axOX5ks/+8uXLGDFiBNTV1dG9e3exac06d+4MU1NT/PXXX8jMzISrq6vEejp27AhNTU1ERUVh8ODB5bY3Z84c7N69G+7u7vjxxx+hqqqKTZs2oaCgAEuWLOHyzZo1i5s6SldXFykpKfj111+RlZVVpQfSQMl1c/DgQXTr1g29e/fG6dOn0axZM277x48fsW3bNgBAdnY2kpKSEBMTg8ePH8PLy0ukw6Znz56Qk5NDv379MG7cOHz+/Bm//fYbtLS0JHbOScve3h7jxo3D0qVLcfPmTfTs2RP169fHo0ePEB0djbVr18LDwwPa2tqYOnUqQkJC4Orqil69euHWrVs4fPgwNDQ0REaU9OzZE4aGhhg1ahRmzpwJGRkZ/PHHH9DU1MSzZ88qjKdv376IiYnBgAED4OLiguTkZGzatAlWVlYiP06o6DspODgYvXv3RqdOnTBq1Cjk5ORg/fr1UFVVrfKUnCNGjODur9JMKZeZmQl9fX14eHigZcuWUFJSwvHjx3HlyhWR0XqjR4/Gnj170KtXL3h6euLx48fYtm1bufchab7nq1pndVR03Mu6dOkSkpKSRL4zStPT00ObNm0QFRVV7qhBS0tLmJmZYcaMGXj58iVUVFSwd+/eKq1nU5P3iJq+DhcuXIgzZ87AxcUFRkZGePv2LUJDQ6Gvr8+NGpT2Pl2eLy1PCCGEEPLNYYQQQsh/wOHDh9nIkSOZpaUlU1JSYnJycszc3JxNnjyZvXnzRiz/3r17WdeuXZmioiJTVFRklpaWbOLEiezBgwdcHnt7e2ZtbS2xPXt7e2Zvb8+9T0hIYABYdHS0SL7k5GQGgIWHh4uk37hxgw0cOJCpq6szPp/PjIyMmKenJztx4gRjjLENGzYwVVVVVlBQINb269evmYuLC1NWVmYAuDjCw8MZAHblyhWR/MLYEhISuDQjIyPm4uIicd8yMzPZnDlzmLm5OZOTk2MaGhqsc+fObOXKlSw/P18k3w8//MB0dXVZ/fr1mYWFBQsODmbFxcUi9QFgEydOFGvHyMiI+fj4cO+F8ScnJ1caZ9njX5W4JREeox07drA5c+YwLS0tJhAImIuLC3v69KlY2+WdF2/evGF+fn5MQ0ODycnJsebNm4t99owxdv78eWZjY8Pk5OQYABYQEMBte/z4MRsxYgTT0dFh9evXZ3p6eqxv375sz549YvGW/kzLi8vHx4cZGRlVuP+M/e9z2rZtG7OwsGB8Pp+1bt1apA3GGAsICGAA2Lt370TSJX1+BQUFLCgoiJmYmLD69eszAwMDNmfOHJabm8vluX79OvP29maGhoaMz+czLS0t1rdvX3b16lUuj/A6Cg4OZiEhIczAwIDx+Xxma2vLbt26Jba/ioqKYvsnjLu0LVu2cPtqaWnJwsPDJea7f/8+s7OzYwKBgAHgztsPHz5wn7eSkhJzdnZm9+/fFzu3Fy1axNq3b8/U1NSYQCBglpaWbPHixWLnpTSfvSTCY1/eS3gO9uvXj8nLy7OsrKxy6/L19WX169dnaWlpFbb5+PFjNmDAAKaiosIEAgHr1q0bu3z5skie7du3Mzs7O6apqclkZWWZhoYGGzBgALt27VqFdQtJ+izT0tKYlZUV09HRYY8ePWKMlZz7pfdXSUmJWVhYsGHDhrFjx45JrPuvv/5iLVq0YPLy8szY2JgtX76c/fHHH2LnsLRxlbZ582ZmY2PDBAIBU1ZWZs2bN2ezZs1iqampXJ7CwkI2f/58pqOjwx2/xMREpq6uzvz9/UXqu3btGuvQoQOTk5NjhoaGbNWqVRKvt7Lfi8XFxWzJkiXMyMiIu54PHjwo8Tuhou+k48ePsy5dujCBQMBUVFRYv3792L1790TKl/e9UNqrV6+YjIwMa9y4cbl5SsvLy2MzZ85kLVu2ZMrKykxRUZG1bNmShYaGiuUNCQlhenp6jM/nsy5durCrV6+We5+W5nu+qnVKc++v6nEvbfLkyQwAe/z4cbnHKzAwkAEQ+04s7d69e8zJyYkpKSkxDQ0NNmbMGHbr1i2xWCV9D36Ne8SXXIdlYzxx4gTr378/09XVZXJyckxXV5d5e3uzhw8fipST9j5d3t8f0pQvfc8qL+6yx6e875WK/uYghBBCCPlSPMZodUNCCCHkW9OnTx8oKSlh9+7ddR3Kd+HUqVNwdHREdHQ0PDw86jqcOsHj8TBx4kSJU1vVtZSUFJiYmCA4OPirjuwjpC5lZGSgQYMGWLRoEebOnVvX4dS4tLQ0NGrUCAsWLMD8+fPrOhxSRd/yPYIQQgghhFQPTStHCCGEfIMcHBxga2tb12EQQgj5CnJyciAQCETShOtzOTg41H5AtSAiIgJFRUUYPnx4XYdCCCGEEEIIAXUOEUIIId+k6qyhQggh5N9h165diIiI4EaJnj17Fjt27EDPnj3RpUuXug6vRp08eRL37t3D4sWL4ebmBmNj47oOiRBCCCGEEALqHCKEEEIIIYSQWtWiRQvIyspixYoV+PTpE7S1tTF16lQsWrSorkOrcQsXLsT58+fRpUsXrF+/vq7DIYQQQgghhPw/WnOIEEIIIYQQQgghhBBCCCHkO1KvrgMghBBCCCGEEEIIIYQQQgghtYc6hwghhBBCCCGEEEIIIYQQQr4j1DlECCGEEEI4vr6+tGA8IRVISUkBj8dDREREXYdCCCGEEEIIIdVGnUOEEEIIIZU4f/48AgMDkZGRUdehfNPi4uIQGBj4xfXweDzweDyEhISIbYuIiACPx8PVq1e5tMDAQPB4PNSrVw/Pnz8XK/Pp0ycIBALweDxMmjSpyvEkJiaiV69eUFJSQsOGDTF8+HC8e/dOLN/ixYvh6uoKbW1t8Hg8qY9Fjx49JMb2/PlzBAUFoX379mjQoAE0NDTg4OCA48ePi9Vx4sQJjBw5Eo0bN4aCggJMTU0xevRovHr1Siyvg4MDd4xLv3r16iXdAQHw5s0bjBs3Dnp6epCXl4exsTFGjRoldfm6tGTJEuzbt6+uw5BKda8F4UtBQQFWVlaYN28ePn36VOX2//rrL7Rp0wby8vIwNDREQEAACgsLJcYh6fX69WuRvMbGxhLz+fv7i7WdkZGBsWPHQlNTE4qKinB0dMT169crjPfx48eQl5cXOy5fUicAFBcXIyIiAq6urjAwMICioiKaNWuGRYsWITc3t9LyhBBCCCGEfItk6zoAQgghhJBv3fnz5xEUFARfX1+oqanVdThf1W+//Ybi4uJqlY2Li8PGjRtrpIMIAIKDgzF+/HgoKChIlZ/P52PHjh2YNWuWSHpMTEy1Y3jx4gXs7OygqqqKJUuW4PPnz1i5ciVu376Ny5cvQ05Ojss7b9486OjooHXr1jh69KhU9cfExODChQsSt+3fvx/Lly+Hm5sbfHx8UFhYiD///BM9evTAH3/8AT8/Py7vTz/9hPfv32PQoEGwsLDAkydPsGHDBhw8eBA3b96Ejo6OSN36+vpYunSpSJqurq5UMT9//hxdunQBAPj7+0NPTw+pqam4fPmyVOXr2pIlS+Dh4QE3N7dqlTcyMkJOTg7q169fs4FVoKrXQlhYGJSUlPD582ccO3YMixcvxsmTJ3Hu3DnweDyp6jh8+DDc3Nzg4OCA9evX4/bt21i0aBHevn2LsLAwsfwLFy6EiYmJSJqk78tWrVph+vTpImmNGzcWeV9cXAwXFxfcunULM2fOhIaGBkJDQ+Hg4IBr167BwsJCYsw//PADZGVlkZeXJ7atunUCQHZ2Nvz8/NCxY0f4+/tDS0sLFy5cQEBAAE6cOIGTJ09KfVwJIYQQQgj5ZjBCCCGEEFKh4OBgBoAlJydXmreoqIjl5OR8/aBq2OfPn7+4jokTJ7Ka+PMSAGvVqhUDwEJCQkS2hYeHMwDsypUrXFpAQAADwAYOHMhatWolVl+PHj2Yu7s7A8AmTpxYpVjGjx/PBAIBe/r0KZcWHx/PALBff/1VJK/w/Hj37h0DwAICAiqsOycnhxkbG7OFCxdKjO3OnTvs3bt3Imm5ubnM0tKS6evri6SfPn2aFRUViaUBYHPnzhVJt7e3Z9bW1hXGVpHevXszExMTlpaWVu06alJVz11FRUXm4+PzdYKpYdW9FsqeNwMHDmQA2Pnz56Vu28rKirVs2ZIVFBRwaXPnzmU8Ho8lJiZWGEd5jIyMmIuLS6X5du3axQCw6OhoLu3t27dMTU2NeXt7Syxz5MgRJicnx+bNmycxnurUKZSXl8fOnTsnlh4UFMQAsPj4+Er3iRBCCCGEkG8NTStHCCGEEFKBwMBAzJw5EwBgYmLCTYOUkpICANx0YFFRUbC2tgafz8eRI0cAACtXrkTnzp2hrq4OgUAAGxsb7NmzR6wNYR379u1Ds2bNwOfzYW1tzdUjlJmZiWnTpsHY2Bh8Ph9aWlro0aOH2LRIly5dQp8+fdCgQQMoKiqiRYsWWLt2Lbfd19cXSkpKePz4Mfr06QNlZWUMHTqU21Z6zSHh+iorV67E6tWrYWRkBIFAAHt7e9y5c0ekzo0bN3L7I3wJvXr1Cvfv30dBQYFUx71Lly7o1q0bVqxYgZycHKnKDBkyBDdv3sT9+/e5tNevX+PkyZMYMmSIVHWUtXfvXvTt2xeGhoZcmpOTExo3bozdu3eL5K3qWk0rVqxAcXExZsyYIXG7tbU1NDQ0RNL4fD769OmDFy9eIDMzk0u3s7NDvXqif9rb2dmhYcOGSExMlFh/YWEhPn/+XKWY79+/j8OHD2PmzJlQV1dHbm6u1J9peaQ9x4CKz92srCxMnz4dBgYG4PP5aNKkCVauXAnGGFeex+MhKysLW7du5c5RX19fbvvLly8xcuRIaGtrc9fhH3/8ITHe0msOCeN6+fIl3NzcoKSkBE1NTcyYMQNFRUUi5WvjWiirW7duAIDk5GSp8t+7dw/37t3D2LFjISv7v8kmJkyYAMaYxO8xoOQ7quz+SpKfn4+srKxyt+/Zswfa2toYOHAgl6apqQlPT0/s379fbGRQQUEBpk6diqlTp8LMzKxG6ixNTk4OnTt3FksfMGAAAJR7jRFCCCGEEPIto84hQgghhJAKDBw4EN7e3gCA1atXIzIyEpGRkdDU1OTynDx5Ej/88AMGDx6MtWvXcp0Ea9euRevWrbFw4UIsWbIEsrKyGDRoEA4dOiTWztmzZzFhwgR4eXlhxYoVyM3Nhbu7O9LT07k8/v7+CAsLg7u7O0JDQzFjxgwIBAKRB5Px8fGws7PDvXv3MHXqVISEhMDR0REHDx4Uaa+wsBDOzs7Q0tLCypUr4e7uXuFx+PPPP7Fu3TpMnDgRc+bMwZ07d9CtWze8efMGADBu3Dj06NEDALhjFBkZyZWfM2cOmjZtipcvX0pz2AGUdMy9efNG4hRWktjZ2UFfXx/bt2/n0nbt2gUlJSW4uLhI3a7Qy5cv8fbtW7Rt21ZsW/v27XHjxo0q1yn07NkzLFu2DMuXL4dAIKhS2devX0NBQaHSKcY+f/6Mz58/i3UwAcDDhw+hqKgIZWVl6OjoYP78+VJ1VgjXO9LW1kb37t0hEAggEAjQu3dvrsO0uio7x4QknbuMMbi6umL16tXo1asXVq1ahSZNmmDmzJn48ccfubKRkZHg8/mwtbXlztFx48YBKFlHqWPHjjh+/DgmTZqEtWvXwtzcHKNGjcKaNWsqjb+oqAjOzs5QV1fHypUrYW9vj5CQEGzevFkkX21cC2U9fvwYAKCuri5VfuG5Xfbc19XVhb6+vsRz39HRESoqKlBQUICrqysePXokse6TJ09CQUEBSkpKMDY2Fum4Lt1+mzZtxDo827dvj+zsbDx8+FAkfc2aNfjw4QPmzZtX4T5VpU5pCNdUknSNEUIIIYQQ8q2jNYcIIYQQQirQokULtGnTBjt27ICbm5vE0SEPHjzA7du3YWVlJZL+8OFDkQf/kyZNQps2bbBq1SqxzorExETcu3eP+9W7o6MjWrZsiR07dmDSpEkAgEOHDmHMmDEii9OXXl+nqKgI48aNQ6NGjXDz5k2R9T5Kj54AgLy8PAwaNEhs3ZnyJCUl4dGjR9DT0wMA9OrVCx06dMDy5cuxatUqdOrUCY0bN0Z8fDyGDRsmVZ2VsbW1haOjI7feSmWdKDweD15eXtixYwcWLlwIAIiKisLAgQPB5/Or3P6rV68AAI0aNRLb1qhRI7x//x55eXnVqnv69Olo3bo1vLy8qlQuKSkJMTExGDRoEGRkZCrMu2bNGuTn52Pw4MEi6WZmZnB0dETz5s2RlZWFPXv2YNGiRXj48CF27dpVYZ3CB/5jx45Fu3btsGvXLjx79gxBQUFwcnLCP//8I/W6OJL2raJzTEjSubt//36cPHkSixYtwty5cwEAEydOxKBBg7B27VpMmjQJZmZmGDZsGPz9/WFqaip2ns6dOxdFRUW4ffs214ni7+8Pb29vBAYGYty4cRWeg7m5uRg8eDDmz5/PlW3Tpg22bNmC8ePHV+uYCFX1Wnj//j0AcGsOhYaGQltbG7a2tlK1V9m5n5qayr1XUFCAr68v1zl07do1rFq1Cp07d8b169dhYGDA5W3RogW6du2KJk2aID09HREREZg2bRpSU1OxfPlykfbt7Owktg0AqampaN68OYCSDppffvkFK1euhIqKSoX7JG2d0lqxYgVUVFTQu3fvKpUjhBBCCCHkW0AjhwghhBBCvpC9vb1YxxAAkQe4Hz58wMePH2Frays2DRxQMlVZ6emQWrRoARUVFTx58oRLU1NTw6VLl0QezJZ248YNJCcnY9q0aWILwUtaLL0qD6zd3Ny4h/ZAya/tO3TogLi4OKnKR0REgDFW5anXAgMD8fr1a2zatEmq/EOGDEFSUhKuXLnC/be6U8oJp/CS1PkjLy8vkqcqEhISsHfvXqlGo5SWnZ2NQYMGQSAQYNmyZRXmPXPmDIKCguDp6clNKSa0ZcsWBAQEYODAgRg+fDj279+PMWPGYPfu3bh48WKF9QqnodPR0cGhQ4fg6emJGTNm4LfffsPjx49FRm1VVVXOsbLnblxcHGRkZDBlyhSR9OnTp4MxhsOHD1fYNmMMe/fuRb9+/cAYQ1paGvdydnbGx48fJV63Zfn7+4u8t7W1FbmGgdq5Fpo0aQJNTU2YmJhg3LhxMDc3x6FDh6TuuKvs3C993nt6eiI8PBwjRoyAm5sbfvnlFxw9ehTp6elYvHixSNm//voLs2bNQv/+/TFy5EicPn0azs7OWLVqFV68eCHSvrTX3U8//QRTU1OMHj260n2qyWt5yZIlOH78OJYtWyb2fUsIIYQQQsi/AXUOEUIIIYR8IRMTE4npBw8eRMeOHSEvL4+GDRtCU1MTYWFh+Pjxo1je0mvaCDVo0AAfPnzg3q9YsQJ37tyBgYEB2rdvj8DAQJEHz8Kpo5o1a1ZpzLKystDX1680n5CFhYVYWuPGjb94KrHK2NnZwdHRUer1Vlq3bg1LS0ts374dUVFR0NHREesckZawc0/SWiS5ubkieaRVWFiIKVOmYPjw4WjXrp3U5YqKiuDl5YV79+5hz5490NXVLTfv/fv3MWDAADRr1gy///67VPVPnz4dwP+mjSuPcH89PT1FpucaNGgQZGVlcf78eanak0Tac0zSufv06VPo6upCWVlZJL1p06bc9oq8e/cOGRkZ2Lx5MzQ1NUVefn5+AIC3b99WWIe8vLzIdJOA+DX8JapyLezduxfx8fE4deoUkpKScOfOHdjY2EjdVmXnfmXnfdeuXdGhQ4dKzycej4cffvgBhYWFOHXqlEj70lx3Fy9eRGRkJFavXi02XZykfaqpa3nXrl2YN28eRo0a9cWjwgghhBBCCKkrNK0cIYQQQsgXkvRQ8e+//4arqyvs7OwQGhqKRo0aoX79+ggPD5c4uqK8KcJKTwfn6ekJW1tbxMbG4tixYwgODsby5csRExNT5WmN+Hx+pQ9TvxUBAQFwcHDAr7/+KtUv9IcMGYKwsDAoKytj8ODB1d5P4XRTwim2Snv16hUaNmxY5Snl/vzzTzx48AC//vqrWKdHZmYmUlJSoKWlJTbCY8yYMTh48CCioqIq7Ox6/vw5evbsCVVVVcTFxYl1lpRHOPWXcDqy8gg7pbS1tUXSZWRkoK6uXmMdIRX5GuducXExAGDYsGHw8fGRmKdFixYV1lHZNH81Qdprwc7O7ovWwSl97peeFk6Y1r59+0rrMDAwwIMHD6TKB4iee40aNSr3ugP+dx7OmjULtra2MDEx4a6ntLQ0Lu+zZ8+4jndp66xMfHw8RowYARcXF6lHNBJCCCGEEPIt+nc8ESCEEEIIqUOSpmSrzN69eyEvL4+jR49i5MiR6N27N5ycnL44lkaNGmHChAnYt28fkpOToa6uzk3dJJyW7s6dO1/cTlmSFpd/+PChyNRY1TlO0rC3t4eDgwOWL18u1eihIUOG4NWrV3j48GG1p5QDAD09PWhqauLq1ati2y5fvoxWrVpVuc5nz56hoKAAXbp0gYmJCfcCSjqOTExMcOzYMZEyM2fORHh4OFavXg1vb+9y605PT0fPnj2Rl5eHo0ePSlwvpjzCEWhlR76UJRx98vLlS5H0/Px8pKWlVVq+ItKcY+UxMjJCamoqMjMzRdLv37/PbReSdJ5qampCWVkZRUVFcHJykvjS0tKq4h7VvKpeC9UlPLfLnvupqal48eKFVOf+kydPpDofJJ17rVq1wvXr17lOO6FLly5BQUEBjRs3BlByPZ05c0bkWpo5cyYAwNXVVaRDT9o6K3Lp0iUMGDAAbdu2xe7duyErS7+1JIQQQggh/17UOUQIIYQQUglFRUUAQEZGhtRlZGRkwOPxUFRUxKWlpKRg37591YqhqKhIbDo6LS0t6OrqclMltWnTBiYmJlizZo1YrKVHIFXHvn37RDoELl++jEuXLomMWKroOL169Qr3799HQUFBtdoXrreyefPmSvOamZlhzZo1WLp0qVQjHCri7u6OgwcP4vnz51zaiRMn8PDhQwwaNKjK9Xl5eSE2NlbsBQB9+vRBbGwsOnTowOUPDg7GypUr8fPPP2Pq1Knl1puVlYU+ffrg5cuXiIuLkzhFGwB8+vRJbGotxhgWLVoEAHB2dubSs7Ozcf/+fW4kBgA4ODhAS0sLUVFR3HRcQMk6OkVFRejRo0cVjoYoac6x8vTp0wdFRUXYsGGDSPrq1avB4/HEztOy56iMjAzc3d2xd+9eiZ2r7969q+LelK82r4Xqsra2hqWlJTZv3izyHRYWFgYejwcPDw8uTdKxiYuLw7Vr19CrVy8u7f379yJ1AUBBQQGWLVsGOTk5ODo6cukeHh548+YNYmJiuLS0tDRER0ejX79+3Ii9zZs3i11LkydPBgCsXLkSUVFRVa4TKJmiUzhNp1BiYiJcXFxgbGyMgwcPVnlKSUIIIYQQQr419FMnQgghhJBKCEdLzJ07F15eXqhfvz769evHdYZI4uLiglWrVqFXr14YMmQI3r59i40bN8Lc3Bz//PNPlWPIzMyEvr4+PDw80LJlSygpKeH48eO4cuUKQkJCAAD16tVDWFgY+vXrh1atWsHPzw+NGjXC/fv3cffuXRw9erR6BwCAubk5unbtivHjxyMvLw9r1qyBuro6Zs2axeURHqcpU6bA2dkZMjIy8PLyAgDMmTMHW7duRXJyslQjQcqyt7eHvb09Tp8+LVX+ijpSquLnn39GdHQ0HB0dMXXqVHz+/BnBwcFo3rw5txaNUGRkJJ4+fYrs7GwAwJkzZ7hOl+HDh8PIyAiWlpawtLSU2JaJiQnc3Ny497GxsZg1axYsLCzQtGlTbNu2TSR/jx49uOndhg4disuXL2PkyJFITExEYmIil09JSYmr9/r16/D29oa3tzfMzc2Rk5OD2NhYnDt3DmPHjkWbNm24cpcvX4ajoyMCAgIQGBgIoGRKt+DgYPj4+MDOzg7Dhw/Hs2fPsHbtWtja2mLgwIFc+VOnTomVr4g051h5+vXrB0dHR8ydOxcpKSlo2bIljh07hv3792PatGncqDqg5Dw9fvw4Vq1aBV1dXZiYmKBDhw5YtmwZEhIS0KFDB4wZMwZWVlZ4//49rl+/juPHj1c65Z60avtaqK7g4GC4urqiZ8+e8PLywp07d7BhwwaMHj2aW8sJADp37ozWrVujbdu2UFVVxfXr1/HHH3/AwMAAP//8M5fvr7/+wqJFi+Dh4QETExO8f/8e27dvx507d7BkyRLo6OhweT08PNCxY0f4+fnh3r170NDQQGhoKIqKihAUFMTl69mzp1jcwo4/e3t7tG3btsp1AkD37t0BgJuqLjMzE87Ozvjw4QNmzpyJQ4cOieQ3MzNDp06dqniECSGEEEIIqWOMEEIIIYRU6pdffmF6enqsXr16DABLTk5mjDEGgE2cOFFimS1btjALCwvG5/OZpaUlCw8PZwEBAazsn2Dl1WFkZMR8fHwYY4zl5eWxmTNnspYtWzJlZWWmqKjIWrZsyUJDQ8XKnT17lvXo0YPL16JFC7Z+/Xpuu4+PD1NUVJQYs4+PDzMyMuLeJycnMwAsODiYhYSEMAMDA8bn85mtrS27deuWSNnCwkI2efJkpqmpyXg8nsh++vj4iBy3ipR3PBISEhgABoBduXKFSxce03fv3lWr3srcuXOH9ezZkykoKDA1NTU2dOhQ9vr1a7F89vb2XHxlXwkJCVWOTbhf0tRpZGRUbr7Sn+eTJ0/YoEGDmLGxMZOXl2cKCgrMxsaGbdq0iRUXF4u0LzzeAQEBYvHu2LGDtWzZkvH5fKatrc0mTZrEPn36JJLnwIEDDADbtGlThftelXOsonM3MzOT/fDDD0xXV5fVr1+fWVhYsODgYLH9un//PrOzs2MCgYAB4K4xxhh78+YNmzhxIjMwMGD169dnOjo6rHv37mzz5s1i8YaHh1cal6Tr/Vu4FqQVGxvLWrVqxfh8PtPX12fz5s1j+fn5Innmzp3LWrVqxVRVVVn9+vWZoaEhGz9+vNg1cvXqVdavXz+mp6fH5OTkmJKSEuvatSvbvXu3xLbfv3/PRo0axdTV1ZmCggKzt7cX2dfyhIeHix2XqtZpZGQk8XuwvFfpc4gQQgghhJB/Cx5jXzjHCCGEEEII+c9KSUmBiYkJgoODMWPGjLoOh/yLzJo1Czt27EBSUpLIlF1l0TlGCCGEEEIIIbWP1hwihBBCCCGE1LiEhATMnz+/wo4hQgghhBBCCCF1g9YcIoQQQggh3513796hqKio3O1ycnJo2LBhLUb033PlypW6DoGUUVRUhHfv3lWYR0lJCUpKSrUUESGEEEIIIaSuUOcQIYQQQgj57rRr1w5Pnz4td7u9vT1OnTpVewERUgueP38OExOTCvMEBAQgMDCwdgIihBBCCCGE1Blac4gQQgghhHx3zp07h5ycnHK3N2jQADY2NrUYESFfX25uLs6ePVthHlNTU5iamtZSRIQQQgghhJC6Qp1DhBBCCCGEEEIIIYQQQggh35F6dR0AIYQQQgghhBBCCCGEEEIIqT3UOUQIIYQQQgghhBBCCCGEEPIdka3rAMh/x7Nnz9C0aVNkZ2fXdSiEkH85BQUFJCYmwtDQsK5DIYQQQgghhPwL0DMJQggpQc9UiLSoc4jUmLS0NGRnZ2Pbtm1o2rRpXYdDCPmXSkxMxLBhw5CWlkZ/yBBCCCGEEEKkQs8kCCGEnqmQqqHOIVLjmjZtijZt2tR1GIQQQgghhBBCCPnO0DMJQgghRDq05hAhhBBCCCGEEEIIIYQQQsh3hDqHCCGEEEIIIYQQQgghhBBCviPUOUQIIYQQQgghhBBCCCGEEPIdoTWHSJ149eoVfv/9dyScPIHMTxlQVFJGh46d4e/vDxMTkyrVdfv2bQQFBeHKlSt48+YN1NXVYWVlBVdXV0yePJnLt2TJElhZWcHNza2G9wY4f/48jh07hmnTpkFNTa3S/L6+vtizZw8+f/5c47FIEhoaCgUFBfj6+tZKe4QQQgghhBBCyLfk2bNnSEtLq+swqk1DQ6PCxeUjIiLg5+eHK1euoG3btmLbHRwckJaWhjt37nBpxsbGePr0qcT6nJ2dceTIEbH0WbNmITg4GJ6enti1axeXzuPxpNqPhIQEODg4SJWXEELI10WdQ6RWZWZmYuKECdixcwfkZBicmxbDTAn4lAVs3ngBwcHB6OvSB7/9vgXa2tqV1nf+/Hk4OjrC0NAQY8aMgY6ODp4/f46LFy9i7dq1Yp1DHh4eX61zKCgoCL6+vlJ1DtW20NBQaGhoUOcQIYQQQgghhJDvzrNnz2BpaYmcnJy6DqXaBAIB7t+/X2EHUXW0atUK06dPF0vX1dUVS2OMYceOHTA2NsaBAweQmZkJZWVlAEBkZKRI3j///BPx8fFi6U2bNq3B6AkhhHwJ6hwitSYjIwPdHO3w+OE9rBxYBJ9OgJrC/7Zn5xdhx2Vg3oEj6NSxHc78fR76+voV1rl48WKoqqriypUrYp0yb9++/Qp7ISorKwuKiopfvR1CCCGEEEIIIYRUT1paGnJycjBp0iTo6ekhOjoaubm5GD58uEi+J0+e4LfffsOCBQsgEAi495L8/PPPXMcIAHz8+BHBwcHQ0NDAtGnTAADHjx/HiRMnKoxt6dKllcb/8uVLbNiwAWlpaTXeOaSnp4dhw4ZJlffUqVN48eIFTp48CWdnZ8TExMDHxwcAxOq4ePEi4uPjpa6bEEJI7aPOIVJrvAYPQkrSPfw9vQgtJPT5KMgBo7oCTk2LYBvyCn379MLV6zchK1v+afr48WNYW1tLHK2jpaXF/b9wePPWrVuxdetWAICPjw8iIiLw9OlTLF++HCdOnMCzZ8+goKCAbt26ITg4GMbGxlwdwiHap06dwq5du7Bnzx4UFBRg6tSpCAoKAgCRKfGSk5NFyldHdHQ0li1bhnv37kFRURG9evXC8uXLoaenx+V5/fo15syZg/j4eLx79w4NGzZE+/btsXbtWhgbG4sMExceB3t7e5w6deqLYiOEEEIIIYQQQv5N9PT0YGpqCmVlZdSrVw+mpqYi24Uji4yNjaGoqMi9X716NRQUFETyqqiooF69/y3lvXfvXnTq1AmJiYkoKiqChYUFRowYAU9PTy7Pzz//jO7du6N79+5c2rc4+0h5oqKiYGVlBUdHRzg5OSEqKorrHCKEEPLvQ51DpFZcuXIFR48dx55xkNgxVJqROrB7dCE6Lb+LQ4cOoX///uXnNTLChQsXcOfOHTRr1qzcfJGRkRg9ejTat2+PsWPHAgDMzMy42M6fPw8vLy/o6+sjJSUFYWFhcHBwwL1798T+AJwwYQI0NTWxYMECZGVloXfv3nj48CF27NiB1atXQ0NDAwCgqakpzaEpl7Azql27dli6dCnevHmDtWvX4ty5c7hx4wb3B6S7uzvu3r2LyZMnw9jYGG/fvkV8fDyePXsGY2NjrFmzBpMnT4aSkhLmzp0LAFJN2UcIIYQQQgghhBBAVVW1wllDGGM4ffo0Ro4cCXV1dSQkJMDCwgLy8vKQl5fn8tWrVw/y8vJftUPo48ePEtdWKigokJi/oKBAYn5FRUUIBALufV5eHvbu3ctNQeft7Q0/Pz+8fv0aOjo6NRQ9IYSQ2kSdQ6RWhIWGwlBDFm6tCqXK39EU6GAqg9CN6yvsHJoxYwZ69+6NVq1aoX379rC1tUX37t3h6OiI+vXrc/mGDRsGf39/mJqaig1pdnFxgYeHh0hav3790KlTJ+zdu1dsmHnDhg1x4sQJyMjIcGlt2rTBjh074Obm9sWjhYCSP85++uknNGvWDGfOnOH+mOzatSv69u2L1atXIygoCBkZGTh//jyCg4MxY8YMrvycOXO4/3dzc8O8efOgoaFBw7kJIYQQQgghhJAadvfuXeTl5aFFixZo2LAhFixYgBEjRoh0DNUWJyencrdZW1uLpR07dkzij1uXLl2K2bNnc+8PHjyIjIwMeHl5ASh51jB27Fjs3LmTm0aPEELIvwt1DpFaEROzB1NtCyFTr/K8QiM7FWFc1AmRBQ7L6tGjBy5cuIClS5fi6NGjuHDhAlasWAFNTU38/vvvcHV1rbSd0r+EKSgowKdPn2Bubg41NTVcv35drHNozJgxIh1DX8PVq1fx9u1bBAYGivwx6eLiAktLSxw6dAhBQUEQCASQk5PDqVOnMGrUKDRo0OCrxkUIIYQQQgghhPwXXL9+HSNGjBBJKy4ulph3/PjxIu81NTUREhLCvT958iQ6d+6MevXqwdDQEFpaWrh48SIcHBxqPO7KbNy4EY0bNxZLnz59OoqKisTSO3TogEWLFomlW1hYiLyPiopC27ZtYW5uDgBQVlaGi4sLoqKiqHOIEEL+pahziHx1hYWF+PjpM4zUq1bOuGR2Nrx//77cziEAaNeuHWJiYpCfn49bt24hNjYWq1evhoeHB27evAkrK6sK28nJycHSpUsRHh6Oly9fgjHGbfv48aNY/tLrCn0twjWCmjRpIrbN0tISZ8+eBQDw+XwsX74c06dPh7a2Njp27Ii+fftixIgRNKybEEIIIYQQQggph7W1NUaPHi2S9ujRI2zYsEEsr/DHmUKlfzCalZWFy5cvY+HChVyara0tEhIS6qRzqH379mjbtq1YeoMGDSROH6ehoVHhaCMAyMjIQFxcHCZNmoSkpCQuvUuXLti7dy8ePnwosUOKEELIt406h8hXJyMjAxmZesgrlPwLnPLk/f90uHw+X6r8cnJyaNeuHdq1a4fGjRvDz88P0dHRCAgIqLDc5MmTER4ejmnTpqFTp05QVVUFj8eDl5eXxF8Nlf6D8Fswbdo09OvXD/v27cPRo0cxf/58LF26FCdPnkTr1q3rOjxCCCGEEEIIIeSbw+fzxX5UmZ6eLjGvlpZWuWsOnT17FgUFBdwav0DJGkSMMaSmpkJXV7fmgq4j0dHRyMvLQ0hIiMiIKaGoqCgEBQXVQWSEEEK+BHUOka+Ox+OhsbkZTj9Mwnh7VnmB/3fqIdCwgSrU1as45AjgfiXz6tUrkTgk2bNnD3x8fET+wMnNzUVGRobU7ZVXd3UZGRkBAB48eIBu3bqJbHvw4AG3XcjMzAzTp0/H9OnT8ejRI7Rq1QohISHYtm3bV4mPEEIIIYQQQgghQEJCAvr27Qt7e3uR9C1btuDUqVMYMmRIHUVWc6KiotCsWTOJP7799ddfsX37duocIoSQf6EqrABDSPWNGz8Re28Ar8VnaZMoOx8IvyiDUaPHon79+uXmS0hIEJkGTiguLg6A6LRsioqKEjt8ZGRkxOpYv369xLl4yyP8BVFVOpQq0rZtW2hpaWHTpk3Iy8vj0g8fPozExES4uLgAALKzs5GbmytS1szMDMrKyiLlytt3QgghhBBCCCGEVOzjx4/IyMgQeRUWFiIlJQXJycno1q0bDA0NRV5dunTB6dOnq/Rs4Vv0/PlznDlzBp6envDw8BB7+fn5ISkpCZcuXarrUAkhhFQRjRwitcLHxwc/z5mNn/flYssIoLKBLMuOABlZxRg3blyF+SZPnozs7GwMGDAAlpaWyM/Px/nz57Fr1y4YGxvDz8+Py2tjY4Pjx49j1apV0NXVhYmJCTp06IC+ffsiMjISqqqqsLKywoULF3D8+PEqjViysbEBAMydOxdeXl6oX78++vXrV+6wcwAoKCiQuOhjw4YNMWHCBCxfvhx+fn6wt7eHt7c33rx5g7Vr18LY2Bg//PADAODhw4fo3r07PD09YWVlBVlZWcTGxuLNmzfw8vISiS8sLAyLFi2Cubk5tLS0xEYkEUIIIYQQQgghRJzw3+Cl/fLLLzh79iz09fWhp6cntr19+/b4448/cOPGDYlrAH0rXr58yc06UpqSkhLc3Nywfft2MMbg6uoqsXyfPn0gKyuLqKgodOjQ4WuHSwghpAZR5xCpFWpqatgYGgY/Pz8oywOrBgEyEsatMQYsPwr8cghYvHgRzMzMKqx35cqViI6ORlxcHDZv3oz8/HwYGhpiwoQJmDdvHtTU1Li8q1atwtixYzFv3jzk5OTAx8cHHTp0wNq1ayEjI4OoqCjk5uaiS5cuOH78OJydnaXev3bt2uGXX37Bpk2bcOTIERQXFyM5ObnCzqH8/HzMnz9fLN3MzAwTJkyAr68vFBQUsGzZMvz0009QVFTEgAEDsHz5cm6/DAwM4O3tjRMnTiAyMhKysrKwtLTE7t274e7uztW5YMECPH36FCtWrEBmZibs7e2pc4gQQgghhBBCyHdpwoQJEtOtra2xa9euct+X1bhx43K3qampYefOnSJpGzZsqGKkX9/NmzcxfPhwsXQjIyO4ubkhKioKhoaGaNmypcTyampq6Nq1K3bt2oVVq1ZBVpYeNRJCyL8Fj0mak4uQarh+/TpsbGxw7do1tGnTRmKesLAwTJo0EYbqMvDvWojBbQF1JeBTDhB7Ewg9I4vE1ELMnz8fQUFBtFYOId8hab5LCCGEEEIIIaS0iv4dIdy2dOlSmJqa1lGE1ffkyRPMmTOH/o1ECKkUPVMhVUHd+aRWjR8/Hu3bt8f69esRsHM7ZscWcNtkZOphgFt/hE2eLLaQIyGEEEIIIYQQQsiXePnyZV2HUC3/1rgJIYR826hziNQ6GxsbREREICQkBBcvXsSnT5+gpKSENm3aSJynlxBCCCGEEEIIIaS6NDQ0IBAIvslp3aQlEAigoaFR12EQQgj5D6HOIVJn1NXV4eLiUtdhEEIIIYQQQggh5D/M0NAQ9+/fR1paWl2HUm0aGhowNDSs6zAIIYT8h1DnECGEEEIIIYQQQgj5TzM0NKTOFUIIIaSUenUdACGEEEIIIYQQQgghhBBCCKk91DlECCGEEEIIIYQQQgghhBDyHaHOIVJnGGNIT09HSkoK3r17h+Li4lptn8fjITAwsFbbJICDgwMcHBzqOgxCCCGEEEIIIYT8C0RERIDH4+Hq1at10v638BzD2NgYffv2rTTfqVOnwOPxcOrUqRprW1Kdvr6+MDY2rrE2CCF1g9YcIrUuMzMTUVFRCF23HrcT73HpZkbG8J80EX5+flBXV5eqroiICPj5+QEA/v77b3Tt2lVkO2MMhoaGePHiBVxcXHDw4MGa25H/gOvXr8PGxgZz587FokWLJOZ59OgRGjdujB9++AGrVq2q5QirJy4uDpcvX6bOP0IIIYQQQgghAIBnz54hLS2trsOoNg0NjQrXTCr9fAQA+Hw+DA0N0bNnT8yfPx/a2toASh70Ozo6cvlkZWVhYGCArl27IjAwEKampiL1JiYmwsrKCnw+H69fv4aamlrN7ti/UGpqKjZv3gw3Nze0atWq0vxlPxsA0NTUhLW1NWbNmoXevXt/pUgJIaRi1DlEatX58+fRv29fvM/IgCvU8DNMoA5ZfEIR9j39iLk/zUbgggXYsWsX+vXrJ3W98vLy2L59u1jn0OnTp/HixQvw+XyxMjk5OZCV/b4vgTZt2sDS0hI7duwot3No+/btAIBhw4bVSJvHjh2rkXoqEhcXh40bN1LnECGEEEIIIYQQPHv2DE2bNkV2dnZdh1JtCgoKSExMrLCDCAAWLlwIExMT5Obm4uzZswgLC0NcXBzu3LkDBQUFLt+UKVPQrl07FBQU4Pr169i8eTMOHTqE27dvQ1dXl8u3bds26Ojo4MOHD9izZw9Gjx791fbxW1X2OUZqaiqCgoJgbGwsVeeQkPCzYYzhzZs3iIiIQJ8+fXDgwAGpRgVJw87ODjk5OZCTk6uR+srz22+/1foMQISQmvd9PxknterixYtw6tYN7Qr4iGTNYAjRG5U7GiCkuAD+uc/h1t8N+/bvk7qDqE+fPoiOjsa6detEOny2b98OGxsbib8OkpeX/7Id+o8YOnQo5s+fj4sXL6Jjx45i23fs2AFLS0u0adPmi9rJzs6GgoLCV/8DhRBCCCGEEEIIKS0tLQ3Z2dmIiFiKpk1NsHBhKA4dOgMAkJWVgY6OBnr3toOvrxtkZWVw7dpdTJjwC1e+QQNVtGzZBFOmDIWenjaXfvToOQQGbsCAAT0wa9bIrxZ/YmIyfH3nIC0trdLOod69e6Nt27YAgNGjR0NdXR2rVq3C/v374e3tzeWztbWFh4cHAMDPzw+NGzfGlClTsHXrVsyZMwdAyWws27dvx5AhQ5CcnIyoqKjvsnOopp5jlP5sAGDUqFHQ1tbGjh07aqxzqF69erXyvKt+/fpfvQ1CyNdHaw6RWpGfnw/3/m5oU8jHkWIzsY4hIS3URzQzQX+ownvwYKSnp0tVv7e3N9LT0xEfHy/S5p49ezBkyBCJZcquOZSZmYlp06bB2NgYfD4fWlpa6NGjB65fv87lefToEdzd3aGjowN5eXno6+vDy8sLHz9+FKl727ZtsLGxgUAgQMOGDeHl5YXnz5+L5HFwcECzZs1w7949ODo6QkFBAXp6elixYoVYrOvXr4e1tTUUFBTQoEEDtG3blhvRI/Ty5UuMHDkS2tra4PP5sLa2xh9//FHpsRs6dCgAiNUHANeuXcODBw+4PPv374eLiwt0dXXB5/NhZmaGX375BUVFRRL37dq1a7Czs4OCggJ+/vlnblvpuXrz8/OxYMEC2NjYQFVVFYqKirC1tUVCQoJInSkpKeDxeFi5ciU2b94MMzMz8Pl8tGvXDleuXOHy+fr6YuPGjQBKPmPhixBCCCGEEELI961pUxO0bm2Fhg3V4OzcBc+enURi4iH89NNo/P77Hpw8eQmtW1vBwsIYAHDnzl94+vQE9uxZjdTUt5g7dx1atGiC1q2t0Lq1FU6duozp00fi5MmLaNrUjEuv6VfTpibV3udu3boBAJKTk6uc79y5c0hJSYGXlxe8vLxw5swZvHjxQqp2X79+DT8/P+jr64PP56NRo0bo378/UlJSuDzlrQVtbGwMX19fsfTs7GyMGzcO6urqUFFRwYgRI/DhwweRPNLWKVzH6Ny5c/jxxx+hqakJRUVFDBgwAO/evRMpW/o5xqlTp9CuXTsAJZ1qwmcOERER0hwWEWpqahAIBFWa1ebYsWNo1aoV5OXlYWVlhZiYGJHtktYHqsrzpxcvXsDNzQ2KiorQ0tLCDz/8gLy8PLF8ZdcckvaZjVB0dDSsrKwgLy+PZs2aITY2ltYxIqQOUOcQqRUxMTFIffsGvxbpQ1DJaScDHjYxAxTk5Ut9czU2NkanTp2wY8cOLu3w4cP4+PEjvLy8pKrD398fYWFhcHd3R2hoKGbMmAGBQIDExEQAJZ0Yzs7OuHjxIiZPnoyNGzdi7NixePLkCTIyMrh6Fi9ejBEjRsDCwgKrVq3CtGnTcOLECdjZ2YnkA4APHz6gV69eaNmyJUJCQmBpaYmffvoJhw8f5vL89ttvmDJlCqysrLBmzRoEBQWhVatWuHTpEpfnzZs36NixI44fP45JkyZh7dq1MDc3x6hRo7BmzZoK99vExASdO3fG7t27xTp5hB1Gwg62iIgIKCkp4ccff8TatWthY2ODBQsWYPbs2WL1pqeno3fv3mjVqhXWrFkjMqdxaZ8+fcLvv/8OBwcHLF++HIGBgXj37h2cnZ1x8+ZNsfzbt29HcHAwxo0bh0WLFiElJQUDBw5EQUEBAGDcuHHo0aMHACAyMpJ7EUIIIYQQQgghQnJyctDR0YCRkS7GjRuM7t074uDBUyJ5tLQaolEjTdjatsXcueOQmPgYSUnPAADJyS9w4cItzJo1EhYWRoiNPV5he4wxLFwYCjOznlBSsoGRUXf88MOyr7V7nMePHwNApWs7S8oXFRUFMzMztGvXDv369YOCgoLIc5eKuLu7IzY2Fn5+fggNDcWUKVOQmZmJZ8+eVXNPgEmTJiExMRGBgYEYMWIEoqKi4ObmBsZYteucPHkybt26hYCAAIwfPx4HDhzApEmTys3ftGlTLFy4EAAwduxY7pmDnZ1dpW19/PgRaWlpePfuHe7evYvx48fj8+fPUk/j/+jRIwwePBi9e/fG0qVLISsri0GDBon8ULo80jx/ysnJQffu3XH06FFMmjQJc+fOxd9//41Zs2ZJFR9Q+TMbADh06BAGDx6M+vXrY+nSpRg4cCBGjRqFa9euSd0OIaRm0LRypFaErl8PBxlVWBcJpMqvhfoYVKyKsPUb8MMPP6Bevcr7MYcMGYI5c+YgJycHAoEAUVFRsLe3F5krtyKHDh3CmDFjEBISwqWVvgHeu3cPycnJiI6O5oZeA8CCBQu4/3/69CkCAgKwaNEibqQMAAwcOBCtW7dGaGioSHpqair+/PNPDB8+HEDJkGIjIyNs2bKFW5Dw0KFDsLa2RnR0dLmxz507F0VFRbh9+zb3h5y/vz+8vb0RGBiIcePGQSAo/9gPHToUEydOxIkTJ9CzZ08AQHFxMXbt2oVOnTpxC1Ju375dpB5/f3/4+/sjNDQUixYtElnb6fXr19i0aRPGjRtXbrsA0KBBA6SkpIgM0x4zZgwsLS2xfv16bNmyRST/s2fP8OjRIzRo0AAA0KRJE/Tv3x9Hjx5F37590alTJzRu3Bjx8fE1tk4SIYQQQgghhJD/Nnl5PtLTM8rdLhCUTNWVn1/ykHvr1v3o3dsWqqrKGDKkLyIiYuHt7VJu+ZiYeKxbtw3bti2HlZU53rxJwz//PKjRfQD+1wGRm5uLc+fOYeHChRAIBGLTlmVmZiItLQ0FBQW4ceMGpk6dCh6PB3d3dwBAQUEBoqOj4e/v///7L4CrqyuioqIwc+bMCmPIyMjA+fPnERwcjBkzZnDpwunqqktOTg4nTpzgpjQzMjLCrFmzcODAAbi6ularTnV1dRw7doybcaS4uBjr1q3Dx48foaqqKpZfW1sbvXv3xoIFC9CpU6cqPXdwcnISec/n8/HHH39wP3CtzMOHD7F3714MHDgQQMkzJGEnT2V1SPP8afPmzXj48CF2796NQYMGASh5PtOyZUup97GyZzZAyXmgp6eHc+fOQUlJCQDQvXt3ODg4wMjISOq2CCFfjkYOka+OMYZLV66gf5Fylcq5QQ2Pn6bg/fv3UuX39PRETk4ODh48iMzMTBw8eLDcKeUkUVNTw6VLl5Camipxu/CPgqNHj5a7iGVMTAyKi4vh6emJtLQ07qWjowMLCwuxqdKUlJRE/pCQk5ND+/bt8eTJE5G4Xrx4IXEYLlByfPfu3Yt+/fqBMSbSrrOzMz5+/CgyNZ4kwl9slJ5a7vTp03j58iU3pRwAkY4h4R+Stra2yM7Oxv3790Xq5PP58PPzq7BdAJCRkeE6hoqLi/H+/XsUFhaibdu2EuMePHgw90cGUDJPMgCRY0YIIYQQQgghhEiDMYYTJy4iPv48HB3bS8zz6tU7rF69FXp6WmjSxATFxcWIjNyPIUNKHnZ7evbCuXM3kJxc/pRrz5+/hra2Orp37whDw0Zo1645Ro3yKDd/dTk5OUFTUxMGBgbw8vKCkpISYmNjoaenJ5Jv5MiR0NTUhK6uLlxcXJCVlYWtW7dya+IcPnwY6enpIusUeXt749atW7h7926FMQgEAsjJyeHUqVNi0759ibFjx4qsdTN+/HjIysoiLi7ui+osPRW9ra0tioqK8PTp0y+KVZKNGzciPj4e8fHx2LZtGxwdHTF69GixqeHKo6uriwEDBnDvhVPr3bhxA69fv66wrDTPn+Li4tCoUSORH0QrKChg7Nix0u5ipc9sUlNTcfv2bYwYMYLrGAIAe3t7NG/eXOp2CCE1gzqHyFdXVFSE/IICqECmSuVU/z//58+fpcqvqakJJycnbN++HTExMSgqKhK5oVVmxYoVuHPnDgwMDNC+fXsEBgaK3CRNTEzw448/4vfff4eGhgacnZ2xceNGkfWGHj16BMYYLCwsoKmpKfJKTEzE27dvRdrU19cXWw+nQYMGIn88/fTTT1BSUkL79u1hYWGBiRMn4ty5c9z2d+/eISMjA5s3bxZrU9g5U7bdstTV1eHs7IzY2Fjk5uYCKBklJCsrC09PTy7f3bt3MWDAAKiqqkJFRQWamprcHxdl113S09OTetHGrVu3okWLFpCXl4e6ujo0NTVx6NAhsToBiC2+Kfyjoyb/4CSEEEIIIYQQ8t8WF3cGDRp0gLJyW/TrNwGDBjlj/vzxInlMTHpATa09jIy6IysrB7t2rYKcXH0cP34BWVk56N27KwBAQ6MBnJw6ISJiX7ntubv3RG5uHpo06QN//0Ds23cChYWFNb5fwg6IhIQE3Lt3D0+ePIGzs7NYvgULFiA+Ph4nT57EP//8g9TUVG5UCVCylrKJiQn4fD6SkpKQlJQEMzMzKCgoICoqqsIY+Hw+li9fjsOHD0NbWxt2dnZYsWJFpR0YlbGwsBB5r6SkhEaNGomsY1RVtfmMoX379nBycoKTkxOGDh2KQ4cOwcrKCpMmTUJ+fn6l5c3NzcWeITVu3BgAKj0G0jx/evr0qcQ2mjRpUmlsQpUdT2Gnm7m5uVhZSWmEkK+LppUjX52srCzk5fhIz6/aHz1pKMmvoqIidZkhQ4ZgzJgxeP36NXr37g01NTWpy3p6esLW1haxsbE4duwYgoODsXz5csTExHBDbENCQuDr64v9+/fj2LFjmDJlCpYuXYqLFy9CX18fxcXF4PF4OHz4MGRkxDvDSv8qAoDEPABE5stt2rQpHjx4gIMHD+LIkSPYu3cvQkNDsWDBAgQFBaG4uBgAMGzYMPj4+Eisr0WLFpXu/7Bhw3Dw4EEcPHgQrq6u2Lt3L3r27AlNTU0AJcPC7e3toaKigoULF8LMzAzy8vK4fv06fvrpJy4OoYqmsStt27Zt8PX1hZubG2bOnAktLS3IyMhg6dKl3JzHpUlzzAghhBBCCCGEkIo4OLTD+vXzICdXH7q6mpCVFX9EdvJkBFRUlKCl1RDKyopcenh4LN6//wgVlf+NNCouLsbt2w8REDBB4tT4BgY6uHPnL5w4cREnTlzElCmLsWpVBE6c+ENkNMyXat++PTf6pyLNmzcXm+ZM6NOnTzhw4AByc3PFOmSAkh+TLl68WKwTobRp06ahX79+2LdvH44ePYr58+dj6dKlOHnyJFq3bl1hbGXXQ64J5dVZl88Y6tWrB0dHR6xduxaPHj2CtbX1V2urtvaTntkQ8u9CnUOkVjg6OmD38fOYWaQjdZldvAy0aGotMhy1MgMGDMC4ceNw8eJF7Nq1q8pxNmrUCBMmTMCECRPw9u1btGnTBosXL+Y6h4CSP6CaN2+OefPm4fz58+jSpQs2bdqERYsWwczMDIwxmJiYcL/eqAmKiooYPHgwBg8ejPz8fAwcOBCLFy/GnDlzoKmpCWVlZRQVFZX7h500XF1doaysjO3bt6N+/fr48OGDyJRyp06dQnp6OmJiYkQWWkxOTv6ifduzZw9MTU0RExMj8odlQEBAteus6A9UQgghhBBCCCFEQUEAc3PDCvOYmOhBTU30B6vp6Rk4cCAB27atgJWVGZdeVFQMR0cfxMefh7NzV4n1CQTy6NvXAX37OsDffzCaN++PO3ceoXVrqy/foRoUExOD3NxchIWFQUNDQ2TbgwcPMG/ePJw7dw5du0reTyEzMzNMnz4d06dPx6NHj9CqVSuEhIRg27ZtAEpGlWRkZIiUyc/Px6tXryTW9+jRIzg6OnLvP3/+jFevXqFPnz5cWlXrrI6afOYgHD0mzaw5SUlJYIyJtP/w4UMAgLGx8RfHYmRkhDt37oi18eBBza2NJVxTKCkpSWybpDRCyNdF08qRWjFh0iRcLcrEZWRJlf8Z8nEAGZgwZXKVbrpKSkoICwtDYGAg+vXrJ3W5oqIisSnMtLS0oKuri7y8PAAlv5wpO+S7efPmqFevHpdn4MCBkJGRQVBQkNivIhhjSE9PlzomobJl5OTkYGVlBcYYCgoKICMjA3d3d+zduxd37twRK//u3Tup2hEIBBgwYADi4uIQFhYGRUVF9O/fn9su/PVH6f3Kz89HaGholfepNEn1Xrp0CRcuXKh2nYqKJb/oKvsHISGEEEIIIYQQ8iWiog5AXV0NgwY5o1kzC+7VsmUT9Opli/DwWInl/vxzP8LDY3DnziM8efIC27cfgkAgD0ND3Vreg8pt27YNpqam8Pf3h4eHh8hrxowZUFJSqnBquezsbG7KeiEzMzMoKytzz0+EaWfOnBHJt3nz5nJH+WzevBkFBQXc+7CwMBQWFor8oLeqdVZHTT1zKCgowLFjxyAnJ4emTZtWmj81NRWxsf87vz59+oQ///wTrVq1go6O9D/GLk+fPn2QmpqKPXv2cGnZ2dnYvHnzF9ctpKuri2bNmuHPP/8U6RA7ffo0bt++XWPtEEKkQyOHSK3o3bs3mpiZw+/pc5wtNEeDCk69HBRjmMxTNFRRExm5Iq3yplarSGZmJvT19eHh4YGWLVtCSUkJx48fx5UrVxASEgIAOHnyJCZNmoRBgwahcePGKCwsRGRkJNc5A5T8EbJo0SLMmTMHKSkpcHNzg7KyMpKTkxEbG4uxY8dixowZVYqtZ8+e0NHRQZcuXaCtrY3ExERs2LABLi4uUFZWBgAsW7YMCQkJ6NChA8aMGQMrKyu8f/8e169fx/Hjx/H+/Xup2ho2bBj+/PNPHD16FEOHDuX+4AGAzp07o0GDBvDx8cGUKVPA4/EQGRn5xUOD+/bti5iYGAwYMAAuLi5ITk7Gpk2bYGVlJfV6U2XZ2NgAAKZMmQJnZ2fIyMjAy8vri+IkhBBCCCGEEEIiIvahf/9uEn/IOmCAE/z8fkZa2gdoaIjOgqKqqozg4C2YOXMlioqK0KyZBWJj10FdXa2WIpdOamoqEhISMGXKFInb+Xw+nJ2dER0djXXr1kmcEu/hw4fo3r07PD09YWVlBVlZWcTGxuLNmzci/zYfPXo0/P394e7ujh49euDWrVs4evSo2Gglofz8fK7eBw8eIDQ0FF27doWrq2u166wOMzMzqKmpYdOmTVBWVoaioiI6dOgAExOTCssdPnwY9+/fB1CyNvT27dvx6NEjzJ49W6olFRo3boxRo0bhypUr0NbWxh9//IE3b94gPDy8RvZrzJgx2LBhA0aMGIFr166hUaNGiIyMhIKCQo3UL7RkyRL0798fXbp0gZ+fHz58+IANGzagWbNm1X4ORAipHuocIrVCRkYG+w8dRJeOHdH1cxL+KDRAeyiAB9E/pu4gB+NkXuCGbB5OHEoQW6Pna1FQUMCECRNw7NgxxMTEoLi4GObm5ggNDcX48SULUrZs2RLOzs44cOAAXr58CQUFBbRs2RKHDx9Gx44dubpmz56Nxo0bY/Xq1QgKCgIAGBgYoGfPniJ/sEhr3LhxiIqKwqpVq/D582fo6+tjypQpmDdvHpdHW1sbly9fxsKFCxETE4PQ0FCoq6vD2toay5cvl7qtbt26oVGjRnj16pVYx5y6ujoOHjyI6dOnY968eWjQoAGGDRuG7t27S1zcUlq+vr54/fo1fv31Vxw9ehRWVlbYtm0boqOjcerUqWrVOXDgQEyePBk7d+7Etm3bwBijziFCCCGEEEIIIQCALVsWVbjd3r4d8vP/kbjt+vW95ZYbNMgZgwZJ/vdx//7d0L9/N+mDrCM7d+5EcXFxhbOx9OvXD3v37sXhw4clPucwMDCAt7c3Tpw4gcjISMjKysLS0hK7d+/mflwLlHRGJCcnY8uWLThy5AhsbW0RHx+P7t27S2x3w4YNiIqKwoIFC1BQUABvb2+sW7dOpKOuqnVWR/369bF161bMmTMH/v7+KCwsRHh4eKWdQwsWLOD+X15eHpaWlggLC8O4ceOkatfCwgLr16/HzJkz8eDBA5iYmGDXrl1f9EymNAUFBZw4cQKTJ0/G+vXroaCggKFDh6J3797o1atXjbQBlJw/O3bsQGBgIGbPng0LCwtERERg69atuHv3bo21QwipHI/RimCkhly/fh02Nja4du0a2rRpIzHPgwcP0N+lLx48ToKNjDIGF6lAHbL4hCLEymTiTNFH6GprI2b/fnTo0KGW94AQ8i2Q5ruEEEIIIYQQQkqr6N8Rwm0REUvRtGnFD/C/RYmJyfD1nUP/RiL/aa1atYKmpibi4+PrOpR/NXqmQqqCRg6RWtWkSRPcfXAfR44cQeiGDQhISEBOXh749eXQoX077Jw8GQMGDICcnFxdh0oIIYQQQgghhJD/AA0NDSgoKMDXd05dh1JtCgoKNTo1GiF1paCgADweD7Ky/3ssferUKdy6dQuLFlU8qo8QUrOoc4jUOhkZGbi4uMDFxQUAUFhYKHJDIIQQQgghhBBCCKkphoaGSExMRFpaWl2HUm0aGhowNDSs6zAI+WIvX76Ek5MThg0bBl1dXdy/fx+bNm2Cjo4O/P396zo8Qr4r9ESe1DnqGCKEEEIIIYQQQsjXZGhoSJ0rhHwDGjRoABsbG/z+++949+4dFBUV4eLigmXLlkFdXb2uwyPku0JP5QkhhBBCCCGEEEIIIYR8daqqqti1a1ddh0EIAVCvrgMghBBCCCGEEEIIIYQQQgghtYc6hwghhBBCCCGEEEIIId+kiIgI8Hg8XL16tU7ad3BwgIODQ520XTqGZs2aVZovJSUFPB4PERERNda2pDoDAwPB4/FqrA1CSN2gaeVIjUtMTKzrEAgh/2L0HUIIIYQQQgipac+ePUNaWlpdh1FtGhoaFa6ZFBERAT8/P+49n8+HoaEhevbsifnz50NbWxsAcOrUKTg6OnL5ZGVlYWBggK5duyIwMBCmpqYi9SYmJsLKygp8Ph+vX7+Gmppaze7Yv1Bqaio2b94MNzc3tGrVSupyjx8/xooVKxAfH4/U1FTIycmhefPm8PT0xNixYyEQCL5e0IQQIgF1DpEao6GhAQUFBQwbNqyuQyGE/MspKChAQ0OjrsMghBBCCCGE/Ac8e/YMTZs2QXZ2bl2HUm0KCvJITHxQYQcRACxcuBAmJibIzc3F2bNnERYWhri4ONy5cwcKCgpcvilTpqBdu3YoKCjA9evXsXnzZhw6dAi3b9+Grq4ul2/btm3Q0dHBhw8fsGfPHowePfqr7eO36tixYyLvU1NTERQUBGNjY6k7hw4dOoRBgwaBz+djxIgRaNasGfLz83H27FnMnDkTd+/exebNm784ViMjI+Tk5KB+/fpfXFdF5s2bh9mzZ3/VNgghXx91DpEaY2hoiMTExH/1L3EIId+Gyn4VRwghhBBCCCHSSktLQ3Z2LrZtA5o2BQICgIMHS7bJygI6OoCLCzByZMn7q1eBceP+V75hQ6BVK2DqVEBf/3/pR44A8+cD7u7A13xOnpgIDBuWi7S0tEr/ndS7d2+0bdsWADB69Gioq6tj1apV2L9/P7y9vbl8tra28PDwAAD4+fmhcePGmDJlCrZu3Yo5c+YAABhj2L59O4YMGYLk5GRERUV9l51DcnJyX1Q+OTkZXl5eMDIywsmTJ9GoUSNu28SJE5GUlIRDhw59aZgAAB6PB3l5+RqpqyKysrKQlaXHyoT86zFCCCGEEEIIIYQQQv7Frl27xgCwa9euVbANjDEwHx+wXr3AXr0CS0kBCw0F4/HAliwp2Z6QAAaAPXgAlpoKdvo0WJMmYFZWYIWFJXkYA+veHWz2bLAGDcBycv6XXtOva9dQ7r4JhYeHMwDsypUrIukHDx5kANjixYsZY4wlJCQwACw6Olok3507dxgANmbMGC7t77//ZgDY5cuX2a5du1i9evXY8+fPK/8wGGOvXr1ivr6+TE9Pj8nJyTEdHR3m6urKkpOTuTwAWEBAgFhZIyMj5uPjI7Zvp0+fZmPHjmUNGzZkysrKbPjw4ez9+/ciZata59mzZ9kPP/zANDQ0mIKCAnNzc2Nv374VKWtvb8/s7e0ZY/87fmVf4eHh5R4Lf39/BoCdO3eu3DyVsbe3Z9bW1uzq1ausU6dOTF5enhkbG7OwsDCRfMnJyWLx+Pj4MEVFRfbixQvWv39/pqioyDQ0NNj06dNZYWGhSPkPHz4wHx8fpqKiwlRVVdmIESPYjRs3xOoMCAhgZR8rA2ATJ05ksbGxzNramsnJyTErKyt2+PBhsf1JSEhgNjY2jM/nM1NTU7Zp0yaJdZKqq+i7kJCy6tVaLxQhhBBCCCGEEEIIId8APr9kxJCRETB+PODkBPz1l2geLS2gUSPAzg5YsAC4dw9ISirZlpwMnD9fMmKocWMgJqbi9hgDAgMBQ8OStnV1gSlTvsquiXj8+DEAQF1dvcr5oqKiYGZmhnbt2qFfv35QUFDAjh07pGrX3d0dsbGx8PPzQ2hoKKZMmYLMzEw8e/asmnsCTJo0CYmJiQgMDMSIESMQFRUFNzc3MMaqXefkyZNx69YtBAQEYPz48Thw4AAmTZpUbv6mTZti4cKFAICxY8ciMjISkZGRsLOzK7fMgQMHYGpqis6dO1c7TgD48OED+vTpAxsbG6xYsQL6+voYP348/vjjj0rLFhUVwdnZGerq6li5ciXs7e0REhIiMpUdYwz9+/dHZGQkhg0bhkWLFuHFixfw8fGROsazZ89iwoQJ8PLywooVK5Cbmwt3d3ekp6dzeW7cuIFevXohPT0dQUFBGDVqFBYuXIh9+/ZV6XgQQr4cjf8jhBBCCCGEEEIIId81gQAo9fxa4nYAyM8v+W94eMlUdKqqwLBhwJYtwJAh5ZffuxdYvRrYuROwtgZevwZu3aq5+IU+fvyItLQ05Obm4ty5c1i4cCEEAgH69u0rki8zMxNpaWkoKCjAjRs3MHXqVPB4PLi7uwMACgoKEB0dDX9/fwCAQCCAq6sroqKiMHPmzApjyMjIwPnz5xEcHIwZM2Zw6cLp6qpLTk4OJ06c4NbTMTIywqxZs3DgwAG4urpWq051dXUcO3YMPB4PAFBcXIx169bh48ePUFVVFcuvra2N3r17Y8GCBejUqVOl625/+vQJL1++RP/+/asVX2mpqakICQnBjz/+CAAYN24cOnTogDlz5mD48OEVrjOUm5uLwYMHY/78+QAAf39/tGnTBlu2bMH48eMBAH/99RfOnDmDFStWcJ/x+PHj4ejoKHWMiYmJuHfvHszMzAAAjo6OaNmyJXbs2MF1ugUEBEBGRgbnzp3j1rfy9PRE06ZNq3hECCFfikYOEUIIIYQQQgghhJDvEmPA8ePA0aNAt26S87x6BaxcCejpAU2aAMXFQERESacQAHh5AWfPlowmKs+zZyUjlZycSkYPtW8PjBlT47sDJycnaGpqwsDAAF5eXlBSUkJsbCz09PRE8o0cORKamprQ1dWFi4sLsrKysHXrVm69osOHDyM9PV1knSJvb2/cunULd+/erTAGgUAAOTk5nDp1Ch8+fKixfRs7dqxIB8j48eMhKyuLuLi4L6pT2DEElKzFVFRUhKdPn35RrEKfPn0CACgrK39xXbKyshhXajEsOTk5jBs3Dm/fvsW1a9cqLS/s6BOytbXFkydPuPdxcXGQlZXlOosAQEZGBpMnT5Y6RicnJ65jCABatGgBFRUVrp2ioiIcP34cbm5uXMcQAJibm6N3795St0MIqRnUOUQIIYQQQgghhBBCvisHDwJKSoC8PNC7NzB4cMm0b6Xp6wOKiiVTwGVllYz+kZMD4uNL3vfpU5JPQwPo0QOoaHavQYOAnBzA1LSkUyg2FigsrPn92rhxI+Lj45GQkIB79+7hyZMncHZ2Fsu3YMECxMfH4+TJk/jnn3+QmpqK4cOHc9u3bdsGExMT8Pl8JCUlISkpCWZmZlBQUEBUVFSFMfD5fCxfvhyHDx+GtrY27OzssGLFCrx+/fqL9s3CwkLkvZKSEho1aoSUlJRq12loaCjyvkGDBgBQY51aKioqAEpGan0pXV1dKCoqiqQ1btwYACo9BvLy8tDU1BRJa9Cggch+Pn36FI0aNYKSkpJIviZNmkgdY9njWbadt2/fIicnB+bm5mL5JKURQr4umlaOEEIIIYQQQgghhHxXHB2BsLCSzh5dXUBWwhOyv/8GVFRK1h4qPfBjyxbg/fv/TTUHlIwm+ucfICgIqCfhp9gGBsCDByWjlOLjgQkTgOBg4PRpoILZwKqsffv23OifijRv3hxOTk4St3369AkHDhxAbm6uWIcMAGzfvh2LFy8WGXFT1rRp09CvXz/s27cPR48exfz587F06VKcPHkSrVu3rjC2oqKiSuOvqvLqlJGRkZj+JesYlaaiogJdXV3cuXOnRuqrrvL2s7baqanjSQipWTRyiBBCCCGEEEIIIYR8VxQVAXPzkineJHUMAYCJCWBmJtoxlJ4O7N9fsnbQzZv/e924AXz4ABw7Vn6bAgHQrx+wbh1w6hRw4QJw+3bN7VNNiYmJQW5uLsLCwhAdHS3yWrRoEZ4+fYpz585VWo+ZmRmmT5+OY8eO4c6dO8jPz0dISAi3vUGDBsjIyBApk5+fj1evXkms79GjRyLvP3/+jFevXsHY2LjadVZHRZ1ikvTt2xePHz/GhQsXvqjd1NRUZGVliaQ9fPgQAESOQXUZGRnh1atX+Pz5s0j6gwcPvrhuIS0tLcjLyyMpKUlsm6Q0QsjXRZ1DhBBCCCGEEEIIIYRIITISUFcHPD2BZs3+92rZsmSauS1bJJeLiCjZducO8OQJsG1bSWeRkVGthi+Vbdu2wdTUFP7+/vDw8BB5zZgxA0pKShVOLZednY3c3FyRNDMzMygrKyMvL08k7cyZMyL5Nm/eXO4on82bN6OgoIB7HxYWhsLCQpG1aqpaZ3UIp3Yr2wlVnlmzZkFRURGjR4/GmzdvxLY/fvwYa9eurbSewsJC/Prrr9z7/Px8/Prrr9DU1ISNjY10wVegT58+KCwsRFhYGJdWVFSE9evXf3HdQjIyMnBycsK+ffuQmprKpSclJeHw4cM11g4hRDo0rRwhhBBCCCGEEEIIIVL44w9gwABA0uARd3dg+HAgLa1kHaLS1NSAZcuAH38EioqA5s2BAwdKOpq+JampqUhISMCUKVMkbufz+XB2dkZ0dDTWrVuH+hLmxHv48CG6d+8OT09PWFlZQVZWFrGxsXjz5g28vLy4fKNHj4a/vz/c3d3Ro0cP3Lp1C0ePHoVG2YP3//Lz87l6Hzx4gNDQUHTt2hWurq7VrrM6zMzMoKamhk2bNkFZWRmKioro0KEDTExMys2/fft2DB48GE2bNsWIESPQrFkz5Ofn4/z584iOjoavr2+l7erq6mL58uVISUlB48aNsWvXLty8eRObN2+W+DlUVb9+/dClSxfMnj0bKSkpsLKyQkxMDD5+/PjFdZcWGBiIY8eOoUuXLhg/fjyKioqwYcMGNGvWDDdv3qzRtgghFaPOIUIIIYQQQgghhBDy3YiIqHi7gwNQ3hIp//xTfjlPz5KXJG5uJa9v3c6dO1FcXIx+/fqVm6dfv37Yu3cvDh8+LNIxI2RgYABvb2+cOHECkZGRkJWVhaWlJXbv3g13d3cu35gxY5CcnIwtW7bgyJEjsLW1RXx8PLp37y6x3Q0bNiAqKgoLFixAQUEBvL29sW7dOpFp3qpaZ3XUr18fW7duxZw5c+Dv74/CwkKEh4eX2zkEAK6urvjnn38QHByM/fv3IywsDHw+Hy1atEBISAjGjBlTabsNGjTA1q1bMXnyZPz222/Q1tbGhg0bpCorjXr16uGvv/7CtGnTsG3bNvB4PLi6uiIkJKTSdaKqwsbGBocPH8aMGTMwf/58GBgYYOHChUhMTMT9+/drrB1CSOV4jFYEI4QQQgghhBBCCCH/YtevX4eNjQ2uXbuGNm3alLMNKLPpX+H6dcDGBhL3jZD/Cjc3N9y9e1dsbSlSNRV9FxJSFo0cIoQQQgghhBBCCCH/eYmJdR1B9fxb4yakPDk5ORAIBNz7R48eIS4uDj4+PnUYFSHfH+ocIoQQQgghhBBCCCH/WRoaGlBQkMewYbl1HUq1KSjI1+i6OYTUJVNTU/j6+sLU1BRPnz5FWFgY5OTkMGvWrLoOjZDvCnUOEUIIIYQQQgghhJD/LENDQyQmPkBaWlpdh1JtGhoaMDQ0rOswCKkRvXr1wo4dO/D69Wvw+Xx06tQJS5YsgYWFRV2HRsh3hTqHCCGEEEIIIYQQQsh/mqGhIXWuEPKNCA8Pr+sQCCEA6tV1AIQQQgghhBBCCCGEEEIIIaT2UOcQIYQQQgghhBBCCCGEEELId4Q6hwghhBBCCCGEEEIIIRwHBwc4ODjUdRjfhYiICPB4PKSkpHBp39rxlxTjtyglJQU8Hg8RERFcWmBgIHg8Xt0F9S926tQp8Hg8nDp1qq5DqVU8Hg+BgYFftc5v5ZqiNYcIIYQQQgghhBBCyH/as2fPkJaWVtdhVJuGhkaFayZFRETAz8+Pe8/n82FoaIiePXti/vz50NbWro0wCSEVCAwMRFBQUKX57O3ta7VDJjQ0FAoKCvD19a21NmtT2e9HANDU1IS1tTVmzZqF3r1711FkdY86hwghhBBCCCGEEELIf9azZ89g2aQpcnKz6zqUahPIK+D+g8QKO4gAYOHChTAxMUFubi7Onj2LsLAwxMXF4c6dO1BQUKilaAn5dsybNw+zZ8+u6zAAAAMHDoS5uTn3/vPnzxg/fjwGDBiAgQMHcum13ZkbGhoKDQ0Nsc4hOzs75OTkQE5Orlbj+VqE34+MMbx58wYRERHo06cPDhw4gL59+3L5cnJyICv7dbtNhg8fDi8vL/D5/K/aTmWoc4gQQgghhBBCCCGE/GelpaUhJzcbXnprocUveTAb/3YVEj+fAADUgyyUZTVhqdwN7dQGox5PpsL67mXG40z6b/A33v3VYweAt3lJ2PlyKtLS0irtHOrduzfatm0LABg9ejTU1dWxatUq7N+/H97e3rURLiHfFFlZ2a/+oF9aLVq0QIsWLbj3aWlpGD9+PFq0aIFhw4bVYWSS1atXD/Ly8nUdRo0p/f0IAKNGjYK2tjZ27Ngh0jlUG/ssIyMDGZmK7zW1gdYcIoQQQgghhBBCCCH/eVp8c+gLmkNf0BwKsg3QRMkB8xtfxU8WZ9BNcxIuf9iOpKyzXJ7yXg3rG6AeZCrNV1MvYYdWdXTr1g0AkJycDAAoLCzEL7/8AjMzM/D5fBgbG+Pnn39GXl5euXV8/vwZioqKmDp1qti2Fy9eQEZGBkuXLuXSnjx5gkGDBqFhw4ZQUFBAx44dcejQIZFywrVMdu/ejaCgIOjp6UFZWRkeHh74+PEj8vLyMG3aNGhpaUFJSQl+fn5iMYaHh6Nbt27Q0tICn8+HlZUVwsLCpDour1+/hp+fH/T19cHn89GoUSP0799fZP0PY2Nj9O3bF6dOnULbtm0hEAjQvHlzbrqvmJgYNG/eHPLy8rCxscGNGzdE2vjnn3/g6+sLU1NTyMvLQ0dHByNHjkR6erpUMUpD2mMg3JezZ8+iffv2kJeXh6mpKf7880+xvHfv3kW3bt0gEAigr6+PRYsWobi4WKp4fH19oaSkhGfPnqFv375QUlKCnp4eNm7cCAC4ffs2unXrBkVFRRgZGWH79u1idWRkZGDatGkwMDAAn8+Hubk5li9fLhZDRkYGfH19oaqqCjU1Nfj4+CAjI0OsvrJrDklal0io7LowwrIPHz7EsGHDoKqqCk1NTcyfPx+MMTx//hz9+/eHiooKdHR0EBISItVx+hJPnz7FhAkT0KRJEwgEAqirq2PQoEFia9f89ddf4PF4OHfuHH788UdoampCUVERAwYMwLt377h8xsbGuHv3Lk6fPg0ejwcej8etd1XemkMbN/4fe/cZHlW1/n38NzNpk15pgSQkQAg1ELp0pQoqSFMUG3ZAFPTo4SjqXxSxHhuKckQFUUFEFBRQUYTQpIgivUPopFASUmY/L/JkdEgCASaZJPP9XNdcZNZee+373pkJMHfWWm8rNjZWVqtVrVq10q+//lrkPlnnzp3T+PHjVadOHXl7e6tWrVp67LHHCr2XTSaTRowYoblz56pRo0by9vZWw4YN9f333zv0O3XqlEaPHq2YmBh5e3urSpUq6tatm9atW3dZ9zI4OFhWq7VQ8bC418GOHTt0++23Kzg4WEFBQbrjjjt09qzjjNRz587p4YcfVkREhAICAnTdddfpwIEDha5d1J5Dl/I+3bhxozp16uTwPv3www8veR+j8lE2BQAAAAAAAMqQh8lLAZ5VJEltQ2/Vnxnf669TP6hN6C2ad+hpbT71g3KNbMX6tdF11Z5RhHftQmOczN6vF7e314jYeaplbWpv//XEB/r1xFQ9Xne5zCazdp5ZqQVHJigla7N8LUFKCh6gHlUelcVUuh/N7dy5U5IUFhYmKX820UcffaQBAwZozJgxWrVqlV544QVt3rxZX331VZFj+Pv7q1+/fvr888/16quvOvy2+8yZM2UYhoYOHSpJOnLkiNq1a6ezZ89q1KhRCgsL00cffaTrrrtOs2fPVr9+/RzGfuGFF2S1WvX4449rx44devPNN+Xp6Smz2azU1FQ9/fTTWrlypaZNm6batWvrqaeesp87efJkNWzYUNddd508PDz0zTff6IEHHpDNZtODDz54wfty4403atOmTRo5cqRiYmJ09OhRLV68WPv27VNMTIy9344dO3TzzTfr3nvv1S233KKXX35Zffv21bvvvqt///vfeuCBB+x5DBo0SFu3bpXZnP+7+IsXL9auXbt0xx13qFq1atq0aZOmTJmiTZs2aeXKlQ4Fi8t1Kfdgx44dGjBggO666y7ddttt+t///qfbb79dSUlJatiwoaT8olmXLl2Um5urxx9/XH5+fpoyZYqsVmuJY8rLy1OvXr3UsWNHTZo0STNmzNCIESPk5+encePGaejQoerfv7/effddDRs2TG3btlXt2vnvrbNnz6pTp046ePCg7r33XkVFRSk5OVlPPPGEDh06pNdff12SZBiGrr/+ei1btkz33XefEhIS9NVXX+m222674ntalMGDByshIUETJ07U/Pnz9dxzzyk0NFTvvfeeunbtqhdffFEzZszQ2LFj1bJlS3Xs2LFU4pCkNWvWKDk5WUOGDFHNmjW1Z88eTZ48WZ07d9Zff/1VqP/IkSMVEhKi8ePHa8+ePXr99dc1YsQIff7555Kk119/XSNHjpS/v7/GjRsn6cLL2k2ePFkjRoxQhw4d9PDDD2vPnj264YYbFBISopo1a9r72Ww2XXfddVq2bJnuueceJSQk6I8//tBrr72mbdu2ae7cuQ7jLlu2THPmzNEDDzyggIAAvfHGG7rxxhu1b98++8+v++67T7Nnz9aIESPUoEEDnThxQsuWLdPmzZvVvHnzi9679PR0HT9+XIZh6OjRo3rzzTd1+vTpEs/aGjRokGrXrq0XXnhB69at0wcffKAqVaroxRdftPcZPny4pk+frptvvlnt2rXTTz/9pGuvvbZE40sle58ePHhQXbp0kclk0hNPPCE/Pz998MEHl7dEnQEAAAAAAABUYGvXrjUkGWvXri322KjY+cakhvuMSQ33GUnBA4yGAd3tzyc13Gc0COhmRPo0MhoEdDOqeNcx7o+ZZYyO+96o59/JCPOKMV5osNOY1HCfMajGK4aPOdB+Xl2/DkbbkGEOY1X3TjCuiXjImNRwnzGu3mrD02Q12oYMM8bW+ckYVut9w88SalwTMdrhnOIeo2LnF5tbgQ8//NCQZPzwww/GsWPHjP379xufffaZERYWZlitVuPAgQPGhg0bDEnG8OHDHc4dO3asIcn46aef7G2dOnUyOnXqZH++cOFCQ5Lx3XffOZzbpEkTh36jR482JBm//vqrve3UqVNG7dq1jZiYGCMvL88wDMNYsmSJIclo1KiRkZ2dbe970003GSaTyejVq5fDddq2bWtER0c7tJ09e7bQfejRo4cRGxtb7H0yDMNITU01JBkvvfTSBftFR0cbkozk5GR7W8F9sFqtxt69e+3t7733niHJWLJkyQXjmzlzpiHJWLp0qb2t4Hu3e/due9v59784Jb0HBbn887pHjx41vL29jTFjxtjbCr5/q1atcugXFBRUKMai3HbbbYYk4/nnn7e3paamGlar1TCZTMZnn31mb9+yZYshyRg/fry97f/+7/8MPz8/Y9u2bQ7jPv7444bFYjH27dtnGIZhzJ0715BkTJo0yd4nNzfX6NChgyHJ+PDDD+3t48ePN/75Efju3bsL9SlwfjwF595zzz0O16lZs6ZhMpmMiRMnFsrztttuu+A9+qdjx44VuubFFPU9X7FihSHJ+Pjjj+0/7wpiv+aaawybzWbv+/DDDxsWi8VIS0uztzVs2LDI11vB+7TgdX3u3DkjLCzMaNmypZGTk2PvN23aNEOSwxiffPKJYTabHX4WGIZhvPvuu4YkY/ny5fY2SYaXl5exY8cOe9vvv/9uSDLefPNNe1tQUJDx4IMPXvwmnafgPXb+w9vb25g2bVqh/sW9Du68806Hfv369TPCwsLszwt+xj7wwAMO/W6++eZCYxb1vi/p+3TkyJGGyWQy1q9fb287ceKEERoaWqL36T+xrBwAAAAAAADclmEY2n76V207vVTBnpH669RiDagxSbX9WquGTwPdFPmGMnIOa1PGwiLPbxUyRBsyvlauLX+ppAOZf+jwuS1qETxIkrTi5McK9qyhG6r/n6p411GjwB7qVuURLT3xvmxGyZbrKqlrrrlGERERqlWrloYMGSJ/f3999dVXioyM1IIFCyRJjzzyiMM5Y8aMkaRCS7+dP26NGjU0Y8YMe9uff/6pjRs3OvzW/YIFC9SqVSu1b9/e3ubv76977rlHe/bsKTSzYdiwYfL09LQ/b926tQzD0J133unQr3Xr1tq/f79yc3Ptbf+czVIwI6BTp07atWuX0tPTi83FarXKy8tLP//8s1JTU4vtJ0kNGjRQ27ZtHeKQ8pfr++f+TwXtu3btKjK+rKwsHT9+XG3atJGky14G63yXcg8aNGigDh062J9HREQoPj7eIeYFCxaoTZs2atWqlUO/gplhJTV8+HD718HBwYqPj5efn58GDRpkb4+Pj1dwcLDD9WfNmqUOHTooJCREx48ftz+uueYa5eXlaenSpfY4PTw8dP/999vPtVgsGjly5CXFeTn5WCwWtWjRQoZh6K677iqU5z/zKQ3//J7n5OToxIkTqlOnjoKDg4t8Xd1zzz0Os9Q6dOigvLw87d2795Kv/dtvv+nEiRO6++67HZZiGzp0qEJCQhz6zpo1SwkJCapfv77D97JgqcslS5Y49L/mmmsUFxdnf96kSRMFBgY63M/g4GCtWrVKKSkplxy7lL8c3uLFi7V48WJNnz5dXbp00fDhwzVnzpwSnX/fffc5PO/QoYNOnDihjIwMSbL/jB01apRDv9GjR5c4xpK8T7///nu1bdtWiYmJ9rbQ0NBLfp9KLCsHAAAAAAAAN7T51I/6z+b6yjNyZRg2NQu6Xo0Ce2nzqR8VZW1m7+fnEaII7zgdObejyHEaBvTQ3ENP6s9TC5UYdJ3Wps1SnF9bhXrVkiQdPbdD0b7NHT6gjfFtoWzbGaXnHFKIV6TTcnr77bdVr149eXh4qGrVqoqPj7cvc7Z3716ZzWbVqeO4h1G1atUUHBx8wQ+LzWazhg4dqsmTJ+vs2bPy9fXVjBkz5OPjo4EDB9r77d27114o+aeEhAT78UaNGtnb/1lgkaSgoCBJUq1atQq122w2paen25eYWr58ucaPH68VK1YU2vcjPT3dPtb5vL299eKLL2rMmDGqWrWq2rRpoz59+mjYsGGqVq2aQ99LiU+SQ7Hp5MmTeuaZZ/TZZ5/p6NGjheJzhku5B+fnIkkhISEOMRf3/YuPjy9xTD4+PoqIiHBoCwoKUs2aNQstpRcUFORw/e3bt2vjxo2Fzi9QcB/37t2r6tWry9/f/7LjvBRFvQ58fHwUHh5eqN2Ze0oVJTMzUy+88II+/PBDHTx4UPkTXfIV9bo6P/aCIs7FCqNFKfgZcf7PEA8PD4flGKX87+XmzZsv+r0sLs6CWP8Z56RJk3TbbbepVq1aSkpKUu/evTVs2DDFxsaWKP5WrVqpRYsW9uc33XSTmjVrphEjRqhPnz7y8vK64PkXupeBgYH2n7H/LHJJl/a6LOn79J9F6wLnf19KguIQAAAAAAAA3E6cX1v1q/68LCZPBXpWlcXkoU0Ziy55HA+zl5oH36jfUr9Qo4CeWp/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"text/plain": [ "
" ] @@ -431,10 +430,10 @@ } ], "source": [ - "from genophenocorr.model.genome import GRCh38\n", + "from gpsea.model.genome import GRCh38\n", "\n", - "from genophenocorr.preprocessing import configure_protein_metadata_service, VVMultiCoordinateService\n", - "from genophenocorr.view import ProteinVisualizable, ProteinVisualizer\n", + "from gpsea.preprocessing import configure_protein_metadata_service, VVMultiCoordinateService\n", + "from gpsea.view import ProteinVisualizable, ProteinVisualizer, ProteinViewable\n", "\n", "txc_service = VVMultiCoordinateService(genome_build=GRCh38)\n", "tx_coordinates = txc_service.fetch(PPP2R1A_transcript_id)\n", @@ -449,17 +448,253 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, + "id": "065ec9e0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " \n", + " Cohort\n", + " \n", + "\n", + "\n", + "\n", + "

GPSEA protein analysis

\n", + "

The UniProt API successfully returned protein information for ID: NP_055040.2

\n", + "

Protein Name: Serine/threonine-protein phosphatase 2A 65 kDa regulatory subunit A alpha isoform

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\n", + "

Protein Features

\n", + "
Feature NameFeature TypeFeature CoordinatesVariants in Feature
HEAT 1repeat8 - 461
PP2A subunit B bindingregion8 - 39958
HEAT 2repeat47 - 840
Polyoma small and medium T antigens Bindingregion47 - 32157
HEAT 3repeat85 - 1230
SV40 small T antigen bindingregion85 - 23948
HEAT 4repeat124 - 1612
HEAT 5repeat162 - 20035
HEAT 6repeat201 - 23911
HEAT 7repeat240 - 2789
HEAT 8repeat279 - 3210
HEAT 9repeat322 - 3600
HEAT 10repeat361 - 3990
HEAT 11repeat400 - 4380
PP2A subunit C bindingregion400 - 5890
HEAT 12repeat439 - 4770
HEAT 13repeat478 - 5160
HEAT 14repeat517 - 5550
HEAT 15repeat556 - 5890
\n", + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "viewer = ProteinViewable()\n", + "html_prot = viewer.process(cohort, pvis)\n", + "display(HTML(html_prot))" + ] + }, + { + "cell_type": "code", + "execution_count": 10, "id": "5821c60b-e9b3-42ad-a5b1-2a33c8037d6d", "metadata": {}, "outputs": [], "source": [ - "from genophenocorr.analysis import configure_cohort_analysis, CohortAnalysisConfiguration\n", + "from gpsea.analysis import configure_cohort_analysis, CohortAnalysisConfiguration\n", "\n", "analysis_config = CohortAnalysisConfiguration()\n", "analysis_config.missing_implies_excluded = True\n", "analysis_config.pval_correction = 'fdr_bh'\n", - "analysis_config.heuristic_strategy(threshold_HPO_observed_frequency=0.25)\n", + "#analysis_config.hpo_mtc_strategy(0.25)\n", "hpo_analysis = configure_cohort_analysis(cohort, hpo, config=analysis_config)" ] }, @@ -481,30 +716,204 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 11, "id": "fedd725c", "metadata": {}, "outputs": [ { - "ename": "ZeroDivisionError", - "evalue": "float division by zero", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mZeroDivisionError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[7], line 5\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mgenophenocorr\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01manalysis\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpredicate\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m PatientCategories\n\u001b[1;32m 4\u001b[0m is_variant \u001b[38;5;241m=\u001b[39m VariantPredicates\u001b[38;5;241m.\u001b[39mvariant_key(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m19_52212726_52212726_C_T\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m----> 5\u001b[0m by_var \u001b[38;5;241m=\u001b[39m \u001b[43mhpo_analysis\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcompare_hpo_vs_genotype\u001b[49m\u001b[43m(\u001b[49m\u001b[43mis_variant\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 6\u001b[0m by_var\u001b[38;5;241m.\u001b[39msummarize(hpo, PatientCategories\u001b[38;5;241m.\u001b[39mYES)\n", - "File \u001b[0;32m~/ielis/phenotypes/genophenocorr/src/genophenocorr/analysis/_gp_impl.py:66\u001b[0m, in \u001b[0;36mGpCohortAnalysis.compare_hpo_vs_genotype\u001b[0;34m(self, predicate)\u001b[0m\n\u001b[1;32m 63\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(predicate, VariantPredicate), \\\n\u001b[1;32m 64\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtype\u001b[39m(predicate)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m is not an instance of `VariantPredicate`\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 65\u001b[0m bool_predicate \u001b[38;5;241m=\u001b[39m wrap_as_boolean_predicate(predicate) \n\u001b[0;32m---> 66\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_apply_poly_predicate_on_hpo_terms\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbool_predicate\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/ielis/phenotypes/genophenocorr/src/genophenocorr/analysis/_gp_impl.py:174\u001b[0m, in \u001b[0;36mGpCohortAnalysis._apply_poly_predicate_on_hpo_terms\u001b[0;34m(self, gt_predicate)\u001b[0m\n\u001b[1;32m 170\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(gt_predicate, GenotypePolyPredicate), \\\n\u001b[1;32m 171\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtype\u001b[39m(gt_predicate)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m is not an instance of `GenotypePolyPredicate`\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 173\u001b[0m pheno_predicates \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_prepare_phenotype_predicates()\n\u001b[0;32m--> 174\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_apply_poly_predicate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpheno_predicates\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgt_predicate\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/ielis/phenotypes/genophenocorr/src/genophenocorr/analysis/_gp_impl.py:182\u001b[0m, in \u001b[0;36mGpCohortAnalysis._apply_poly_predicate\u001b[0;34m(self, pheno_predicates, gt_predicate)\u001b[0m\n\u001b[1;32m 177\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_apply_poly_predicate\u001b[39m(\n\u001b[1;32m 178\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 179\u001b[0m pheno_predicates: typing\u001b[38;5;241m.\u001b[39mIterable[PhenotypePolyPredicate[P]],\n\u001b[1;32m 180\u001b[0m gt_predicate: GenotypePolyPredicate,\n\u001b[1;32m 181\u001b[0m ):\n\u001b[0;32m--> 182\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_gp_analyzer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43manalyze\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 183\u001b[0m \u001b[43m \u001b[49m\u001b[43mpatients\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_patient_list\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 184\u001b[0m \u001b[43m \u001b[49m\u001b[43mpheno_predicates\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpheno_predicates\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 185\u001b[0m \u001b[43m \u001b[49m\u001b[43mgt_predicate\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgt_predicate\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 186\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/ielis/phenotypes/genophenocorr/src/genophenocorr/analysis/_gp_analysis.py:521\u001b[0m, in \u001b[0;36mFisherExactAnalyzer.analyze\u001b[0;34m(self, patients, pheno_predicates, gt_predicate)\u001b[0m\n\u001b[1;32m 519\u001b[0m \u001b[38;5;66;03m# 3) Multiple correction\u001b[39;00m\n\u001b[1;32m 520\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_correction \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 521\u001b[0m _, pvals_corrected, _, _ \u001b[38;5;241m=\u001b[39m \u001b[43mmultitest\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmultipletests\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpvals\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43malpha\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_mtc_alpha\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_correction\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 522\u001b[0m corrected_idx \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mIndex(n_usable\u001b[38;5;241m.\u001b[39mkeys(), name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mp_val_corrected\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 523\u001b[0m corrected_pvals_series \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mSeries(\n\u001b[1;32m 524\u001b[0m data\u001b[38;5;241m=\u001b[39mpvals_corrected, index\u001b[38;5;241m=\u001b[39mcorrected_idx, name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCorrected p value\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m 525\u001b[0m )\n", - "File \u001b[0;32m~/ielis/phenotypes/genophenocorr/venv/lib/python3.12/site-packages/statsmodels/stats/multitest.py:156\u001b[0m, in \u001b[0;36mmultipletests\u001b[0;34m(pvals, alpha, method, maxiter, is_sorted, returnsorted)\u001b[0m\n\u001b[1;32m 153\u001b[0m pvals \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mtake(pvals, sortind)\n\u001b[1;32m 155\u001b[0m ntests \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlen\u001b[39m(pvals)\n\u001b[0;32m--> 156\u001b[0m alphacSidak \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m \u001b[38;5;241m-\u001b[39m np\u001b[38;5;241m.\u001b[39mpower((\u001b[38;5;241m1.\u001b[39m \u001b[38;5;241m-\u001b[39m alphaf), \u001b[38;5;241;43m1.\u001b[39;49m\u001b[38;5;241;43m/\u001b[39;49m\u001b[43mntests\u001b[49m)\n\u001b[1;32m 157\u001b[0m alphacBonf \u001b[38;5;241m=\u001b[39m alphaf \u001b[38;5;241m/\u001b[39m \u001b[38;5;28mfloat\u001b[39m(ntests)\n\u001b[1;32m 158\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m method\u001b[38;5;241m.\u001b[39mlower() \u001b[38;5;129;01min\u001b[39;00m [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mb\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbonf\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbonferroni\u001b[39m\u001b[38;5;124m'\u001b[39m]:\n", - "\u001b[0;31mZeroDivisionError\u001b[0m: float division by zero" - ] + "data": { + "text/html": [ + "
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variant has ID of 19_52212726_52212726_C_TYesNo
CountPercentCountPercentp valueCorrected p value
Hypoplasia of the corpus callosum [HP:0002079]4/4100%11/11100%1.01.0
Kyphosis [HP:0002808]0/00%2/2100%1.01.0
Increased head circumference [HP:0040194]1/1100%12/12100%1.01.0
Abnormal cardiac ventricle morphology [HP:0001713]0/00%2/2100%1.01.0
Abnormality of limbs [HP:0040064]0/00%3/3100%1.01.0
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Short attention span [HP:0000736]0/00%6/6100%1.01.0
Aplasia/Hypoplasia of the corpus callosum [HP:0007370]11/11100%26/26100%1.01.0
Abnormal vascular morphology [HP:0025015]2/2100%4/4100%1.01.0
Abnormal volitional state [HP:0025780]0/00%7/7100%1.01.0
Abnormality of lower limb joint [HP:0100491]0/00%2/2100%1.01.0
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" + ], + "text/plain": [ + "variant has ID of 19_52212726_52212726_C_T Yes No \\\n", + " Count Percent Count \n", + "Hypoplasia of the corpus callosum [HP:0002079] 4/4 100% 11/11 \n", + "Kyphosis [HP:0002808] 0/0 0% 2/2 \n", + "Increased head circumference [HP:0040194] 1/1 100% 12/12 \n", + "Abnormal cardiac ventricle morphology [HP:0001713] 0/0 0% 2/2 \n", + "Abnormality of limbs [HP:0040064] 0/0 0% 3/3 \n", + "... ... ... ... \n", + "Short attention span [HP:0000736] 0/0 0% 6/6 \n", + "Aplasia/Hypoplasia of the corpus callosum [HP:0... 11/11 100% 26/26 \n", + "Abnormal vascular morphology [HP:0025015] 2/2 100% 4/4 \n", + "Abnormal volitional state [HP:0025780] 0/0 0% 7/7 \n", + "Abnormality of lower limb joint [HP:0100491] 0/0 0% 2/2 \n", + "\n", + "variant has ID of 19_52212726_52212726_C_T \\\n", + " Percent p value \n", + "Hypoplasia of the corpus callosum [HP:0002079] 100% 1.0 \n", + "Kyphosis [HP:0002808] 100% 1.0 \n", + "Increased head circumference [HP:0040194] 100% 1.0 \n", + "Abnormal cardiac ventricle morphology [HP:0001713] 100% 1.0 \n", + "Abnormality of limbs [HP:0040064] 100% 1.0 \n", + "... ... ... \n", + "Short attention span [HP:0000736] 100% 1.0 \n", + "Aplasia/Hypoplasia of the corpus callosum [HP:0... 100% 1.0 \n", + "Abnormal vascular morphology [HP:0025015] 100% 1.0 \n", + "Abnormal volitional state [HP:0025780] 100% 1.0 \n", + "Abnormality of lower limb joint [HP:0100491] 100% 1.0 \n", + "\n", + "variant has ID of 19_52212726_52212726_C_T \n", + " Corrected p value \n", + "Hypoplasia of the corpus callosum [HP:0002079] 1.0 \n", + "Kyphosis [HP:0002808] 1.0 \n", + "Increased head circumference [HP:0040194] 1.0 \n", + "Abnormal cardiac ventricle morphology [HP:0001713] 1.0 \n", + "Abnormality of limbs [HP:0040064] 1.0 \n", + "... ... \n", + "Short attention span [HP:0000736] 1.0 \n", + "Aplasia/Hypoplasia of the corpus callosum [HP:0... 1.0 \n", + "Abnormal vascular morphology [HP:0025015] 1.0 \n", + "Abnormal volitional state [HP:0025780] 1.0 \n", + "Abnormality of lower limb joint [HP:0100491] 1.0 \n", + "\n", + "[127 rows x 6 columns]" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "from genophenocorr.analysis.predicate.genotype import VariantPredicates\n", - "from genophenocorr.analysis.predicate import PatientCategories\n", + "from gpsea.analysis.predicate.genotype import VariantPredicates\n", + "from gpsea.analysis.predicate import PatientCategories\n", "\n", "is_variant = VariantPredicates.variant_key(\"19_52212726_52212726_C_T\")\n", "by_var = hpo_analysis.compare_hpo_vs_genotype(is_variant)\n", @@ -513,13 +922,246 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "id": "c056afec", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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variant affects REGION feature type on the protein encoded by transcript NM_014225.6YesNo
CountPercentCountPercentp valueCorrected p value
Hypoplasia of the corpus callosum [HP:0002079]15/15100%0/00%1.01.0
Kyphosis [HP:0002808]2/2100%0/00%1.01.0
Increased head circumference [HP:0040194]12/12100%1/1100%1.01.0
Abnormal cardiac ventricle morphology [HP:0001713]2/2100%0/00%1.01.0
Abnormality of limbs [HP:0040064]3/3100%0/00%1.01.0
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Short attention span [HP:0000736]6/6100%0/00%1.01.0
Aplasia/Hypoplasia of the corpus callosum [HP:0007370]37/37100%0/00%1.01.0
Abnormal vascular morphology [HP:0025015]6/6100%0/00%1.01.0
Abnormal volitional state [HP:0025780]7/7100%0/00%1.01.0
Abnormality of lower limb joint [HP:0100491]2/2100%0/00%1.01.0
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127 rows × 6 columns

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" + ], + "text/plain": [ + "variant affects REGION feature type on the protein encoded by transcript NM_014225.6 Yes \\\n", + " Count \n", + "Hypoplasia of the corpus callosum [HP:0002079] 15/15 \n", + "Kyphosis [HP:0002808] 2/2 \n", + "Increased head circumference [HP:0040194] 12/12 \n", + "Abnormal cardiac ventricle morphology [HP:0001713] 2/2 \n", + "Abnormality of limbs [HP:0040064] 3/3 \n", + "... ... \n", + "Short attention span [HP:0000736] 6/6 \n", + "Aplasia/Hypoplasia of the corpus callosum [HP:0... 37/37 \n", + "Abnormal vascular morphology [HP:0025015] 6/6 \n", + "Abnormal volitional state [HP:0025780] 7/7 \n", + "Abnormality of lower limb joint [HP:0100491] 2/2 \n", + "\n", + "variant affects REGION feature type on the protein encoded by transcript NM_014225.6 \\\n", + " Percent \n", + "Hypoplasia of the corpus callosum [HP:0002079] 100% \n", + "Kyphosis [HP:0002808] 100% \n", + "Increased head circumference [HP:0040194] 100% \n", + "Abnormal cardiac ventricle morphology [HP:0001713] 100% \n", + "Abnormality of limbs [HP:0040064] 100% \n", + "... ... \n", + "Short attention span [HP:0000736] 100% \n", + "Aplasia/Hypoplasia of the corpus callosum [HP:0... 100% \n", + "Abnormal vascular morphology [HP:0025015] 100% \n", + "Abnormal volitional state [HP:0025780] 100% \n", + "Abnormality of lower limb joint [HP:0100491] 100% \n", + "\n", + "variant affects REGION feature type on the protein encoded by transcript NM_014225.6 No \\\n", + " Count \n", + "Hypoplasia of the corpus callosum [HP:0002079] 0/0 \n", + "Kyphosis [HP:0002808] 0/0 \n", + "Increased head circumference [HP:0040194] 1/1 \n", + "Abnormal cardiac ventricle morphology [HP:0001713] 0/0 \n", + "Abnormality of limbs [HP:0040064] 0/0 \n", + "... ... \n", + "Short attention span [HP:0000736] 0/0 \n", + "Aplasia/Hypoplasia of the corpus callosum [HP:0... 0/0 \n", + "Abnormal vascular morphology [HP:0025015] 0/0 \n", + "Abnormal volitional state [HP:0025780] 0/0 \n", + "Abnormality of lower limb joint [HP:0100491] 0/0 \n", + "\n", + "variant affects REGION feature type on the protein encoded by transcript NM_014225.6 \\\n", + " Percent \n", + "Hypoplasia of the corpus callosum [HP:0002079] 0% \n", + "Kyphosis [HP:0002808] 0% \n", + "Increased head circumference [HP:0040194] 100% \n", + "Abnormal cardiac ventricle morphology [HP:0001713] 0% \n", + "Abnormality of limbs [HP:0040064] 0% \n", + "... ... \n", + "Short attention span [HP:0000736] 0% \n", + "Aplasia/Hypoplasia of the corpus callosum [HP:0... 0% \n", + "Abnormal vascular morphology [HP:0025015] 0% \n", + "Abnormal volitional state [HP:0025780] 0% \n", + "Abnormality of lower limb joint [HP:0100491] 0% \n", + "\n", + "variant affects REGION feature type on the protein encoded by transcript NM_014225.6 \\\n", + " p value \n", + "Hypoplasia of the corpus callosum [HP:0002079] 1.0 \n", + "Kyphosis [HP:0002808] 1.0 \n", + "Increased head circumference [HP:0040194] 1.0 \n", + "Abnormal cardiac ventricle morphology [HP:0001713] 1.0 \n", + "Abnormality of limbs [HP:0040064] 1.0 \n", + "... ... \n", + "Short attention span [HP:0000736] 1.0 \n", + "Aplasia/Hypoplasia of the corpus callosum [HP:0... 1.0 \n", + "Abnormal vascular morphology [HP:0025015] 1.0 \n", + "Abnormal volitional state [HP:0025780] 1.0 \n", + "Abnormality of lower limb joint [HP:0100491] 1.0 \n", + "\n", + "variant affects REGION feature type on the protein encoded by transcript NM_014225.6 \n", + " Corrected p value \n", + "Hypoplasia of the corpus callosum [HP:0002079] 1.0 \n", + "Kyphosis [HP:0002808] 1.0 \n", + "Increased head circumference [HP:0040194] 1.0 \n", + "Abnormal cardiac ventricle morphology [HP:0001713] 1.0 \n", + "Abnormality of limbs [HP:0040064] 1.0 \n", + "... ... \n", + "Short attention span [HP:0000736] 1.0 \n", + "Aplasia/Hypoplasia of the corpus callosum [HP:0... 1.0 \n", + "Abnormal vascular morphology [HP:0025015] 1.0 \n", + "Abnormal volitional state [HP:0025780] 1.0 \n", + "Abnormality of lower limb joint [HP:0100491] 1.0 \n", + "\n", + "[127 rows x 6 columns]" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "from genophenocorr.model import FeatureType\n", - "from genophenocorr.analysis.predicate.genotype import ProteinPredicates\n", + "from gpsea.model import FeatureType\n", + "from gpsea.analysis.predicate.genotype import ProteinPredicates\n", "\n", "pp = ProteinPredicates(pms)\n", "is_region = pp.protein_feature_type(feature_type=FeatureType.REGION, tx_id=PPP2R1A_transcript_id)\n", @@ -552,7 +1194,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.3" + "version": "3.10.10" } }, "nbformat": 4, diff --git a/notebooks/RPGRIP1/RPGRIP1.ipynb b/notebooks/RPGRIP1/RPGRIP1.ipynb index 12db217a..235650b5 100644 --- a/notebooks/RPGRIP1/RPGRIP1.ipynb +++ b/notebooks/RPGRIP1/RPGRIP1.ipynb @@ -21,55 +21,19 @@ "name": "stdout", "output_type": "stream", "text": [ - "Using genophenocorr version 0.1.1dev\n" - ] - } - ], - "source": [ - "import genophenocorr\n", - "\n", - "print(f\"Using genophenocorr version {genophenocorr.__version__}\")" - ] - }, - { - "cell_type": "markdown", - "id": "e19d81af", - "metadata": {}, - "source": [ - "## Setup" - ] - }, - { - "cell_type": "markdown", - "id": "06e46d4f", - "metadata": {}, - "source": [ - "### Load HPO\n", - "\n", - "We use HPO `v2023-10-09` release for this analysis." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "3a8fe0bf", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loaded HPO v2023-10-09\n" + "Loaded HPO v2023-10-09\n", + "Using gpsea version 0.2.1.dev0\n" ] } ], "source": [ + "import gpsea\n", "import hpotk\n", "\n", "store = hpotk.configure_ontology_store()\n", - "\n", "hpo = store.load_minimal_hpo(release='v2023-10-09')\n", - "print(f'Loaded HPO v{hpo.version}')" + "print(f'Loaded HPO v{hpo.version}')\n", + "print(f\"Using gpsea version {gpsea.__version__}\")" ] }, { @@ -79,22 +43,12 @@ "source": [ "### Load Phenopackets\n", "\n", - "We will load phenopackets from the *RPGRIP1* cohort of Phenopacket Store `0.1.18`." - ] - }, - { - "cell_type": "markdown", - "id": "fec13c15", - "metadata": {}, - "source": [ - "### Load Phenopackets\n", - "\n", - "We will load phenopacket JSON files located in `phenopackets` folder that is next to the notebook." + "We will load phenopackets from the *RPGRIP1* cohort of Phenopacket Store `0.1.19`." ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "id": "497568ec", "metadata": {}, "outputs": [ @@ -102,19 +56,18 @@ "name": "stdout", "output_type": "stream", "text": [ - "Patients Created: 100%|██████████| 229/229 [00:16<00:00, 14.26it/s]\n", + "Patients Created: 100%|██████████| 229/229 [04:42<00:00, 1.23s/it]\n", "Validated under none policy\n", - "No errors or warnings were found\n", - "\n" + "No errors or warnings were found\n" ] } ], "source": [ "from ppktstore.registry import configure_phenopacket_registry\n", - "from genophenocorr.preprocessing import configure_caching_cohort_creator, load_phenopackets\n", + "from gpsea.preprocessing import configure_caching_cohort_creator, load_phenopackets\n", "\n", "cohort_name = 'RPGRIP1'\n", - "phenopacket_store_release = '0.1.18'\n", + "phenopacket_store_release = '0.1.19'\n", "\n", "registry = configure_phenopacket_registry()\n", "with registry.open_phenopacket_store(phenopacket_store_release) as ps:\n", @@ -146,7 +99,8 @@ "metadata": {}, "outputs": [], "source": [ - "tx_id = 'NM_020366.4'" + "RPGRIP1_MANE_transcript = 'NM_020366.4'\n", + "RPGRIP1_protein_id = 'NP_065099.3'" ] }, { @@ -233,8 +187,8 @@ "\n", "\n", "\n", - "

genophenocorr cohort analysis

\n", - "

Successfully ingested 229 phenopackets.

\n", + "

GPSEA cohort analysis

\n", + "

Successfully loaded 229 individuals.

\n", " \n", "

No errors encountered.

\n", " \n", @@ -248,7 +202,7 @@ " \n", " HPO Term\n", " ID\n", - " Annotation Count\n", + " Seen in n individuals\n", " \n", " \n", " \n", @@ -258,14 +212,14 @@ " \n", " \n", " \n", - " Eye poking\n", - " HP:0001483\n", + " Abnormal electroretinogram\n", + " HP:0000512\n", " 28\n", " \n", " \n", " \n", - " Abnormal electroretinogram\n", - " HP:0000512\n", + " Eye poking\n", + " HP:0001483\n", " 28\n", " \n", " \n", @@ -321,92 +275,92 @@ " \n", " \n", " \n", - " Variant\n", + " Count\n", + " Variant key\n", " Variant Name\n", " Protein Variant\n", " Variant Class\n", - " Variant Count\n", " \n", " \n", " \n", + " 25\n", " 14_21312457_21312458_GA_G\n", " c.1107del\n", " p.Glu370AsnfsTer5\n", " FRAMESHIFT_VARIANT\n", - " 25\n", - " \n", + " \n", " \n", " \n", - " SO:1000029_HGNC:13436_RPGRIP1\n", - " 1000029_HGNC\n", - " None\n", - " TRANSCRIPT_ABLATION\n", " 12\n", - " \n", - " \n", - " \n", " 14_21325943_21325943_G_T\n", " c.2480G>T\n", " p.Arg827Leu\n", " MISSENSE_VARIANT\n", - " 12\n", - " \n", + " \n", " \n", " \n", - " 14_21302530_21302531_AG_A\n", - " c.535del\n", - " p.Glu179SerfsTer11\n", - " FRAMESHIFT_VARIANT\n", - " 8\n", - " \n", + " 12\n", + " SO:1000029_HGNC:13436_RPGRIP1\n", + " 1000029_HGNC\n", + " None\n", + " TRANSCRIPT_ABLATION\n", + " \n", " \n", " \n", + " 8\n", " 14_21345145_21345145_C_T\n", " c.3565C>T\n", " p.Arg1189Ter\n", " STOP_GAINED\n", + " \n", + " \n", + " \n", " 8\n", - " \n", + " 14_21302530_21302531_AG_A\n", + " c.535del\n", + " p.Glu179SerfsTer11\n", + " FRAMESHIFT_VARIANT\n", + " \n", " \n", " \n", + " 7\n", " 14_21345139_21345146_CAAGGCCG_C\n", " c.3565_3571del\n", " p.Arg1189GlyfsTer7\n", " FRAMESHIFT_VARIANT\n", - " 7\n", - " \n", + " \n", " \n", " \n", + " 7\n", " 14_21325252_21325252_G_A\n", " c.2236G>A\n", " p.Gly746Arg\n", " MISSENSE_VARIANT\n", - " 7\n", - " \n", + " \n", " \n", " \n", + " 6\n", " 14_21327671_21327671_A_AT\n", " c.2759_2760insT\n", " p.Gln920HisfsTer14\n", " FRAMESHIFT_VARIANT\n", - " 6\n", - " \n", + " \n", " \n", " \n", + " 5\n", " 14_21303542_21303542_C_T\n", " c.799C>T\n", " p.Arg267Ter\n", " STOP_GAINED, SPLICE_REGION_VARIANT\n", - " 5\n", - " \n", + " \n", " \n", " \n", + " 5\n", " 14_21325265_21325265_A_G\n", " c.2249A>G\n", " p.Tyr750Cys\n", " MISSENSE_VARIANT\n", - " 5\n", - " \n", + " \n", " \n", " \n", " \n", @@ -447,11 +401,6 @@ " \n", " \n", " \n", - " FRAMESHIFT_VARIANT\n", - " 95\n", - " \n", - " \n", - " \n", " MISSENSE_VARIANT\n", " 50\n", " \n", @@ -462,13 +411,13 @@ " \n", " \n", " \n", - " TRANSCRIPT_ABLATION\n", - " 12\n", + " FRAMESHIFT_VARIANT\n", + " 95\n", " \n", " \n", " \n", - " SPLICE_ACCEPTOR_VARIANT\n", - " 12\n", + " SPLICE_REGION_VARIANT\n", + " 20\n", " \n", " \n", " \n", @@ -477,6 +426,11 @@ " \n", " \n", " \n", + " INFRAME_DELETION\n", + " 2\n", + " \n", + " \n", + " \n", " SPLICE_DONOR_5TH_BASE_VARIANT\n", " 5\n", " \n", @@ -492,13 +446,13 @@ " \n", " \n", " \n", - " TRANSCRIPT_AMPLIFICATION\n", - " 2\n", + " TRANSCRIPT_ABLATION\n", + " 12\n", " \n", " \n", " \n", - " SPLICE_REGION_VARIANT\n", - " 20\n", + " SPLICE_ACCEPTOR_VARIANT\n", + " 12\n", " \n", " \n", " \n", @@ -507,7 +461,7 @@ " \n", " \n", " \n", - " INFRAME_DELETION\n", + " TRANSCRIPT_AMPLIFICATION\n", " 2\n", " \n", " \n", @@ -534,10 +488,10 @@ ], "source": [ "from IPython.display import display, HTML\n", - "from genophenocorr.view import CohortViewable\n", + "from gpsea.view import CohortViewable\n", "\n", "cv = CohortViewable(hpo=hpo)\n", - "html = cv.process(cohort=cohort, transcript_id=tx_id)\n", + "html = cv.process(cohort=cohort, transcript_id=RPGRIP1_MANE_transcript)\n", "\n", "display(HTML(html))" ] @@ -560,7 +514,7 @@ }, { "data": { - "image/png": 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OGZP+eCsrK4uc40Lu7u5MXl6ePXv2jEv7559/mKqqKuvYsaNYnB06dJDqui97/TL2+ddKVY6ro6MjA8DWrFkjFps051lhYSEzMjJihoaG7MOHDyJ5S99bKzrvy7svDR06VCRf7969mZaWFve9Kvc6SYTnjr6+PsvMzOTSd+3axQCwZcuWcWnl1VNYWBgDwLZs2cKlFRQUsHbt2jEVFRWu3JSUlHJj6ty5M7O2tubqlLGSurO3t2dmZmZi8ZY+14Vxbd68mUvLz89nenp6rG/fvhXuf1k9evQQu24IIYQQ8uOioZkIIYSQ/wjh06v169eHlZVVjZfftGlT7ml9oOQp2SZNmuD58+dieV+/fg1HR0d8+vQJ58+fh6GhIbds9+7dsLCwgLm5OVJTU7mPcKiYmJgYAMCxY8cAAOPGjRMpe8KECVWK29vbW+QJ1Nu3b+PJkycYOHAg0tLSuO1nZ2ejc+fOOH/+PIqLi6u0jdL8/PxEhghxcHBAUVERXrx4Ue0yS9PQ0EB2djaioqKqtb67u7vIkF1t2rSBnZ0dV9+SCIdv8ff3F0kfO3ZsuesMHz6c+38+n49WrVqBMYZhw4Zx6RoaGmLnkLTnh1CXLl24J8yBkieC1dTUJJ6XZQn3+ddffxVJ/+233wAAR48e5eIEgCNHjuDTp0/lllf6PPvw4QMyMjLg4OCAmzdvVhiHcLiwsm9DZGRkQEdHR+Rz+/btSvdLkqCgIOjo6EBPTw9OTk549uwZFixYwD1JXTr2rKwspKamwsHBATk5OYiPjwcA7o2HkydPig1nI7Rv3z4UFxfD09NT5Pjp6enBzMxM7PiVZ9SoUdUq9/r160hLS8OIESNE5kkYNGgQ6tSpI2VtSae6xxsAioqKcOrUKbi7u8PY2JhLr1evHgYOHIgLFy5w54XQiBEjPmv+ns+5Vqp6XBUUFODr6ytWjjTn2a1bt5CQkIAJEyaIvT1Tdsipqip7Xjk4OCAtLY2r6+rc6yQZMmQIVFVVue8eHh6oV6+e2H1WUj0dO3YMenp6GDBgAJcmJyeHcePG4ePHjzh37lyF237//j2io6Ph6enJ1XFqairS0tLg4uKCJ0+eICkpqcIyVFRUROaUkZeXR5s2baQ6VwghhBDy30VDMxFCCCH/Aa9evUJQUBCsrKxw//59LFy4ENOmTeOWv3//HgUFBdx3gUDANSpKy8DAQCytTp06+PDhg1j6L7/8AllZWcTFxUFPT09k2ZMnTxAXF1fuEEfv3r0DALx48QIyMjIiDWcA0KRJkyrFbWRkJLZ9ANzE3pJkZGRUu9GybD0Jy5FUT9Xh7++PXbt2wdXVFfr6+vjpp5/g6emJbt26SbW+mZmZWFrjxo2xa9euctcRHouydWlqalruOmXrQV1dHYqKilyHWen0tLQ07ru050d52wHKPy/LEu5X2f3Q09ODhoYG13nk6OiIvn37IiQkBKGhoXBycoK7uzsGDhwoMmn5kSNHMHv2bNy+fVtkjonKGk+FDZYfP34USVdRUeE6nE6dOiUydE1V+fn5oV+/fpCRkYGGhgYsLS1FYn/w4AGmTZuG6OhosQZw4fwPRkZG+PXXX7F06VJs3boVDg4OcHNz4+YCAUqOH2NM4nkGSD/5uqTrVppyhces7DGVlZUVm2vlc1X3eANASkoKcnJyJN7PLCwsUFxcjFevXokMe1e6TgoKCvD+/XuR9XR0dCrsqPica6Wqx1VfX1/iXCfSnGfCYcO+RId6RfdnNTW1at3rJClbTzweD6ampmLzkUiqpxcvXsDMzAwyMqLPFFpYWHDLK/L06VMwxjB9+nRMnz5dYp53795VOIdUgwYNxM7jOnXq4O7duxVumxBCCCH/bdQRQQghhPwHBAQEAACOHz+OX3/9FXPmzMHAgQO5J2379Okj8hSlt7c3NxmytMpr4GL/P357aX369MHmzZuxbNkyzJs3T2RZcXExrK2tsXTpUonlNWzYsEpxVab0E7jC7QPAokWL0Lx5c4nrfM44/VWpp+rQ1dXF7du3cfLkSRw/fhzHjx9HeHg4hgwZIjKZaW2TVA/S1E1Vz4+aqO/KGo55PB727NmDy5cv4/Dhwzh58iSGDh2KJUuW4PLly1BRUUFsbCzc3NzQsWNHrFq1CvXq1YOcnBzCw8Oxbdu2Css3NzcHALFJw2VlZdGlSxcAkHqS9vKYmZlxZZWVnp4OR0dHqKmpYebMmTAxMYGioiJu3ryJP/74Q+QNoSVLlsDHxwcHDx7EqVOnMG7cOMybNw+XL19GgwYNUFxcDB6Ph+PHj0s8NtJeW5Ku25oot7TyjrukiZPL+pzjXV2l6+TSpUtwdnYWWZ6QkFBhZ8vnXCtVrf+yxw+o2nn2pXzp+3NVSaqnzyWsx8DAQLi4uEjMU1nHyrdWT4QQQgj5PlBHBCGEEPKD279/Pw4dOoTQ0FA0aNAAYWFhOHnyJMaMGYPjx48DKGk8LP3Ua/369b9oTGPHjoWpqSlmzJgBdXV1TJ48mVtmYmKCO3fuoHPnzhU2ABsaGqK4uBjPnj0TeWr40aNHnxWb8A0LNTW1chtmhT53GBBpVXU78vLy6NWrF3r16oXi4mL4+/tj7dq1mD59eqUNTMI3Qkp7/PhxhQ2YwmORkJAg8qTv06dPqxS3NKQ9P6qivHKE+/XkyRPuaWMASE5ORnp6usiQYgDQtm1btG3bFnPmzMG2bdswaNAg7NixA8OHD8fevXuhqKiIkydPirxpEB4eXml8TZo0gZmZGQ4cOICwsDAoKytXc0+r5+zZs0hLS8O+ffu4ScOBkoZtSaytrWFtbY1p06bh0qVLaN++PdasWYPZs2fDxMQEjDEYGRmhcePGNRajtOUKj9nTp09FGuoLCwuRmJjITeYL/Ps0fHp6ukgZ0gyjVpXjLen809HRgZKSksT7WXx8PGRkZCrslLWxsREbnq3s22fVUd61UhPHVdrzTHiPvn//foX36C9xf66pe13Z+yxjDE+fPhU5/yqK4e7duyguLhZ5K0I4dJXwHC9v/4UPIMjJyVX6N44QQgghpCbRHBGEEELIDywrKwvjxo1DixYtuDGs69evj1mzZuHEiRPYvXs3AMDW1hZdunThPk2bNv3isU2fPh2BgYGYMmUKVq9ezaV7enoiKSkJ69evF1snNzcX2dnZAABXV1cAwPLly0XyhIWFfVZctra2MDExweLFi8WGwgFKhkwREjYIl22orGlV2U7pYYwAQEZGhmvcKj08THkOHDggMj741atXceXKFa6+JRE+Vbtq1SqR9BUrVlS6vaqS9vyoivLqt3v37gDEzynh2xg9evQAUDJsS9kngYVv0wjrnM/ng8fjiTxNn5iYiAMHDkgVY3BwMFJTUzFixAiJ81B8ySeRhU8/l95GQUGB2PHOzMxEYWGhSJq1tTVkZGS4eujTpw/4fD5CQkLEYmaMiZ2/0pK23FatWkFLSwvr168XiXXr1q1iQxAJG7zPnz/PpRUVFWHdunWVxlOV462srCx27vH5fPz00084ePCgyHA9ycnJ2LZtGzp06AA1NbVyt1+nTh2Re3qXLl2gqKhYadyVKe9aqYnjKu151rJlSxgZGSEsLEwsjtLrfon7c03d6zZv3oysrCzu+549e/DmzZsK77NC3bt3x9u3b7Fz504urbCwECtWrICKigocHR0BAEpKSgDE919XVxdOTk5Yu3Yt3rx5I1Z+6b9xhBBCCCE1id6IIIQQQn5g06ZNwz///IN9+/aJDKUwZswYbNq0CRMmTEC3bt1EJs38mhYtWoSMjAyMGTMGqqqqGDx4MH755Rfs2rULo0aNQkxMDNq3b4+ioiLEx8dj165dOHnyJFq1aoXmzZtjwIABWLVqFTIyMmBvb48zZ8589lP4MjIy2LBhA1xdXWFpaQlfX1/o6+sjKSkJMTExUFNTw+HDhwGUdFoAwJ9//gkvLy/IycmhV69eNf7EuomJCTQ0NLBmzRqoqqpCWVkZdnZ2YuOUAyWTQL9//x6dOnVCgwYN8OLFC6xYsQLNmzcXeaq/PKampujQoQNGjx6N/Px8hIWFQUtLC5MmTSp3HVtbW/Tt2xdhYWFIS0tD27Ztce7cOTx+/BhAzT6ZLO35URXlHUcbGxt4e3tj3bp13LAxV69exaZNm+Du7s49Ub9p0yasWrUKvXv3homJCbKysrB+/XqoqalxnRk9evTA0qVL0a1bNwwcOBDv3r3DypUrYWpqKtW46gMHDsT9+/cxb948XL16FV5eXjAyMkJ2djbu37+P7du3Q1VVtcYnXAYAe3t71KlTB97e3hg3bhx4PB4iIyPFGpyjo6MREBCAfv36oXHjxigsLERkZCT4fD769u0LoORcnj17NqZMmYLExES4u7tDVVUVCQkJ2L9/P/z8/BAYGFjlGKUtV15eHsHBwRg7diw6deoET09PJCYmIiIiAiYmJiLnqqWlJdq2bYspU6bg/fv30NTUxI4dO8Q6WySpyvG2tbXF6dOnsXTpUtSvXx9GRkaws7PD7NmzERUVhQ4dOsDf3x+ysrJYu3Yt8vPzsXDhwirXUU0o71qpieMq7XkmIyOD1atXo1evXmjevDl8fX1Rr149xMfH48GDBzh58qRIrOPGjYOLiwv4fD68vLw+e/9r4l6nqamJDh06wNfXF8nJyQgLC4OpqSlGjBhR6bp+fn5Yu3YtfHx8cOPGDTRq1Ah79uzBxYsXERYWxv09FwgEaNq0KXbu3InGjRtDU1MTVlZWsLKywsqVK9GhQwdYW1tjxIgRMDY2RnJyMv7++2+8fv0ad+7cqX4lVeLu3bs4dOgQgJI3STIyMjB79mwAJW/y9OrV64ttmxBCCCG1jBFCCCHkh3T9+nXG5/NZQECAxOVXr15lMjIybNy4cRWWA4AFBQVx38PDwxkAlpCQwKUZGhqyHj16iK3r6OjIHB0dxda9du0al1ZUVMQGDBjAZGVl2YEDBxhjjBUUFLAFCxYwS0tLpqCgwOrUqcNsbW1ZSEgIy8jI4NbNzc1l48aNY1paWkxZWZn16tWLvXr1SixmSWJiYhgAtnv3bonLb926xfr06cO0tLSYgoICMzQ0ZJ6enuzMmTMi+WbNmsX09fWZjIyMSL0YGhoyb2/vCve9dBwxMTHl1htjjB08eJA1bdqUycrKMgAsPDycMcaYt7c3MzQ05PLt2bOH/fTTT0xXV5fJy8szAwMDNnLkSPbmzZsK6yMhIYEBYIsWLWJLlixhDRs2ZAoKCszBwYHduXNHJG9QUBAr+8/I7OxsNmbMGKapqclUVFSYu7s7e/ToEQPA5s+fL7ZuSkqKyPre3t5MWVlZLC5HR0dmaWkpkibt+QGAjRkzRqzMsseGsfKP46dPn1hISAgzMjJicnJyrGHDhmzKlCksLy+PW/fmzZtswIABzMDAgCkoKDBdXV3Ws2dPdv36dZFtbNy4kZmZmTEFBQVmbm7OwsPDJdZlRc6ePcs8PDxYvXr1mJycHFNTU2OtWrViQUFB5R7jMWPGlLuN0se9IhcvXmRt27ZlAoGA1a9fn02aNImdPHlS5Nx9/vw5Gzp0KDMxMWGKiopMU1OTOTs7s9OnT4uVt3fvXtahQwemrKzMlJWVmbm5ORszZgx79OhRhXGUd/5Utdzly5czQ0NDpqCgwNq0acMuXrzIbG1tWbdu3UTyPXv2jHXp0oUpKCiwunXrsqlTp7KoqCixa7bsdciY9Mc7Pj6edezYkQkEAgZA5Ny8efMmc3FxYSoqKkxJSYk5OzuzS5cuiaxf3r2lsjosrSauFcakq39J17SQNOeZ0IULF1jXrl2ZqqoqU1ZWZs2aNWMrVqzglhcWFrKxY8cyHR0dxuPxRPa57N+I8s4rSX/vpL3XSSK832/fvp1NmTKF6erqMoFAwHr06MFevHghkreiekpOTma+vr5MW1ubycvLM2tra+5vQmmXLl1itra2TF5eXmyfnz17xoYMGcL09PSYnJwc09fXZz179mR79uwRi7fs3ydJcUm6BiQR1qmkT9lzjRBCCCE/Fh5jNKMUIYQQQsh/XWJiIoyMjLBo0aJqPZEuye3bt9GiRQts2bIFgwYNqpEyCfkSiouLoaOjgz59+kgc9ouQikh7rzt79iycnZ2xe/dueHh4fMUICSGEEEJqH80RQQghhBBCPltubq5YWlhYGGRkZEQmniWktuXl5YkN97N582a8f/8eTk5OtRMU+W7QvY4QQgghpHpojghCCCGEEPLZFi5ciBs3bsDZ2RmysrI4fvw4jh8/Dj8/PzRs2LC2wyOEc/nyZUycOBH9+vWDlpYWbt68iY0bN8LKygr9+vWr7fDIN47udYQQQggh1UMdEYQQQggh5LPZ29sjKioKs2bNwsePH2FgYIDg4GD8+eeftR0aISIaNWqEhg0bYvny5dwk1EOGDMH8+fMhLy9f2+GRbxzd6wghhBBCqofmiCCEEEIIIYQQQgghhBBCyBdDc0QQQgghhBBCCCGEEEIIIeSLoY4IQgghhBBCCCGEEEIIIYR8MdQRQQghhBBCOD4+PmjUqFFth0HIN+vs2bPg8Xg4e/ZsbYdCCCGEEELId4M6IgghhBBCKnHp0iUEBwcjPT29tkP5ph07dgzBwcGfXQ6PxwOPx8OSJUvElkVERIDH4+H69etcWnBwMHg8HmRkZPDq1SuxdTIzMyEQCMDj8RAQEFDleOLi4tCtWzeoqKhAU1MTv/zyC1JSUkTyxMfHY9KkSWjevDlUVVVRr1499OjRQyTO0pKSkuDp6QkNDQ2oqanh559/xvPnz0Xy5ObmYtiwYbCysoK6ujpUVFRgY2ODZcuW4dOnTxLLPX36NDp16gR1dXWoqqrC1tYWO3fuFMuXlZWFSZMmwcjICAoKCtDX14eHhwdycnKqVDdbt24Fj8eDiopKldarLTk5OQgODv4uOhESExO5a2Hv3r1iy4XnfWpqKpfm4+PDrcPj8aCmpgYbGxssWbIE+fn5VY5h48aNsLCwgKKiIszMzLBixYpy8+7cuRPt2rWDsrIyNDQ0YG9vj+joaJE8pWMr/Zk/f75YedJcIwCQnJwMX19f6OrqQiAQoGXLlti9e7fEGHfs2IGWLVtCUVEROjo6GDZsmEj9VWTu3Llo27YtdHR0uPqYMGGC2L2AEEIIIYRIJlvbARBCCCGEfOsuXbqEkJAQ+Pj4QENDo7bD+aLWr1+P4uLiaq177NgxrFy5skY6IwBg0aJFGD16NJSUlKTKr6CggO3bt2PSpEki6fv27at2DK9fv0bHjh2hrq6OuXPn4uPHj1i8eDHu3buHq1evQl5eHgCwYcMGbNy4EX379oW/vz8yMjKwdu1atG3bFidOnECXLl24Mj9+/AhnZ2dkZGRg6tSpkJOTQ2hoKBwdHXH79m1oaWkBKOmIePDgAbp3745GjRpBRkYGly5dwsSJE3HlyhVs27ZNJNbw8HAMGzYMXbt2xdy5c8Hn8/Ho0SOxzpmMjAw4Ojri9evX8PPzg6mpKVJSUhAbG4v8/Hyp6/vjx4+YNGkSlJWVq12/X1tOTg5CQkIAAE5OTtUqo2PHjsjNzeWO/dcwc+ZM9OnTBzwer9K8CgoK2LBhAwAgPT0de/fuRWBgIK5du4YdO3ZIvc21a9di1KhR6Nu3L3799VfExsZi3LhxyMnJwR9//CGSNzg4GDNnzoSHhwd8fHzw6dMn3L9/H0lJSWLldu3aFUOGDBFJa9Gihch3aa+RzMxMdOjQAcnJyRg/fjz09PSwa9cueHp6YuvWrRg4cCBX5urVq+Hv74/OnTtj6dKleP36NZYtW4br16/jypUrUFRUrLA+bty4gebNm8PLywuqqqqIi4vD+vXrcfToUdy+ffu7ug4IIYQQQmoFI4QQQgghFVq0aBEDwBISEirNW1RUxHJzc798UDXs48ePn13GmDFjWE388xIAa968OQPAlixZIrIsPDycAWDXrl3j0oKCghgA1qdPH9a8eXOx8rp27cr69u3LALAxY8ZUKZbRo0czgUDAXrx4waVFRUUxAGzt2rVc2vXr11lWVpbIuqmpqUxHR4e1b99eJH3BggUMALt69SqXFhcXx/h8PpsyZUqlMQUEBDAA7M2bN1xaQkICEwgEbNy4cVLtk4aGBnv+/HmleSvyxx9/sCZNmrBBgwYxZWXlzyrrc2RnZ0udNyUlhQFgQUFBXy6gGpKQkCByLezdu1dkufC8T0lJ4dK8vb3FjkVRURFr1aoVA8CSkpKk2nZOTg7T0tJiPXr0EEkXHuv3799zaX///Tfj8Xhs6dKllZYr7TUo7TWycOFCBoCdOXOGSysqKmKtW7dmenp6LD8/nzHGWH5+PtPQ0GAdO3ZkxcXFXN7Dhw8zAGz58uWVxiTJnj17GAC2ffv2aq1PCCGEEPJfQkMzEUIIIYRUIDg4GL///jsAwMjIiBtKJDExEQC44X62bt0KS0tLKCgo4MSJEwCAxYsXw97eHlpaWhAIBLC1tcWePXvEtiEs48CBA7CysoKCggIsLS25coSysrIwYcIENGrUCAoKCtDV1UXXrl1x8+ZNkXxXrlxB9+7dUadOHSgrK6NZs2ZYtmwZt9zHxwcqKip49uwZunfvDlVVVQwaNIhbVnqOCOHwMIsXL0ZoaCgMDQ0hEAjg6OiI+/fvi5S5cuVKbn+EH6E3b94gPj6+3CGFymrfvj06deqEhQsXIjc3V6p1Bg4ciNu3byM+Pp5Le/v2LaKjo0WejK6KvXv3omfPnjAwMODSunTpgsaNG2PXrl1cmq2trdjwRFpaWnBwcEBcXJxI+p49e9C6dWu0bt2aSzM3N0fnzp1FyiyP8PiUHipszZo1KCoqwsyZMwGUPFHOGBNbNz09HeHh4fDz84ORkREKCgqqNWTPkydPEBoaiqVLl0JW9vNeshbOubBz505MnToVenp6UFZWhpubm9jbHE5OTrCyssKNGzfQsWNHKCkpYerUqQCAd+/eYdiwYahbty4UFRVhY2ODTZs2cesmJiZCR0cHABASEsKdo6Xf4ImPj4eHhwc0NTWhqKiIVq1a4dChQxLjLT28kzCuhw8fwtnZGUpKStDX18fChQvF9vfly5ci52hlvLy80LhxY8ycOVPiMa2MjIwM9/aH8L5VmZiYGKSlpcHf318kfcyYMcjOzsbRo0e5tLCwMOjp6WH8+PFgjOHjx4+Vlp+bm4u8vLxyl0t7jcTGxkJHRwedOnXi0mRkZODp6Ym3b9/i3LlzAID79+8jPT0d/fv3F7kv9ezZEyoqKlV6U6Q0SdciIYQQQgiRjDoiCCGEEEIq0KdPHwwYMAAAEBoaisjISERGRnINmgAQHR2NiRMnon///li2bBnXOLVs2TK0aNECM2fOxNy5cyErK4t+/fqJNOIJXbhwAf7+/vDy8sLChQuRl5eHvn37Ii0tjcszatQorF69Gn379sWqVasQGBgIgUAg0tAdFRWFjh074uHDhxg/fjyWLFkCZ2dnHDlyRGR7hYWFcHFxga6uLhYvXoy+fftWWA+bN2/G8uXLMWbMGEyZMgX3799Hp06dkJycDAAYOXIkunbtCgBcHUVGRnLrT5kyBRYWFhKHailPcHAwkpOTsXr1aqnyd+zYEQ0aNBAZsmjnzp1QUVFBjx49pN6uUFJSEt69e4dWrVqJLWvTpg1u3bpVaRlv376FtrY29724uBh3794tt8xnz54hKytLJL2goACpqal49eoV9u/fj8WLF8PQ0BCmpqZcntOnT8Pc3BzHjh1DgwYNoKqqCi0tLUyfPl1kqK0LFy4gLy8Ppqam8PDwgJKSEgQCAdq3b4/bt29LUy0AgAkTJsDZ2Rndu3eXep3KzJkzB0ePHsUff/yBcePGISoqCl26dBHriEpLS4OrqyuaN2+OsLAwODs7Izc3F05OToiMjMSgQYOwaNEiqKurw8fHh+uE09HR4c6l3r17c+donz59AAAPHjxA27ZtERcXh8mTJ2PJkiVQVlaGu7s79u/fX2n8Hz58QLdu3bg5GczNzfHHH3/g+PHjIvmGDBkCCwsLqeuFz+dj2rRpuHPnjlRxSPLs2TMA4IY0qozw3C57ntra2kJGRkbk3D9z5gxat26N5cuXQ0dHh5sj5a+//pJYdkREBJSVlSEQCNC0aVOxIcaqco3k5+dDIBCI5RMOL3bjxg0uHwCJeQUCAW7duiXVkHSMMaSmpuLt27fcUFV8Pr/aw3wRQgghhPyn1O4LGYQQQggh376KhmYCwGRkZNiDBw/EluXk5Ih8LygoYFZWVqxTp05iZcjLy7OnT59yaXfu3GEA2IoVK7g0dXX1Coc1KSwsZEZGRszQ0JB9+PBBZFnp4Ui8vb0ZADZ58mSxMry9vZmhoSH3XTg8jEAgYK9fv+bSr1y5wgCwiRMncmkVDc0k3KY0w1uh1PAtzs7OTE9Pj6vLioZmSklJYYGBgczU1JRb1rp1a+br6ytWrjSuXbvGALDNmzeLLfv9998ZAJaXl1fu+ufPn2c8Ho9Nnz6dSxMODTRz5kyx/CtXrmQAWHx8vEj69u3bGQDu06pVK3b37l2RPGpqaqxOnTpMQUGBTZ8+ne3Zs4cNHDhQ7DgvXbqUAWBaWlqsTZs2bOvWrWzVqlWsbt26rE6dOuyff/6ptF6OHDnCZGVluXNe0nBAVRETE8MAMH19fZaZmcml79q1iwFgy5Yt49IcHR0ZALZmzRqRMsLCwhgAtmXLFi6toKCAtWvXjqmoqHDlVjQ0U+fOnZm1tbXIMS0uLmb29vbMzMxMLN6YmBixuEqfK/n5+UxPT4/17dtXZDvCvJURXnuLFi1ihYWFzMzMjNnY2HDXckVDM6WkpLCUlBT29OlTNnfuXMbj8VizZs0q3abQmDFjGJ/Pl7hMR0eHeXl5McYYe//+PXc+qaiosEWLFrGdO3eybt26STxO9vb2LCwsjB08eJCtXr2aWVlZMQBs1apVXJ6qXCNjx45lMjIyLDExUSSfl5cXA8ACAgK4Mnk8Hhs2bJhIvvj4eO66Sk1NrbRe3rx5I3ItNmjQgO3cubPS9QghhBBCCA3NRAghhBDy2RwdHdG0aVOx9NJP33748AEZGRlwcHAQG0oJKBnux8TEhPverFkzqKmp4fnz51yahoYGrly5gn/++UdiHLdu3UJCQgImTJggNqm2pEluR48eXem+Cbm7u0NfX5/73qZNG9jZ2eHYsWNSrR8REQHGmMiwT9IIDg7G27dvsWbNGqnyDxw4EE+fPsW1a9e4/1Z3WCbhk/gKCgpiy4QT25Y3bNS7d+8wcOBAGBkZiUyeXZ0ynZ2dERUVhd27d2PUqFGQk5NDdna2SJ6PHz/iw4cPCAkJwcyZM9G3b19s3boV3bp1w7Jly7gnyIXD5vB4PJw5cwYDBw7E6NGjceDAAXz48IEbXqs8BQUFmDhxIkaNGiXxnP8cQ4YMgaqqKvfdw8MD9erVEzvHFBQU4OvrK5J27Ngx6OnpcW8vAYCcnBzGjRuHjx8/ckP0lOf9+/eIjo6Gp6cnsrKykJqaitTUVKSlpcHFxQVPnjyp9G0eFRUVDB48mPsuLy+PNm3aiFzDQMnQTqyKQyyVfiviwIEDFebNzs6Gjo4OdHR0YGpqiqlTp6Jdu3ZVepuiosm4FRUVuXNUeD6lpaVhw4YNCAwMhKenJ44ePYqmTZti9uzZIutevHgR48ePh5ubG0aNGoUbN27AysoKU6dO5cqsyjUyfPhw8Pl8eHp64tKlS3j27BnmzZvH7aswn7a2Njw9PbFp0yYsWbIEz58/R2xsLPr37w85OTmRvBXR1NREVFQUDh8+jJkzZ0JbW1uqoagIIYQQQggNzUQIIYQQ8tmMjIwkph85cgRt27aFoqIiNDU1uaFhMjIyxPKWnoNAqE6dOvjw4QP3feHChbh//z4aNmyINm3aIDg4WKSRUzj8ipWVVaUxy8rKokGDBpXmEzIzMxNLa9y4sdRjzldXx44d4ezsLPVcES1atIC5uTm2bduGrVu3Qk9PT2T8+KoQdiRJmkNBOL69pKFesrOz0bNnT2RlZeHgwYMic0dUp8y6deuiS5cu8PDwwOrVq9GzZ0907doVb9++FSu3dEO88Htubi43lI4wX69evUTiatu2LYyMjHDp0iWJdSEUGhqK1NRUhISEVJivOsqeYzweD6ampmLnmL6+vlgj+YsXL2BmZgYZGdGfN8IhkF68eFHhtp8+fQrGGKZPn8414gs/QUFBAEo6lyrSoEEDsQ6/stfw5xg0aBBMTU0rnStCUVERUVFRiIqKwvnz5/Hq1StcvHgRxsbGUm9LIBCgoKBA4rK8vDzuPBL+V05ODh4eHlweGRkZ9O/fH69fv8bLly/L3Y68vDwCAgKQnp7ODaNUlWukWbNm2LZtG549e4b27dvD1NQUy5cvR1hYGACInONr165F9+7dERgYCBMTE3Ts2BHW1tbo1auXWN6K4u3SpQt69uyJ6dOnY+XKlRg2bJjY0HeEEEIIIUQcdUQQQgghhHwmSY3RsbGxcHNzg6KiIlatWoVjx44hKioKAwcOlNiIyOfzJZZdOq+npyeeP3+OFStWoH79+li0aBEsLS3FxqCXhoKCglij7bcqKCgIb9++xdq1a6XKP3DgQOzcuRPbtm1D//79q72f9erVA1Ay0XZZb968gaampthT2wUFBejTpw/u3r2LgwcPinUKCdcpr0wAqF+/foVxeXh44OPHjzh48CCXJlynbt26Inl1dXUBgGsMLy+fMG9FjeYZGRmYPXs2RowYgczMTCQmJiIxMZGbGDsxMbHSxvqaIOl6+1zC+QECAwO5Rvyyn9JzckgizTX8OYRvRdy+fVvk2EvK16VLF3Tp0gUODg5V6nAUqlevHoqKisSOZ0FBAdLS0rjzSDipt5aWltj+lz33ytOwYUMAJW+lCMusyjXi4eGBf/75B1evXsXff/+NFy9ecJ0ujRs35vKpq6vj4MGDePHiBc6dO4fExERERkbizZs30NHREXuLTBr29vaoV68etm7dWuV1CSGEEEL+a76PX5+EEEIIIbVI0rBGldm7dy8UFRVx8uRJDB06FK6urujSpctnx1KvXj34+/vjwIEDSEhIgJaWFubMmQMA3NBO9+/f/+ztlPXkyROxtMePH4sMtVSdepKGo6MjnJycsGDBAqneihg4cCDevHmDx48fV3tYJqDkyXsdHR1cv35dbNnVq1fRvHlzkbTi4mIMGTIEZ86cwbZt2+Do6Ci2noyMDKytrSWWeeXKFRgbG4sMTySJsA5Kv1lja2sLAGLDBwmH8RJOrl5ePmHe0pOwl/Xhwwd8/PgRCxcuhJGREffZu3cvcnJyYGRkBD8/vwpjr0jZc4wxhqdPn0o1nJehoSGePHkiNuFwfHw8txwo/xwVNlzLyclxjfhlP5Udl69h8ODBMDU1RUhISI11cEgiPLfLnqfXr19HcXExt1xGRgbNmzdHSkqK2BsUZc+98gjf6hLmq841Ii8vj9atW6Nt27aQl5fH6dOnAUDiPdfAwAAdO3aEoaEh9ybG59yb8/LyJL7lRgghhBBCRFFHBCGEEEJIJZSVlQEA6enpUq/D5/PB4/FQVFTEpSUmJlY6vnt5ioqKxBq7dHV1Ub9+fW4Ik5YtW8LIyAhhYWFisX5uo+WBAwdEGq+vXr2KK1euwNXVlUurqJ7evHmD+Ph4fPr0qVrbF84VsW7dukrzmpiYICwsDPPmzUObNm2qtT2hvn374siRI3j16hWXdubMGTx+/Bj9+vUTyTt27Fjs3LkTq1atQp8+fcot08PDA9euXRNpaH306BGio6NFykxNTZV43DZs2AAAaNWqFZfWv39/AMDGjRu5tOLiYoSHh0NTU5PrgGjSpAlsbGxw8OBBpKamcnlPnTqFV69eoWvXrlxaRkYG4uPjufNOV1cX+/fvF/s4OztDUVER+/fvx5QpU8rd78ps3ryZm8sCAPbs2YM3b96InGPl6d69O96+fYudO3dyaYWFhVixYgVUVFS4TiElJSUA4ueorq4unJycsHbtWolP4qekpFRnlyR6+fIl10FSVaXfijh06FCNxVRWp06doKmpidWrV4ukr169GkpKSujRoweX1r9/fxQVFWHTpk1cWl5eHrZu3YqmTZtyby9IqsOsrCyEhYVBW1ubO0cB6a8RSZ48eYI1a9agZ8+eIm9ESDJlyhQUFhZi4sSJIunx8fEiQ0plZ2cjJydHbP29e/fiw4cPItciIYQQQgiRTLa2AyCEEEII+dYJG8j+/PNPeHl5QU5ODr169eIa3iXp0aMHli5dim7dumHgwIF49+4dVq5cCVNTU9y9e7fKMWRlZaFBgwbw8PCAjY0NVFRUcPr0aVy7dg1LliwBUPIk8erVq9GrVy80b94cvr6+qFevHuLj4/HgwQOcPHmyehUAwNTUFB06dMDo0aORn5+PsLAwaGlpiUzELKyncePGwcXFBXw+H15eXgBKGvw2bdqEhISEKk9YDZS8FeHo6FjppMNC48ePr/I2JJk6dSp2794NZ2dnjB8/Hh8/fsSiRYtgbW0tMmFyWFgYVq1ahXbt2kFJSQlbtmwRKad3797c+eLv74/169ejR48eCAwMhJycHJYuXYq6devit99+49bZsmUL1qxZA3d3dxgbGyMrKwsnT55EVFQUevXqJTL3xc8//4zOnTtj3rx5SE1NhY2NDQ4cOIALFy5g7dq1IkNIhYaGomvXrujQoQNGjhyJjIwMLF26FI0bNxaZwHz//v3w9fVFeHg4fHx8oKSkBHd3d7E6OnDgAK5evSq2LCIiQmT9ymhqaqJDhw7w9fVFcnIywsLCYGpqihEjRlS6rp+fH9auXQsfHx/cuHEDjRo1wp49e3Dx4kWEhYVxT9ALBAI0bdoUO3fuROPGjaGpqQkrKytYWVlh5cqV6NChA6ytrTFixAgYGxsjOTkZf//9N16/fo07d+5UGoc0hgwZgnPnzlW7c3DQoEGYNWsWbt++XSPxSCIQCDBr1iyMGTMG/fr1g4uLC2JjY7FlyxbMmTMHmpqaXN6RI0diw4YNGDNmDB4/fgwDAwNERkbixYsXOHz4MJdv5cqVOHDgAHr16gUDAwO8efMG//vf//Dy5UtERkaKzPsh7TUCAE2bNkW/fv1gYGCAhIQErF69GpqammIT3M+fPx/379+HnZ0dZGVlceDAAZw6dQqzZ89G69atRfJaWFjA0dERZ8+eBVDSudGlSxf0798f5ubmkJGRwfXr17FlyxY0atSoxu43hBBCCCE/NEYIIYQQQio1a9Yspq+vz2RkZBgAlpCQwBhjDAAbM2aMxHU2btzIzMzMmIKCAjM3N2fh4eEsKCiIlf0nWHllGBoaMm9vb8YYY/n5+ez3339nNjY2TFVVlSkrKzMbGxu2atUqsfUuXLjAunbtyuVr1qwZW7FiBbfc29ubKSsrS4zZ29ubGRoact8TEhIYALZo0SK2ZMkS1rBhQ6agoMAcHBzYnTt3RNYtLCxkY8eOZTo6OozH44nsp7e3t0i9VaS8+oiJiWEAGAB27do1Ll1YpykpKdUqtzL3799nP/30E1NSUmIaGhps0KBB7O3btyJ5hPtX3qfsfr969Yp5eHgwNTU1pqKiwnr27MmePHkikufatWusX79+zMDAgCkoKDBlZWXWsmVLtnTpUvbp0yexOLOystj48eOZnp4ek5eXZ9bW1mzLli0S9ykqKoq1bduWKSoqMk1NTfbLL7+wN2/eiOQJDw9nAFh4eHiF9VPe+bRixQoGgJ04caLC9YXHdfv27WzKlClMV1eXCQQC1qNHD/bixQuRvI6OjszS0lJiOcnJyczX15dpa2tz+y8p9kuXLjFbW1smLy/PALCgoCBu2bNnz9iQIUOYnp4ek5OTY/r6+qxnz55sz549YvHGxMRUGlfZ60mYV5qfYaWvvbKEx6bseV/RtV0d69atY02aNGHy8vLMxMSEhYaGsuLiYrF8ycnJzNvbm2lqajIFBQVmZ2cndtxPnTrFunbtytWthoYG++mnn9iZM2ckbluaa4Qxxry8vFjDhg2ZvLw8q1+/Phs1ahRLTk4Wy3fkyBHWpk0bpqqqypSUlFjbtm3Zrl27JG4bAHN0dOS+p6SkMD8/P2Zubs6UlZWZvLw8MzMzYxMmTKj0vkMIIYQQQkrwGPuCg4sSQgghhJDvWmJiIoyMjLBo0SIEBgbWdjjkO+Lp6YnExERcvXq1wnxnz56Fs7Mzdu/eDQ8Pj68UHSGEEEIIIeRroqGZCCGEEEIIITWKMYazZ8+KDVFFCCGEEEII+W+ijghCCCGEEPKfk5KSIjKReFny8vIi4+CTquHxeHj37l1th0HKKCgowPv37yvMo66uDoFA8JUiIoQQQggh/xXUEUEIIYQQQv5zWrdujRcvXpS7vPREtYT8KC5dugRnZ+cK80g7uTghhBBCCCFVQXNEEEIIIYSQ/5yLFy8iNze33OV16tSBra3tV4yIkC/vw4cPuHHjRoV5LC0tUa9eva8UESGEEEII+a+gjghCCCGEEEIIIYQQQgghhHwxMrUdACGEEEIIIYQQQgghhBBCflzUEUEIIYQQQgghhBBCCCGEkC+GJqsmNebly5ewsLBATk5ObYdCCPnOKSkpIS4uDgYGBrUdCiGEEEIIIeQ7QG0ShBBS4lttU6GOCFJjUlNTkZOTgy1btsDCwqK2wyGEfKfi4uIwePBgpKamfnN/NAkhhBBCCCHfJmqTIISQb7tNhToiSI2zsLBAy5YtazsMQgghhBBCCCGE/MdQmwQhhHybaI4IQgghhBBCCCGEEEIIIYR8MdQRQQghhBBCCCGEEEIIIYSQL4Y6Igj5AQQHB4PH49XKts+ePQsej4ezZ8/WyvYJIYQQQgghhBBCCCHfNpojgtSKN2/eYMOGDYiJOYOszHQoK6vCrq09Ro0aBSMjoyqVde/ePYSEhODatWtITk6GlpYWmjZtCjc3N4wdO5bLN3fuXDRt2hTu7u41vDdVc/fuXYSGhuLs2bN48+YNZGVlYWpqip9++gmjRo2CsbFxrcZHCCGEEEIIIYT8aF6+fInU1NTaDqPatLW1pZ549tmzZ1i4cCGioqLwzz//QF5eHtbW1vD09ISfnx8EAsEXjpYQQsRRRwT5qrKysjBmjD+2b98OeTkGl7bFMGkIZGYD69b8jUWLFqFnj+5Yv2Ej6tatW2l5ly5dgrOzMwwMDDBixAjo6enh1atXuHz5MpYtWybWEeHh4VGrHRHr16/H6NGjoa2tjUGDBsHc3ByFhYW4f/8+Nm/ejLCwMOTm5oLP51ep3GnTpmHy5MlfKGpCCCGEEEIIIeT79fLlS5ibmyM3N7e2Q6k2gUCA+Pj4Sjsjjh49in79+kFBQQFDhgyBlZUVCgoKcOHCBfz+++948OAB1q1b95WiJoSQf1FHBPlq0tPT0cm5I549fYjF44vg3RPQUP13eU5eEbafAKatOYF2bVvjfOwlNGjQoMIy58yZA3V1dVy7dg0aGhoiy969e/cF9qL6Ll26hNGjR6N9+/Y4cuQIVFVVRZYvWbIEc+bMqVbZsrKykJWly5kQQgghhBBCCCkrNTUVubm5CAgIgL6+PpeelZWFmJgYxMfHIzMzEyoqKqhXrx7at28PU1PTWoxYVFJSEv766y+kpqZW2BGRkJAALy8vGBoaIjo6GvXq1eOWjRkzBk+fPsXRo0e/RsiEECKG5oggX41X/35IfP4QseuLMH6AaCcEACgpAsPcgcvhRSjMe4OePbqhsLCwwjKfPXsGS0tLsU4IANDV1eX+n8fjITs7G5s2bQKPxwOPx4OPjw+3/NatW3B1dYWamhpUVFTQuXNnXL58WaS8iIgI8Hg8nD9/HiNHjoSWlhbU1NQwZMgQfPjwodL9DwkJAY/Hw9atW8U6IQBAUVERs2bNEnkbIjY2Fv369YOBgQEUFBTQsGFDTJw4UewpDklzRPB4PAQEBODAgQOwsrKCgoICLC0tceLECbFtJyUlYejQoahbty6X73//+59YvtevX8Pd3R3KysrQ1dXFxIkTkZ+fX+m+E0IIIYQQQgghtU1fXx/GxsYwNjaGiooKVq9ejVevXsHX1xeLFy/G9OnT0bp1axw/fpzL9y18SneeVGThwoX4+PEjNm7cKNIJIWRqaorx48fXdLUSQohU6BFq8lVcu3YNJ0+dxp4FQDOzivMa1gN2zStEO98HOHr0KH7++efy8xoa4u+//8b9+/dhZWVVbr7IyEgMHz4cbdq0gZ+fHwDAxMQEAPDgwQM4ODhATU0NkyZNgpycHNauXQsnJyecO3cOdnZ2ImUFBARAQ0MDwcHBePToEVavXo0XL15wkzZLkpOTg+joaDg5OVX6lkdpu3fvRk5ODkaPHg0tLS1cvXoVK1aswOvXr7F79+5K179w4QL27dsHf39/qKqqYvny5ejbty9evnwJLS0tAEBycjLatm3LdVzo6Ojg+PHjGDZsGDIzMzFhwgQAQG5uLjp37oyXL19i3LhxqF+/PiIjIxEdHS31/hBCCCGEEEIIId+CjRs3gsfjYc6cOVBUVOTSGzZsCGdn51qMrPoOHz4MY2Nj2Nvb13YohBAihjoiyFexetUqGNSThbtTxW84CLW1Buys+Vi1ckWFHRGBgYFwdXVF8+bN0aZNGzg4OKBz585wdnaGnJwcl2/w4MHcRNCDBw8WKWPatGn49OkTLly4wE0UPWTIEDRp0gSTJk3CuXPnRPLLy8vjzJkzXPmGhoaYNGkSDh8+DDc3N4lxPn36FIWFhRI7S96/f4/i4mLuu5qaGuTl5QEACxYsEJlEys/PD6amppg6dSpevnxZ6diQcXFxePjwIdfp4uzsDBsbG2zfvh0BAQEAgD///BNFRUW4d+8e1zkxatQoDBgwAMHBwRg5ciQEAgHWrVuHx48fY9euXejXrx8AYMSIEbCxsakwBkIIIYQQQggh5Fvy8eNH3LlzB/379xfphBBSVlauhag+T2ZmJpKSkipsQyGEkNpEQzORr2Lf/j3w6VmIqszBPLRXEU5FnUFWVla5ebp27Yq///4bbm5uuHPnDhYuXAgXFxfo6+vj0KFDlW6jqKgIp06dgru7O9cJAQD16tXDwIEDceHCBWRmZoqs4+fnJ9LJMXr0aMjKyuLYsWPlbkdYhoqKitgyY2Nj6OjocJ/ScZfuhMjOzkZqairs7e3BGMOtW7cq3b8uXbpwnRAA0KxZM6ipqeH58+cAAMYY9u7di169eoExhtTUVO7j4uKCjIwM3Lx5EwBw7Ngx1KtXDx4eHlx5SkpK3BsmhBBCCCGEEELI9+Dt27dgjEk95NH3QNjuIGkoaEII+RZQRwT54goLC5GR8RGGelVbr1H9kv++f/++wnytW7fGvn378OHDB1y9ehVTpkxBVlYWPDw88PDhwwrXTUlJQU5ODpo0aSK2zMLCAsXFxXj16pVIupmZ6NhSwsmsEhMTy92O8B8CHz9+FFt28OBBREVFYfHixWLLXr58CR8fH2hqakJFRQU6OjpwdHQEAGRkZFS4bwAkvjFRp04dbk6LlJQUpKenY926dSKdITo6OvD19QXw76TfL168gKmpqdjwU5LqjhBCCCGEEEII+VYxxmo7hBqnpqYGABU+zEkIIbWJhmYiXxyfzwefL4P8T8WVZy4lv6DkvwoKClLll5eXR+vWrdG6dWs0btwYvr6+2L17N4KCgqoaco0zNTWFrKws7t+/L7ZM2LEgKyt6ORYVFaFr1654//49/vjjD5ibm0NZWRlJSUnw8fERGc6pPPxyXkER/qNLWMbgwYPh7e0tMW+zZs0q3Q4hhBBCCCGEEPK9qFevHng8HpKSkmo7lBqjpqaG+vXrS2x3IISQbwF1RJAvjsfjobGZCc7deIrRHtI/dXD2BqCpqc7NW1AVrVq1AgC8efNGJI6ydHR0oKSkhEePHokti4+Ph4yMDBo2bCiS/uTJE5GJqz5+/Ig3b96ge/fu5cajrKzMTX6dlJQk1euf9+7dw+PHj7Fp0yYMGTKES4+Kiqp0XWnp6OhAVVUVRUVF6NKlS4V5DQ0Ncf/+fTDGROpSUt0RQgghhBBCCCHfKhUVFdjY2ODUqVNwdXUVmyciOzv7u5wnomfPnli3bh3+/vtvtGvXrrbDIYQQETQ0E/kqRo4ag70xwNtU6fLn5AHhh/kYNkx0PoayYmJiJL5SKZyvofSwQcrKykhPTxfJx+fz8dNPP+HgwYMiQyslJydj27Zt6NChA/d6o9C6devw6dMn7vvq1atRWFgIV1fXCvdpxowZKCoqwuDBgyUO0VR2P4RvM5ROZ4xh2bJlFW6nKvh8Pvr27Yu9e/dKfGoiJSWF+//u3bvjn3/+wZ49e7i0nJwcrFu3rsbiIYQQQgghhBBCvoahQ4eiuLgYf/75J65cuYI3b97g9evXOH78OKZNm1bb4VXLpEmToKysjOHDhyM5OVls+bNnz2q0TYEQQqqC3oggX4W3tzemTp2MqSvzsHEGIOHlBBHzI4D0rGKMHDmywnxjx45FTk4OevfuDXNzcxQUFODSpUvYuXMnGjVqxM1zAAC2trY4ffo0li5divr168PIyAh2dnaYPXs2oqKi0KFDB/j7+0NWVhZr165Ffn4+Fi5cKLbNgoICdO7cGZ6ennj06BFWrVqFDh06wM3NrcJYHRwc8Ndff2Hs2LEwMzPDoEGDuJgfP36MrVu3Ql5eHnp6JZNpmJubw8TEBIGBgUhKSoKamhr27t3Lze9QU+bPn4+YmBjY2dlhxIgRaNq0Kd6/f4+bN2/i9OnT3BwdI0aMwF9//YUhQ4bgxo0bqFevHiIjI6GkpFSj8RBCCCGEEEIIIV9a3bp1MX/+fOzfvx+RkZH48OED1NTUYGxsjOHDh9d2eNViYmKCbdu2oX///rCwsMCQIUNgZWXFtZXs3r0bPj4+tR0mIeQ/ijoiyFehoaGBlStXw9fXF6rKwNKJgKTpCxgDFmwCZm0A5syZDRMTkwrLXbx4MXbv3o1jx45h3bp1KCgogIGBAfz9/TFt2jRoaGhweZcuXQo/Pz9MmzYNubm58Pb2hp2dHSwtLREbG4spU6Zg3rx5KC4uhp2dHbZs2QI7Ozuxbf7111/YunUrZsyYgU+fPmHAgAFYvny5xKGfyho9ejTatWuH0NBQ7N69G2/fvoWcnBxMTEzg7e2N0aNHc/ssJyeHw4cPY9y4cZg3bx4UFRXRu3dvBAQEwMbGptJtSatu3bq4evUqZs6ciX379mHVqlXQ0tKCpaUlFixYwOVTUlLCmTNnMHbsWKxYsQJKSkoYNGgQXF1d0a1btxqLhxBCCCGEEEII+Rrq1KmDoUOHYujQobUdSo1xc3PD3bt3sWjRIhw8eBCrV6+GgoICmjVrhiVLlmDEiBG1HSIh5D+KxySNa0NINdy8eRO2tra4ceMGWrZsKTHP6tWrERAwBgZ6fIzqU4j+PwFa6kBmNrA/Bli1VxZxzwsxffp0hISESNW4/7VERETA19cX165d4+agIITUPGnuJYQQQgghhBBSWkW/I4TL5s2bB2Nj41qKsPqeP3+OKVOm0G8kQkilvuU2FXojgnxVo0ePRps2bbBixQoErd+GyX/9O9cCny+D3r1/xur/jYWjo2MtRkkIIYQQQgghhJAfTVJSUm2HUC3fa9yEEFIadUSQr87W1hYRERFYsmQJLl++jMzMTKioqKBly5bQ19ev7fAIIYQQQgghhBDyA9HW1oZAIMBff/1V26FUm0AggLa2dm2HQQgh1UYdEaTWaGlpoUePHrUdBiGEEEIIIYQQQn5gBgYGiI+PR2pqam2HUm3a2towMDCo7TAIIaTaqCOCECn5+PjAx8entsMghBBCCCGEEEJIFRkYGFBDPiGE1CKZ2g6AEEIIIYQQQgghhBBCCCE/LuqIIIQQQgghhBBCCCGEEELIF0MdEaTWMMaQlpaGxMREpKSkoLi4uLZD+i7weDwEBARUmi8iIgI8Hg+JiYki6YsWLYKxsTH4fD6aN2/+ZYKsIWfPngWPx8PZs2drOxRCCCGEEEIIIeSHERwcDB6PV9thcL61eIR4PB6Cg4NrOwxCfgg0RwT56rKysrB161b8tfwvPIh7wKUbGRrBP8Afvr6+0NLSkqqsiIgI+Pr6Slz2xx9/YP78+TUS84/i1KlTmDRpEgYPHozg4GBoa2vjn3/+wbp16+Du7l5px4SbmxtOnz6N5ORkqKqqSswzaNAg7N69G2/evJH6ONamnJwcLFy4EE5OTnBycqrtcAghhBBCCCGEfAEvX778oSerLts+oqCgAE1NTVhbW6NHjx7w9fUt93c8IYR8DdQRQb6qS5cuwa2nGz6kf0AzNMNwDIcylJGHPNx+cRtT/piCoBlB2LFzB3r16iV1uTNnzoSRkZFImpWVVU2H/1355Zdf4OXlBQUFBS4tOjoaMjIy2LhxI+Tl5QEA169fR0hICBo1alRpR8SgQYNw+PBh7N+/H0OGDBFbnpOTg4MHD6Jbt2410gnRsWNH5ObmcrF+CTk5OQgJCQEA6ogghBBCCCGEkB/Qy5cvYWFhgZycnNoOpdqUlJQQFxdX6YTbwvaRT58+4e3btzh79iwmTJiApUuX4tChQ2jWrBkAYNq0aZg8efLXCJ0QQgBQRwT5ii5fvozOnTrD4JMBAlkgNKEpsrwlWsKj2APb8rbB/Wd3HDh4QOrOCFdXV7Rq1UqqvHl5eZCXl4eMzI89MhmfzwefzxdJe/fuHQQCQbUb9t3c3KCqqopt27ZJ7Ig4ePAgsrOzMWjQoGqVL1T6GCkqKn5WWYQQQgghhBBC/ttSU1ORk5ODiIh5sLAoeYhx5sxVOHr0PICS389qaiowMzNA16726NnTkWszSEtLh6qqMuTl5Wot/ri4BPj4TEFqamqlHRFl20emTJmC6Oho9OzZE25uboiLi4NAIICsrCxkZb9es2BhYSGKi4u/6IOGpeXk5EBJSemrbIsQIp0fuyWWfDMKCgrQ5+c+aFDYAGOLx4p1QgipQQ1+zA82sIFXfy+kpaV91naFcwzs2LED06ZNg76+PpSUlJCZmYn3798jMDAQ1tbWUFFRgZqaGlxdXXHnzh2JZezatQshISHQ19eHqqoqPDw8kJGRgfz8fEyYMAG6urpQUVGBr68v8vPzxWLZsmULbG1tIRAIoKmpCS8vL7x69Uokz5MnT9C3b1/o6elBUVERDRo0gJeXFzIyMsTKO3DgAKysrKCgoABLS0ucOHFCZHnZOSJ4PB7Cw8ORnZ0NHo8HHo+HiIgItG7dGgDg6+srki6JQCBAnz59cObMGbx7905s+bZt26Cqqgo3N7cq16+kYyRpjojY2Fj069cPBgYGUFBQQMOGDTFx4kTk5uaKlOvj4wMVFRUkJSXB3d0dKioq0NHRQWBgIIqKigAAiYmJ0NHRAQCEhIRw+0/jPxJCCCGEEELIj8fCwggtWjRFixZNoampAReX9nj5MhpPn57AiRNr0bOnE5Yti0RIyCpYWzdGixZN0aWLPezsbLj1avJjaWkmVT5h50l1derUCdOnT8eLFy+wZcsWAJLnZIiKikKHDh2goaEBFRUVNGnSBFOnThXJ8+7dOwwbNgx169aFoqIibGxssGnTJpE8iYmJ4PF4WLx4McLCwmBiYgIFBQU8fPgQAHDhwgW0bt0aioqKMDExwdq1a8uNXZq2FCcnJ1hZWeHGjRvo2LEjlJSUuLjz8/MRFBQEU1NTrg1h0qRJYu02+fn5mDhxInR0dLh2jdevX1ehlgkhlaE3IshXsW/fPrx59wZBCII8Ku79loEMBrKBmJI/BREREfjtt98qLT8jI0NsrEdtbW3u/2fNmgV5eXkEBgYiPz8f8vLyePjwIQ4cOIB+/frByMgIycnJWLt2LRwdHfHw4UPUr19fpLx58+ZBIBBg8uTJePr0KVasWAE5OTnIyMjgw4cPCA4OxuXLlxEREQEjIyPMmDGDW3fOnDmYPn06PD09MXz4cKSkpGDFihXo2LEjbt26BQ0NDRQUFMDFxQX5+fkYO3Ys9PT0kJSUhCNHjiA9PR3q6upceRcuXMC+ffvg7+8PVVVVLF++HH379sXLly/LHRIpMjIS69atw9WrV7FhwwYAgJmZGWbOnIkZM2bAz88PDg4OAAB7e/ty63rQoEHYtGkTdu3aJTJp9vv373Hy5EkMGDAAAoEADx48qFL9SjpGkuzevRs5OTkYPXo0tLS0cPXqVaxYsQKvX7/G7t27RfIWFRXBxcUFdnZ2WLx4MU6fPo0lS5bAxMQEo0ePho6ODlavXo3Ro0ejd+/e6NOnDwBwr6oSQgghhBBCCPlxycvLQ0+vpO1AX78uWrRoCju7ZnBxGYHNmw9i6NC+kJdvht27w/Dzz51QUPAJv/++CPv3n8aHD5moW1cLI0b0wx9/DAcAvHz5BhMmzENMzBXIyMjgp5/aIyxsCurWLfmdPnPmKhw6FAN/fy/Mn78eL168QX7+nXLjq0m//PILpk6dilOnTmHEiBFiyx88eICePXuiWbNmmDlzJhQUFPD06VNcvHiRy5ObmwsnJyc8ffoUAQEBMDIywu7du+Hj44P09HSMHz9epMzw8HDk5eXBz8+Pm7Pi3r17+Omnn6Cjo4Pg4GAUFhYiKCgIdevWFYtJmrYUobS0NLi6usLLywuDBw9G3bp1UVxcDDc3N1y4cAF+fn6wsLDAvXv3EBoaisePH+PAgQPc+sOHD8eWLVswcOBA2NvbIzo6Gj169Pj8iieE/IsRUkNu3LjBALAbN26ILetg34GZ883ZWqyV+mMHO2ZkaMSKiorK3WZ4eDgDIPHDGGMxMTEMADM2NmY5OTki6+bl5YmVnZCQwBQUFNjMmTO5NGEZVlZWrKCggEsfMGAA4/F4zNXVVaSMdu3aMUNDQ+57YmIi4/P5bM6cOSL57t27x2RlZbn0W7duMQBs9+7d5e4vY4wBYPLy8uzp06dc2p07dxgAtmLFCrG6SUhI4NK8vb2ZsrKySHnXrl1jAFh4eHiF2xUqLCxk9erVY+3atRNJX7NmDQPATp48yRirev1KOkbCZTExMVxa2TyMMTZv3jzG4/HYixcvRPYVgMi2GGOsRYsWzNbWlvuekpLCALCgoCCp9p98eRXdSwghhBBCCCFEkop+RwiXXbmygxUU3GUFBXfZL7+4sV69nLnvpT/NmjVh3bp1YAUFd///d3oYKyi4y+bP/5U1bKjHoqPD2ZMnJ1hMTATbvHk+Kyi4y/LybjMbG3PWvn0LdvnyDnbhwhbWsmVT1rFjK67cadNGMWVlAXNxac+uXNnJrl/fI3H7ZT9Xruyo9DeSsA3g2rVr5eZRV1dnLVq0YIwxFhQUxEo3C4aGhjIALCUlpdz1w8LCGAC2ZcsWLq2goIC1a9eOqaiosMzMTMZYyW9/AExNTY29e/dOpAx3d3emqKgo8vv94cOHjM/ni8QjbVsKY4w5OjoyAGzNmjUieSMjI5mMjAyLjY0VSRe2X1y8eJExxtjt27cZAObv7y+Sb+DAgdReQL4733KbCg3NRL44xhiuXrsK6yLrKq1nAxskvEjA+/fvK827cuVKREVFiXxK8/b2hkAgEElTUFDgxnwsKipCWloa9+rhzZs3xbYxZMgQyMn9OyaknZ0dGGMYOnSoSD47Ozu8evUKhYWFAEreBikuLoanpydSU1O5j56eHszMzBATEwMA3BsPJ0+erHQCrS5dusDExIT73qxZM6ipqeH58+cVrlcT+Hw+vLy88Pfff3PDPgElwzLVrVsXnTt3BlD1+pV0jCQpnSc7Oxupqamwt7cHYwy3bt0Syz9q1CiR7w4ODl+lngghhBBCCCGEfJ+aNDFCYuI/YumvXr2FqakB2rdvCUPD+mjfviW8vLoDAKKjr+D+/SfYvHkBWrZsijZtmuF//5uD8+ev4/r1+1wZBQWf8L//zUGLFhZo1qzxV9snAFBRUUFWVpbEZcK3Cw4ePIji4mKJeY4dOwY9PT0MGDCAS5OTk8O4cePw8eNHnDt3TiR/3759ueGQgZK2gZMnT8Ld3V1krgsLCwu4uLiIrCttW4qQgoICfH19RdJ2794NCwsLmJubi5TRqVMnAODKOHbsGABg3LhxIutPmDBBYj0QQqqHOiLIF1dUVISCTwUQoPJG5tKE+T9+/Fhp3jZt2qBLly4in9KMjMTHUywuLkZoaCjMzMygoKAAbW1t6Ojo4O7duxLnZCg7IZSw46Bhw4Zi6cXFxVwZT548AWMMZmZm0NHREfnExcVxcy0YGRnh119/xYYNG6CtrQ0XFxesXLlSqlgAoE6dOvjw4UNF1VRjhJNRb9u2DQDw+vVrxMbGwsvLi5sgu6r1K+kYSfLy5Uv4+PhAU1OTm/fB0dERAMTKVVRUFPlHD/B164kQQgghhBBCyPeHMSY2fwIADBnihjt3HsHS0g0TJ85HVNQlbll8/HM0bFgXDRvqcWlNm5pAQ0MVcXH/PgxnaFgfOjqS58380j5+/AhVVVWJy/r374/27dtj+PDhqFu3Lry8vLBr1y6RTokXL17AzMyMe+hQyMLCglteWtnf+SkpKcjNzYWZmZnY9ps0aSLyXdq2FCF9fX2xIZ6fPHmCBw8eiK3fuHFJB5CwjBcvXkBGRkbkgU9JMRFCPg/NEUG+OFlZWSjKK+JjQeUdCqV9REl+NTW1z45B0pP2c+fOxfTp0zF06FDMmjULmpqakJGRwYQJEyT2/gsb2KVNZ4wBKGmQ5/F4OH78uMS8Kioq3P8vWbIEPj4+OHjwIE6dOoVx48Zh3rx5uHz5Mho0aCD1Nr80W1tbmJubY/v27Zg6dSq2b98OxhjXQQFUvX6leRuiqKgIXbt2xfv37/HHH3/A3NwcysrKSEpKgo+Pj1i55dUTIYQQQgghhBBSnvj45zAy0hdLb9GiKR4/Po4TJy4gOvoyBg78HZ062WHnzqVSl62kVLWHNGvK69evkZGRAVNTU4nLBQIBzp8/j5iYGBw9ehQnTpzAzp070alTJ5w6dapav6+l+Z1fnqq0pZS3reLiYlhbW2PpUsnHp+yDpYSQL4s6IshX4eTshFunb8GlyKXSvEI3eDdgbWGNOnXqfJGY9uzZA2dnZ2zcuFEkPT09XWSi689lYmICxhiMjIy4XveKWFtbw9raGtOmTcOlS5fQvn17rFmzBrNnz66xmEqT9JSHNAYNGoTp06fj7t272LZtG8zMzNC6dWtu+Zeo33v37uHx48fYtGkThgwZwqWXHYqrKqq7/4QQQgghhBBCfjwxMSVDLI0f/4vE5WpqKvD07AZPz27o06crevYcjffvM2BuboxXr5Lx6tVb7q2Ihw+fIT09C02bmkgs62uKjIwEALEhkEqTkZFB586d0blzZyxduhRz587Fn3/+iZiYGHTp0gWGhoa4e/cuiouLRd6KiI+PBwAYGhpWGIOOjg4EAgGePHkituzRo0ci36valiKJiYkJ7ty5g86dO1f429/Q0BDFxcV49uyZyFsQZWMihHweGpqJfBVjAsYgoSgBCUiQKv97vMdd3MWYcWO+WEMxn88Xe4Ng9+7dSEpKqtHt9OnTB3w+HyEhIWLbY4whLS0NAJCZmcnNKyFkbW0NGRkZ5Ofn12hMpSkrKwMo6SCoCuHbDzNmzMDt27dF3oYAvkz9Cp+CKF0uYwzLli2rdplKSkoAqr7/hBBCCCGEEEK+bwUFBXj7NhVJScm4desh5s9fj759x6N7944YPLiXWP6wsM3YseMY4uMT8PhxIvbuPQU9PW1oaKiic+e2sLIyg7f3ZNy69RDXrt3D0KF/omPHVrC1tayFvftXdHQ0Zs2aBSMjI7Hf7kKS5uds3rw5AHBtEt27d8fbt2+xc+dOLk9hYSFWrFgBFRUVbtjk8vD5fLi4uODAgQN4+fIllx4XF4eTJ0+K5JW2LaUinp6eSEpKwvr168WW5ebmIjs7GwDg6uoKAFi+fLlInrCwsEq3QQiRHr0RQb4KV1dXmJmYIfJFJH4r/A3KUC43bwEKEM4PRx21OuX+gawJPXv2xMyZM+Hr6wt7e3vcu3cPW7duhbGxcY1ux8TEBLNnz8aUKVOQmJgId3d3qKqqIiEhAfv374efnx8CAwMRHR2NgIAA9OvXD40bN0ZhYSEiIyPB5/PRt2/fGo2pbHwaGhpYs2YNVFVVoaysDDs7u0rnbDAyMoK9vT0OHjwIAGLH6kvUr7m5OUxMTBAYGIikpCSoqalh7969nzXng0AgQNOmTbFz5040btwYmpqasLKygpWVVbXLJIQQQgghhBDy7Tt58iIMDDpBVlYWdeqoolmzJggNnYxffnETmwcBAFRVlbBkSQSePn0BPp+PVq0scfDgSi7v3r3LMGHCPHTq5AsZGRn89FN7hIVN+ar7dPz4ccTHx6OwsBDJycmIjo5GVFQUDA0NcejQISgqKkpcb+bMmTh//jx69OgBQ0NDvHv3DqtWrUKDBg3QoUMHAICfnx/Wrl0LHx8f3LhxA40aNcKePXtw8eJFhIWFlTv/RGkhISE4ceIEHBwc4O/vz3VkWFpa4u7du1w+adtSKvLLL79g165dGDVqFGJiYtC+fXsUFRUhPj4eu3btwsmTJ9GqVSs0b94cAwYMwKpVq5CRkQF7e3ucOXMGT58+rULNE0IqQx0R5Kvg8/k4fPQw7NvaY8nHJfil8Bc0QiPwIPq2QxKSsI2/Da9lXyP6aLTYmH81aerUqcjOzsa2bduwc+dOtGzZEkePHsXkyZNrfFuTJ09G48aNERoaipCQEAAlYxH+9NNPcHNzAwDY2NjAxcUFhw8fRlJSEpSUlGBjY4Pjx4+jbdu2NR6TkJycHDZt2oQpU6Zg1KhRKCwsRHh4uFSTRw8aNAiXLl1CmzZtxMaZ/BL1Kycnh8OHD3NzZygqKqJ3794ICAiAjY1NtcvdsGEDxo4di4kTJ6KgoABBQUHUEUEIIYQQQgghP7CNG2dj48bKh0AuKPi3cXzYMA8MG+ZRbl4Dg3rYt295uctnzPDHjBn+VQu0imbMmAEAkJeXh6amJqytrREWFgZfX98KOwrc3NyQmJiI//3vf0hNTYW2tjYcHR0REhICdXV1ACUP8p09exaTJ0/Gpk2bkJmZiSZNmiA8PBw+Pj5SxdesWTOcPHkSv/76K2bMmIEGDRogJCQEb968EemIAKRrS6mIjIwMDhw4gNDQUGzevBn79++HkpISjI2NMX78eJEhn/73v/9BR0cHW7duxYEDB9CpUyccPXqU5pEgpAbx2Nea3Zb88G7evAlbW1vcuHEDLVu2lJjn0aNH6NWjF548e4JG/EZoWdQSKlBBLnJxh38Hj4seo17deth/cD/s7Oy+8h4QQr4F0txLCCGEEEIIIaS0in5HCJdFRMyDhUXlD919a+LiEuDjM4V+IxFCKvUtt6nQGxHkq2rSpAniHsXhxIkTWPnXShyLOYa8/DwoyCmgTZs2mDl2Jnr37g15efnaDpUQQgghhBBCCCE/AG1tbSgpKcHH5+sOk1STlJSUoK2tXdthEEJItVFHBPnq+Hw+evTogR49egAomdhIVpZORUIIIYQQQgghhNQ8AwMDxMXFITU1tbZDqTZtbW0YGBjUdhiEEFJt1PpLah11QhBCCCGEEEIIIeRLMjAwoIZ8QgipRTK1HQAhhBBCCCGEEEIIIYQQQn5c1BFBCCGEEEIIIYQQQgghhJAvhjoiyH/ax48fMXz4cOjp6YHH42HChAm1HdI37+zZs+DxeDh79myNltuoUSP4+PjUaJmEEEIIIYQQQgj5b/Dx8UGjRo1qOwypBQcHg8fjVSnv9zzPCSE0OD+pNfHx8Th79iyysrKgrKwMOzs72NraVqmMiIgI+Pr64tq1a2jVqlWVY5g7dy4iIiIwffp0mJiYwMLCosplfIuE9SKkoKAATU1NWFtbo0ePHvD19YWqqupXj+vSpUs4deoUJkyYAA0Nja++fUIIIYQQQggh/00vX778rhtxK5us+nPaR3JycrBw4UI4OTnBycnpMyP9sv755x+sW7cO7u7uaN68eW2HU+Pmzp2Lpk2bwt3dvUbL9fHxwaZNm7jv8vLyMDQ0hJeXF6ZOnQpFRUWR/KU7SHg8HvT09GBlZYWpU6eKnSPFxcXYsmULIiMjcevWLWRkZKBOnTpo3rw5PDw84O3tDQUFBYllA4CqqipatGiBSZMmoUePHjW41+RbQx0R5Ks7fPgwQpcsQcy5c5ABIODzkVdcjCLG0LplS4ybOBGDBg2Sulf4c0RHR6Nt27YICgr64tuqDTNnzoSRkRE+ffqEt2/f4uzZs5gwYQKWLl2KQ4cOoVmzZl81nkuXLiEkJAQ+Pj5iHRGPHj2CjAy9pEUIIYQQQgghpGa9fPkSFhZNkJOTV9uhVJuSkiLi4h59kQm3c3JyEBISAgDfRUdESEgIGjVqJNYRsX79ehQXF9dOYNUwbdo0TJ48WSRt7ty58PDwqPGOCKDkIdUNGzYAADIyMnDw4EHMmjULz549w9atW8Xyd+3aFUOGDAFjDAkJCVi1ahU6deqEo0ePwtXVFQCQm5uL3r174+TJk7C3t0dgYCDq1q2L9+/f49y5c/D398eVK1ewcePGcst+8eIFVq9ejV69euH48eNwcXGp8X0n3wbqiCBfDWMMf/75J+bNmwdTPh/DALQAIFdUhCIA9wGcu30bv/zyC86cPo0NGzeCz+d/0ZjevXuHpk2bVpovLy8P8vLy311Duaurq8iTEFOmTEF0dDR69uwJNzc3xMXFQSAQ1GKE/yrdO04IIYQQQgghhNSU1NRU5OTkYcsWQDgQQlAQkJUFLF0qXRm2tsDixYCz85eLszxxccDgwXlITU39Ih0RX0p2djaUlZW/2vbk5OS+2rZqgqysLGRlv17TrKysLAYPHsx99/f3h729PbZv346lS5eibt26IvkbN24skr93795o1qwZwsLCuI6IiRMn4uTJkwgLC8P48eNF1v/tt9/w5MkTREVFicVStuy+ffuiadOmWLZsGXVE/MC+r1ZV8l1bsGAB5s2bh74AAouK0AaA8E8EH4ANgHHFxfAFsHnzZrEbmLR8fHygoqKCpKQkuLu7Q0VFBTo6OggMDERRURGAf+c5SEhIwNGjR8Hj8cDj8ZCYmMgt27FjB6ZNmwZ9fX0oKSkhMzMT79+/R2BgIKytraGiogI1NTW4urrizp07IjEIy9i1axdCQkKgr68PVVVVeHh4ICMjA/n5+ZgwYQJ0dXWhoqICX19f5Ofni+3Lli1bYGtrC4FAAE1NTXh5eeHVq1fVqhehTp06Yfr06Xjx4gW2bNkisiw+Ph4eHh7Q1NSEoqIiWrVqhUOHDklV7pUrV9CtWzeoq6tDSUkJjo6OuHjxIrc8ODgYv//+OwDAyMhIpM4ByXNEPH/+HP369YOmpiaUlJTQtm1bHD16VCRP6bqeM2cOGjRoAEVFRXTu3BlPnz6tYu0QQgghhBBCCPlRWVgALVuWfLS0AA2Nf79X9gEAExPp85f3sbau+jrVHUVamvaRxMRE6OjoAABCQkK43+rBwcFcOdK0FURERIDH43FPwevq6qJBgwYAgBcvXsDf3x9NmjSBQCCAlpYW+vXrx7UHlJaeno6JEyeiUaNGUFBQQIMGDTBkyBCkpqbi7NmzaN26NQDA19eXizUiIoLb37JzRGRnZ+O3335Dw4YNoaCggCZNmmDx4sVgjInk4/F4CAgIwIEDB2BlZQUFBQVYWlrixIkTFdYxYwza2tr49ddfubTi4mJoaGiAz+cjPT2dS1+wYAFkZWXx8eNHAOJzRPB4PGRnZ2PTpk3cvpVtJ0lPT+dGmVBXV4evry9ycnIqjLE8PB4PHTp0AGMMz58/rzS/tbU1tLW1kZCQAAB49eoVNmzYgG7dupXbhmdmZgZ/f/9Ky7awsIC2tjaePXtWtZ0g3xV6I4J8FW/fvsWM6dPxE4CfKsnbFkAOY1i5ciVGjhwJa2vrKm+vqKgILi4usLOzw+LFi3H69GksWbIEJiYmGD16NCwsLBAZGYmJEyeiQYMG+O233wAAOjo63B/CWbNmQV5eHoGBgcjPz4e8vDwePnyIAwcOoF+/fjAyMkJycjLWrl0LR0dHPHz4EPXr1xeJY968eRAIBJg8eTKePn2KFStWQE5ODjIyMvjw4QOCg4Nx+fJlREREwMjICDNmzODWnTNnDqZPnw5PT08MHz4cKSkpWLFiBTp27Ihbt2591hwLv/zyC6ZOnYpTp05hxIgRAIAHDx6gffv20NfXx+TJk6GsrIxdu3bB3d0de/fuRe/evcstLzo6Gq6urrC1tUVQUBBkZGQQHh6OTp06ITY2Fm3atEGfPn3w+PFjbN++HaGhodDW1ubqXJLk5GTY29sjJycH48aNg5aWFjZt2gQ3Nzfs2bNHLJ758+dDRkYGgYGByMjIwMKFCzFo0CBcuXKl2vVECCGEEEIIIeTH5+QENGsGKCoCGzYA8vLAqFGAsC1e2LYt/BlqaAgI29APHgRCQoCHD4H69QFvb+DPPwHhg+48HrBqFXD8OHDmDPD77/+W+zVU1j6io6OD1atXY/To0ejduzf69OkDANxQzlVtK/D394eOjg5mzJiB7OxsAMC1a9dw6dIleHl5oUGDBkhMTMTq1avh5OSEhw8fQklJCQDw8eNHODg4IC4uDkOHDkXLli2RmpqKQ4cO4fXr17CwsMDMmTMxY8YM+Pn5wcHBAQBgb28vcd8ZY3Bzc0NMTAyGDRuG5s2b4+TJk/j999+RlJSE0NBQkfwXLlzAvn374O/vD1VVVSxfvhx9+/bFy5cvoaWlJXEbPB4P7du3x/nz57m0u3fvIiMjAzIyMrh48SI370FsbCxatGgBFRUViWVFRkZi+PDhaNOmDfz8/AAAJiYmInk8PT1hZGSEefPm4ebNm9iwYQN0dXWxYMECiWVWRtgGVqdOnUrzfvjwAR8+fICpqSkA4Pjx4ygqKhJ5s6G6MjIy8OHDB7H9JT8YRkgNuXHjBgPAbty4IbZs9uzZTEFGhi0F2FopPqsApiEry0aPHl3hNsPDwxkAdu3aNS7N29ubAWAzZ84UyduiRQtma2srkmZoaMh69OghkhYTE8MAMGNjY5aTkyOyLC8vjxUVFYmkJSQkMAUFBZHtCcuwsrJiBQUFXPqAAQMYj8djrq6uImW0a9eOGRoact8TExMZn89nc+bMEcl37949JisrK5ZelqR6KUtdXZ21aNGC+965c2dmbW3N8vLyuLTi4mJmb2/PzMzMxPYtJiaGy2NmZsZcXFxYcXExly8nJ4cZGRmxrl27cmmLFi1iAFhCQoJYPIaGhszb25v7PmHCBAaAxcbGcmlZWVnMyMiINWrUiDsOwngsLCxYfn4+l3fZsmUMALt3714FNUW+RRXdSwghhBBCCCFEkop+R/y7DIyxko+3N9jPP5f8v6MjmJoaWHAw2OPHYJs2gfF4YKdOlSx/9w4MAAsPB3vzpuQ7Y2Dnz5esFxEB9uxZSf5GjUrKEW4HANPVBfvf/0ryvHjx7zJpPzduoNLfSJ/TPpKSksIAsKCgILFypW0rEG6/Q4cOrLCwUKSMsm0rjDH2999/MwBs8+bNXNqMGTMYALZv3z6x/ML2hmvXrv3/sQgXy+Pt7S3StnLgwAEGgM2ePVskn4eHB+PxeOzp06dcGgAmLy8vknbnzh0GgK1YsUJsW6UtWrSI8fl8lpmZyRhjbPny5czQ0JC1adOG/fHHH4wxxoqKipiGhgabOHEit15QUBAr2zSrrKws0jZSNu/QoUNF0nv37s20tLQqjI+xkrpRVlZmKSkpLCUlhT19+pQtXryY8Xg8ZmVlJdKeI6yPYcOGsZSUFPbu3Tt25coV1rlzZwaALVmyhDHG2MSJExkAdvv2bZF18/Pzue2kpKSw1NTUCsu+fv0669atGwPAFi1aVOm+kIp9y20qNDQT+SrWr12LVsXFkHZkQD6A9oWF2BwRIXHIImmMGjVK5LuDg4NUr5oJeXt7i82foKCgwM0TUVRUhLS0NKioqKBJkya4efOmWBlDhgwRGaPQzs4OjDEMHTpUJJ+dnR1evXqFwsJCAMC+fftQXFwMT09PpKamch89PT2YmZkhJiZG6v0oj4qKCrKysgAA79+/R3R0NDw9PZGVlcVtLy0tDS4uLnjy5AmSkpIklnP79m08efIEAwcORFpaGrdudnY2OnfujPPnz1drsqhjx46hTZs26NChg0jMfn5+SExMxMOHD0Xy+/r6Ql5envsufDKiKsecEEIIIYQQQsh/U7NmJfNGmJkBQ4YArVqVvMEAAMIX+TU0AD29f7+HhACTJ5e8BWFsDHTtCsyaBaxdK1r2wIGAr29JntqY4qG67SPVaSsYMWKE2HyfpdtWPn36hLS0NJiamkJDQ0OkLWXv3r2wsbGROCJD6SGMpHXs2DHw+XyMGzdOJP23334DYwzHjx8XSe/SpYvIE/nNmjWDmppapXXl4OCAoqIiXLp0CUDJmw8ODg5wcHBAbGwsAOD+/ftIT0/n2iqqS9KxTEtLQ2ZmZqXrZmdnQ0dHBzo6OjA1NUVgYCDat2+PgwcPSqzfjRs3QkdHB7q6urCzs8PFixfx66+/YsKECQDAbbPsGx7Hjh3jtqOjowNDQ8MKy27VqhXOnDmDSZMmiQxxRX48NDQT+eKKi4vx8vVrdKg8qwgjAEdzc5GWliY25FFlFBUVxYb8qVOnDj58+CD99o2MxNKKi4uxbNkyrFq1CgkJCdyYigAkvqZXdhIpdXV1AEDDhg3F0ouLi5GRkQEtLS08efIEjDGYmZlJjK0mJmD6+PEjdHV1AQBPnz4FYwzTp0/H9OnTJeZ/9+4d9PX1xdKfPHkCoKTjpjwZGRlSveZX2osXL2BnZyeWbvH/g2O+ePECVlZWXHrZuhZuryrHnBBCCCGEEELIf9P/j0TEqVcPePeu4nXu3AEuXgTmzPk3ragIyMsDcnKA/x9xCK1a1WysVfE57SPVaSuQ1JaSm5uLefPmITw8HElJSSLzM2RkZHD//+zZM/Tt27fSuKT14sUL1K9fH6qqqiLppdsVSpM0Ebg0ddWyZUsoKSkhNjYWLi4uiI2NRUhICPT09LBixQrk5eVxHRKlH7asjoraPtTU1CpcV1FREYcPHwYAvH79GgsXLsS7d+/EHsIV+vnnnxEQEAAejwdVVVVYWlqKTD4urFfhnBdC7du35yaoXrRokcj8oWXLLigowLVr1zB37lzk5ORwD/+SHxN1RJAvjv3/u4RVvZUI+2JLN/ZLq2zve3VIuhHPnTsX06dPx9ChQzFr1ixoampCRkYGEyZMkPjUf3lxlJcu/GNcXFwMHo+H48ePS8xb3niC0nr9+jUyMjK4cf2EsQcGBsLFxUXiOsK8ZQnXXbRoEZo3by4xz+fGK43K6pQQQgghhBBCCClP2ef9eDygspf7P34seSvi/6dVEKGo+O//K0s7PMQX8DntI9VpK5DUljJ27FiEh4djwoQJaNeuHdTV1cHj8eDl5VWtERS+lOq2K8jJycHOzg7nz5/H06dP8fbtWzg4OKBu3br49OkTrly5gtjYWJibm5c7T+aXjlG4bpcuXbjvLi4uMDc3x8iRI8UmHweABg0aiOQvy9zcHEDJ2x42NjZcuo6ODrfeli1bJK5buuzu3btDW1sbAQEBcHZ25uYpIT8e6oggXxyfz4eWhgaS09OrtN47ADIyMuVOCFQb9uzZA2dnZ2zcuFEkPT09nZt8uSaYmJiAMQYjIyM0bty4xsoVioyMBADuHxLGxsYASv54VvRHRhLha4tqamqVrluVVykNDQ3x6NEjsfT4+HhuOSGEEEIIIYQQ8jXIyZW87VBay5bAo0dAOc/tfTfK+63+OW0Fpe3Zswfe3t5YsmQJl5aXl4f0Mu1EJiYmuH//frVilcTQ0BCnT59GVlaWyFsRX6JdwcHBAQsWLMDp06ehra0Nc3Nz8Hg8WFpaIjY2FrGxsejZs2el5VRnCKrqqlevHiZOnIiQkBBcvnwZbdu2rdL6rq6u4PP52Lp1KwYNGvRZsYwcORKhoaGYNm0aevfu/VXrgXw99L4L+SoGDB6MK7KyKJQyPwNwUVYWbr16QUn4LuM3gM/ni/Uy7969u9z5E6qrT58+4PP5CAkJEdseYwxpaWnVLjs6OhqzZs2CkZER94dCV1cXTk5OWLt2Ld68eSO2TkpKSrnl2drawsTEBIsXLxZ7Ha/susJX+Mr+Y0OS7t274+rVq/j777+5tOzsbKxbtw6NGjVC06ZNKy2DEEIIIYQQQgipCY0alcwZ8fYtIBypZ8YMYPPmkrciHjwA4uKAHTuAadNqNdQqE7a7lP2t/jltBaVJaktZsWKF2AgYffv2xZ07d7B//36xMoTrV7VdoaioCH/99ZdIemhoKHg8HlxdXaWKXxoODg7Iz89HWFgYOnTowDWkOzg4IDIyEv/8849U80MoKytLtW81ZezYsVBSUsL8+fOrvK6BgQGGDh2K48ePi9WxkLSjVMjKyuK3335DXFwcDh48WOVYyPeB3oggX4W/vz/++usvXAakmiviAYDXhYWIGDv2C0dWNT179sTMmTPh6+sLe3t73Lt3D1u3buWeEqgpJiYmmD17NqZMmYLExES4u7tDVVUVCQkJ2L9/P/z8/BAYGFhpOcePH0d8fDwKCwuRnJyM6OhoREVFwdDQEIcOHYJiqXdFV65ciQ4dOsDa2hojRoyAsbExkpOT8ffff+P169e4c+eOxG3IyMhgw4YNcHV1haWlJXx9faGvr4+kpCTExMRATU2NG4PQ1tYWAPDnn3/Cy8sLcnJy6NWrl8gYg0KTJ0/G9u3b4erqinHjxkFTUxObNm1CQkIC9u7dS+MGEkIIIYQQQgj5apYsAX79FVi/HtDXBxITARcX4MgRYOZMYMGCkrcmzM2B4cNrO9qqEQgEaNq0KXbu3InGjRtDU1MTVlZWsLKyqnZbQWk9e/ZEZGQk1NXV0bRpU/z99984ffq02AgYv//+O/bs2YN+/fph6NChsLW1xfv373Ho0CGsWbMGNjY2MDExgYaGBtasWQNVVVUoKyvDzs5O4twUvXr1grOzM/78808kJibCxsYGp06dwsGDBzFhwgSRiak/V7t27SArK4tHjx7Bz8+PS+/YsSNWr14NAFJ1RNja2uL06dNYunQp6tevDyMjI4nzZ9YULS0t+Pr6YtWqVYiLi+Pmz5BWWFgYEhISMHbsWOzYsQO9evWCrq4uUlNTcfHiRRw+fBhNmjSRqiwfHx/MmDEDCxYsgLu7ezX2hnzrqCOCfBUWFhYYOGAAdu7cCe3iYphXkPcVgHA+H07t26NTp05fK0SpTJ06FdnZ2di2bRt27tyJli1b4ujRo5g8eXKNb2vy5Mlo3LgxQkNDERISAqBkkuuffvoJbm5uUpUxY8YMAIC8vDw0NTVhbW2NsLAw+Pr6ik3W1LRpU1y/fh0hISGIiIhAWloadHV10aJFC66c8jg5OeHvv//GrFmz8Ndff+Hjx4/Q09ODnZ0dRo4cyeVr3bo1Zs2ahTVr1uDEiRMoLi5GQkKCxI6IunXr4tKlS/jjjz+4yZ2aNWuGw4cPo0ePHlLtPyGEEEIIIYQQIklExL//f/as+PIDB0S/9+pV8inLxaXkU57vZdrCDRs2YOzYsZg4cSIKCgoQFBQEKyurz2orEFq2bBk3hE9eXh7at2+P06dPi807oaKigtjYWAQFBWH//v3YtGkTdHV10blzZzRo0ABAyTBRmzZtwpQpUzBq1CgUFhYiPDxcYkeEjIwMDh06hBkzZmDnzp0IDw9Ho0aNsGjRIvz222+fX2mlKCsro0WLFrh27ZrIhNTCzoeGDRtKNRTU0qVL4efnh2nTpiE3Nxfe3t5ftCMCAH799VesWbMGCxYsQETpC0MKSkpKOHHiBCIjIxEZGYmFCxciMzMTGhoasLGxwapVq+Dt7S1VWQKBAAEBAQgODsbZs2fh5ORU9Z0h3zQeo5lcSQ25efMmbG1tcePGDbRs2VJseV5eHtx69ULMmTNwYgyOAHRLLf8AIBZANJ8PCysrnI6JQZ06db5S9ISQb0Vl9xJCCCGEEEIIKaui3xH/LiuZ1+F7c/MmYGsL+o1ECKnUt9ymQm9EkK9GUVERR48dQ3BwMFb/9RdOZ2bCiM+HUnEx8mRkkFBcDIGiIoYOHYr58+dDRUWltkMmhBBCCCGEEELIDyIurrYjqJ7vNW5CCCmNOiLIVyUnJ4c5c+Zg2rRp2LVrF2JiYpCZmQkVFRVMtLPDL7/8AjU1tdoOkxBCCCGEEEIIIT8IbW1tKCkpYvDgvNoOpdqUlBShra1d22EQQki1UUcEqRUCgQDe3t5SjxNHCCGEEEIIIYQQUh0GBgaIi3uE1NTU2g6l2rS1tWFgYFDbYRBCSLVRRwQhhBBCCCGEEEII+aEZGBhQQz4hhNQimdoOgBBCCCGEEEIIIYQQQgghPy7qiCCEEEIIIYQQQgghhBBCyBdDHRHkP4vH4yE4OLi2w/jPcXJygpOTU22HQQghhBBCCCGEEEII+UpojghSK968eYMNGzYg5swZZKVnQFlVFXb27TBq1CgYGRlJXU5ERAR8fX0BALGxsejQoYPIcsYYDAwM8Pr1a/To0QNHjhyp0f343t28eRO2trb4888/MXv2bIl5njx5gsaNG2PixIlYunTpV46weo4dO4arV69SRxMhhBBCCCGE/MfExcXVdgiEEFJrvuV7IHVEkK8qKysLY/z9sX37dsgzHlyKVWACWWSiCOv+voJFixahZ/fuWL9xI+rWrSt1uYqKiti2bZtYR8S5c+fw+vVrKCgoiK2Tm5sLWdn/9iXQsmVLmJubY/v27eV2RGzbtg0AMHjw4BrZ5qlTp2qknIocO3YMK1eupI4IQgghhBBCCPmP0NbWhpKSUo39diWEkO+VkpIStLW1azsMMf/tVljyVaWnp6NTR0c8exiHxUX14A0taJQ6BXOKirEd7zHtxBm0a90G5y9dRIMGDaQqu3v37ti9ezeWL18u0rmwbds22NraIjU1VWwdRUXFz9+pH8CgQYMwffp0XL58GW3bthVbvn37dpibm6Nly5aftZ2cnBwoKSlBXl7+s8ohhBBCCCGEEELKMjAwQFxcnMTf/4QQ8l+ira0NAwOD2g5DHCOkhty4cYMBYDdu3JC43KVLV1aHL8/uwIIx2Jb7SYQVaygrYDaWVuzTp08VbjM8PJwBYLt372Y8Ho8dO3aMW5afn8/q1KnDlixZwgwNDVmPHj1E1gXAgoKCuO+ZmZls/PjxzNDQkMnLyzMdHR3WpUsXkf15/Pgx69OnD6tbty5TUFBg+vr6rH///iw9PV2k7MjISNayZUumqKjI6tSpw/r3789evnwpksfR0ZFZWlqyBw8eMCcnJyYQCFj9+vXZggULxPZz+fLlrGnTpkwgEDANDQ1ma2vLtm7dKpLn9evXzNfXl+nq6jJ5eXnWtGlTtnHjxgrrjzHGnj9/zgCwsWPHii27fv06A8BmzZrFGGPswIEDrHv37qxevXpMXl6eGRsbs5kzZ7LCwkKJ+3b9+nXm4ODABAIBGz9+PLfM0dGRy5ufn8+mT5/OWrZsydTU1JiSkhLr0KEDi46OFikzISGBAWCLFi1ia9euZcbGxkxeXp61atWKXb16lcvn7e3NAIh9yPejsnsJIYQQQgghhBBCCPm+0BsR5Ku4du0aTp6Owh4YoxmUKsxrCAXsKjRAuwf3cfToUfz888+Vlt+oUSO0a9cO27dvh6urKwDg+PHjyMjIgJeXF5YvX15pGaNGjcKePXsQEBCApk2bIi0tDRcuXEBcXBxatmyJgoICuLi4ID8/H2PHjoWenh6SkpJw5MgRpKenQ11dHQAwZ84cTJ8+HZ6enhg+fDhSUlKwYsUKdOzYEbdu3YKGhga3zQ8fPqBbt27o06cPPD09sWfPHvzxxx+wtrbm9mP9+vUYN24cPDw8MH78eOTl5eHu3bu4cuUKBg4cCABITk5G27ZtwePxEBAQAB0dHRw/fhzDhg1DZmYmJkyYUO5+GxkZwd7eHrt27UJoaCj4fD63TDgsk3A7ERERUFFRwa+//goVFRVER0djxowZyMzMxKJFi0TKTUtLg6urK7y8vDB48OByh9rKzMzEhg0bMGDAAIwYMQJZWVnYuHEjXFxccPXqVTRv3lwk/7Zt25CVlYWRI0eCx+Nh4cKF6NOnD54/fw45OTmMHDkS//zzD6KiohAZGVnpcSeEEEIIIYQQQgghhHxhtd0TQn4cFT3F7OvjwwxkBawQLSt8G6L0x46vyn7q3KXCbQrfiLh27Rr766+/mKqqKsvJyWGMMdavXz/m7OzMGGNSvRGhrq7OxowZU+62bt26xb19UZ7ExETG5/PZnDlzRNLv3bvHZGVlRdIdHR0ZALZ582YuLT8/n+np6bG+fftyaT///DOztLSsoBYYGzZsGKtXrx5LTU0VSffy8mLq6upcnZRn5cqVDAA7efIkl1ZUVMT09fVZu3btuDRJ5YwcOZIpKSmxvLw8sX1bs2aNWP6yb0QUFhb+H3v3HR9Vlf4P/HOnZzLpnfReCDWE3jsqKIKKggru13WxsNa1K8W6Yvvpirq66iq4iCAIoiBVSmihtxBCEkII6X0yM5m59/cHyywhhZSZTBI+79dreGXuPfec5w6TSXKfe84jGY3GOm1KS0slPz8/6YEHHrBuuzIjwsvLSyopKbFuX7NmjQRAWrt2rXXbI488wlkQnRhnRBARERERERERdS0yB+U/6Aaz6seVmG12hxxCs495wOKBjZs3obKyslnt77zzTtTU1GDdunWorKzEunXrrHfyN4e7uzv27t2LixcvNrj/yoyHDRs2QK/XN9hm1apVEEURd955J4qKiqwPf39/REdHY+vWrXXa63S6OoW0VCoV+vfvj3PnztWJ68KFC9i/f3+DY0qShJUrV2Ly5MmQJKnOuBMmTEB5eTkOHjzY5LnfddddUCqV1hkQwOVC37m5uZg5c6Z1m5OTk/XryspKFBUVYdiwYdDr9Th9+nSdPtVqNebMmdPkuAAgl8utdSNEUURJSQnMZjP69evXYNx33XUXPDw8rM+HDRsGAHVeMyIiIiIiIiIiIuo4mIgguzObzSivqkQoWlakOAxqAEBJSUmz2vv4+GDs2LFYtmwZVq1aBYvFgunTpzd7vL///e84fvw4goOD0b9/f8yfP7/Oxe3w8HA8+eST+OKLL+Dt7Y0JEybgH//4B8rLy61t0tPTIUkSoqOj4ePjU+dx6tQpFBQU1BkzKCgIglA3OePh4YHS0lLr82effRY6nQ79+/dHdHQ0HnnkEezatcu6v7CwEGVlZfj888/rjXklEXDtuNfy8vLChAkT8NNPP8FgMAC4vASSQqHAnXfeaW134sQJTJ06FW5ubnB1dYWPj481kXL16wAAgYGBzS5M/c0336Bnz57QaDTw8vKCj48Pfvnll3p9AqhXbOdKUuLq14yIiIiIiIiIiIg6DtaIILuTy+WQy+QwilKLjjNCBHD5zvrmuueee/Dggw/i0qVLmDRpUp16DNdz5513YtiwYfjpp5+wceNGvPPOO3j77bexatUqa72Gd999F7Nnz8aaNWuwceNGzJs3D2+++Sb27NmDoKAgiKIIQRDw66+/1qm1cIVOp6vzvKE2wOVZDlfEx8cjLS0N69atw2+//YaVK1fik08+wSuvvIIFCxZAFC+/TrNmzcL999/fYH89e/a87vnPmjUL69atw7p16zBlyhSsXLkS48ePh4+PDwCgrKwMI0aMgKurKxYuXIjIyEhoNBocPHgQzz77rDWOK66ePdGU7777DrNnz8Ztt92GZ555Br6+vpDL5XjzzTeRkZFRr31zXjMiIiIiIiIiIiLqOJiIILsTBAExkZHYfrYIcyWfZh+3DZXwdHODl5dXs4+ZOnUqHnroIezZswfLly9vcawBAQF4+OGH8fDDD6OgoAB9+/bF66+/bk1EAECPHj3Qo0cPvPTSS9i9ezeGDBmCTz/9FK+99hoiIyMhSRLCw8MRExPT4vEb4+zsjLvuugt33XUXTCYTbr/9drz++ut4/vnn4ePjAxcXF1gsFowdO7bVY0yZMgUuLi5YtmwZlEolSktL6yzLtG3bNhQXF2PVqlUYPny4dXtmZmabzu3HH39EREQEVq1aVWd2yKuvvtrqPq+dZUJERERERERERESOw6WZqF089MjDWIkyXEJts9rrIeIreRn+9Oc/Q6lUNnscnU6HJUuWYP78+Zg8eXKzj7NYLPWWAfL19UW3bt1gNBoBABUVFTCbzXXa9OjRAzKZzNrm9ttvh1wux4IFC+rdoS9JEoqLi5sd0xXXHqNSqZCQkABJklBbWwu5XI5p06Zh5cqVOH78eL3jCwsLmzWOk5MTpk6divXr12PJkiVwdnbGrbfeat1/ZSbC1edlMpnwySeftPicrtZQv3v37kVKSkqr+3R2dgZweRYHERERERERERERORZnRFC7uP/++/HCc8/hBUMuvkQohOsUrX4Ll1Am1uKhhx5q1VgtVVlZiaCgIEyfPh29evWCTqfDpk2bsH//frz77rsAgC1btuDRRx/FHXfcgZiYGJjNZnz77bfWRAAAREZG4rXXXsPzzz+PrKws3HbbbXBxcUFmZiZ++ukn/PnPf8bTTz/dotjGjx8Pf39/DBkyBH5+fjh16hQ+/vhj3HzzzXBxcQEAvPXWW9i6dSsGDBiABx98EAkJCSgpKcHBgwexadOmZtfZmDVrFv79739jw4YNmDlzpvWCPgAMHjwYHh4euP/++zFv3jwIgoBvv/22zUsi3XLLLVi1ahWmTp2Km2++GZmZmfj000+RkJCAqqqqVvWZlJQEAJg3bx4mTJgAuVyOGTNmtClOIiIiIiIiIiIiah0mIqhduLu74x9LlmDOnDlwgRzvIQjyBpIREiS8jXwsQh5ef+11REZGtkt8Wq0WDz/8MDZu3IhVq1ZBFEVERUXhk08+wdy5cwEAvXr1woQJE7B27Vrk5uZCq9WiV69e+PXXXzFw4EBrX8899xxiYmLw/vvvY8GCBQCA4OBgjB8/HlOmTGlxbA899BCWLl2K9957D1VVVQgKCsK8efPw0ksvWdv4+flh3759WLhwIVatWoVPPvkEXl5e6N69O95+++1mjzV69GgEBAQgLy+vzrJMwOWC1uvWrcNTTz2Fl156CR4eHpg1axbGjBmDCRMmtPi8rpg9ezYuXbqEzz77DBs2bEBCQgK+++47rFixAtu2bWtVn7fffjsee+wx/Oc//8F3330HSZKYiCAiIiIiIiIiInIQQWKFV7KRgwcPIikpCampqejbt2+DbZYsWYJHH3kUIXIN/mL2wF3wgBcUqIAFP6EMnyhKcMpcjZdffhkLFizgWv9EN6DmfJYQEREREREREVHnwRoR1K7mzp2Lffv3YcTMO/CqshDhOA5XHEYQjuFxWS663zoR27Ztw8KFC5mEICIiIiIiIiIiIuoCuDQTtbukpCR8/fXXePfdd7Fnzx5UVFRAp9Ohb9++CAwMdHR4RERERERERERERGRDTESQw3h5eeHmm292dBhEREREREREREREZEdcmomIiIiIiIiIiIiIiOyGiQgiIiIiIiIiIiIiIrIbJiKIiIiIiIiIiIiIiMhumIggh5EkCcXFxcjKykJhYSFEUXR0SACAr7/+GoIgICsry7pt5MiRGDlypMNicoSwsDDMnj3b0WHY1bZt2yAIArZt2+boUIiIiIiIiIiIiLosJiKo3VVWVuLTTz9F9x7d4e3tjfDwcPj6+iIiKgKLFy9GcXFxi/o7duwYpk+fjtDQUGg0GgQGBmLcuHH46KOP7HQG7WP+/PkQBMH6UCqVCAsLw7x581BWVubo8OxiypQp0Gq1qKysbLTNzJkzoVKpWvw+cRS9Xo/58+cz2UFERERERERERDcshaMDoBvL7t27ccuUW1BWWgb0BPB/AJwBGIDsw9l49vln8cqrr2D5f5Zj8uTJzepv1KhRCAkJwYMPPgh/f3/k5ORgz549+PDDD/HYY4/ZJO6NGzfapJ/WWLJkCXQ6Haqrq7F582Z89NFHOHjwIHbu3GnXcdPS0iCTtW+ucubMmVi7di1++ukn3HffffX26/V6rFmzBhMnToSXl1ebxxs+fDhqamqgUqna3Fdj9Ho9FixYAAA33KwaIiIiIiIiIiIigIkIakd79uzB6DGjURtSC+lpCfC8pkFfQJwuwrDMgFtvuxVrVq+5bjLi9ddfh5ubG/bv3w93d/c6+woKCmwWuz0vVF/P9OnT4e3tDQB46KGHMGPGDCxfvhz79u1D//797TauWq22W9+NmTJlClxcXLBs2bIGExFr1qxBdXU1Zs6c2aZxDAYDVCoVZDIZNBpNm/oiIiIiIiIiIiKipnFpJmoXJpMJt95+K8xBZoiPifWTEFe4AtKfJaAXcNeMu667/E5GRga6d+9eLwkBAL6+vnWeC4KARx99FEuXLkVsbCw0Gg2SkpLwxx9/XDf+hmpEGAwGzJ8/HzExMdBoNAgICMDtt9+OjIwMaxtRFPHBBx+ge/fu0Gg08PPzw0MPPYTS0tLrjtmYYcOGAUCdcQBg7969mDhxItzc3KDVajFixAjs2rWr3vHbtm1Dv379oNFoEBkZic8++8y6DNTVGqoRce7cOdxxxx3w9PSEVqvFwIED8csvv9TrXxAE/PDDD3j99dcRFBQEjUaDMWPG4OzZs02em5OTE26//XZs3ry5wUTSsmXL4OLigilTpqCkpARPP/00evToAZ1OB1dXV0yaNAlHjhxpMJ7//Oc/eOmllxAYGAitVouKiooGa0Ts2LEDd9xxB0JCQqBWqxEcHIwnnngCNTU1dfqdPXs2dDodcnNzcdttt0Gn08HHxwdPP/00LBYLACArKws+Pj4AgAULFliX2Zo/f36TrwMREREREREREVFXwhkR1C5WrVqFgrwC4FUA15tcIAOkeyQYnzfi66+/xlNPPdVo09DQUKSkpOD48eNITEy8bhzbt2/H8uXLMW/ePKjVanzyySeYOHEi9u3b16zjr7BYLLjllluwefNmzJgxA3/9619RWVmJ33//HcePH0dkZCSAyzMYvv76a8yZMwfz5s1DZmYmPv74Yxw6dAi7du2CUqls9phXXCmi7eHhYd22ZcsWTJo0CUlJSXj11Vchk8nw1VdfYfTo0dixY4d15sShQ4cwceJEBAQEYMGCBbBYLFi4cKH1YnlT8vPzMXjwYOj1esybNw9eXl745ptvMGXKFPz444+YOnVqnfZvvfUWZDIZnn76aZSXl+Pvf/87Zs6cib179zY5zsyZM/HNN9/ghx9+wKOPPmrdXlJSgg0bNuDuu++Gk5MTTpw4gdWrV+OOO+5AeHg48vPz8dlnn2HEiBE4efIkunXrVqffRYsWQaVS4emnn4bRaGx0lsuKFSug1+sxd+5ceHl5Yd++ffjoo49w4cIFrFixok5bi8WCCRMmYMCAAVi8eDE2bdqEd999F5GRkZg7dy58fHywZMkSzJ07F1OnTsXtt98OAOjZs+d1X28iIiIiIiIiIqIuQyKykdTUVAmAlJqaWm/f4KGDJXmcXMJnaP5jAKTQ8FDJYrE0OubGjRsluVwuyeVyadCgQdLf/vY3acOGDZLJZKrXFoAEQDpw4IB1W3Z2tqTRaKSpU6dat3311VcSACkzM9O6bcSIEdKIESOsz//1r39JAKT33nuv3jiiKEqSJEk7duyQAEhLly6ts/+3335rcPu1Xn31VQmAlJaWJhUWFkpZWVnSv/71L8nJyUny8fGRqqurreNFR0dLEyZMsI4tSZKk1+ul8PBwady4cdZtkydPlrRarZSbm2vdlp6eLikUCunaj4PQ0FDp/vvvtz5//PHHJQDSjh07rNsqKyul8PBwKSwszPr/tHXrVgmAFB8fLxmNRmvbDz/8UAIgHTt2rMnzNpvNUkBAgDRo0KA62z/99FMJgLRhwwZJkiTJYDDUe29kZmZKarVaWrhwoXXblXgiIiIkvV5fp/2VfVu3bq3zul3rzTfflARBkLKzs63b7r//fglAnbEkSZL69OkjJSUlWZ8XFhZKAKRXX321yfOm/2nqs4SIiIiIiIiIiDofLs1EdidJEvbv2w9LD0vLDuwFZGdmo6SkpNEm48aNQ0pKCqZMmYIjR47g73//OyZMmIDAwED8/PPP9doPGjQISUlJ1uchISG49dZbsWHDButyOs2xcuVKeHt7N1gM+8oSRytWrICbmxvGjRuHoqIi6yMpKQk6nQ5bt25t1lixsbHw8fFBWFgYHnjgAURFReHXX3+FVqsFABw+fBjp6em45557UFxcbB2nuroaY8aMwR9//AFRFGGxWLBp0ybcdtttdWYLREVFYdKkSdeNY/369ejfvz+GDh1q3abT6fDnP/8ZWVlZOHnyZJ32c+bMqTPr4MqSUufOnWtyHLlcjhkzZiAlJcU6+wO4vCyTn58fxowZA+ByDYsrxbQtFguKi4uh0+kQGxuLgwcP1uv3/vvvh5OT03XP8+o21dXVKCoqwuDBgyFJEg4dOlSv/V/+8pc6z4cNG3bdcyQiIiIiIiIiIrqRMBFBdmexWFBrqgWufw24rv+2r6qqarJZcnIyVq1ahdLSUuzbtw/PP/88KisrMX369HoXx6Ojo+sdHxMTA71ej8LCwmaHlpGRgdjYWCgUja9ulp6ejvLycvj6+sLHx6fOo6qqqtnFtFeuXInff/8dy5Ytw8CBA1FQUFDnYnl6ejqAyxfarx3niy++gNFoRHl5OQoKClBTU4OoqKh6YzS07VrZ2dmIjY2ttz0+Pt66/2ohISF1nl9ZSqo59TGuFKNetmwZAODChQvYsWMHZsyYAblcDuBy/Y33338f0dHRUKvV8Pb2ho+PD44ePYry8vJ6fYaHh193XAA4f/48Zs+eDU9PT2vdhxEjRgBAvX41Gk29Za08PDzaVAOEiIiIiIiIiIioq2GNCLI7hUIBlUYFU5WpZQf+N//g6urarOYqlQrJyclITk5GTEwM5syZgxUrVuDVV19tYcS2IYoifH19sXTp0gb3N6cuAwAMHz4c3t7eAIDJkyejR48emDlzJlJTUyGTySCKIgDgnXfeQe/evRvsQ6fTwWAwtPwk2uBKwuBakiRd99ikpCTExcXh+++/xwsvvIDvv/8ekiRZExQA8MYbb+Dll1/GAw88gEWLFsHT0xMymQyPP/649TW5WnNmQ1gsFowbNw4lJSV49tlnERcXB2dnZ+Tm5mL27Nn1+m3sHImIiIiIiIiIiOh/mIigdjFq5ChsOrQJlgnNX/5ISBUQ3yO+TlHm5urXrx8AIC8vr872K7MHrnbmzBlotdpmJwYAIDIyEnv37kVtbW2jBacjIyOxadMmDBkypFkXwZtDp9Ph1VdfxZw5c/DDDz9gxowZ1sLYrq6uGDt2bKPH+vr6QqPR4OzZs/X2NbTtWqGhoUhLS6u3/fTp09b9tjRz5ky8/PLLOHr0KJYtW4bo6GgkJydb9//4448YNWoUvvzyyzrHlZWVWRM3LXXs2DGcOXMG33zzDe677z7r9t9//711J4H/LdVFRERERERERER0o+LSTNQuHn3kUVgyLUBmMw8oAXAUmPfIvCYv5G7durXBO+zXr18PAPWWEkpJSalTPyAnJwdr1qzB+PHjW3R3+7Rp01BUVISPP/643r4r8dx5552wWCxYtGhRvTZmsxllZWXNHu9qM2fORFBQEN5++20Al2cPREZGYvHixQ0uY3VlySm5XI6xY8di9erVuHjxonX/2bNn8euvv1533Jtuugn79u1DSkqKdVt1dTU+//xzhIWFISEhoVXn05grsx9eeeUVHD58uM5sCODy+Vz7f79ixQrk5ua2eswr74Gr+5UkCR9++GGr+7xSy6O1/99ERERERERERESdHWdEULuYNGkSIqMjkf1tNsxPmQHnJhqbAPlXcrh6uNa7+Hytxx57DHq9HlOnTkVcXBxMJhN2796N5cuXIywsDHPmzKnTPjExERMmTMC8efOgVqvxySefAAAWLFjQovO577778O9//xtPPvkk9u3bh2HDhqG6uhqbNm3Cww8/jFtvvRUjRozAQw89hDfffBOHDx/G+PHjoVQqkZ6ejhUrVuDDDz/E9OnTWzQuACiVSvz1r3/FM888g99++w0TJ07EF198gUmTJqF79+6YM2cOAgMDkZubi61bt8LV1RVr164FAMyfPx8bN27EkCFDMHfuXFgsFnz88cdITEzE4cOHmxz3ueeew/fff49JkyZh3rx58PT0xDfffIPMzEysXLnSWjjaVsLDwzF48GCsWbMGAOq9F2655RYsXLgQc+bMweDBg3Hs2DEsXboUERERrR4zLi4OkZGRePrpp5GbmwtXV1esXLmyTTUfnJyckJCQgOXLlyMmJgaenp5ITExEYmJiq/skIiIiIiIiIiLqTDgjgtqFXC7HL2t/ga5GB8W7isszIxoqFZALyD+UQ3Fecbm9Ttdkv4sXL8aoUaOwfv16PPnkk9bEwMMPP4y9e/fC3d29TvsRI0bggw8+wLfffotXXnkFnp6e+PXXX9GzZ88Wn8/69evx4osvYu/evXj88cfx3nvvwdXVFT169LC2+/TTT/H555+joKAAL7zwAp5//nls2bIFs2bNwpAhQ1o05tX+/Oc/w83NDW+99RYAYOTIkUhJSUG/fv3w8ccf47HHHsPXX38Nf39/PPHEE9bjkpKS8Ouvv8LDwwMvv/wyvvzySyxcuBBjxoyBRqNpckw/Pz/s3r0b48aNw0cffYTnn38eKpUKa9euxdSpU1t9Lk25knzo379/vYLaL7zwAp566ils2LABf/3rX3Hw4EH88ssvCA4ObvV4SqUSa9euRe/evfHmm29iwYIFiI6Oxr///e82nccXX3yBwMBAPPHEE7j77rvx448/tqk/IiIiIiIiIiKizkSQmlM5lqgZDh48iKSkJKSmpqJv374NtklLS8PNk29GRnoG5GFyWPpaAB2AGkB+RA7LGQv8Avyw5qc1GDBggE3jEwQBjzzySIPLKd3obrvtNpw4caLBGhpE7a05nyVERERERERERNR5cEYEtavY2FiknUrDunXrMC5+HNTr1cC/AeXPSgz0G4j//Oc/OJ913uZJCPqfmpqaOs/T09Oxfv16jBw50jEBERERERERERERUZfGGhHU7uRyOW6++WbcfPPNAC4XblYo+FZsLxEREZg9ezYiIiKQnZ2NJUuWQKVS4W9/+5ujQyMiIiIiIiIiIqIuiFd/yeGYhGhfEydOxPfff49Lly5BrVZj0KBBeOONNxAdHe3o0IiIiIiIiIiIiKgL4hVgumGwHMplX331laNDICIiIiIiIiIiohsIa0QQEREREREREREREZHdMBFBdI2vv/4agiAgKyvLum3kyJEs5tyJzZ49G2FhYY4Og4iIiIiIiIiI6IbERAQ5zOnTp/Hpp5/inXfewSeffILU1NRW9XPs2DFMnz4doaGh0Gg0CAwMxLhx4/DRRx/ZOOL2NX/+fAiCYH1otVqEhIRg8uTJ+Oqrr2A0Gh0dYqsUFBRAoVBg1qxZjbaprKyEk5MTbr/99naMrG12796N+fPno6yszNGhEBERERERERERdSisEUHtbu3atfjg3fexZftWyCCDi9wJ1aIBZsmC/n2T8dgT8zBz5kwIgnDdvnbv3o1Ro0YhJCQEDz74IPz9/ZGTk4M9e/bgww8/xGOPPWaTmDdu3GiTflpjyZIl0Ol0MBqNyM3NxYYNG/DAAw/ggw8+wLp16xAcHOyw2FrD19cX48aNw5o1a6DX66HVauu1WbVqFQwGQ5PJipb45z//CVEUbdJXY3bv3o0FCxZg9uzZcHd3t+tYREREREREREREnQkTEdRuJEnCiy++iDfffBOD5VFYij9hGvpAbVHCDAvW4zj+cXg77r33XmzetBlffPkF5HJ5k32+/vrrcHNzw/79++td/C0oKLBZ7CqVymZ9tdT06dPh7e1tff7KK69g6dKluO+++3DHHXdgz549DoutKaIowmQyQaPR1Ns3c+ZM/Pbbb/j5558xY8aMevuXLVsGNzc33HzzzW2Kobq6Gs7OzlAqlW3qh4iIiIiIiIiIiFqPSzNRu3n77bfx5ptv4h1Mw07L07gH/aHG5QvECsgxBb2wQZyHbzEH3/7733j8r3+9bp8ZGRno3r17g3eg+/r61nkuCAIeffRRLF26FLGxsdBoNEhKSsIff/xx3XEaqhFhMBgwf/58xMTEQKPRICAgALfffjsyMjKsbURRxAcffIDu3btDo9HAz88PDz30EEpLS687ZlNmzpyJ//u//8PevXvx+++/19m3YsUKJCUlwcnJCd7e3pg1axZyc3PrtJk9ezZ0Oh1yc3Nx2223QafTwcfHB08//TQsFkudttXV1XjqqacQHBwMtVqN2NhYLF68GJIk1Wl39evbvXt3qNVq/Pbbbw3GP3XqVDg7O2PZsmX19hUUFGDz5s2YPn061Go1duzYgTvuuAMhISFQq9UIDg7GE088gZqamgbPKSMjAzfddBNcXFwwc+ZM675ra0QsXrwYgwcPhpeXF5ycnJCUlIQff/yxXjxXzmv16tVITEyEWq1G9+7d65zb/Pnz8cwzzwAAwsPDrctpXV1nhIiIiIiIiIiI6EbFRAS1i0uXLuGVl1/B3zAeT2M8BDS+7NIsDMT70h34+B//wLFjx5rsNzQ0FKmpqTh+/Hiz4ti+fTsef/xxzJo1CwsXLkRxcTEmTpzY7OOvsFgsuOWWW7BgwQIkJSXh3XffxV//+leUl5fX6euhhx7CM888gyFDhuDDDz/EnDlzsHTpUkyYMAG1tbUtGvNa9957L4C6y0Z9/fXXuPPOOyGXy/Hmm2/iwQcfxKpVqzB06NB6tQssFgsmTJgALy8vLF68GCNGjMC7776Lzz//3NpGkiRMmTIF77//PiZOnIj33nsPsbGxeOaZZ/Dkk0/Wi2nLli144okncNddd+HDDz9stEC0s7Mzbr31VmzYsAElJSV19i1fvhwWi8WaRFixYgX0ej3mzp2Ljz76CBMmTMBHH32E++67r16/ZrMZEyZMgK+vLxYvXoxp06Y1+vp9+OGH6NOnDxYuXIg33ngDCoUCd9xxB3755Zd6bXfu3ImHH34YM2bMwN///ncYDAZMmzYNxcXFAIDbb78dd999NwDg/fffx7fffotvv/0WPj4+jY5PRERERERERER0w5CIbCQ1NVUCIKWmptbb99prr0lOMrVUgvckCZ9d92HCJ5K/wl2aO3duk2Nu3LhRksvlklwulwYNGiT97W9/kzZs2CCZTKZ6bQFIAKQDBw5Yt2VnZ0sajUaaOnWqddtXX30lAZAyMzOt20aMGCGNGDHC+vxf//qXBEB677336o0jiqIkSZK0Y8cOCYC0dOnSOvt/++23Brdf69VXX5UASIWFhQ3uLy0tlQBYYzeZTJKvr6+UmJgo1dTUWNutW7dOAiC98sor1m3333+/BEBauHBhnT779OkjJSUlWZ+vXr1aAiC99tprddpNnz5dEgRBOnv2rHUbAEkmk0knTpxo8ryu+OWXXyQA0meffVZn+8CBA6XAwEDJYrFIkiRJer2+3rFvvvmmJAiClJ2dXe+cnnvuuXrt77//fik0NLTOtmv7NZlMUmJiojR69Og62wFIKpWqzrkeOXJEAiB99NFH1m3vvPNOvfcNtU5TnyVERERERERERNT5cEYEtYsvP/sn7hb7wQPOzWqvhBwPmofg26//DaPR2Gi7cePGISUlBVOmTMGRI0fw97//HRMmTEBgYCB+/vnneu0HDRqEpKQk6/OQkBDrnfnXLknUlJUrV8Lb27vBYthXimyvWLECbm5uGDduHIqKiqyPpKQk6HQ6bN26tdnjNUSn0wEAKisrAQAHDhxAQUEBHn744Tp1GW6++WbExcU1eKf/X/7ylzrPhw0bhnPnzlmfr1+/HnK5HPPmzavT7qmnnoIkSfj111/rbB8xYgQSEhKaFf/48ePh4+NTZ3mmzMxM7NmzB3fffTdksssfT05OTtb91dXVKCoqwuDBgyFJEg4dOlSv37lz5zZr/Kv7LS0tRXl5OYYNG4aDBw/Wazt27FhERkZan/fs2ROurq51XisiIiIiIiIiIiJqGBMRZHeiKCLrwnn0R1iLjhuAcFTVVFuXv2lMcnIyVq1ahdLSUuzbtw/PP/88KisrMX36dJw8ebJO2+jo6HrHx8TEQK/Xo7CwsNmxZWRkIDY2FgpF4/Xe09PTUV5eDl9fX/j4+NR5VFVVtbmYdlVVFQDAxcUFAJCdnQ0AiI2Nrdc2Li7Ouv8KjUZTb+kgDw+POvUrsrOz0a1bN+sYV8THx9cZ84rw8PBmx69QKHDXXXdhx44d1hoWV5ISV5ZlAoDz589j9uzZ8PT0tNayGDFiBACgvLy8Xp9BQUHNGn/dunUYOHAgNBoNPD094ePjgyVLltTrE7icsLrWta8VERERERERERERNazxq6hENiJJEiRJgryFeS/5f+tINHemgkqlQnJyMpKTkxETE4M5c+ZgxYoVePXVV1scsy2IoghfX18sXbq0wf1trR9wpRZFVFRUq46Xy+VtGr8hV88yaI5Zs2bh448/xvfff4+nn34a33//PRISEtC7d28Al//vx40bh5KSEjz77LOIi4uDs7MzcnNzMXv2bIiiWKc/tVptnUnRlB07dmDKlCkYPnw4PvnkEwQEBECpVOKrr75qsIB2Y6+VdE3BbiIiIiIiIiIiIqqPiQiyO7lcDm93L6SX5bfouDMogEwmg5eXV4vH7NevHwAgLy+vzvb09PT645w5A61W26LEQGRkJPbu3Yva2loolcpG22zatAlDhgxp8QX65vj2228BABMmTABwuXA3AKSlpWH06NF12qalpVn3t0RoaCg2bdqEysrKOrMiTp8+XWfM1howYAAiIyOxbNkyjBs3DidOnMDrr79u3X/s2DGcOXMG33zzTZ3i1L///nubxl25ciU0Gg02bNgAtVpt3f7VV1+1us8rS3IRERERERERERFRXVyaidrFjFl342vFXphgblZ7CRL+qdiFWydPgVarbbTd1q1bG7wrff369QDqL1OUkpJSpwZATk4O1qxZg/Hjx7dohsC0adNQVFSEjz/+uH7s/43nzjvvhMViwaJFi+q1MZvNKCsra/Z411q2bBm++OILDBo0CGPGjAFwOfni6+uLTz/9tE5djV9//RWnTp3CzTff3OJxbrrpJlgslnrn+f7770MQBEyaNKnV53DFzJkzcejQIbz66qsQBAH33HOPdd+V/5Or/48lScKHH37YpjHlcjkEQagz2yYrKwurV69udZ/Ozpfrn7Tl/5WIiIiIiIiIiKgr4owIahcPP/wwPv74Y3yLPfgThl63/QacwHHzBXzw2NdNtnvssceg1+sxdepUxMXFwWQyYffu3Vi+fDnCwsIwZ86cOu0TExMxYcIEzJs3D2q1Gp988gkAYMGCBS06n/vuuw///ve/8eSTT2Lfvn0YNmwYqqursWnTJjz88MO49dZbMWLECDz00EN48803cfjwYYwfPx5KpRLp6elYsWIFPvzwQ0yfPv26Y/3444/Q6XQwmUzIzc3Fhg0bsGvXLvTq1QsrVqywtlMqlXj77bcxZ84cjBgxAnfffTfy8/Px4YcfIiwsDE888USLzhEAJk+ejFGjRuHFF19EVlYWevXqhY0bN2LNmjV4/PHH6xRwbq1Zs2Zh4cKFWLNmDYYMGYKwsDDrvri4OERGRuLpp59Gbm4uXF1dsXLlyjbXZrj55pvx3nvvYeLEibjnnntQUFCAf/zjH4iKisLRo0db1eeVIugvvvgiZsyYAaVSicmTJ1sTFERERERERERERDcqJiKoXcTHx2Pm3ffg0eXLES56YzTiGm17GDm4R/4VRg0ZWW+JoWstXrwYK1aswPr16/H555/DZDIhJCQEDz/8MF566SW4u7vXaT9ixAgMGjQICxYswPnz55GQkICvv/4aPXv2bNH5yOVyrF+/Hq+//jqWLVuGlStXwsvLC0OHDkWPHj2s7T799FMkJSXhs88+wwsvvACFQoGwsDDMmjULQ4YMadZYc+fOBXC5uLS3tzd69+6Nf/3rX7jnnnvqLCsEALNnz4ZWq8Vbb72FZ599Fs7Ozpg6dSrefvvteq9Fc8hkMvz888945ZVXsHz5cnz11VcICwvDO++8g6eeeqrF/TUkOjoaycnJ2L9/f50i1cDl5MratWsxb948vPnmm9BoNJg6dSoeffRR9OrVq9Vjjh49Gl9++SXeeustPP744wgPD8fbb7+NrKysVicikpOTsWjRInz66af47bffIIoiMjMzmYggIiIiIiIiIqIbniCx2irZyMGDB5GUlITU1FT07du33n6DwYBbJ0/B1s1b8Zg0EnMxAlHwte7PRSk+xw68J9+C2MR4/L51Ezw8PGwWnyAIeOSRRxpcTomIOo7rfZYQEREREREREVHnwhkR1G40Gg3Wrf8F8+fPx6cfL8F7FZvQTx4Ob1GLcpkB+8RMOGmccP8DD+Ctt96CTqdzdMhERERERERERERE1EZMRFC7UiqVeP311/HSSy/hhx9+wNatW1FRUQEfnQ4zBzyBe++9F66uro4Ok4iIiIiIiIiIiIhshIkIcggnJyfcf//9uP/++x0dChERERERERERERHZERMRdMNgORQiIiIiIiIiIiKi9idzdABERERERERERERERNR1MRFBRERERERERERERER2w0QEERERERERERERERHZDWtEkM2dOnXK0SEQUSfGzxAiIiIiIiIioq6FiQiyGW9vb2i1WsyaNcvRoRBRJ6fVauHt7e3oMIiIiIiIiIiIyAYESZIkRwdBXcf58+dRVFTk6DCIqJPz9vZGSEiIo8MgIiIiIiIiIiIbYCKCiIiIiIiIiIiIiIjshsWqiYiIiIiIiIiIiIjIbpiIICIiIiIiIiIiIiIiu2EigoiIiIiIiIiIiIiI7IaJCCIiIiIiIiIiIiIishsmIoiIiIiIiIiIiIiIyG6YiCAiIiIiIiIiIiIiIrthIoKIiIiIiIiIiIiIiOyGiQgiIiIiIiIiIiIiIrIbJiKIiIiIiIiIiIiIiMhumIggIiIiIiIiIiIiIiK7YSKCiIiIiIiIiIiIiIjshokIIiIiIiIiIiIiIiKyGyYiiIiIiIiIiIiIiIjIbpiIICIiIiIiIiIiIiIiu2EigoiIiIiIiIiIiIiI7IaJCCIiIiIiIiIiIiIishuFowMgIiKiy0RRRGVlJYoLipGXkYfK0kqIZhGQAEEmQKaUwcvPC/5R/vD09ISTkxMEQXB02ERERERERERETRIkSZIcHQQREdGNrKysDMdTjqM4qximdBOqD1TDnG6GmH85CWGlAOTBcqgSVNAmaaEKVSE4IRhxveOg0WgcFj8RERERERERUVOYiCAiInKQnKwcHN99HNVHq1G2tAziBbFlHQiAsrcS7ve4wz3KHUmjk+Dq6mqfYImIiIiIiIiIWomJCCIionZmNBqRsjEF+b/mo/yrcsDQ9j5lPjJ4PuOJqOFRSExOhEzGMlBERERERERE1DEwEUFERNSOzp87j0ObDqH4nWKYz5pt3r/TJCd4zfLC0FuGcnYEEREREREREXUITEQQERG1k2P7juHUilMo+6AMsH0OwkrwEOC90BtD7hgCXz9f+w1ERERERERERNQMTEQQERG1g8Mph3H6m9Oo+KyifQbUAN5veGPIjCHwC/BrnzGJiIiIiIiIiBrABaSJiIjs7NShU0hbltZ+SQgAMABFzxdh94rdKCkuab9xiYiIiIiIiIiuwUQEERGRHZWVleHkhpMo/7i8/Qc3AkUvFGH3L7thNttxLSgiIiIiIiIioiYwEUFERGQnoihi9y+7UfKW42YkSNUSij4pwsEdBx0WAxERERERERHd2JiIICIispOje46i6OsiSOWOLcdk2mvC+a3nUVRY5NA4iIiIiIiIiOjGxEQEERGRHRgMBmTuy4Rhk8HRoQAASt8rxf7f9zs6DCIiIiIiIiK6ATERQUREZAdpR9JQ9u8yR4dhJVVLqDpYhZISFq4mIiIiIiIiovbFRAS1u+HDh0MQhHqP2bNn12nXUJsrDyKijkwURZw/cR61h2sdHUod5cvKcXz3cUeHQUREREREREQ3GIWjA6Abz44dOwBcTjQ4OzvDYDDAbDbjm2++QXl5OX766ac67d3d3es81+l07RUqEVGr5OXmoXJzJeDY0hD1iHkiSjJKYDKZoFKpHB0OEREREREREd0gOCOC2l1ycjLS09MhiiIqKytRW1uLBx54AACwevXqeu1LS0vrPHJycto5YiKilsk6ngX9Zr2jw2hQ1ZYqFBYWOjoMIiIiIiIiIrqBMBFB7W7fvn2Iioqqs+3LL79stH1JSQnWrVtn77CIiGymvLgcYr7o6DAaZDxpxKWMS44Og4iIiIiIiIhuIFyaiToEk8nU6D4vL686zzdv3ozRo0fbOyQiolYRRRGmssY/0xzNkm1BUV6Ro8MgIiIiIiIiohsIZ0RQh+Dn5wcAcHFxsW6TyWSQyWTw8fGpk4wYM2YMLl3i3bxE1DFVVlaiNqNjFamuoxYwlhsdHQURERERERER3UA4I4IcbubMmSgrKwMAZGVlWbdbLJZ6bZVKJcxmMxITE1FUxDt6iajjMRgMqL3YgRMRACyG+p+vRERERERERET2wkQEOdTixYuxbNkyAMBrr70GT0/PJtunpKQgOTkZxcXFTbZ5+eWXG9xXXl6Os2fP4u2338af//zn1gdORNQIURQhGjpmfQgr8+U4ZTJOjCQiIiIiIiIi+xMkSZIcHQTdmFavXo2pU6cCAKZPn44VK1Y06zhBEAAAjb11x44di82bNzfZh7u7O0pLS1sQLRFR8+Tl5WHz3M3Qr9E7OpRG+bzvg1vn3cpEBBERERERERG1C86IIIc4evSoNQnRv3//ZichNmzYcN02ixYtanRfamoqysrKEBER0bxAiYhaSC6XQ6bt2Bf4BbnAJAQRERERERERtRsmIqjdlZSUoFevXgCA0NBQ7N27t16bo0ePIi8vDxMmTKiz/aabbgIA+Pv7N9r/oEGDsGnTpgb3XZkt4eHh0drwiYia5OzsDFWEytFhNEnhzB//RERERERERNR+eDsktTtvb2/r1+Xl5fDw8KjzAICvvvoKEydOhCAI0Gg00Gg0EAQBonh53fX09HSHxE5EdD1arRaq4I6biBCcBTi5OTk6DCIiIiIiIiK6gTARQe3u6toOZWVl9R4AMHjwYGsbo9EIo9EIAFAoFMjOzoZOp2vXmImImksQBKhcVYDc0ZE0TB4hh2+or6PDICIiIiIiIqIbCNdmoHbXnProd9xxR7PaERF1RF7+XsgNyYUl0+LoUOpx6uMEvxA/R4dBRERERERERDcQzoggIiKysajeUXC93dXRYTRIO0xbZ4k8IiIiIiIiIiJ7YyKCiIjIxjw8PODcxxlQOzqSuhTdFQiKDYJMxh//RERERERERNR+eCWCiIjIDmL7xcJpTMcqCu0+yx3xSfGODoOIiIiIiIiIbjBMRBAREdlBWFTY5eWZlI6O5DJZoAwe8R7QarWODoWIiIiIiIiIbjBMRBAREdmBXC5Hn1F94Pagm6NDAWSA9wveGDBugKMjISIiIiIiIqIbEBMRREREdhISEQK/iX5QRCkcGofLTBckjkzkbAgiIiIiIiIicggmIoiIiOxo0PhB8PqbF+CgchHycDl8bvNBZHykYwIgIiIiIiIiohseExFERER2pFarMfjWwfB+3bvd60XIAmTwW+iHYTcPgyAI7Ts4EREREREREdF/MRFBRERkZz6+Phh0xyB4v+UNqNtnTFmgDP5v+WPMtDFQq9tpUCIiIiIiIiKiBjARQURE1A4CggIwdOZQ+L7vC1mAfX/8qgao0O3v3TD2zrFwcnLQmlBERERERERERP/l2OqZRERENxBfP1+Mu28cdvnsQtHKIlQtrwIk2/UvOAvweNIDwSODkTQ8CQoFf8wTERERERERkePxCgUREVE7cnZ2xrhp45Aem46TQ06i5LMSmI+b29apEtCM0sDzHk8MmDQAPr4+tgmWiIiIiIiIiMgGmIggIiJqZ4IgIKZHDIKjgnGy10nkpuWi4tcK6H/VAzXN70fmK4PrDFc493NGdFI0ImMjIZfL7Rc4EREREREREVErMBFBRETkIE5OTkgaloQ+Q/ogZ0wOztx1BjWlNai9WAt9qh6msybAAEgWCYJSgKAToOmhgaanBiofFVx8XJA4OBGenp4QBMHRp0NERERERERE1CAmIoiIiBxMJpMhNDwUoeGhkCQJNTU1KCkpQXFOMUwGEworCuHr7guNTgPfYF+4u7tDpVI5OmwiIiIiIiIiomZhIoKIiKgDEQQBWq0WWq0WQUFBAICdO3ei39B+Do6MiIiIiIiIiKh1ZI4OgG48w4cPhyAI9R6zZ8+u13bGjBn12i1ZsqT9gyYiIiIiIiIiIiKiVmEigtrdjh07AFy+61en00GhuDwx55tvvsHUqVOt7V555RUsX74cAKBWq63tHn74YZw9e7adoyYiIiIiIiIiIiKi1mAigtpdcnIy0tPTIYoiKisrUVtbiwceeAAAsHr1amu7RYsWAQCeffZZGAwG1NbWIiYmBgDQu3fv9g6biIiIiIiIiIiIiFqBiQhqd/v27UNUVFSdbV9++WWj7d966y3r12lpaQCA6upq+wRHRERERERERERERDbFRAR1CCaTqc7z+fPnAwCUSqUDoiEiIiIiIiIiIiIiW2EigjoEPz8/AICLiwsAYNu2bQAAZ2fnem1lsstv25MnT7ZPcERERERERERERETUagpHB0A0c+ZMlJWVAQCysrIAwPq8qRkRJ06cQEJCgp2jIyIiIqKuQq/Xo6SoCCVns1BZVAzJbAEEAXKVEu6B/vAIC4GXlxdn5RIRERER2RgTEeRQixcvxrJlywAAr732Gjw9PQEA7u7uAIDa2tpGj+3evXuD21NSUvDyyy83uC81NRUAUFpa2tqQiYiIiKgTEUUReedzkLk3FaozF+C9+wSCT+XA7UIRZBYRAFCrUaEs3BclPSNwdlAC1JFBiBoyAB4eHg6OnoiIiIioaxAkSZIcHQTdmFavXo2pU6cCAKZPn44VK1ZY982fPx8LFiyAUqmsVz9CEAQAQGNv3bFjx2Lz5s1Nju3u7s5kBBF1Gjt37sTQoUMdHQYRUadTUlyMo+s3IWD9PoSt3Qt1taFZx1UGeCB95miYhvZC7wmjodFo7BwpEREREVHXxkQEOcTRo0fRq1cvAED//v2xd+/eem0aSzhcLxFxvRkRZWVl6Nu3r3V2BBFRR8dEBBFRy4iiiBM790C/fT96v/Mj1JU1reqnNNwPR569E9FjhyMwMtzGURIRERER3Ti4NBO1u5KSEmsSIjQ0tMEkxNVefPFFvP766wCAuLg4AIBWq220/aBBg7Bp06YG912ZLcFp9kRERERdkyiK2L9+I3yXrEaP9Qfa1JdHZj6GPfIPHHy2AIY7JyGyV6KNoiQiIiIiurHIHB0A2cakSZPg5eVVZ1tUVBQEQYAgCB1qOrm3t7f16/Lycnh4eNR5XPHCCy8AAN544w04OTlBqVQiLS0NALB///72DZqIiIiIOjxJkpD62yZ0e/cHhLcxCXGF3CKi35vLUfb9L8g+edomfRIRERER3Wg4I6KL+O233+o8/+tf/4qMjAzrc6PRCJ1Oh6qqqvYOrZ6rl1QqKytrtN3rr7+OtLQ0rFy5EgbD/9bzfe+995CQkGDPEImIiIioE8o8cQouSzcieNtRm/YrSECfv/+I3b7u8AwOhIuLi037JyIiIiLq6jgjogtRqVTWrz/55BMAwOeff2698F9dXe2QuK4lSVKTj6v9+OOP9fY/8cQTDoqciIiIiDoqvV6PC9tSEPP9drv0L5Mk9HnjPzj0y0aIomiXMYiIiIiIuiomIroQtVpt/dpsNgMAHnzwQQCATqdzSExERERERO3h6Mat6P3GcsiuubHFlpyLK9Ft+TZkn06z2xhERERERF0RExFdyJUZD2+99RaAugWda2trHRITEREREZG91dTUQErLgmteid3HCv9lH86nHq03k5eIiIiIiBrHGhFdhCAIEEURMpnM+kfRa6+9Zt1vNBodFRoRERERkV2dSz2MiKVb2mUsea0F7vvTUHJTCby8vNplTCIiIiKizo4zIrqITZs2AfhfIWhfX19rLYV169YBAGQy/ncTERERUddTnJEN32PZ7TZe6JoUXDh8vN3GIyIiIiLq7HhluosYPXp0nYLO+fn51n0jR47ETz/9hNzcXAdGSERERERke2azGfKSCgjtOKbrhSJU5he244hERERERJ0bExFdhCAIGDJkSIP7dDodFi9ejICAgHaOioiIiIjIvsrLy+F2Oqddx5SJEsTyKtaJICIiIiJqJiYiupDTp0+3ah8RERERUWdVU1MD54yL7T6uskIPi8XS7uMSEREREXVGTETcIKqqqhwdAhERERGRzYmiCHmNqd3HlZktTEQQERERETUTExGdWGBgIARBgCBcXhG3pKTE+vzah9FodHC0RERERES2J5PJYNGq231cUSmHXC5v93GJiIiIiDojJiI6sYKCgha1v//+++0UCRERERGRY2i1WlRHdWv3cc2uWiYiiIiIiIiaSeHoAKj1amtrrV8LggBPT08UFxc7MCIiIiIiovbl5uaGU7HB7TqmKJdBcHOxzkwmIiIiIqKmMRHRRUiS5OgQiIiIiIjanVwuh8XDBZIACO30K3F5sDdc/XzaZzAiIiIioi6AiYguaMuWLdi8eXODxfPeeustB0RERERERGQ/3jERyO8dAf9D59plvOzbhyK0T492GYuIiIiIqCtgIqKLOHr0KPr06QNRFJtsx0QEUfspKSnBuWPnUJxXDFOlCWKtCEEQIHeSw9nDGd2iuyE8OhxKpdLRoRIR0Q2opqYGOafSUJKVA1N5BWQGEyABkloJuasOHsGBCOoeBxcXF0eHel0RfXri4D2j2iURYVYpUNEnCh4eHnYfi4g6BkmScOnSReTnnkBleSFgqYIACyRBBknQQefiBd+AOHQLCoVMxlKcREREDWEioovo1auX9Wu1Wg21Wu3AaIhubOfPncfx3cdRdbAKlT9XwpJtAcx12wjuArJ7Z+PktJPwjvJGv5H9oNFoHBMwERHdUCoqKnBy8x+Q0s8j9Ic/EHr6AtTVhjptajUqlEb649Qdw1EbH4rYkUPg7evroIivT6PRQBEbhrJgb7jnFNl1rIypgxGa3NuuYxBRxyCKIs6eOYyL2Ufgp9qKCM0W6NwuQnbVOnCSJKDK7ItL54bij1MT4ROQgLjuA1jMnoiI6BpMRHQxrBVB5DgGgwEpG1OQ/0s+Kr6uAEyNt5XKJBi3GVGwrQDFEcUoeqYIvUb3QnhMePsFTERENxRRFHHmwCEUb9+H3u+sgHNhRaNtlQYTfE+ch++J72B0ccLRv57DhVHJSBw+GApFx/wTosfYkdj3/CUMe2wJZJamZwm3VqWfOwqnDceQmGi79E9EHUd5eTkO7/8FIcqlGO71a53kw9UEQYKLMh8uypWI0q3ExbJB2LH5L+jRdxK8vFlLhoiI6ArOGexCtFqto0Noli1btkAul0MQBOvDzc2tXrur91/7IOpoqqqq8Pv3v+Pc4+dQ8XnTSYhrWc5ZUPBoAfZ+sBf7t+9nQpGIiGzOYrFg79rfoHztXxj8ty+bTEJcS11Zg+TXvofvC59j98qfYTK14IdcO3JyckLYqCE4NXusXfoX5TIceuEu9LlpHH8fJeri8i/l4kjKN0h2eRThuvWNJiGuJQhAoHMKBrs/jLTUf+J81mk7R0pERNR5dMzbmahV9Hq9o0NolrVr1163lsXV3N3d6zzX6XQ2joiobfR6Pbb8sAX5z+RDKmllEsECVCypQHp5OmQyGZKGJdk2SCIiumFJkoT9v2xA6NvL0C2l9RfFuu09Dc1LX2EPJAyedmuHnBkREhuNQ3eNQ2ZeKcLX77dZv6IgYP+LMxBx02g4OzvbrF8i6niKCvORfmQ5Bns9D4WsdYlXlbwaA71exv50I2SyexEUEmXjKImIiDqfjvfXA7XKQw89hM8++wyCIGD06NGYPn16gxfs7733XgdEV9eDDz6IwMBAPP300xg5ciS2b9/eZPvS0tJ2ioyo5SRJws71O1HwUkHrkxBXqVpWhXPB5xAYGQj/bv42iJCIiG50Zw8fhde3G9uUhLjC82weot9diePe3ug9ZoQNorMtQRDQZ+xIpFosqHXWIHrFDrR17oJZpcCBF2cg4I5JCIqKtEmcRNQx1dbW4vjBXzDE88VWJyGukAkikj1ex85TvvD07tZpVjAgIiKyFyYiuojPPvvM+vWWLVuwZcuWBtt1hEREQkICEhISmt2+pKQEu3fvxi233GLHqIha58zxMyhaWQQxz3ZrUZd+WIp9sfswadYkKJVKm/VLREQ3nqqqKlzavhdDV+60WZ8B+8/gwtb9KEqMg7efn836tRVBEJA0YQzSPNyR0i8avd/6AdrSqlb1VZgQghNP3o748SPhFxxk40g7jurqamSnZ+NS5iXUVNRAMkuAAKicVfDq5oWQuBB4e3tzSSrq8o6mbkaC8/tQygw26U8mWNDb9Q0c2uePwSNu5/cQERHd0JiI6CKu1Fzoiry8vOo837x5M0aPHu2gaIj+RxRFpO1LQ9Xy1l3caJQBKP2qFGf7nEV873jb9k1ERDeU03+koOe7K9HM5c2brecHq7E/MQJDZ9xu245tRBAExA3oh/K4aBwI9oXXlkOIWLkLTs1MSJSG+yH9vrEQ+nfH4LEjoVKp7ByxY5SUlODgloMoTytH+cpyWM5aIFVe9WZRAjkhOTg77iycBzkjrl8cIuMju+zfHXRjq66uRm3lQfh6Hrdpv66qi9Dpt6OkZGS9v22JiIhuJExEdBFms9nRIdicTHa5lrqXlxdEUURxcTEAYMyYMcjLy4O/P5etIce6cP4CKjdUAnaoLW3cbUTGkQzE9YrjH/tERNQqJpMJhrPZcLtQbPO+1dUGqE9mobKyEi4uLjbv31bc3NwwbMY0FI4aiiMTB0LMvACv/WfgeTQTbjmFUBhqIQkCTC5OKA3zRWm/GBT3iYJrVCjiB/br0OfWFhaLBYd3H0bW9iyUvFsCqayRX2ZqAUuGBeUZ5Sj/Zzkq7qxA5u2ZGHLTEC4zQ13OuTMHEOX0b7v0Hen8H5w8NQZeQ2+1S/9ERESdARMR1GFZLJZ625RKJcxmMxITE1FUVNTgcSkpKXj55Zcb3JeamgqAdSfINs6knoF+vZ2KxFsA/SE9yqaUwcPDwz5jEBFRl5Z3PgdBP6fYrf+wH7bj/PhB6D50kN3GsAVBEODr7w/faZNhNBpRWlqKoqzzOJdXAHNtLcpgga/GGR5hwfAPDECcuzvkcrmjw7Ybs9mMbT9vw8V/XIRhSwuWn7EAVd9XQb9ND32pHiPuGAF3d3e7xUnU3koKM5DomWaXvnXKQhjLcyCKovWGOyIiohsNExFdTL9+/XDixIlGZ0jU1ta2c0S2lZKSguTkZOvsiIa8/PLL2Lx5c5P9nDt3ztah0Q2oprQGUrUdpkP8V/WeahRdKmIigoiIWqXkbCaiTp63W/8e5/JxJveS3fq3B7VaDX9//zoza3fu3InkoUMdGFX7kSQJ29dtx4U3LsCU2rpCvGKeiIKnCrAd2zH2nrFwdna2cZRE7c9oNEIjXII9JyK7Kc+goqKCCTwiIrphMRHRRXz77be47777HB2G3fXr1++6bRYtWtTovtTUVJSVlSEiIsKWYdENyGQyobbIvok9yzkL8jPzER0fbddxiIioa6oqLoXukv1mgSpqzRArbFwniezq9JHTuLT0UquTEFdIlRIKXy7ELvddGDd9HJeRpE6vrKwM7oqjdh3DQ56K0pICJiKIiOiGxUREF3ElCeHl5YV7770XH3zwAaKjozFy5Ej885//BAC89957jgzRJjZs2HDdNoMGDcKmTZsa3Dd27Fhs3ryZd5hTm5lMJohlol3HECtEGGuMdh2jvZnNZlRWVsJsNkMul8PFxQVKpdLRYRERdUmCqdbmRarrMXXu2bY3Er1ej9N/nEb1ymqb9Cfmiyj6sQjp8emISYyxSZ9EjmIymaBCnl3HUMvLoDeU23UMIiKijoyJiC5EEAQUFRXh66+/BnC52PPnn3+Ozz//HIIg4Mknn8QTTzzh2CCb6ejRo8jLy8OECRPqbL/pppsAgIWqqWPgzX/NYjAYkHn2CArzTkMhFcNVcRYKoQoWOCHNHIlaeMPDOxIRMf24vAMRUSfDH4Wdx8n9J1GypASwYXKqamUVztx0BtHdozkrgjo1g8EAhWTv97AAu679RERE1MExEdGFXLmA179/fwBAZWWldZ9Wq4Veb6eiuq3g5OQEs9lsrWVRUVFhvSt6xYoV2L59Oz744AMAl9fyBS6v23lFenp6+wZMdA21Wg25h30LWcrcZdBoNXYdw55EUcTpE3tQcnEfIrX/RpzHYQgN3JorSUBxdSyO7rofGo9k9OgzEgoFfzwREbWZWglRECCT7DctQlJzVltnIIoics/kwnLaYtuOLUDVzioUTCyAn5+fbfsmakcXz12Eh8kPcLPfGAazB9Qad/sNQERE1MHJHB0A2Y7BYAAAJCQkAAAKCgrq7esoDAZDvYLaVxIT27dvx+DBg63bjUajNQmhUCiQnZ0NnU7XrvESXUupVELpbd+LL4ooBfwjO+fsH4PBgJ1blsO5ZD6GeD2BAO2hBpMQwOUbw7yd0jDI6wUEGJ7Hzs3L6iRSiYiodXTeXqjs5mm3/s1qJeSuLnbrn2ynrKwMxsP2We6xelM1sk9k26VvovZSeakShcWxdh2jVEyGh6ePXccgIiLqyHjLaRdy9YV9QRBgNputswxE0b5r2beU1Iw785rThsiRtB5aCG4CpHL7vFedBzvD28/bLn3bk8FgwJ4/VqCvy3NwVV1s0bH+2gNwUz2OvbuMSBoyAy4uvMBFRNRaXtERKO4RBrfcYrv0XxIVAI+QQLv0TbZVlFeE6gO2qQ1xLUuOBSX5JXbpm6g9WCwW1JbWQlJ5QpLst3pSeW0Uf7clIqIbGmdEdBFTpkyBSqXCyZMnAQDff/89ANRZ/ujNN990WHxEXVFscix0U+w0O0cJaHto4erqap/+7USSJKTu+QW9dS+1OAlxhZOiFP09nsPBPT93uCQqEVFnEhAchAuTB9qt/8wZIxHSI8Fu/ZPtlFwsgSXXxssyXWEBzNXm67cj6qD0ej0sFy2o2qvBpeoedhmjwtQNWtdQyGS8BENERDcu/hTsItasWQOj0Whdlumuu+6CJEn44Ycf8Ntvv0GSJDz33HMOjpKoa+kW2A26UTrADqUiNCM1iO7b+Qo/Zp07CS/pJ7irz7epH62iBOHqL3Hq2G4bRUZEdONRKBRwiQ5FSYTtl/mrcXOGJTbEWqOMOjbRIgJ2zBVIImcyU+cliiKkWglVP8txImuaXcY4U3k3IuMGX78hERFRF8ZERBd3xx13YMKECY4Og6hLEgQB3Yd0h8ts206xFpwFeM7yRGRcpE37tTdRFJF9dh9iXJbZpL9g7VaUXDqM2tpam/RHRHQjihs6CMeeuh2izLaJ7SPPTEP8mOE27ZPsR6lWQtDY7+YGQdG5bpwguppCoYCgFSCVSSje54ucsmSb9l9iCEeNYiA8PDxs2i8REVFnwxoRnZS7uzuAywWpVSqV9fn1lJWV2S0mohtReHQ4sm7Jgn6LHpZM2yx54PGMBwbdNAhyuR2mWthR/qWL8FdtgayRotQtJQhAqGYlsjOHIyqmp036vBGZzWZcOH8W+RdPwWQoByQDAAEQtNDqvBAY2ht+/gGdbvYNETWPk5MTQkcOxul7zyDhm02NthMFAZd6h+NiUhRqvFwg/PcOd0kmQF2hh/+RLATuOwO52YLzo3pCNyIZHp72K4RNtuUT6gNFhAKWbDssz6QBNK4a2/dL1E60Wi2UgZdrK5b/U46D3e+Fb7/TUMsr29y3WVRi78V5GDqRNwcSERExEdFJlZeXAwDOnz+PqKgo63Mial+CIGDIpCHQV+px6dlLEPPaVtPA7WE3xN0cBy9vLxtF2H7yco4ixul3m/bZTbsLe3NPMxHRCqIo4sypA8i/cBDB6jXopd0FjdP//qCWJKDa7Ifzpyfh9LGRiEkYgW5BYY4LmIjsJjQ+FqkzxiIrvxRhv6XW258zMBYZY3vD/2gm4lfvgbaoAlenJmvcnHExOQo7n50Gl9xi6G8ejEGD+rffCVCbefl4wbm/M4xbjTbvWxGhgHeQt837JWovgiBA5aa6vNxqLVD4thO2vPoSxiQugEqub3W/FlGJbadfgEkMYpFqIiIiMBHRaT311FMAgKioqDrPiaj9qdVqjJk+BluELSh4twC1h1uxlJAa8HjCA3FT45DYL9H2QbaD6spiOLvn27RPhawWllomWluqpqYG+3evQbDiWwz3Wo+GJjsIAqBT5iNB+TVixKU4kTYXF3Mmok//8Z1uNg4RNU0QBPQdPxoHAeh93RH33RbIRAlmpQKpfx4P54IKDHtzBeSWhpPpTuXViNx0BBGbj+DM5P6oEkWYzWZ+VnQiOp0OmgTN5QutNp4U4Xq7KyISImzbKVE7C0kIQcHAAhh3GSGeF3HxDQ/8/txCDOv+HlxVF1vcn97sgR2nnkb+Kj/EPh5sh4iJiIg6HyYiOqnFixdbvz5//jzS0tLQp08fLFy40IFREd24nJycMP6u8djvux+5W3JR9kkZpMrmLVGk7KOE16NeSJ6QjIDAADtHakeivsEL3m2lQAUsFgsveDVTTU0N9v7xA5Jcn4eLKq9Zxyhktejl/v+Qqz+AvbuMGDh0CmQylpEi6kpkMhmSJoxBVnAgdvSPRff3f8LpqQMRteEg/I9lN6sPQQJif94Hv8OZSDHVYtAdt0KtVts5crIFQRAQ1TsKxcOLbTorQnAW4NLdBa6urjbrk8gRortH48ydZ1C4qxAAYMmQcPE5V2x66kVEJWxDQrfVUMiuf7ORKMlxtmAcTpy5BcWL1fD4szu69+9u7/CJiIg6BSYiugCdTod169bh999/ZyKCyIGUSiUGjx+M/J75ONL3CCpPV6L8p3JYzlkgVVyVlJABsiAZNMkauExwQVBcEHoO7AmlUum44G2ibctSNUYQLBBFkYmIZpAkCQdS1qKv6wvNTkJcLVC7G+aqj3HyqBcSew+zQ4RE5EiCICC8ezz8wkOR4uKEmE/WNjsJcTX384Xo9co3SNU6YdDUW1hjppOISohC+n3pyNuTB9TYpk/3v7qjz6g+tumMyIFUKhVC+oSgYngFjH9cTtZJxRKKXlCgavRYZN46DH5+JxEZuB3u6kwoZQbrsWZRiXJTGLIuDUZufl9UbFBDv06AIkYGn34+0Ol0jjotIiKiDoWJiC7A87+FAkXRPhcBiahl/Pz9MP7O8aiqqsL5KeeRn5UPfbkekvlyMkLhpICHnwcCYwIR0C2g61xgF1R26dYiOXWd18jOMtKPwl/2Q6uWELgiVLcZewpGoqysB9zd3W0XHBF1GPqqKrilXUDw3jOt7sMjMx8ea3bifEIMQuNibRgd2YtCoUD/Cf3xxxN/oPSN0jb3p0pWIWhUEHz9fG0QHZHj9R7cG5cevISLRy5CKv/fTUSGLYBhixpFEf2QOagnNAkWKFxrIchESKIM5moljGlyVO9RwXLqv2ufqQHvp7wxcOxAB50NERFRx8NERBehVqthNBrx7bff4t5773V0OESEy7OVEnonIKF3gnVbamoqkpKSHBiV/ciVrjCLaihktlvyQZIAUebGZYKaQRRFXMhMxQivVW3uq4fr/8Px4z0wYOitNoiMiDqaM9t3o+/Haxvcl/qncUj68vdm9RO7dCt2jO6NkNgYzoroJPwC/BAzOQanCk6h4ouKVvejiFMg4OkAJI9MtmF0RI4ll8sxbMowbKnagsK/FdZbZtVyzoLKc3JUQg7g8g04Tn9yQs2XV6YYXZWEeMMbAycPhEplnxt1iIiIOiNe2ekiJOnyL0n33XcfBEGAUqmEWq2u89BoNA6Okohqamy0FkIH5OkTiiJDnE37rKztBp2Lj0377KryL+XBX7UZgtC82iRNcVYWwqzPgNFou6QSEXUMer0e8vQcaCr0De6v8XRpdl8yiwivlJMoKiqyVXjUDnoN7IX4P8XD43kPoBV/HjhNckLQoiCMnjoaCgXva6OuxdXVFSNnjITf+36QR15/Rq7Ms+4lFVmADD7v+WDI3UM4W4iIiOga/M2xizCZTHWem81mB0VCRDeq0IgeOJx7N/y1R2zW5zn9XQjrM8Bm/XVlhZdOI0S902b9+Sp3oaTkbgQEdOIC6kRUT1FBAfy22O5z2m/bURSmn4OPD5PG7aWkpAQXzx+HyVgJURShUjnB0zcK3QJDmj2DsNfAXvAL8cO+6H0o/aoUxt1G683cjZGHy+Ex1wNRw6OQmJxo89mKkiThYu5F5KblwlBjgAABGmcNwhLD4O3tzVk31G7c3d0xYdYEpPimIH9jPiq+q4BU1vSNHoKzAOdpzvCZ7IMhNw2BVqu17pMkCUVFRbh04ThMxmpIEqBSO8HHPxb+AYEQBMHaJut4FgzVBkiSBI1Wg8DYy0u5cnYwERF1BUxEdBFPPfWUo0Mgohuck5MTZNpYVJi6talGwRUGsxsq0c9aB4eaVlFWAFfXCzbrz0N5AgVFmUxEEHUxZRnZCDlju88K96wCpOdesll/1DCLxYKc7HScP3cArsJBBGt+hkZeDkFmQa3JGfkZg/DHyfHwCYhHRHRfODk5XbdP/27+mDRrEtJ7p+Pc0XOoOVaD6r3VMGeaIdVIEOQCZN1kcOrjBG1/LXyjfNFjUA+bF941Go04c/QMso5noWprFaq3VUOqlgABkLnKcG7SOTgPcEZUnyhExkVCqVTadHyihqjVaoycPBL5yfk4NvIYKjIqoN+jh/G4EWLJ5dqMMlcZZH4yeC/yhnO0M7oP6o7A4EBr0qy2thbnM0/jQtYheCr2IlDzK9SyCggyCSaDM/LSRuL00VFQyINQdtEA/T49qn6tglghAiIg6AScHXEWutE6hHUPQ2yvWKjVake+LERERG3CREQXsXjxYkeHQESEHn3H48Afz2Oo9zzI2rBEkCQBh8r/hsTkSTaMrmuTRBNkgmiz/tTyCpgMlTbrj4g6BlO1HupK2y0TqDTWQrxmZi7Zll6vx75dqxGi/A6D3TZDIbv29S6Fm2oFonUrUFDeA/u2/wVxvabALyDoun0rlUok9ElAfO94VFRUoKigCIVZhTAZTag0VSI8PBw+QT7w8PCwyzJMRYVF2LV6F0r+WYLaA7XANb86WIotKP+kHOWfl6NkWAnOzj6LEVNHwMWl+UuIEbWFn78f/Kb5wWAwoPTeUuRl5EFfrkeZsQz+Xv4oNZViyJC6MyAAoLKyEgd2/4QIzTcY6vkH5ELdFQu0ihIoZBtxNr0vSv5jgXGHCFyzqIFUKqHquypULa1CcVIxMh/MxOCpgzkDjYiIOi0mIoiIyGa0Wi1CY8bgyLnH0dv9fbR2FYUzlffAvdsoeHh42DbALs22U/ZFyCGTXX9tZCLqXAS5DKLcdp8XEgBJxiVz7KWmpgb7dixHkusLcFE1PfNEEAA/7TF4aR7HvqNlsIj3oFtgWLPGEQQBbm5ucHNzQ2R0JABg586dSOiT0NZTaFRBfgF2LtuJoheLgOvlxsyAYasBeUfysMWwBaPvHs1kBLUrjUaDgIAA60zRnTt3ot/Qfti5c2eDSYjUXd9jgMfz0CpKGuyvstYPW1JfQsErakgl17mRRAJqD9Qi/0Q+dlTtwNB7hsLXn/UniIio8+FCg13I5MmTIQhCk4+OYMuWLZDL5XXicnNza7DtjBkz6p3DkiVL2jliImqJ0Ih4OAfeg4Olz8AitSzfLUoCTpQ/gBrXPyGu+0A7Rdg1qTQ6GC22uyhTaQqCzi3QZv0RUcegC/BDZTfbLXlX7esGZzdXm/VH/2OxWLBv56pmJSGuppDVYoDXq8g4vhplpaV2jLD1qqqqsHvVbhS90IwkxFWkEgkFzxVg26pt9WrkEXUEJpMJqbtXYYDHc40mIUwWJ2w7+hwKXlZDKmnBDOIaoOiFIuxetRtVVVU2ipiIiKj9MBHRRfTo0QPr1q1zdBjNsnbtWoji9ZcPeeWVV7B8+XIAl9fovDId/OGHH8bZs2ftGiMRtU1MfD/4x/8FO4r+gSJDbLOOKTcFYWfxh3AKfhy9+o7qMMnTzsLDOwwlhiib9Vds7g8PL3+b9UdEHYNncCBK+sXYrL+S2CB4RIbZrD/6n5zsdAQrl7UoCXGFXDAjyf01nD6+zfaB2cCRnUdQ9FYRYGj5sVKphOIlxThz9IztAyNqo8yzRxHttAROisaTgGfyJ6H4a2dIpa1YxtQIFL1VhMM7Drc+SCIiIgdhIqKLOH78OIDLF/k///xzAEB8fDwkScLgwYMBAGFhYY4Kr44HH3wQ77zzDiRJwogRIxptt2jRIgDAs88+C4PBgNraWsTEXP7DuXfv3u0RKhG1QWBQBAaNuh8X1B/jj6JPcLbiFpQaQyFKl5f7ESUB5aZAZFWOwc7iD3AGnyJp6AOIiO7BJEQrBIXEIts43SZ9iZIMpZY+cHd3t0l/RNRxeHl5oWBI92uX4m+1nNsGo1tYqI16o6udP3cAIc6bWn28VlEMqSYdNTW2qwliC7W1tSg8WwjxfOvrGpn2mZB5LBOSZKt3MlHbSZKEvJzjCNDua6KNgMyLw2Da2/r3rpgjoiijCLW1ta3ug4iIyBFYI6ILUalUuOWWW6yzCMzmy9Wudu3aBblcjqysLAdG9z8JCQlISGj+erNvvfWW9eu0tDQIgoDq6mp7hEZENqZWq9G731iYzSNxKe9m5BSexcnyQkiSBWVVcgR184Bntyj0DwiCSqVydLidmlarhaSOQ1WtD3TKwjb1lVM9Et2CezIhRNQFyWQy+MZHI69/DLrta9sd5eVBXlBGh0KtVtsoOrqipKQELsKhBgpTt0y40zJkpg9FQs/BNoqs7TJOZaB8ZXnbOpGAqj+qkDchD926dbNNYERtlH8pD77K7ZAJjScZ8vWJqNqtBlqfhwMAlP9YjrPDzyK+Z3zbOiIiImpHnBHRhSiVSgDA1KlTAQBFRUXWfU5OTg6JqbXmz58P4H/nRESdm0KhQFBwCHr2HY0ho+7C0NH3wN0zEL37jUdIaASTEDbSo+94HC5/HpLU+gSCweKCTONsRMb0smFkRNSRxPRPwpm5t8Dk1PrPXlEm4PDzdyFxzHAbRkZXXDx/HCGatW3ux8/pKIoKOtaSptkns2HcaWxzP9W/VuPc4XM2iIjINi6eP4wQ7a9Ntsm4MArVG9v+N65xpxHnT55vcz9ERETtiYmILkSv1wOA9YJe6VXF6TrbDIJt27YBAJydnevtk8kuv21PnjzZniEREXV4Op0OAeGjcaz8L2jNahUWUYkDpfPRq9/NkMvltg+QiDoEhUKBxEljcOCVmbDIW/7ngATg8OO3IXz0UGi1WtsHSDAZq6CWl7W5H0GQIEgdq6izxWQBzG3vRywTYaxpe0KDyFaMxprrft8aal0hlrdxOgQAWP77vURERNSJcGmmLkImk0EURVy6dAn+/v5wdXVFRUVFnWU1OtPsgrKyMgBNx3zixIkWLfF0I5MkCYUF+cg6uw9GQzUAETK5En7d4hAaHm99nS0WCy7knENu9hFYzJf/aFUqNQiOSEJAt2BrEsiRJElCUWEhss7uhcFQBUgiZDIlfLtFIzS8O++spy6npqYGlRUl2Ll1OSTRDEGQw1nngYjYgXBzc6vXPjKmF06Z7sOhS1r0cvt/kMuat35wjdkDB0pfQUyvafDw9LTpOZSXl+Pc3lRUl5RCsogQFHJ4BHVDRN9enW7GXnuwWCw4f+48Mo5moNZ4+f9PpVEhum80gkKCOsRnsSOYTCZkHT+FwrPnINaaAZkAjU6H8IFJ8PL2tvtSYqIo4mL2eeQcOgaLwQgJEhRqNQJ7dUdgeFinS955+/nBeOfNSFHI0G/R99BU6Jt1nFmtxKGnbof7tHEIibtcu0uv1+NceirKii9CksyAIIeLixciYgfA1dXVnqfhcDU1Ncg8ewglRTmQxMvn7qzzQETMAGudnWvbSJBDkOlgKFbDXGsGREChUiC0eygiYiKgUCggiSJkMhtcrAQAm1UEsQ2b1XUQAKNYge2bV8CgL4UomiETAKVaB2/fUETG9GvwpiYie5FEEbLrrLkkSQrARvkDSexY39tERETXw0REJzZ16lT89NNPAIDMzExs2bIFOp0OwOWLPh4eHtYL+q6urigvb+NarO3oyh9uTRXg6t69e4PbU1JS8PLLLze4LzU1FUDd2SJdmSRJyDx7DDmZh+Cl2IVE51VwciuGIFy+8zk3bzD2ZE6HxjUSCqUKFcUZCFKvQ7LzJihllwsbGi0uyEq/CX+cGAe/wO6ITejvkItgkiQh+9xJZJ9Lhad8DxK0P0LrWnj5XCQF8vIHYF/2ndC4RqF7r1G8uEmdXnl5OU4d3QzJcBaqWhMG+74CmSBBkoAKUzAy9t+DaqEnouKHIqBbSJ1j4xMH4qK7P3aciEWs02fw1x6E0Mh6xWZRhazqicg134Xeg25uMLnRWpdyLiB95x5oj51D5NKtcMsphCABoiCgqHsIDs8aDUSFIGHMMLixMDbMZjMO7zqM3LRcVK6rhH6THvhvjVlBJyD/pnzoxusQ0j0EPfv3vGESEnq9Hie37oTh7HmErdiOgXvPQG62QAJQ7eOGzDuG4sSAeIQm90FYfKzNx7dYLEjbl4qCk2fQbcMB9P31ANRVBgCAyUmF8xOSsPOmZHjGRiJ+8AAoFJ3n1+vAiDBoH7gLe/y9ELJ0C0I3H2q0rSgTcHFALM7+3yTEjR8J/6BAlJWW4tSxLRCMZxDhtBQJrif/+zkloMwUgjP7ZsIgS0R0/DD4BQS145nZX0VFBU4e2QSp5iwinJYizuU4ZIL4v8/o1LtRbukFuUyAUsxBuNMyxOqOI+3SLcjIHYnKXU6oXieDVPbfz2YVcGnEJZy87ST8ov3g5K6CqVYLraK47cEKHetmJJmyjZ9dasDtfgu0g0xQqU5Aa8pBT6/f4a7KgiBIECUBRVVxOPTHnZA5dUd8z9Hw8PSyTfBETVCq1DCJznCSlTXaRiWvguAsQDK1PYnQ5u8lIiKidiZINrslhdrblTv/VCoVnn32WSxcuNDBEbXcyJEjsX379nqJkvnz52PBggVQKpUwmepOJ79y3o29dceOHYvNmzc3Oa67u3uXT0aIooiD+zZCV/NvxLj8AJnQ8K03RosLdl16ErHua9FNexCN3VAqSQLOV4/GBelh9B8yuV1n2EiShEMHNkNTuRSxrt9C3si5AECZMQSHK19F30HTO+RdmDt37sTQoUMdHUaHwNeicQWXLuD04Z/Qz2MBtIpi7Mx7BkMD3qnXrlbU4EjZE3ANuh0x8f3q7TeZTMg4cwj5uSfgqkiHh/wAnOSFkCQBVZZwlJr7wiCEIySiH0LCom16YTv94GGUrfodvd/7CcqaxpcF0XvqcODVmYidOgl+wV3rQmVLGI1GbPlpC/Lfy4dpfxPLqAiAZpQGAQ8HYOSUkZ1qtmNrlJeV4dCPa9F74VK45xQ12s6ikOP0/WNguns8eo8eYbPZEbW1tdi7Zj1CP16NoO3H0FivEoC8AbE4+8Q0DLjt5k5XvNlisSA77QxyDh5FpdGIuJ/3wjm7AAIk6P09UdovBpVR3RCQGI+IXolQKpXIu5iNs8dWoZ/7IjgpGv+dymRxwuHyp+EVejsiY3q330m1QEt/HhVcysXpwz8hyX0+nJUNJwoKauJxqnQq+vl8DmdlESySHDvSnsGFH8KgXys0OUlBEa+A14tuiOu2ArGuy1t6OnVUmAJxBp+h36CbW3ysvX5O79++H0cfOgpLWstvCxdcBHgvMCEsbg+Mkjv6ev8LKnnjs3lqzG44UPYyIrvfjm5B4W0Jm6iOK98fV3+fnDt7ErLcvyJMt6nR49ILx2HPu9NR03iTZpFHy9Hz855IHpncto6IiIjaUee5ZYvqubIck8lkwqJFi7Bo0SLodDp8++23uO222xwdXptcSUQ0NSOiMYsWLWp0X2pqKsrKyhAREdGW8Do8SZJwOHULvE0fIMx1Q6PtzKIKe/MfRV/vr+CubrrYmSBICNVthlNNPvbvFjBw2K3tdjfu0UPb4Vb9ESLdfr5uW3f1eQyUP4E9uyX0H343166mTqekpBhpR1ZgsNezUFxnWSWlzIAkjzdxJLca55RqRET1qLNfpVIhPnEA4rr3R1VVFcpKi1FZU4acCwWIT+iBYA8PaDQam59D1olTqPpuHfq9v7rRC7dXaEuqMOTpL5BitkB533R4envbPJ6Ozmw2Y+tPW5H7XC7E7OssxSIBhi0G5BTkYLtsO0bfOrrLzoyorq7GoRVrMfCpz6GprGmyrdxsQfcvN+JsSRWOK5ToMWJIm8cXRRH7fv4Vca98De9TOU22FQB025sGp+e/xF4BGDLt1k61VJNcLkdEQjwiEuKxfft2aD8Zg6riEuTk5SE6LhaxXl5wdna2JniKigpw7tgKDPZ6HnKh6cX+VfIaJHsswsHsGmQrNQgNj2uPU7Kb0pJipB2+8hndcNKw1BiK02W3Yoj/YihkJkgSsOvME8j+JBzG3dcfw3zKjMKnS6H8eAhiXH5odEZbc2To70Fk38GtPt4eEvolIOfeHBS91HhysUEqwHuBCfE91qOqNhADfP9x3dfGSVGOwV7PYs+JWihVs+Hj69+GyImaFhIWg90ZU5tMRIR7bcfJWyajZlPbEtbu97ojIZnLFBMRUefSNf9yvUFYLBZIkoT+/ftb/zCsqqrC1KlTIQgC/P39cfbsWQdH2XYvvvii9eu4uMt/vDZ1cXnQoEHYtGlTg4+kpCQAgIeHh32DdrDCwkLIyn9GmHPjSQgASCubjEi3jddNQlzN1+k4fKWvkHXuVFvDbJbi4mKYS35FpMv1kxBXaBQVSHJ7GUcONH3+RB2NJEk4lroeAzxevm4S4gpBAHq5f4TcjC0wGAyNtBHg4uKC4JAwRMf2hkqtRUBAgF2SEEajEee37ELvD66fhLhCXmtB/5f+jaPrN9lu7fBO5Pi+47j0/y5dPwlxFfNxM/L+lYf0k+l2jMyxjv62Gf1e/ua6SYirRf20G8YNu1BSUtLm8c8dP4GAL9ZfNwlxNY/MfIR/tAZnDhxs8/iOIpfL0S0wENE9e0DlrEVwaCh0Ol2dGanHD/6K/p4vXzcJcYUgAH083kVW2uZ6M107E0mScDT1V/T3fKnRJIQkAUeLZ2KA78fWNrmVycj9LapZSYgrxEsizHoZLul7XL9xI8yiGpVSrw73e6+zszNcYlwguLVs5pLrTAv69FiG/Jo+6OvzZbMTNHLBgv6e83Hi0K8QRVvV3SCqT6FQwMUzGqXGxmffKGQm+Pqfgjy89clqwVWAa6wra6AQEVGnw0REF7B3716IoojKykpERkZat+fn5yM6OhqCIHS4os5OTk5QKpXYvn07gMvr7CqVSiiVSqxevRoA8MILLwAA3njjDWv7tLQ0AMD+/fsdEndnce70TsS4fNdkG1GSo9AQj27a1Bb3H+78G3IyD7bLBcOM07sQq/umxce5qPIAwxno9c0rvknUERQVFsJTvhsqeXWLjhMECVHab5B59oidImu+zKPHEfXtZrT0Bl5VjQk+O46jID/fPoF1UKIo4vyp8zDtafnF2ZrfanD20Nkumbyprq6GLC0buvyyFh8b+/XvyNi9r03jS5KE3MMnEPp7yxMKQbtOIP/EmS57wTP/0kX4KrZCKWs48dkYmSAhUrsMWRnH7RSZ/RUVFcFTngJ1E5/RxcYYeKgzoZZXWbedyLwVVata9meXPEoOb/VpnCmfDLOoalW8JyoeRETswFYda289h/WE+6PuzT9ADugGGlAr6BDuugUyoWXfX0qZEf6qTbiUd6FlgRK1UFTcQByvfAyi1HiiITF0FTzmGoFW5iLcH3FHj2GtT1ISERE5ChMRXYhOp8PZs5cvSJw4cQK+vr7WfadOnbLZesm2YDAYYDbXvYvObDbDbDZbkxOvv/46pk2bVq/9e++91+ESKx1JTU0NJONZaJtYrxkALlYnoZv2QKM1IZoil9XCXbbfJnecNsVoNKK2OgM6ZesuTEZqv8O59M57VyrdeDLSdiHSeUWrjvV3Ooz83BMOvfgpSRIuHU+D/4HWzcaLWLkT53a17QJyZ3Ph/AVUbaxqcr34RpmBqr1VKCpq4fImncC5A4cR8V3T9Z4a45JXCmNGTpvuvC8qLIRnyknILS3/fhIkwH/zIVy6kNvq8Tuyc2d2I8J5dauO7abdg4s5xzpt8uxc2i5EaH9osk1G+ThEuv5vWZZKkz+qMtwhVbfsnN1nGNAzZAXi3FdjX8EjsIgtqwdzqvQ2CB7TERQcef3GDuDn74f42+Lh+ufm1fNSDRAQHrANufr+CHRu3c+JcOefkXlmT6uOJWouFxcXhMdPxoGSFyBKDV9ucVHmI6n3d/B41tLiKzKu/+eK+Knx8A/gMmNERNT5MBHRRSUkJODUqVMICuqYhT8lSWr08f7771vb/fjjj/X2P/HEEw6MvOO7lHcBgao11213Ud8XwbqUVo8T7LQOF88fbfXxzVGQn49uqnWtPt5HcxylhedsGBGRfZn0l6BVNFz49HoEQYKn8ggqKipsHFXzVVRUwP14JmStvMjoVFqF2ktFnfYiZWtkHs1E9e8tmwFztapfqpB5LNOGEXUMpVnn4X2q9Xcud/t1PwoKClp9/MWjJxH8S+uTYsEbUnHx6IlWH99RSZIEiyEfGkV5q46XCSLc5CdRVVV1/cYdkFGfB2dl04k/o8UFzspC6/Oc4gEo/8WpxWNpgmugU+bDT3sC4a5bsDv/SVTXXr+GTq2oxpGimSgw9EG3kO4tHrc9dU/qju7/1x0ez3lA0DZ9Z4zrGCMCPI/BRZkHudDyItcAoJZXQTLlwWJp3fFEzRUUEgX/mFlIKX4DNWa3BtuEeqag34hl8F5khuB+/TvDBK0Aj2c9kPB/Cejer2N/bxMRETWGxaq7oF69euHo0foXiMPCwto/GGp3JkM5POTXv0BQKzpDLa9s9TgaeQVMxtZfPGsOo6EczrLWz7oQBECQOu9a1HRjsVgskAvNXwu/IWqhwKHrr5tMJmgKW3eB8gql3giLxQKF4sb4FcVoMEKqaH3iRSwXYahu2RI5nYFgMje7xkhD1EUVMFS2/meUqVoPTUXrl/bTVOhhqjG2+viOqra2FkpZ6393AAC1rKhT1okQRRFyNP29JkoCZNfUzdDXeLTqe1yu+F+doADtEWjlJThechcskgoRrlvg53S0To2EClMgzlaMQ3WtH8JdtkCrrkZtbfNqDTmKIAjokdwDXgFeONbrGCpPVKL8u3JYsuomCuQJcijjFJAAqOVtS7ar5BWora3tVMXkqXMKCYuBzuVPOHwiCkLFGUQ4fQcfzck6s9HddRfg27sMWBwO8ZICFd9Xwnyi7meIPEwOt1lucOnugsQhiQgMDmzfEyEiIrKhG+Ov/BvApEmTsHHjxnpLcnh4eGD16tUYPny4gyKj9iYIMkiNTAOuS4LUhss8kiSDINh3UpUgk0Nq68StjrMiGVGTLi+f17Y3rASFQ5fhEwQBUhsv7ogyoUMtJWhvgiBcnp/a2ht0ZYBMxgmu15LkAmTy1r8ugkwGqQ2vqyiTQZB1vfexIAiQWruo+X85+nOqtS6fe9PvCeGqf63bZC1fegVAveXa3NQ5GOD3DxjMbsisHIX08okQIP63qQAnRQmiXH+HuzobAHCmMrTTvM7dgrqh24xuqKiowInhJ1BysQRS7eUXQFAJ8A/3h96UDgFSM3/HbZwEWad5Xajz8/TyxqDh06DX63HuzFCkFWVCgAmQAElQQucWgJ5DB8LV1RV6vR6nhp/CpcxLkEyXVwKQqWTwCPBA4sBEuLo2bxkzIiKijoyJiE7skUceweeff16v1oJGo8F7772HuXPnOigyciSNsyeq87sBONZ0O3kZ9GZvuLSy/kK12RsaV/dWHdtcTlp3VFuCAbSuzoNFkkMSNLYNishOZDIZzJJzm/rQW4IQqHHce16j0aA6yKtNfZh1TjfUnapOOifIvGUQ81pX20PmI4PWXWvjqDoAJzVEuQyyVtRoAAB9iB9cXHStHl7j5opqb1c4lbZuCSG9tys0urZ9P3dECoUCJrFtF8P0lgBoHPg51VqCIMCCpr/XBEGCRapby8HFuQAyX8CS0bLxzLUaSBLq1fLSKMoR77Ha+jy18E9I8vmy3vF6SzB8O9nr7OrqikHjBlmfp6amIikp6fLXey5BsiigN19/eaqmGC3uUCpbVm+DqK20Wi0Sew8DMAxA3ff21W2ShiVdadJgGyIios6Ot9B1Yp988ok1CaFQKPDggw9CkiTU1NQwCXEDCwgIRK7xluu2C9HtRlbFyFaPk224E8HhPVp9fHP4+vriomkiWrtcfG71EAQE2TdGIlty8QhFmTG0VceaRSUqpe7Q6Vp/8bWtdDod9LEhMKtad59DebA3dCHdbBxVxxbbLxYuU11afbzbnW6I7hltw4g6Br/usbg4MK5Vx0oA8sb1ha+vb6vHD+mdiKw7Wj+bNGvaUIT0693q4zsqQRDg7BqEClNAq46vFdWoEWKg1XbO5JmrRxhKjeFNtnFT5aDEEGF9HuK5G+6TW77snv64GiXG6xearjF71ttmkeQot/SCm1vDa9N3FjU1/3vdQiKScdE4BibRGSZLy2tuAEBVrR80uiDOIiOHu/q93ZY2REREnQ1/C+vEBEHAkCFDIEkSamtr8fnnnzs6JOoAlEolnN2jUGYMbrKdt+Y0SoxRsEgtv/PYaHGGSR4PF5fWXzxrDoVCAXfv6Gb9Id6QbMMdCI1IsHFURPYTFTcIZ/UzW3XsBf1IBIf3dfiSEyH9eiFndK9WHXt25mhEDkq2cUQdm7e3N5wHOLdqjqrgLMAl3sXun8WOENY9vtWJgOLYQHjEhLdpZo2rqysMieEwOalafKxZqUBZcgw8PetfIO4KIuOGIEN/T6uOPV89HqGRnfd7PDJ2ADKu8xkd6boJGRXjrM818kq4B16EzKdlf3ZV/KjCsXPTWxXnxeohCAzt7fCfB7bk7eODYvNghOj+QHbViFb1kVF9FyLjhto4MiIiIiJqLi7N1IldWw+C6IrIuEE4sXcuBqlerFPI8GqCAATrUnC2fCJi3X9pUf+nK+cgIn6wLUK9rsjYATi8ay4Gq5+B7JpzKTWG42z5OBgs7v/dIsBJUYwo140wiN7QecRw+n07kSQJBfmXcO7MbpiNJQAskKCGh3cEImL6wdnZvkuUGAwGnDlyBjmnc2AxWCAIAlSuKsQPiEdQaMe++9FiseBc+jmcSDkBY4URGp9gxDh3g6vqYrOON1qckVExFtlVo6Ez/oHczJ0Q5C4IieyPwKCwZl+MNZlMOHPsDLJOZMFsqYHCuRiC3AKZXIRcqIVCoYBc6Qw3z1BExg5o9OJ3UFQkdkwfhqDtx6CsaX5B2soAD9T0iYa7u3uzj2kpk8mE7MyTyMs5BkHSAxAgyHUICktCcGiUQ5aEEgQBMUkxqJhegar/tGwZINfZrug+uLudInMslUoFbVw4CnqGw/doZrOPE2UCTj10E/om921zDOGD+uH0fWPR87P1LTou/c5hCE3q1aEvAlssFmSlZ+HMwTOoraoFJEChVSC8R/h1f7/08PDAcSEZVbW+0CkL6u2vMHVDRsU4VNX640qhA7W8EiHOO5BdMxUjgiPqHdOa+C+cy0TO/sNAlR6SJEFy0sA/MQ5hifFQqVqeQGoOV1dXGBW9UGEKhKsqt8E2Lqo81IpalJuC4Ka6AADoGfkDiv70FErfav5njJgvoqwiFGXGELirzzf7OLOoQkbNTAyx040YVVVVOLn/JAqyCiAaRQhyAVpPLRKHJMLXz9du73tBEBAc3gc1OaHIq4pFiG4H1PLmF6SvrvVBuTQQPbtogtCRJEnCpUsXkXlmN0RTGSRYIEEDT99IREQnddoZUPZisVigr6rCzmUrIFXVQJAAUatGQI94hHWP7xB/u1RWVuL4nuMozimGaLr8fa7z0SFxSCK8vb079M83IiLq2JiIIOqC3Nzc4Bd+Mw5n56O3+/9rNBkR7rIF+wvnIqdqIIJ1e5rVd3rlNMBjGgK6NT3jwlZ0Oh2CY27CoYwC9HV/B4IgwWhxwYHCP0OrKESs+1q4qvKs7ctNgUgvn4RCYz+MGMe73tpDRUUFDu39GT6KLeitXQUn53IAgCQBRdVxOL57FpSu/dA7eZzNEwKSJOHw7sPI2puFsmVlqD1Yay3wKWgFFN1UBJebXDDwpoHw9Wv9Mi32kns+FynrU2DMNcK43gjzITMqXOXY9tazGNN7IZyVxY0eK0lAWtlkFBriEe6yFROCHrcm62pFDbIzJ2LH6VsQ32si/PwDm+hHwvEDx3E25SyqdpXBfUwFwgN2owxR8FRnIMJ1M5yVRdb2JYYInN4zC5K2H/oOmASFou6vEgqFAj0mj8fesioMfPEbKIy1130d9F4uSH3tfgyYPPG6bVvrbNphXMzaiXCn5RjqngKZcLk6tFlU43zOWOw4cytiEsehW1CY3WJoTHRCNC7dewlZ+VkwbDU06xjn250RPj0cQSFBdo7OcXqOGYFdz5VC+eJX8Mi8fj0jURBw8G93IPSmUTZJfnYLDUHa8EScyclHzPrUZh2TPaY3qmdNQFxcbJvHt5fz587j0OZDqFhdgZpNNcCVfKEcKBxcCO2DWpw+chpxvRpfGis2cRR27n4BIwIWwElRCuDyskuphQ9CITMi0nUjPP5bNBkAqmp9kFZ2KwS5EiaTCU5OrVtaBwAu5eTg9IZtCP45BQM2HoLyv58xokzAxQGx2DtjJPyGJCEmqU+rx2hK0sBbsGd7JZLdnoWzsrDBNn29/4U9+fOQ7PspnJWF8NJkIG7gFpyYMwYVXzXv56CyjxLagDAcKHsFAz1faDDpcy2LqMS+kgVI6HOLzS9mWiwWpGxMQf6BfJR9WwZLusW6T/AQUDitEC4jXTB8ynC7zdKKiErE/sJ7ESB9ib35j2GQ/4dQypqxxI3ZDfvLXkfysCm8gGpjZWVlOLzvZ/gpN6Gvdg00ugoAl39HKazsjqO7ZkHjkYyefUd16JtC2svFzCyc2fQHpIpKDHzxO+vvSKJchtxB8Ui5azi6DU1GVO+eDonPbDZj5/qdKNpfhPKl5bBk/u/7XOYjQ8GdBXAd4ooRt45ggomIiFqFiQiiLioyuicypNnYm+WJRNd/NPgHrCAAvb2+wu78v6HEGI1491VQNXJ3mcHshlOVcyD3vA09+7R+3ezWCA2Phyjehz0Zboh1/gJHS2ait9e/G7xD0E2Vi34+X6DYsA37d7lg8Mg7OsSdRV1VRUUFDu5ehgEez8FJUV5nnyAAPk6n4eP0Ei5Uj8DeXQYMGDLZpn+I7t26F+c+O4eqH+rfSS7pJVT/WI3qddXY+dpODLlnCPwC/Gw2dlvlZOUg5acU1GyrgfFno3W7VC6h4BUtNr36KvrGf4tA1wMNHn+89E7IYMZQ/7/XK2aqlBkQ5bIaYc7rse/IAkjiTPg3kjw8tOsQ0r5Kg+FQJYKfuIj+UZ8itfAhJPt+2uDnhqfmHDw1C5Ff0xt7dhgxaPjUejMJvH19EXP3rdjlpELie6vglXGpwbFFQcClflE488gU9Js2uU0XJ5ty+sRemC59g2FeS+q9VgqZERG6XxDqvAH7T74EUZyJoJAou8TRGEEQMHTiUMhkMuSE56BqeRWk6kZms7kJcJ3tiojbItBvRL92jbO9KRQKDLx9MvbJ5Qj6168I2XwEcrOlwbaVAR44Nu9WdJsyFiGxMW0eW5IkpG7YjNDvt6HKwwWHZ41E3M/7oKnQN9je6KxB+t0jYJw+Gn3HjeqwFzuz0rNwYOkBlLxWAlz7UloA4w4jFAMVOLL3CGrvaziJWFlZiZMHV6Ov9xLszX8Use5r4aM5iZSCJxDvvgY+TqfqHaNTFiLJ5wtUmn7Bnj/MGDj8rlZ9v1/MzEL2sp8xbMFSyGvrnoBMlBCUchqBKadx4v8m4OQcExIGD2jxGNej0WiQPPRO7N+lQLTmHwjQ7qs3Y1OjKEey7xLsKXgMMW7rEei8D4mBP0K6VUCa7yiUfaGAVNxIASw1oJ2iRbeZ3RDXOxLpP57Dfs08RMf/jG6uByATGp6xUmoMw7GKJxDd8zb4+tm21o4oitj681bkvpMLY4qx3n6pVELFFxWoXFWJLfotGD1jtF2SEYIgoN/ASUjdK4OT+TfszHsaPTx/gJcmrd5nOwBIkoBLNb1wWj8PSYOn2X125o2mrLQUR/Yuw0DPXJx3bgAAcRNJREFU56GRV9bZJwiAr9MJ+Do9j+zqcdifYkD/wTd12M/G9nAhPQO5y37GsEXfI+XJ2+rcqCGziAjeeQJBO0/g2MPncfr+5s8otRWLxYLNP23GxdcuovZo/c9/sVBE+T/KUfljJTYZNmHcjHF2+72NiIi6LiYiiLqwyJje8PAOwclTvVBbcQ6hmhXQKS5CEMwwWXTINU5ChZSEkPi+UCqdsC/jJmjEkwhWr4JGUQ5JElBj8UK24Q6IqhhEJg6Gn79jCsmGR3aHh1cg9u30RT/PN667TIGX5iy6YxEO73dF8uDrF++mlpMkCQf3rGkwCXGtIOftMFXpkH66G2ITbLM+eHZmNrJ+yGowCVGHASh6sQh7XPfgptk3dYjElMFgwN4Ne2E4ZKiThLhCLBSR/4waO6b8CboRMyH3BooNUVDK9DBLapyvHIlaUYNk3yVNjqOQmTDA8xXsOOoGD68HoFar6+zPy8vD2dVnUbW8Ct0+rMTIuDeQkv8Ekn0/u+7dt35Oh2GW3sOJI97o2Xdkvf2+3QLgfP9dOBsbiePpmQheuwfup3KgMNai1kmFwkEJuDS6F/y7x2Jw7552W0qlqKgIlRfXop9H/STE1eSCGf09F2HnSQ94ev9fu9/pJ5PJMGTCEOTE5eDUuFOoOlWF8lXlEMtEQABkXjK4TXeDa4wrEockIqBb64oFdzZqtRqDp01BVkIsdh46Bo+9p9Dt94NQVRtgUShQFeyD7OlDoY4MRsLQgTZb2iv7dBqcl21E5Lp9AIBLPcNw8E9jITeZEbrzJDQlVRAAGNydkT0kHsWJYUi8eSyCQkM77IU2vV6Pg+sPNpyEuEb5P8px2uM0vEZ51dl++XP/Z/T3eA5aRSnc/d9DRsVYHCmeiZ5eSxtMQlzNRZWHJNcXcXCvM4aMvKNF8RsMBqT/shlDX/0Ockvjy0cJALp/sQEHPVxQEBkGXz/bJ6CdnZ0xZNQMnEuPxx85x+Cn2gZf1W4oZXpYJDXKTDHIMd4Kd/8olKnGIiP/DHyV2xDovQuuE3JxJu4mlOf6onKNHGK+CFgAwVWA62RX6HrpEJcch4DgAOz5ZjmGPv81JLkM527piT9GT4av/xH4+R62jqU3eeOPQ6/AXOuGsLE3IaBbiM3P9+jeo8hbktdgEuJqUomEgucKsEO7A5PumWSX7wWZTIZ+Ayci90I8Mk7vwZHSP/3/9u48Pqrq/v/4+84kk0z2nYSsQAiBQAj7vqOI4oKgWLUu1Vr31u/Pam3rUq1Yt9rFr0u1rW21rUu/1mqrlsUVUXYV2XcIZN/3ZOb+/ogZCUkgQ3IzCbyejwcPkjPnnvncyZk7Z+7n3nNkmNVKCVmlqIA98rPVqcntVF5tjo40nKmE5BGaPH50m88/dI3b7dbGNf/S5Ki7FHBMEuJYqcHLVF8Zrt07k5SecXJrSPV1NTU12vOflZr6wN9kO8Hxa8RT/9bayFA1zO76FIPeWPf+Oh157Ei7SYijufPdKvhxgT4K/khnXnxmD0UHADhVkIgATnFRUVEaP+V81dfX6+D+uSqtLtLhvHKlJPdTSv8hrRbTTE4doIqKCh0+eK4a6iuUV1Cl1NRkjUgZ0iuuInM4HAoLKFRU4Dfzha8vvEZjYn/fbv2YwG3aUbxddXVzFRgY2FNhnjby844ozm/lCZMQLdKC/6MPDy7S4Mwx3XJXxNZPt6rizxWdq1wvlf+9XHsm7dGQ4b6fMmXH5ztUf7he9a8f56ROrVT1sqGqV5wKeSBEKzbern4zItUgp/xUqknRN3fquey2Rg0O+rP27R6nIcNaX0G/+ePNKn++XEFnuTUi7TVVNiYqxD9fIf4nngZHkhKDVmt34TY1NU1tM0WT1HyybuScGWqaMUW5c6bq8OF85ZYWKykmVlFpycro39/yqRp2b/tIw0OfOW4SooXNcGlI8PPas3O0ho/s+andDMNQSlqKUtJSVFlZqX0L9qmmokYlVSVKTk7WgCEDesWxuKfZ7XYNGj5MA7OGqnh+sQou3a3G6hrl11Vr0MCBGj84vVsTWaZpav+6zzXl7x96yuK/2Kf4L/apOjZMh8ZlqGBosvJyBirl4y0a+s/PVJBbKveZM3ttEkKStq7fqtLflZ4wCdGi/Nly+Y9snbgtLCxUtP1DBX09HZPDXq308HdVWJup/sGbPPWO99kc5jisgOrPVVExT2FhYZ2Of8+mLzTkD+8eNwnRwpA07Pl3tGnCMMUtOq/Tz+ENf39/DRk2VhlDxyg/70zlF+5VY0ONCorrNThjiCYnD/QcF01zkvLzzvDUMRLqNXLyQDXOl2rKa1RYUajEfolKzUz1JNN2bPxc6X9ZIb/GJqlRGvLaRmX8Y6Pys1OVnzNXjWEOFaQOkNw2Tb77dbn87Vr3/AANHNy9d3S53W4d+OqAapedeAokqTkZUflxpQrPKFRcnDVTIhqGoaTkNCUlp6miokKH9m9TfslobS2slmHYlJQUq9i0dGUm9O41ovqyI4cPqr/jnRMmIVqkh7yuD/ct0KDB2b36OGmVPes3KfO5t4+bhGhhSMr63X+0cuQA6wP7WlNTk45sP6KGNZ27E8Od51bFxgqVnVlm6dpeAIBTDyMz4DQREBCg9IwRGjFqloJDYzR0+KRWSYgWYWFhyswap+zRcxQUEq0hQ0f3mhNfe3au10Dni63KapuOv+jggMC/au+uTRZGdfrau+MTDQh+vdP1bYapfo6Vys/r3CLMx1NeXq6qL6ukzp0XkSTVfVinXRt2dfm5u8o0Te3ZukfuQrfMqg6m5mi1QfN0TWUPGyo/HCj/gFAF2fa3mQbheBKC1ujIwc0yzW+er6amRhU7KmSWmwqdXa+k8DXaXTFXg8KWebU/yQFv6OD+ncet4+fnp9TB6Ro+Y4qCY6KUNXWSEpKsP0FUV1cnV+3u4661caw455cqytt2wgV7rRYaGqoR40ZowpwJCo0O1fDRw3vNsdhXDMNQTEyMhk2eoJFnzFJQZIQGZQ3r9rtpSktLFbF+R/MJ4GMEF1ZoyH/WKfvvHymouFKZb61VaF6pUv67QQfWbOzWOLqT2+1W7rZcubZ0Mguh5juzGqsbVV//TcJ0z/ZVGhj8Wqt6ByqnKi30o1ZlJ/psHhT0onZtW93pWEzTVMGWneq3aU+nt3GWVsnYeUC1tV58UJwEwzAUn5CoYdlTNXLsmXIGRyptQEar5Gx7dTIyspQ1OkvjZo1TWHSYRk4c6TmhZ5qmjnyxRf1Xt77DxDCl+M/3a9if1mrkb1fJmV+joMIq+dU3KqCqTv7b9quqyrtF70/k0IFDqvxv5z9vJKni1QptXrW5W+PoSFhYmIaNGK8pM85TZEyyIqITlTNmthITU0hCWGjfzk81IPjNTte3GS7F+K1SYUHnLnQ4lbjdbhVt363Yrzq/6HxwYYXM6lrV1XVu3aiu2rNjjype7+TFPV8re6lMm1f3zPscAHDqYHR2ivjwww9lGIYuu+wyX4cCWKasOFexgcef9uFYsc4vVVp8yKKITm9NDaWdvhuiRZzjU5UU7evyc5eUlKjyA+9OjKhJaihp8PkJ5oaGBjWVNXl1QlCS5JYa8hvU0NCgWP9VXm1qM9wKtOWpqembE6tlZWWq+aRGCpSCg4plM9yqbYpSmMO7RFFs4HqVFu09cUUfKC8vV7Rf+2tsdMQwpFC//T325R+9T8nhI4r98EuvtvGvb5RR6uUxqQfV1taqYZ/3c467ylwqL//mON9YV6Igv5JWdUrqByrWucWrdiMD9qum8sSLL7eor69X8KEiGZ3I3R4t5rPtKisr826jXsDlcsmRXyqb27sdjvvwS5WWlnZrLPl781W7xrtkjllsqqak/fVUcGpwN5Z2uK5cR+Icq1RafNCiiHqv2tpahe7Ll7f3gTjKq1VR4V1y4GTl78lX/cbjT712LPdBtyoKeyY+AMCpg0TEKWLPnuYrxDZubL4a76233pJhGJo3b54vwwK6lWm6ZXh5FsJuNMnt8vKELzrF6Oz8HkexGw1yNXV9Ab7GukaZ9V6ekZKkhuYTPL7kcrlkNpkyG7yP36w3ZZqm7PLuy78k2Y36VvvucrnkqnbJ8Ddkt3n35bN1uw1yudpeOd4buFwu2eX9yTC76n3eT+A7rvoG2RtOok+7XK3uOupNXC6XzLqTiM117DGz7fvCbfrLbpzMcb3zSWGXyyV7vffPYauu65PvZZfLdVJ90FZbL1fj8ed391ZTQ5N0En9e09U73wvoHic3BmyUq+nkxxt9ldvtlr3O+zeR4XL32PGrqekk3+deJksBAGCNiD6sZX7NgIAAxcfHS5LnFuSioiJJ0sGDp99VJzh12f0canQHyt/W+SuV612h8newQKEV3HLINNWpufdb1LvC5QgM7fJzB4UGyS/WT03y7kSNEWy0u5ZBT/L395fNaZMR7v0cybYwm+x2u+pM7xcqrneHt1qo2+FwyNHfoYaPG1Tf9M087W7TJpvR+ROE9a5wOQJ655RBDodDpab3C9XWuSMtWzwbvV9AWKjqo7w/TrkDHb127nOHwyF7lN3r7YwAo9V7wWznuO+wV6nOFS6HvfNJP5dpl4zOv8ccDofqIkI6Xb9FfUKUQvrge9nf318NoUFeb1cfH6Xgbl4TKzA0sPnz6oh329kCuN7tVOY2vB9b17nC5QgMtyCa3s3hcJzUZ4orwL/HxiKBQc3vc7PSi8SCXbI5eJ8DALzDJ0cfFhDQPACsr6/X/v37JUlfffWV/P399dOf/lSSmBsVp5TE1JE6UH2GV9vsr5mvpNQcawI6zUVED1Bx/WCvttlfd6ESkzO7/NxxcXEKO7vzi5xKkhFtKCwhzOcnCv39/RUcEyz/0f4nrnwUI9xQaFKoHA6H8hpme7VtnStUNkd/2e3fnIiMiYlR8IxgySXV5IeprilMcc6vdKRmtFdt7689X0lpOV5t01OioqJU2DhB3lyk3ugOVKMt1fMZi9NPfHKSDp0/yattKhKi5Ez0PunVUwIDA+VMc0redGtDskfaFRkZ6SmKihukwrqsVtUSg9fqQJV3i7vnVk9VfOKwTtf39/eXO7mfGoI6vwOmpIIpWYqOjvYqtt7AZrPJlhirunDvkhFH5o7q9gWiB2YNVPhC704e+w33U/yg+G6NA71LaHiKSutTvdrmYP1C9U8aZFFEvVdAQIAa0uLVGND5cZ9pSA2RIa2Ov1ZKH5Wu0HO9S5YETAxQ6lDv+gAAAJyl7sPq6upkms3TdCxatMhT3tTUpNzcXEnNiQm73a6UlBS98MILPooU6B5JyQN1qH5Bp08qmqahIw1nKCExxdrATlODhozX7ppvd7p+vStEjX5DFRra9TsiHA6HYgbFyJbU+Y+xsMVhGj51eJefuztkTcyS4WfIPqDzVyg7z3Vq+OThstlsCo9OV3Fd57/M762+QAMyJrcqs9vtSshIkH2wXWWvOrXtyNlKC31feytmdrpdl9tfZe5x7S583xvYbDbFJgxVQe2ITm+zv/ospaaPtzAq9HaBgYGyD0pWVWznT77uvnSm0qdMsDCqrsscn6mgMzt/Ytt/pL+cIc5WF7UMHDxGe2pbr0cWG7hFRXVD5DY7fzzbX79YqQM7n4iQpLSJY7TvnM6/N4uGJilqyKBWCdi+ZODkcdqzcPKJK36tdEA/hQ5O6/a7/sLDwxU8Ilhydn6biMsiNGyMd39f9C2DMidqd83lna5f2xQh05GhoCDv7/Q5FaSOH6X98zp/oUd+9gAFhAT32EWFMTExCp4Y7NV8GeEXhys9K926oAAApyQSEX3YNddc4/l5wYIFkqShQ4fKNE1deeWVnsfcbrcOHjyoq6++usdj7IoRI0bIMIx2/02a5N2Vijg12O129U8do60VV564sqTN5dcpZdA47gyySHBwsPxCxym3ZsoJ67pNQxvKfqQhw2d22/OPnDZS0XdGS524a91vsJ+i5kYpNja2256/KxKTExU+NFyBlwdKnZhFwwgyFH1OtBL6N0/JlDFssjZX3q5G94nPDJXVp6rIvUDxCYltHhs+Ybii/1+0Gre7tGfXNNU0xCrccVB7OpGMME1pY9n/0+ChU3x+l8nxDMoYo601t6redeJpXSobEpTbeJESkwb0QGTozTJnTtHGu5bI5Xfik9gFI9JUN3Vkj125erJSBqYoakmUbPEn/kw0Qg1F3xytkPDW75ugoCAFhI/RweqZ39Q1pPSw/2pT8bc7daHArspFio4f1WqquM7on5KsIxdOVXniie9waAgO0Fc/uFDp4727w6s36ZeQoOL5E1Q64MR32jQGOvTFDxcrY7I1SdSc6TmK+lGUOrPabuCsQCWMSZDT6UXmAn1OWFiY3M6JOlIz9oR13aZdG8p+3K1jwL4mccAA5S6Zqcr4E39O1Ic6te2W8xUU7t2dv11hGIZGTB6hiP+J6FT94PODlTwqmWksAQBe4+xcH/aHP/zBc2K+JSnR1NQ8X/rMmTMlfZOY+PzzzzVu3DhfhdolhmEoIiKi1b/vfOc7vg4LPpI+ZLSaIq7Sl2XXyWW2f9lOk9tfn5fdLFvs5RqYnt3DEZ5ecsadof3m7dpbeZZMs/0zFA2uYH1W8oASMxYpJrb7pi4JDQ3VpAsnKfbRWBlRHZ8dcYxzKOH+BM04b0avOWFus9k054I5ChkcouA7gmXEdByXX7afHEMcmr1wtif+oKAgDR+7SJ8UP6bqxo6TK/k12fq8eqnGTz2/3X0PCgrS1EVTFfdYnEr/N0AfrL9d0f5bVVyXoR1lZ8tttj9MaHQHal3pjxWRcrH6Jw30cu97VmBgoHLGL9LqksdV2dDx2hpFdUO0vvJhjZ+6sM9eQY3uEx4RocEXnqXVD1/d4fQ4pqRDU4dp+92Xa9yCeT0b4Emw2+2auXCm4h+Ol33wcfp4oBT3WJymL5re7nth5Ni5yjVu0+7KBXJ/fdxPClmjEL8CbSi6Rk3u9qdPcpt2bau4TOXO6zR0ROev9G9hs9k04YJztPEX16hwWMd3Olb1i9Dqx76rkYvO6dMnww3D0Pjzz9YXD16tvJyB6ijH0+Tw0yePXausC89WcLA16/UkJCYoZ1GOou6LkhHUweeVIQWdG6TkHyRrwpzefXcQuseYifO1x3WX9lfN7jAJWe8K1eripUobdqGiomN6NsBexG63a/zCBVr/8DUqymh7YUiLioQorX7su8pZtKDHxyKp6anKujhLkT+K7HgaP5sU8q0QpV6fqtFT+26iFwDgOyxW3YctWLBA7733nqqrq+V2Ny8sunPnTk9yQvomMZGdna01a9b4LNauiI2NVX5+vq/DQC9hGIayR8/U/r399PHOqapurFJRXYb8bTVqdAUrt/4slbnHaGDGBCWnZvg63FOe3W7XxKnnacfWBH1w6CLFO5YrzrFGdqNBda5wHai/UPX2YRoyarpiY71fYPlE4vv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"text/plain": [ "
" ] @@ -570,22 +524,160 @@ } ], "source": [ - "from genophenocorr.preprocessing import configure_protein_metadata_service\n", - "from genophenocorr.model.genome import GRCh38\n", - "from genophenocorr.preprocessing import VVMultiCoordinateService\n", - "from genophenocorr.view import ProteinVisualizable, ProteinVisualizer\n", + "from gpsea.preprocessing import configure_protein_metadata_service\n", + "from gpsea.model.genome import GRCh38\n", + "from gpsea.preprocessing import VVMultiCoordinateService\n", + "from gpsea.view import ProteinVisualizable, ProteinVisualizer, ProteinViewable\n", "\n", "txc_service = VVMultiCoordinateService(genome_build=GRCh38)\n", - "tx_coordinates = txc_service.fetch(tx_id)\n", + "tx_coordinates = txc_service.fetch(RPGRIP1_MANE_transcript)\n", "\n", "pms = configure_protein_metadata_service()\n", - "protein_meta = pms.annotate('NP_065099.3')\n", + "protein_meta = pms.annotate(RPGRIP1_protein_id)\n", "\n", "pvis = ProteinVisualizable(tx_coordinates=tx_coordinates, protein_meta=protein_meta, cohort=cohort)\n", "drawer = ProteinVisualizer()\n", "drawer.draw_fig(pvis=pvis)" ] }, + { + "cell_type": "code", + "execution_count": 7, + "id": "ae6fe012", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " \n", + " Cohort\n", + " \n", + "\n", + "\n", + "\n", + "

GPSEA protein analysis

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The UniProt API successfully returned protein information for ID: NP_065099.3

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Protein Name: X-linked retinitis pigmentosa GTPase regulator-interacting protein 1

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Protein Features

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Feature NameFeature TypeFeature CoordinatesVariants in Feature
Disorderedregion144 - 19310
C2domain781 - 90642
Disorderedregion934 - 100811
Disorderedregion1058 - 11080
Interaction with RPGRregion1091 - 128134
\n", + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "viewer = ProteinViewable()\n", + "html_prot = viewer.process(cohort, pvis)\n", + "display(HTML(html_prot))" + ] + }, { "cell_type": "markdown", "id": "5c00b472", @@ -596,14 +688,20 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "id": "82b28521", "metadata": {}, "outputs": [], "source": [ - "from genophenocorr.analysis import configure_cohort_analysis, CohortAnalysisConfiguration\n", + "from gpsea.analysis import configure_cohort_analysis, CohortAnalysisConfiguration\n", + "from gpsea.model import VariantEffect\n", + "from gpsea.analysis.predicate import PatientCategories\n", + "from gpsea.analysis.predicate.genotype import VariantPredicates\n", "\n", - "all_hpo_analysis = configure_cohort_analysis(cohort, hpo)" + "analysis_config = CohortAnalysisConfiguration()\n", + "analysis_config.missing_implies_excluded = False\n", + "\n", + "all_hpo_analysis = configure_cohort_analysis(cohort, hpo, config=analysis_config)" ] }, { @@ -622,30 +720,10 @@ "Test for presence of genotype-phenotype correlations between missense variants vs. others." ] }, - { - "cell_type": "code", - "execution_count": 8, - "id": "19112877", - "metadata": {}, - "outputs": [], - "source": [ - "from genophenocorr.model import VariantEffect\n", - "from genophenocorr.analysis.predicate import PatientCategories\n", - "from genophenocorr.analysis.predicate.genotype import VariantPredicates" - ] - }, - { - "cell_type": "markdown", - "id": "3db11203", - "metadata": {}, - "source": [ - "Test for presence of genotype-phenotype correlations between frameshift variants vs. others." - ] - }, { "cell_type": "code", "execution_count": 9, - "id": "b9bf9ccf", + "id": "74f51e78", "metadata": {}, "outputs": [ { @@ -668,7 +746,7 @@ "\n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -685,70 +763,477 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - 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FRAMESHIFT_VARIANT on NM_020366.4MISSENSE_VARIANT on NM_020366.4YesNo
Global developmental delay [HP:0001263]0/500%11/1706%0.0733161.0
Eye poking [HP:0001483]23/2785%5/1242%0.0169830.6283693/560%25/3474%0.6088191.0
Very low visual acuity [HP:0032122]36/36Restricted or repetitive behaviors or interests [HP:0031432]3/3100%45/4525/25100%1.0000001.0000001.0
Restricted or repetitive behaviors or interests [HP:0031432]23/23100%5/5Recurrent maladaptive behavior [HP:5200241]3/3100%25/25100%1.0000001.0
Abnormality of the eye [HP:0000478]24/24100%95/95100%1.0000001.0
Myopia [HP:0000545]0/00%4/4100%1.0000001.0
Abnormality of mental function [HP:0011446]3/3100%25/25100%1.0000001.0
Tactile sensory seeking [HP:5200065]3/3100%25/25100%1.0000001.0
Abnormal movements of face and head [HP:5200017]3/3100%25/25100%1.0000001.0
Mild hypermetropia [HP:0031728]1/1100%1/1100%1.0000001.0
Abnormal nervous system physiology [HP:0012638]3/3100%32/32100%1.0000001.0
Severely reduced visual acuity [HP:0001141]0/00%6/6100%1.0000001.0
Sensory behavioral abnormality [HP:5200046]23/233/3100%5/525/25100%1.0000001.0
Constriction of peripheral visual field [HP:0001133]1/1100%9/9100%1.0000001.0
Abnormality of the eye [HP:0000478]52/52Abnormality of refraction [HP:0000539]4/4100%67/6724/24100%1.0000001.0
Hypermetropia [HP:0000540]4/4100%20/20100%1.0000001.0
Abnormal electroretinogram [HP:0000512]4/4100%23/2496%1.0000001.0
Abnormal eye physiology [HP:0012373]24/24100%95/95100%1.0000001.0
Abnormal repetitive mannerisms [HP:0000733]3/3100%25/25100%1.0000001.0
Very low visual acuity [HP:0032122]15/15100%66/66100%1.0000001.0
High myopia [HP:0011003]0/00%4/4100%1.0000001.0
All [HP:0000001]24/24100%97/97100%1.0000001.0
Abnormality of the nervous system [HP:0000707]3/3100%32/32100%1.0000001.0
Visual impairment [HP:0000505]24/24100%90/90100%1.0000001.0
High hypermetropia [HP:0008499]0/00%9/9100%1.0000001.0
Moderately reduced visual acuity [HP:0030515]9/9100%15/15100%1.0000001.0
Mildly reduced visual acuity [HP:0032037]0/00%3/3100%1.0000001.0
Visual field defect [HP:0001123]1/1100%2/29/9100%1.0000001.0
Reduced visual acuity [HP:0007663]24/24100%90/90100%1.0000001.0
All [HP:0000001]52/52Sensory seeking [HP:4000079]3/3100%69/6925/25100%1.0000001.0
Neurodevelopmental delay [HP:0012758]0/00%11/11100%1.0000001.0
Self-injurious behavior [HP:0100716]3/3100%25/25100%1.0000001.0
Recurrent maladaptive behavior [HP:5200241]Phenotypic abnormality [HP:0000118]24/24100%97/97100%1.0000001.0
Neurodevelopmental abnormality [HP:0012759]0/00%11/11100%1.0000001.0
Moderate hypermetropia [HP:0031729]3/3100%9/9100%1.0000001.0
Atypical behavior [HP:0000708]3/3100%25/25100%1.0000001.0
Abnormal visual electrophysiology [HP:0030453]4/4100%23/23100%1.0000001.0
Abnormality of vision [HP:0000504]24/24100%90/90100%1.0000001.0
\n", + "" + ], + "text/plain": [ + "MISSENSE_VARIANT on NM_020366.4 Yes ... \n", + " Count ... Corrected p value\n", + "Global developmental delay [HP:0001263] 0/50 ... 1.0\n", + "Eye poking [HP:0001483] 3/5 ... 1.0\n", + "Restricted or repetitive behaviors or interests... 3/3 ... 1.0\n", + "Recurrent maladaptive behavior [HP:5200241] 3/3 ... 1.0\n", + "Abnormality of the eye [HP:0000478] 24/24 ... 1.0\n", + "Myopia [HP:0000545] 0/0 ... 1.0\n", + "Abnormality of mental function [HP:0011446] 3/3 ... 1.0\n", + "Tactile sensory seeking [HP:5200065] 3/3 ... 1.0\n", + "Abnormal movements of face and head [HP:5200017] 3/3 ... 1.0\n", + "Mild hypermetropia [HP:0031728] 1/1 ... 1.0\n", + "Abnormal nervous system physiology [HP:0012638] 3/3 ... 1.0\n", + "Severely reduced visual acuity [HP:0001141] 0/0 ... 1.0\n", + "Sensory behavioral abnormality [HP:5200046] 3/3 ... 1.0\n", + "Constriction of peripheral visual field [HP:000... 1/1 ... 1.0\n", + "Abnormality of refraction [HP:0000539] 4/4 ... 1.0\n", + "Hypermetropia [HP:0000540] 4/4 ... 1.0\n", + "Abnormal electroretinogram [HP:0000512] 4/4 ... 1.0\n", + "Abnormal eye physiology [HP:0012373] 24/24 ... 1.0\n", + "Abnormal repetitive mannerisms [HP:0000733] 3/3 ... 1.0\n", + "Very low visual acuity [HP:0032122] 15/15 ... 1.0\n", + "High myopia [HP:0011003] 0/0 ... 1.0\n", + "All [HP:0000001] 24/24 ... 1.0\n", + "Abnormality of the nervous system [HP:0000707] 3/3 ... 1.0\n", + "Visual impairment [HP:0000505] 24/24 ... 1.0\n", + "High hypermetropia [HP:0008499] 0/0 ... 1.0\n", + "Moderately reduced visual acuity [HP:0030515] 9/9 ... 1.0\n", + "Mildly reduced visual acuity [HP:0032037] 0/0 ... 1.0\n", + "Visual field defect [HP:0001123] 1/1 ... 1.0\n", + "Reduced visual acuity [HP:0007663] 24/24 ... 1.0\n", + "Sensory seeking [HP:4000079] 3/3 ... 1.0\n", + "Neurodevelopmental delay [HP:0012758] 0/0 ... 1.0\n", + "Self-injurious behavior [HP:0100716] 3/3 ... 1.0\n", + "Phenotypic abnormality [HP:0000118] 24/24 ... 1.0\n", + "Neurodevelopmental abnormality [HP:0012759] 0/0 ... 1.0\n", + "Moderate hypermetropia [HP:0031729] 3/3 ... 1.0\n", + "Atypical behavior [HP:0000708] 3/3 ... 1.0\n", + "Abnormal visual electrophysiology [HP:0030453] 4/4 ... 1.0\n", + "Abnormality of vision [HP:0000504] 24/24 ... 1.0\n", + "\n", + "[38 rows x 6 columns]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "is_missense = VariantPredicates.variant_effect(VariantEffect.MISSENSE_VARIANT, RPGRIP1_MANE_transcript)\n", + "by_missense = all_hpo_analysis.compare_hpo_vs_genotype(is_missense)\n", + "by_missense.summarize(hpo, PatientCategories.YES)" + ] + }, + { + "cell_type": "markdown", + "id": "3db11203", + "metadata": {}, + "source": [ + "Test for presence of genotype-phenotype correlations between frameshift variants vs. others." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "b9bf9ccf", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -910,34 +1440,34 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -946,8 +1476,8 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -955,16 +1485,16 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -973,10 +1503,10 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -991,29 +1521,38 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -1022,134 +1561,57 @@ "" ], "text/plain": [ - "FRAMESHIFT_VARIANT on NM_020366.4 Yes No \\\n", - " Count Percent Count \n", - "Eye poking [HP:0001483] 23/27 85% 5/12 \n", - "Very low visual acuity [HP:0032122] 36/36 100% 45/45 \n", - "Restricted or repetitive behaviors or interests... 23/23 100% 5/5 \n", - "Sensory behavioral abnormality [HP:5200046] 23/23 100% 5/5 \n", - "Abnormality of the eye [HP:0000478] 52/52 100% 67/67 \n", - "Mildly reduced visual acuity [HP:0032037] 1/1 100% 2/2 \n", - "All [HP:0000001] 52/52 100% 69/69 \n", - "Recurrent maladaptive behavior [HP:5200241] 23/23 100% 5/5 \n", - "Self-injurious behavior [HP:0100716] 23/23 100% 5/5 \n", - "Myopia [HP:0000545] 4/4 100% 0/0 \n", - "Reduced visual acuity [HP:0007663] 48/48 100% 66/66 \n", - "Moderately reduced visual acuity [HP:0030515] 8/8 100% 16/16 \n", - "Atypical behavior [HP:0000708] 23/23 100% 5/5 \n", - "Hypermetropia [HP:0000540] 12/12 100% 12/12 \n", - "Visual field defect [HP:0001123] 5/5 100% 5/5 \n", - "Sensory seeking [HP:4000079] 23/23 100% 5/5 \n", - "High hypermetropia [HP:0008499] 4/4 100% 5/5 \n", - "Abnormality of the nervous system [HP:0000707] 27/27 100% 8/8 \n", - "Visual impairment [HP:0000505] 48/48 100% 66/66 \n", - "Severely reduced visual acuity [HP:0001141] 3/3 100% 3/3 \n", - "Abnormal movements of face and head [HP:5200017] 23/23 100% 5/5 \n", - "Abnormal visual electrophysiology [HP:0030453] 11/11 100% 16/16 \n", - "Abnormal eye physiology [HP:0012373] 52/52 100% 67/67 \n", - "Tactile sensory seeking [HP:5200065] 23/23 100% 5/5 \n", - "Abnormal repetitive mannerisms [HP:0000733] 23/23 100% 5/5 \n", - "Abnormal nervous system physiology [HP:0012638] 27/27 100% 8/8 \n", - "Abnormal electroretinogram [HP:0000512] 11/11 100% 16/17 \n", - "Phenotypic abnormality [HP:0000118] 52/52 100% 69/69 \n", - "Abnormality of vision [HP:0000504] 48/48 100% 66/66 \n", - "Constriction of peripheral visual field [HP:000... 5/5 100% 5/5 \n", - "Moderate hypermetropia [HP:0031729] 7/7 100% 5/5 \n", - "Abnormality of mental function [HP:0011446] 23/23 100% 5/5 \n", - "Abnormality of refraction [HP:0000539] 16/16 100% 12/12 \n", - "Neurodevelopmental abnormality [HP:0012759] 8/8 100% 3/3 \n", - "Mild hypermetropia [HP:0031728] 1/1 100% 1/1 \n", - "Neurodevelopmental delay [HP:0012758] 8/8 100% 3/3 \n", - "High myopia [HP:0011003] 4/4 100% 0/0 \n", - "\n", - "FRAMESHIFT_VARIANT on NM_020366.4 \\\n", - " Percent p value \n", - "Eye poking [HP:0001483] 42% 0.016983 \n", - "Very low visual acuity [HP:0032122] 100% 1.000000 \n", - "Restricted or repetitive behaviors or interests... 100% 1.000000 \n", - "Sensory behavioral abnormality [HP:5200046] 100% 1.000000 \n", - "Abnormality of the eye [HP:0000478] 100% 1.000000 \n", - "Mildly reduced visual acuity [HP:0032037] 100% 1.000000 \n", - "All [HP:0000001] 100% 1.000000 \n", - "Recurrent maladaptive behavior [HP:5200241] 100% 1.000000 \n", - "Self-injurious behavior [HP:0100716] 100% 1.000000 \n", - "Myopia [HP:0000545] 0% 1.000000 \n", - "Reduced visual acuity [HP:0007663] 100% 1.000000 \n", - "Moderately reduced visual acuity [HP:0030515] 100% 1.000000 \n", - "Atypical behavior [HP:0000708] 100% 1.000000 \n", - "Hypermetropia [HP:0000540] 100% 1.000000 \n", - "Visual field defect [HP:0001123] 100% 1.000000 \n", - "Sensory seeking [HP:4000079] 100% 1.000000 \n", - "High hypermetropia [HP:0008499] 100% 1.000000 \n", - "Abnormality of the nervous system [HP:0000707] 100% 1.000000 \n", - "Visual impairment [HP:0000505] 100% 1.000000 \n", - "Severely reduced visual acuity [HP:0001141] 100% 1.000000 \n", - "Abnormal movements of face and head [HP:5200017] 100% 1.000000 \n", - "Abnormal visual electrophysiology [HP:0030453] 100% 1.000000 \n", - "Abnormal eye physiology [HP:0012373] 100% 1.000000 \n", - "Tactile sensory seeking [HP:5200065] 100% 1.000000 \n", - "Abnormal repetitive mannerisms [HP:0000733] 100% 1.000000 \n", - "Abnormal nervous system physiology [HP:0012638] 100% 1.000000 \n", - "Abnormal electroretinogram [HP:0000512] 94% 1.000000 \n", - "Phenotypic abnormality [HP:0000118] 100% 1.000000 \n", - "Abnormality of vision [HP:0000504] 100% 1.000000 \n", - "Constriction of peripheral visual field [HP:000... 100% 1.000000 \n", - "Moderate hypermetropia [HP:0031729] 100% 1.000000 \n", - "Abnormality of mental function [HP:0011446] 100% 1.000000 \n", - "Abnormality of refraction [HP:0000539] 100% 1.000000 \n", - "Neurodevelopmental abnormality [HP:0012759] 100% 1.000000 \n", - "Mild hypermetropia [HP:0031728] 100% 1.000000 \n", - "Neurodevelopmental delay [HP:0012758] 100% 1.000000 \n", - "High myopia [HP:0011003] 0% 1.000000 \n", + "FRAMESHIFT_VARIANT on NM_020366.4 Yes ... \n", + " Count ... Corrected p value\n", + "Eye poking [HP:0001483] 23/27 ... 0.645352\n", + "Global developmental delay [HP:0001263] 8/91 ... 1.000000\n", + "Restricted or repetitive behaviors or interests... 23/23 ... 1.000000\n", + "Recurrent maladaptive behavior [HP:5200241] 23/23 ... 1.000000\n", + "Abnormality of the eye [HP:0000478] 52/52 ... 1.000000\n", + "Myopia [HP:0000545] 4/4 ... 1.000000\n", + "Abnormality of mental function [HP:0011446] 23/23 ... 1.000000\n", + "Tactile sensory seeking [HP:5200065] 23/23 ... 1.000000\n", + "Abnormal movements of face and head [HP:5200017] 23/23 ... 1.000000\n", + "Mild hypermetropia [HP:0031728] 1/1 ... 1.000000\n", + "Abnormal nervous system physiology [HP:0012638] 27/27 ... 1.000000\n", + "Severely reduced visual acuity [HP:0001141] 3/3 ... 1.000000\n", + "Sensory behavioral abnormality [HP:5200046] 23/23 ... 1.000000\n", + "Constriction of peripheral visual field [HP:000... 5/5 ... 1.000000\n", + "Abnormality of refraction [HP:0000539] 16/16 ... 1.000000\n", + "Hypermetropia [HP:0000540] 12/12 ... 1.000000\n", + "Abnormal electroretinogram [HP:0000512] 11/11 ... 1.000000\n", + "Abnormal eye physiology [HP:0012373] 52/52 ... 1.000000\n", + "Abnormal repetitive mannerisms [HP:0000733] 23/23 ... 1.000000\n", + "Very low visual acuity [HP:0032122] 36/36 ... 1.000000\n", + "High myopia [HP:0011003] 4/4 ... 1.000000\n", + "All [HP:0000001] 52/52 ... 1.000000\n", + "Abnormality of the nervous system [HP:0000707] 27/27 ... 1.000000\n", + "Visual impairment [HP:0000505] 48/48 ... 1.000000\n", + "High hypermetropia [HP:0008499] 4/4 ... 1.000000\n", + "Moderately reduced visual acuity [HP:0030515] 8/8 ... 1.000000\n", + "Mildly reduced visual acuity [HP:0032037] 1/1 ... 1.000000\n", + "Visual field defect [HP:0001123] 5/5 ... 1.000000\n", + "Reduced visual acuity [HP:0007663] 48/48 ... 1.000000\n", + "Sensory seeking [HP:4000079] 23/23 ... 1.000000\n", + "Neurodevelopmental delay [HP:0012758] 8/8 ... 1.000000\n", + "Self-injurious behavior [HP:0100716] 23/23 ... 1.000000\n", + "Phenotypic abnormality [HP:0000118] 52/52 ... 1.000000\n", + "Neurodevelopmental abnormality [HP:0012759] 8/8 ... 1.000000\n", + "Moderate hypermetropia [HP:0031729] 7/7 ... 1.000000\n", + "Atypical behavior [HP:0000708] 23/23 ... 1.000000\n", + "Abnormal visual electrophysiology [HP:0030453] 11/11 ... 1.000000\n", + "Abnormality of vision [HP:0000504] 48/48 ... 1.000000\n", "\n", - "FRAMESHIFT_VARIANT on NM_020366.4 \n", - " Corrected p value \n", - "Eye poking [HP:0001483] 0.628369 \n", - "Very low visual acuity [HP:0032122] 1.000000 \n", - "Restricted or repetitive behaviors or interests... 1.000000 \n", - "Sensory behavioral abnormality [HP:5200046] 1.000000 \n", - "Abnormality of the eye [HP:0000478] 1.000000 \n", - "Mildly reduced visual acuity [HP:0032037] 1.000000 \n", - "All [HP:0000001] 1.000000 \n", - "Recurrent maladaptive behavior [HP:5200241] 1.000000 \n", - "Self-injurious behavior [HP:0100716] 1.000000 \n", - "Myopia [HP:0000545] 1.000000 \n", - "Reduced visual acuity [HP:0007663] 1.000000 \n", - "Moderately reduced visual acuity [HP:0030515] 1.000000 \n", - "Atypical behavior [HP:0000708] 1.000000 \n", - "Hypermetropia [HP:0000540] 1.000000 \n", - "Visual field defect [HP:0001123] 1.000000 \n", - "Sensory seeking [HP:4000079] 1.000000 \n", - "High hypermetropia [HP:0008499] 1.000000 \n", - "Abnormality of the nervous system [HP:0000707] 1.000000 \n", - "Visual impairment [HP:0000505] 1.000000 \n", - "Severely reduced visual acuity [HP:0001141] 1.000000 \n", - "Abnormal movements of face and head [HP:5200017] 1.000000 \n", - "Abnormal visual electrophysiology [HP:0030453] 1.000000 \n", - "Abnormal eye physiology [HP:0012373] 1.000000 \n", - "Tactile sensory seeking [HP:5200065] 1.000000 \n", - "Abnormal repetitive mannerisms [HP:0000733] 1.000000 \n", - "Abnormal nervous system physiology [HP:0012638] 1.000000 \n", - "Abnormal electroretinogram [HP:0000512] 1.000000 \n", - "Phenotypic abnormality [HP:0000118] 1.000000 \n", - "Abnormality of vision [HP:0000504] 1.000000 \n", - "Constriction of peripheral visual field [HP:000... 1.000000 \n", - "Moderate hypermetropia [HP:0031729] 1.000000 \n", - "Abnormality of mental function [HP:0011446] 1.000000 \n", - "Abnormality of refraction [HP:0000539] 1.000000 \n", - "Neurodevelopmental abnormality [HP:0012759] 1.000000 \n", - "Mild hypermetropia [HP:0031728] 1.000000 \n", - "Neurodevelopmental delay [HP:0012758] 1.000000 \n", - "High myopia [HP:0011003] 1.000000 " + "[38 rows x 6 columns]" ] }, - "execution_count": 9, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "is_frameshift = VariantPredicates.variant_effect(VariantEffect.FRAMESHIFT_VARIANT, tx_id=tx_id)\n", + "is_frameshift = VariantPredicates.variant_effect(VariantEffect.FRAMESHIFT_VARIANT, tx_id=RPGRIP1_MANE_transcript)\n", "by_frameshift = all_hpo_analysis.compare_hpo_vs_genotype(is_frameshift)\n", "by_frameshift.summarize(hpo, PatientCategories.YES)" ] @@ -1164,7 +1626,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 12, "id": "44006715", "metadata": {}, "outputs": [ @@ -1211,16 +1673,16 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1229,16 +1691,16 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1247,115 +1709,160 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", - " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1364,7 +1871,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1373,79 +1880,79 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", - " \n", " \n", " \n", " \n", @@ -1454,25 +1961,16 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", - " \n", " \n", " \n", " \n", @@ -1481,189 +1979,85 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", - " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", - " \n", " \n", " \n", "
FRAMESHIFT_VARIANT on NM_020366.4YesNo
CountPercentCountPercentp valueCorrected p value
Eye poking [HP:0001483]23/2785%5/1242%0.0169830.645352
Global developmental delay [HP:0001263]8/919%3/1292%0.0546841.000000
Restricted or repetitive behaviors or interests [HP:0031432]23/23100%5/51.000000
Self-injurious behavior [HP:0100716]Recurrent maladaptive behavior [HP:5200241]23/23100%5/51.000000
Abnormality of the eye [HP:0000478]52/52100%67/67100%1.0000001.000000
Myopia [HP:0000545]4/4100%1.000000
Reduced visual acuity [HP:0007663]48/48Abnormality of mental function [HP:0011446]23/23100%66/665/5100%1.0000001.000000
Tactile sensory seeking [HP:5200065]23/23100%5/5100%1.0000001.000000
Abnormal movements of face and head [HP:5200017]23/23100%5/5100%1.0000001.000000
Mild hypermetropia [HP:0031728]1/1100%1/1100%1.0000001.000000
Abnormal nervous system physiology [HP:0012638]27/27100%8/8100%1.0000001.000000
Moderately reduced visual acuity [HP:0030515]8/8Severely reduced visual acuity [HP:0001141]3/3100%16/163/3100%1.0000001.000000
Atypical behavior [HP:0000708]Sensory behavioral abnormality [HP:5200046]23/23100%5/51.000000
Hypermetropia [HP:0000540]12/12Constriction of peripheral visual field [HP:0001133]5/5100%12/125/5100%1.0000001.000000
Visual field defect [HP:0001123]5/5Abnormality of refraction [HP:0000539]16/16100%5/512/12100%1.0000001.000000
Sensory seeking [HP:4000079]23/23Hypermetropia [HP:0000540]12/12100%5/512/12100%1.0000001.000000
High hypermetropia [HP:0008499]4/4100%5/5Abnormal electroretinogram [HP:0000512]11/11100%16/1794%1.0000001.000000
Abnormality of the nervous system [HP:0000707]27/27Abnormal eye physiology [HP:0012373]52/52100%8/867/67100%1.0000001.000000
Visual impairment [HP:0000505]48/48Abnormal repetitive mannerisms [HP:0000733]23/23100%66/665/5100%1.0000001.000000
Severely reduced visual acuity [HP:0001141]3/3Very low visual acuity [HP:0032122]36/36100%3/345/45100%1.0000001.000000
Abnormal movements of face and head [HP:5200017]23/23100%5/5High myopia [HP:0011003]4/4100%0/00%1.0000001.000000
Abnormal visual electrophysiology [HP:0030453]11/11All [HP:0000001]52/52100%16/1669/69100%1.0000001.000000
Abnormal eye physiology [HP:0012373]52/52Abnormality of the nervous system [HP:0000707]27/27100%67/678/8100%1.0000001.000000
Tactile sensory seeking [HP:5200065]23/23Visual impairment [HP:0000505]48/48100%5/566/66100%1.0000001.000000
Abnormal repetitive mannerisms [HP:0000733]23/23High hypermetropia [HP:0008499]4/4100%5/5100%1.000000
Abnormal nervous system physiology [HP:0012638]27/27100%Moderately reduced visual acuity [HP:0030515]8/8100%16/16100%1.0000001.000000
Abnormal electroretinogram [HP:0000512]11/11Mildly reduced visual acuity [HP:0032037]1/1100%2/2100%16/1794%1.0000001.000000
Phenotypic abnormality [HP:0000118]52/52Visual field defect [HP:0001123]5/5100%69/695/5100%1.0000001.000000
Abnormality of vision [HP:0000504]Reduced visual acuity [HP:0007663]48/48100%66/661.000000
Constriction of peripheral visual field [HP:0001133]5/5Sensory seeking [HP:4000079]23/23100%5/5100%1.000000
Moderate hypermetropia [HP:0031729]7/7Neurodevelopmental delay [HP:0012758]8/8100%5/53/3100%1.0000001.000000
Abnormality of mental function [HP:0011446]Self-injurious behavior [HP:0100716]23/23100%5/51.000000
Abnormality of refraction [HP:0000539]16/16Phenotypic abnormality [HP:0000118]52/52100%12/1269/69100%1.0000001.0000001.000000
Mild hypermetropia [HP:0031728]1/1Moderate hypermetropia [HP:0031729]7/7100%1/15/5100%1.0000001.000000
Neurodevelopmental delay [HP:0012758]8/8Atypical behavior [HP:0000708]23/23100%3/35/5100%1.0000001.000000
High myopia [HP:0011003]4/4Abnormal visual electrophysiology [HP:0030453]11/11100%16/16100%1.0000001.000000
Abnormality of vision [HP:0000504]48/48100%66/66100%0/00%1.0000001.000000
12/2352%0.0009190.034010.034930
Very low visual acuity [HP:0032122]17/17100%64/64100%1.0000001.00000Global developmental delay [HP:0001263]4/2516%7/1954%0.0249350.947524
Restricted or repetitive behaviors or interests [HP:0031432]12/12100%1.0000001.000001.000000
Sensory behavioral abnormality [HP:5200046]Recurrent maladaptive behavior [HP:5200241]16/16100%12/12100%1.0000001.000001.000000
Abnormality of the eye [HP:0000478]102/102100%1.0000001.000001.000000
Mildly reduced visual acuity [HP:0032037]Myopia [HP:0000545]0/00%3/34/4100%1.0000001.000001.000000
All [HP:0000001]17/17Abnormality of mental function [HP:0011446]16/16100%104/10412/12100%1.0000001.000001.000000
Recurrent maladaptive behavior [HP:5200241]Tactile sensory seeking [HP:5200065]16/16100%12/12100%1.0000001.000001.000000
Self-injurious behavior [HP:0100716]Abnormal movements of face and head [HP:5200017]16/16100%12/12100%1.0000001.000001.000000
Myopia [HP:0000545]Mild hypermetropia [HP:0031728]0/00%4/42/2100%1.0000001.000001.000000
Reduced visual acuity [HP:0007663]17/17Abnormal nervous system physiology [HP:0012638]16/16100%97/9719/19100%1.0000001.000001.000000
Moderately reduced visual acuity [HP:0030515]Severely reduced visual acuity [HP:0001141]0/00%24/246/6100%1.0000001.000001.000000
Atypical behavior [HP:0000708]Sensory behavioral abnormality [HP:5200046]16/16100%12/12100%1.0000001.000001.000000
Constriction of peripheral visual field [HP:0001133]0/00%10/10100%1.0000001.000000
Abnormality of refraction [HP:0000539]3/3100%25/25100%1.0000001.000000
Hypermetropia [HP:0000540]3/3100%21/2121/21100%1.0000001.000000
Abnormal electroretinogram [HP:0000512]0/00%27/2896%1.0000001.000000
Abnormal eye physiology [HP:0012373]17/17100%102/102100%1.0000001.000000
Abnormal repetitive mannerisms [HP:0000733]16/16100%12/12100%1.0000001.000000
Very low visual acuity [HP:0032122]17/17100%64/64100%1.0000001.000001.000000
Visual field defect [HP:0001123]High myopia [HP:0011003]0/00%10/104/4100%1.0000001.00000
Sensory seeking [HP:4000079]16/16100%12/12100%1.0000001.00000
High hypermetropia [HP:0008499]3/3All [HP:0000001]17/17100%6/6104/104100%1.0000001.000001.000000
Abnormality of the nervous system [HP:0000707]19/19100%1.0000001.000001.000000
Visual impairment [HP:0000505]97/97100%1.0000001.000001.000000
Severely reduced visual acuity [HP:0001141]0/00%High hypermetropia [HP:0008499]3/3100%6/6100%1.0000001.000001.000000
Abnormal movements of face and head [HP:5200017]16/16Moderately reduced visual acuity [HP:0030515]0/00%24/24100%12/121.0000001.000000
Mildly reduced visual acuity [HP:0032037]0/00%3/3100%1.0000001.000001.000000
Abnormal visual electrophysiology [HP:0030453]Visual field defect [HP:0001123]0/00%27/2710/10100%1.0000001.000001.000000
Abnormal eye physiology [HP:0012373]Reduced visual acuity [HP:0007663]17/17100%102/10297/97100%1.0000001.000001.000000
Tactile sensory seeking [HP:5200065]Sensory seeking [HP:4000079]16/16100%12/12100%1.0000001.000001.000000
Abnormal repetitive mannerisms [HP:0000733]16/16Neurodevelopmental delay [HP:0012758]4/4100%12/127/7100%1.0000001.000001.000000
Abnormal nervous system physiology [HP:0012638]Self-injurious behavior [HP:0100716]16/16100%19/1912/12100%1.0000001.00000
Abnormal electroretinogram [HP:0000512]0/00%27/2896%1.0000001.00000
Phenotypic abnormality [HP:0000118]104/104100%1.0000001.000001.000000
Abnormality of vision [HP:0000504]17/17Neurodevelopmental abnormality [HP:0012759]4/4100%97/977/7100%1.0000001.00000
Constriction of peripheral visual field [HP:0001133]0/00%10/10100%1.0000001.00000
Moderate hypermetropia [HP:0031729]12/12100%1.0000001.000001.000000
Abnormality of mental function [HP:0011446]Atypical behavior [HP:0000708]16/16100%12/12100%1.0000001.00000
Abnormality of refraction [HP:0000539]3/3100%25/25100%1.0000001.00000
Neurodevelopmental abnormality [HP:0012759]4/4100%7/7100%1.0000001.00000
Mild hypermetropia [HP:0031728]Abnormal visual electrophysiology [HP:0030453]0/00%2/227/27100%1.0000001.000001.000000
Neurodevelopmental delay [HP:0012758]4/4Abnormality of vision [HP:0000504]17/17100%7/797/97100%1.0000001.00000
High myopia [HP:0011003]0/00%4/4100%1.0000001.00000
\n", "
" ], "text/plain": [ - "variant has ID of 14_21312457_21312458_GA_G Yes No \\\n", - " Count Percent Count \n", - "Eye poking [HP:0001483] 16/16 100% 12/23 \n", - "Very low visual acuity [HP:0032122] 17/17 100% 64/64 \n", - "Restricted or repetitive behaviors or interests... 16/16 100% 12/12 \n", - "Sensory behavioral abnormality [HP:5200046] 16/16 100% 12/12 \n", - "Abnormality of the eye [HP:0000478] 17/17 100% 102/102 \n", - "Mildly reduced visual acuity [HP:0032037] 0/0 0% 3/3 \n", - "All [HP:0000001] 17/17 100% 104/104 \n", - "Recurrent maladaptive behavior [HP:5200241] 16/16 100% 12/12 \n", - "Self-injurious behavior [HP:0100716] 16/16 100% 12/12 \n", - "Myopia [HP:0000545] 0/0 0% 4/4 \n", - "Reduced visual acuity [HP:0007663] 17/17 100% 97/97 \n", - "Moderately reduced visual acuity [HP:0030515] 0/0 0% 24/24 \n", - "Atypical behavior [HP:0000708] 16/16 100% 12/12 \n", - "Hypermetropia [HP:0000540] 3/3 100% 21/21 \n", - "Visual field defect [HP:0001123] 0/0 0% 10/10 \n", - "Sensory seeking [HP:4000079] 16/16 100% 12/12 \n", - "High hypermetropia [HP:0008499] 3/3 100% 6/6 \n", - "Abnormality of the nervous system [HP:0000707] 16/16 100% 19/19 \n", - "Visual impairment [HP:0000505] 17/17 100% 97/97 \n", - "Severely reduced visual acuity [HP:0001141] 0/0 0% 6/6 \n", - "Abnormal movements of face and head [HP:5200017] 16/16 100% 12/12 \n", - "Abnormal visual electrophysiology [HP:0030453] 0/0 0% 27/27 \n", - "Abnormal eye physiology [HP:0012373] 17/17 100% 102/102 \n", - "Tactile sensory seeking [HP:5200065] 16/16 100% 12/12 \n", - "Abnormal repetitive mannerisms [HP:0000733] 16/16 100% 12/12 \n", - "Abnormal nervous system physiology [HP:0012638] 16/16 100% 19/19 \n", - "Abnormal electroretinogram [HP:0000512] 0/0 0% 27/28 \n", - "Phenotypic abnormality [HP:0000118] 17/17 100% 104/104 \n", - "Abnormality of vision [HP:0000504] 17/17 100% 97/97 \n", - "Constriction of peripheral visual field [HP:000... 0/0 0% 10/10 \n", - "Moderate hypermetropia [HP:0031729] 0/0 0% 12/12 \n", - "Abnormality of mental function [HP:0011446] 16/16 100% 12/12 \n", - "Abnormality of refraction [HP:0000539] 3/3 100% 25/25 \n", - "Neurodevelopmental abnormality [HP:0012759] 4/4 100% 7/7 \n", - "Mild hypermetropia [HP:0031728] 0/0 0% 2/2 \n", - "Neurodevelopmental delay [HP:0012758] 4/4 100% 7/7 \n", - "High myopia [HP:0011003] 0/0 0% 4/4 \n", - "\n", - "variant has ID of 14_21312457_21312458_GA_G \\\n", - " Percent p value \n", - "Eye poking [HP:0001483] 52% 0.000919 \n", - "Very low visual acuity [HP:0032122] 100% 1.000000 \n", - "Restricted or repetitive behaviors or interests... 100% 1.000000 \n", - "Sensory behavioral abnormality [HP:5200046] 100% 1.000000 \n", - "Abnormality of the eye [HP:0000478] 100% 1.000000 \n", - "Mildly reduced visual acuity [HP:0032037] 100% 1.000000 \n", - "All [HP:0000001] 100% 1.000000 \n", - "Recurrent maladaptive behavior [HP:5200241] 100% 1.000000 \n", - "Self-injurious behavior [HP:0100716] 100% 1.000000 \n", - "Myopia [HP:0000545] 100% 1.000000 \n", - "Reduced visual acuity [HP:0007663] 100% 1.000000 \n", - "Moderately reduced visual acuity [HP:0030515] 100% 1.000000 \n", - "Atypical behavior [HP:0000708] 100% 1.000000 \n", - "Hypermetropia [HP:0000540] 100% 1.000000 \n", - "Visual field defect [HP:0001123] 100% 1.000000 \n", - "Sensory seeking [HP:4000079] 100% 1.000000 \n", - "High hypermetropia [HP:0008499] 100% 1.000000 \n", - "Abnormality of the nervous system [HP:0000707] 100% 1.000000 \n", - "Visual impairment [HP:0000505] 100% 1.000000 \n", - "Severely reduced visual acuity [HP:0001141] 100% 1.000000 \n", - "Abnormal movements of face and head [HP:5200017] 100% 1.000000 \n", - "Abnormal visual electrophysiology [HP:0030453] 100% 1.000000 \n", - "Abnormal eye physiology [HP:0012373] 100% 1.000000 \n", - "Tactile sensory seeking [HP:5200065] 100% 1.000000 \n", - "Abnormal repetitive mannerisms [HP:0000733] 100% 1.000000 \n", - "Abnormal nervous system physiology [HP:0012638] 100% 1.000000 \n", - "Abnormal electroretinogram [HP:0000512] 96% 1.000000 \n", - "Phenotypic abnormality [HP:0000118] 100% 1.000000 \n", - "Abnormality of vision [HP:0000504] 100% 1.000000 \n", - "Constriction of peripheral visual field [HP:000... 100% 1.000000 \n", - "Moderate hypermetropia [HP:0031729] 100% 1.000000 \n", - "Abnormality of mental function [HP:0011446] 100% 1.000000 \n", - "Abnormality of refraction [HP:0000539] 100% 1.000000 \n", - "Neurodevelopmental abnormality [HP:0012759] 100% 1.000000 \n", - "Mild hypermetropia [HP:0031728] 100% 1.000000 \n", - "Neurodevelopmental delay [HP:0012758] 100% 1.000000 \n", - "High myopia [HP:0011003] 100% 1.000000 \n", + "variant has ID of 14_21312457_21312458_GA_G Yes ... \n", + " Count ... Corrected p value\n", + "Eye poking [HP:0001483] 16/16 ... 0.034930\n", + "Global developmental delay [HP:0001263] 4/25 ... 0.947524\n", + "Restricted or repetitive behaviors or interests... 16/16 ... 1.000000\n", + "Recurrent maladaptive behavior [HP:5200241] 16/16 ... 1.000000\n", + "Abnormality of the eye [HP:0000478] 17/17 ... 1.000000\n", + "Myopia [HP:0000545] 0/0 ... 1.000000\n", + "Abnormality of mental function [HP:0011446] 16/16 ... 1.000000\n", + "Tactile sensory seeking [HP:5200065] 16/16 ... 1.000000\n", + "Abnormal movements of face and head [HP:5200017] 16/16 ... 1.000000\n", + "Mild hypermetropia [HP:0031728] 0/0 ... 1.000000\n", + "Abnormal nervous system physiology [HP:0012638] 16/16 ... 1.000000\n", + "Severely reduced visual acuity [HP:0001141] 0/0 ... 1.000000\n", + "Sensory behavioral abnormality [HP:5200046] 16/16 ... 1.000000\n", + "Constriction of peripheral visual field [HP:000... 0/0 ... 1.000000\n", + "Abnormality of refraction [HP:0000539] 3/3 ... 1.000000\n", + "Hypermetropia [HP:0000540] 3/3 ... 1.000000\n", + "Abnormal electroretinogram [HP:0000512] 0/0 ... 1.000000\n", + "Abnormal eye physiology [HP:0012373] 17/17 ... 1.000000\n", + "Abnormal repetitive mannerisms [HP:0000733] 16/16 ... 1.000000\n", + "Very low visual acuity [HP:0032122] 17/17 ... 1.000000\n", + "High myopia [HP:0011003] 0/0 ... 1.000000\n", + "All [HP:0000001] 17/17 ... 1.000000\n", + "Abnormality of the nervous system [HP:0000707] 16/16 ... 1.000000\n", + "Visual impairment [HP:0000505] 17/17 ... 1.000000\n", + "High hypermetropia [HP:0008499] 3/3 ... 1.000000\n", + "Moderately reduced visual acuity [HP:0030515] 0/0 ... 1.000000\n", + "Mildly reduced visual acuity [HP:0032037] 0/0 ... 1.000000\n", + "Visual field defect [HP:0001123] 0/0 ... 1.000000\n", + "Reduced visual acuity [HP:0007663] 17/17 ... 1.000000\n", + "Sensory seeking [HP:4000079] 16/16 ... 1.000000\n", + "Neurodevelopmental delay [HP:0012758] 4/4 ... 1.000000\n", + "Self-injurious behavior [HP:0100716] 16/16 ... 1.000000\n", + "Phenotypic abnormality [HP:0000118] 17/17 ... 1.000000\n", + "Neurodevelopmental abnormality [HP:0012759] 4/4 ... 1.000000\n", + "Moderate hypermetropia [HP:0031729] 0/0 ... 1.000000\n", + "Atypical behavior [HP:0000708] 16/16 ... 1.000000\n", + "Abnormal visual electrophysiology [HP:0030453] 0/0 ... 1.000000\n", + "Abnormality of vision [HP:0000504] 17/17 ... 1.000000\n", "\n", - "variant has ID of 14_21312457_21312458_GA_G \n", - " Corrected p value \n", - "Eye poking [HP:0001483] 0.03401 \n", - "Very low visual acuity [HP:0032122] 1.00000 \n", - "Restricted or repetitive behaviors or interests... 1.00000 \n", - "Sensory behavioral abnormality [HP:5200046] 1.00000 \n", - "Abnormality of the eye [HP:0000478] 1.00000 \n", - "Mildly reduced visual acuity [HP:0032037] 1.00000 \n", - "All [HP:0000001] 1.00000 \n", - "Recurrent maladaptive behavior [HP:5200241] 1.00000 \n", - "Self-injurious behavior [HP:0100716] 1.00000 \n", - "Myopia [HP:0000545] 1.00000 \n", - "Reduced visual acuity [HP:0007663] 1.00000 \n", - "Moderately reduced visual acuity [HP:0030515] 1.00000 \n", - "Atypical behavior [HP:0000708] 1.00000 \n", - "Hypermetropia [HP:0000540] 1.00000 \n", - "Visual field defect [HP:0001123] 1.00000 \n", - "Sensory seeking [HP:4000079] 1.00000 \n", - "High hypermetropia [HP:0008499] 1.00000 \n", - "Abnormality of the nervous system [HP:0000707] 1.00000 \n", - "Visual impairment [HP:0000505] 1.00000 \n", - "Severely reduced visual acuity [HP:0001141] 1.00000 \n", - "Abnormal movements of face and head [HP:5200017] 1.00000 \n", - "Abnormal visual electrophysiology [HP:0030453] 1.00000 \n", - "Abnormal eye physiology [HP:0012373] 1.00000 \n", - "Tactile sensory seeking [HP:5200065] 1.00000 \n", - "Abnormal repetitive mannerisms [HP:0000733] 1.00000 \n", - "Abnormal nervous system physiology [HP:0012638] 1.00000 \n", - "Abnormal electroretinogram [HP:0000512] 1.00000 \n", - "Phenotypic abnormality [HP:0000118] 1.00000 \n", - "Abnormality of vision [HP:0000504] 1.00000 \n", - "Constriction of peripheral visual field [HP:000... 1.00000 \n", - "Moderate hypermetropia [HP:0031729] 1.00000 \n", - "Abnormality of mental function [HP:0011446] 1.00000 \n", - "Abnormality of refraction [HP:0000539] 1.00000 \n", - "Neurodevelopmental abnormality [HP:0012759] 1.00000 \n", - "Mild hypermetropia [HP:0031728] 1.00000 \n", - "Neurodevelopmental delay [HP:0012758] 1.00000 \n", - "High myopia [HP:0011003] 1.00000 " + "[38 rows x 6 columns]" ] }, - "execution_count": 10, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -1678,7 +2072,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 13, "id": "0179fa6e", "metadata": {}, "outputs": [ @@ -1719,15 +2113,6 @@ " \n", " \n", " \n", - " OMIM:608194\n", - " 20/50\n", - " 40%\n", - " 17/170\n", - " 10%\n", - " 0.000004\n", - " 0.000008\n", - " \n", - " \n", " OMIM:613826\n", " 30/50\n", " 60%\n", @@ -1736,48 +2121,54 @@ " 0.000004\n", " 0.000008\n", " \n", + " \n", + " OMIM:608194\n", + " 20/50\n", + " 40%\n", + " 17/170\n", + " 10%\n", + " 0.000004\n", + " 0.000008\n", + " \n", " \n", "\n", "" ], "text/plain": [ - "MISSENSE_VARIANT on NM_020366.4 Yes No \\\n", - " Count Percent Count Percent p value \n", - "OMIM:608194 20/50 40% 17/170 10% 0.000004 \n", - "OMIM:613826 30/50 60% 153/170 90% 0.000004 \n", + "MISSENSE_VARIANT on NM_020366.4 Yes ... \n", + " Count Percent ... p value Corrected p value\n", + "OMIM:613826 30/50 60% ... 0.000004 0.000008\n", + "OMIM:608194 20/50 40% ... 0.000004 0.000008\n", "\n", - "MISSENSE_VARIANT on NM_020366.4 \n", - " Corrected p value \n", - "OMIM:608194 0.000008 \n", - "OMIM:613826 0.000008 " + "[2 rows x 6 columns]" ] }, - "execution_count": 11, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "is_missense = VariantPredicates.variant_effect(VariantEffect.MISSENSE_VARIANT, tx_id)\n", + "is_missense = VariantPredicates.variant_effect(VariantEffect.MISSENSE_VARIANT, RPGRIP1_MANE_transcript)\n", "by_dis = all_hpo_analysis.compare_disease_vs_genotype(is_missense)\n", "by_dis.summarize(hpo, PatientCategories.YES)" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 14, "id": "d4b6a6a0", "metadata": {}, "outputs": [], "source": [ "analysis_config = CohortAnalysisConfiguration()\n", - "analysis_config.heuristic_strategy()\n", + "analysis_config.hpo_mtc_strategy()\n", "hpo_analysis = configure_cohort_analysis(cohort, hpo, config=analysis_config)\n" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 15, "id": "3c459243", "metadata": {}, "outputs": [ @@ -1812,175 +2203,55 @@ " Percent\n", " Count\n", " Percent\n", - " p value\n", - " Corrected p value\n", - " \n", - " \n", - " \n", - " \n", - " Eye poking [HP:0001483]\n", - " 23/27\n", - " 85%\n", - " 5/12\n", - " 42%\n", - " 0.016983\n", - " 0.033966\n", - " \n", - " \n", - " Abnormal electroretinogram [HP:0000512]\n", - " 11/11\n", - " 100%\n", - " 16/17\n", - " 94%\n", - " 1.000000\n", - " 1.000000\n", - " \n", - " \n", - "\n", - "" - ], - "text/plain": [ - "FRAMESHIFT_VARIANT on NM_020366.4 Yes No \\\n", - " Count Percent Count Percent \n", - "Eye poking [HP:0001483] 23/27 85% 5/12 42% \n", - "Abnormal electroretinogram [HP:0000512] 11/11 100% 16/17 94% \n", - "\n", - "FRAMESHIFT_VARIANT on NM_020366.4 \n", - " p value Corrected p value \n", - "Eye poking [HP:0001483] 0.016983 0.033966 \n", - "Abnormal electroretinogram [HP:0000512] 1.000000 1.000000 " - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "by_frameshift = hpo_analysis.compare_hpo_vs_genotype(is_frameshift)\n", - "by_frameshift.summarize(hpo, PatientCategories.YES)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "de5bd274", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - " \n", - " Cohort\n", - " \n", - "\n", - "\n", - "\n", - "

Statistical analysis: bonferroni (heuristic sampler)

\n", - "

Performed statistical tests for 4 out of the total of 37 HPO terms.

\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Using heuristic sampler, 33 term(s) were omitted from statistical analysis.
ReasonCount
Skipping term because all genotypes have same HPO observed proportions20
Skipping general term8
Skipping term with only 4 observations (not powered for 2x2)2
Skipping term with only 3 observations (not powered for 2x2)1
Skipping term with only 6 observations (not powered for 2x2)1
Skipping term with only 2 observations (not powered for 2x2)1
\n", - "\n", - "" + " p value\n", + " Corrected p value\n", + " \n", + " \n", + " \n", + " \n", + " Eye poking [HP:0001483]\n", + " 23/27\n", + " 85%\n", + " 5/12\n", + " 42%\n", + " 0.016983\n", + " 0.033966\n", + " \n", + " \n", + " Abnormal electroretinogram [HP:0000512]\n", + " 11/11\n", + " 100%\n", + " 16/17\n", + " 94%\n", + " 1.000000\n", + " 1.000000\n", + " \n", + " \n", + "\n", + "" ], "text/plain": [ - "" + "FRAMESHIFT_VARIANT on NM_020366.4 Yes ... \n", + " Count ... Corrected p value\n", + "Eye poking [HP:0001483] 23/27 ... 0.033966\n", + "Abnormal electroretinogram [HP:0000512] 11/11 ... 1.000000\n", + "\n", + "[2 rows x 6 columns]" ] }, + "execution_count": 15, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], "source": [ - "from genophenocorr.view import StatsViewer\n", - "viewer = StatsViewer()\n", - "\n", - "\n", - "display(HTML(viewer.process(by_frameshift.mtc_filter_report)))" + "by_frameshift = hpo_analysis.compare_hpo_vs_genotype(is_frameshift)\n", + "by_frameshift.summarize(hpo, PatientCategories.YES)" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "id": "348818dc", "metadata": {}, "outputs": [ @@ -2032,18 +2303,18 @@ " 12/23\n", " 52%\n", " 0.000919\n", - " 0.03401\n", + " 0.034930\n", " \n", " \n", - " Very low visual acuity [HP:0032122]\n", - " 17/17\n", - " 100%\n", + " Global developmental delay [HP:0001263]\n", + " 4/25\n", + " 16%\n", " 0/0\n", " 0%\n", - " 64/64\n", - " 100%\n", - " 1.000000\n", - " 1.00000\n", + " 7/195\n", + " 4%\n", + " 0.024935\n", + " 0.947524\n", " \n", " \n", " Restricted or repetitive behaviors or interests [HP:0031432]\n", @@ -2054,10 +2325,10 @@ " 12/12\n", " 100%\n", " 1.000000\n", - " 1.00000\n", + " 1.000000\n", " \n", " \n", - " Sensory behavioral abnormality [HP:5200046]\n", + " Recurrent maladaptive behavior [HP:5200241]\n", " 16/16\n", " 100%\n", " 0/0\n", @@ -2065,7 +2336,7 @@ " 12/12\n", " 100%\n", " 1.000000\n", - " 1.00000\n", + " 1.000000\n", " \n", " \n", " Abnormality of the eye [HP:0000478]\n", @@ -2076,32 +2347,32 @@ " 102/102\n", " 100%\n", " 1.000000\n", - " 1.00000\n", + " 1.000000\n", " \n", " \n", - " Mildly reduced visual acuity [HP:0032037]\n", + " Myopia [HP:0000545]\n", " 0/0\n", " 0%\n", " 0/0\n", " 0%\n", - " 3/3\n", + " 4/4\n", " 100%\n", " 1.000000\n", - " 1.00000\n", + " 1.000000\n", " \n", " \n", - " All [HP:0000001]\n", - " 17/17\n", + " Abnormality of mental function [HP:0011446]\n", + " 16/16\n", " 100%\n", " 0/0\n", " 0%\n", - " 104/104\n", + " 12/12\n", " 100%\n", " 1.000000\n", - " 1.00000\n", + " 1.000000\n", " \n", " \n", - " Recurrent maladaptive behavior [HP:5200241]\n", + " Tactile sensory seeking [HP:5200065]\n", " 16/16\n", " 100%\n", " 0/0\n", @@ -2109,10 +2380,10 @@ " 12/12\n", " 100%\n", " 1.000000\n", - " 1.00000\n", + " 1.000000\n", " \n", " \n", - " Self-injurious behavior [HP:0100716]\n", + " Abnormal movements of face and head [HP:5200017]\n", " 16/16\n", " 100%\n", " 0/0\n", @@ -2120,43 +2391,43 @@ " 12/12\n", " 100%\n", " 1.000000\n", - " 1.00000\n", + " 1.000000\n", " \n", " \n", - " Myopia [HP:0000545]\n", + " Mild hypermetropia [HP:0031728]\n", " 0/0\n", " 0%\n", " 0/0\n", " 0%\n", - " 4/4\n", + " 2/2\n", " 100%\n", " 1.000000\n", - " 1.00000\n", + " 1.000000\n", " \n", " \n", - " Reduced visual acuity [HP:0007663]\n", - " 17/17\n", + " Abnormal nervous system physiology [HP:0012638]\n", + " 16/16\n", " 100%\n", " 0/0\n", " 0%\n", - " 97/97\n", + " 19/19\n", " 100%\n", " 1.000000\n", - " 1.00000\n", + " 1.000000\n", " \n", " \n", - " Moderately reduced visual acuity [HP:0030515]\n", + " Severely reduced visual acuity [HP:0001141]\n", " 0/0\n", " 0%\n", " 0/0\n", " 0%\n", - " 24/24\n", + " 6/6\n", " 100%\n", " 1.000000\n", - " 1.00000\n", + " 1.000000\n", " \n", " \n", - " Atypical behavior [HP:0000708]\n", + " Sensory behavioral abnormality [HP:5200046]\n", " 16/16\n", " 100%\n", " 0/0\n", @@ -2164,21 +2435,10 @@ " 12/12\n", " 100%\n", " 1.000000\n", - " 1.00000\n", - " \n", - " \n", - " Hypermetropia [HP:0000540]\n", - " 3/3\n", - " 100%\n", - " 0/0\n", - " 0%\n", - " 21/21\n", - " 100%\n", " 1.000000\n", - " 1.00000\n", " \n", " \n", - " Visual field defect [HP:0001123]\n", + " Constriction of peripheral visual field [HP:0001133]\n", " 0/0\n", " 0%\n", " 0/0\n", @@ -2186,153 +2446,164 @@ " 10/10\n", " 100%\n", " 1.000000\n", - " 1.00000\n", + " 1.000000\n", " \n", " \n", - " Sensory seeking [HP:4000079]\n", - " 16/16\n", + " Abnormality of refraction [HP:0000539]\n", + " 3/3\n", " 100%\n", " 0/0\n", " 0%\n", - " 12/12\n", + " 25/25\n", " 100%\n", " 1.000000\n", - " 1.00000\n", + " 1.000000\n", " \n", " \n", - " High hypermetropia [HP:0008499]\n", + " Hypermetropia [HP:0000540]\n", " 3/3\n", " 100%\n", " 0/0\n", " 0%\n", - " 6/6\n", + " 21/21\n", " 100%\n", " 1.000000\n", - " 1.00000\n", + " 1.000000\n", " \n", " \n", - " Abnormality of the nervous system [HP:0000707]\n", - " 16/16\n", - " 100%\n", + " Abnormal electroretinogram [HP:0000512]\n", " 0/0\n", " 0%\n", - " 19/19\n", - " 100%\n", + " 0/0\n", + " 0%\n", + " 27/28\n", + " 96%\n", + " 1.000000\n", " 1.000000\n", - " 1.00000\n", " \n", " \n", - " Visual impairment [HP:0000505]\n", + " Abnormal eye physiology [HP:0012373]\n", " 17/17\n", " 100%\n", " 0/0\n", " 0%\n", - " 97/97\n", + " 102/102\n", " 100%\n", " 1.000000\n", - " 1.00000\n", + " 1.000000\n", " \n", " \n", - " Severely reduced visual acuity [HP:0001141]\n", - " 0/0\n", - " 0%\n", + " Abnormal repetitive mannerisms [HP:0000733]\n", + " 16/16\n", + " 100%\n", " 0/0\n", " 0%\n", - " 6/6\n", + " 12/12\n", " 100%\n", " 1.000000\n", - " 1.00000\n", + " 1.000000\n", " \n", " \n", - " Abnormal movements of face and head [HP:5200017]\n", - " 16/16\n", + " Very low visual acuity [HP:0032122]\n", + " 17/17\n", " 100%\n", " 0/0\n", " 0%\n", - " 12/12\n", + " 64/64\n", " 100%\n", " 1.000000\n", - " 1.00000\n", + " 1.000000\n", " \n", " \n", - " Abnormal visual electrophysiology [HP:0030453]\n", + " High myopia [HP:0011003]\n", " 0/0\n", " 0%\n", " 0/0\n", " 0%\n", - " 27/27\n", + " 4/4\n", " 100%\n", " 1.000000\n", - " 1.00000\n", + " 1.000000\n", " \n", " \n", - " Abnormal eye physiology [HP:0012373]\n", + " All [HP:0000001]\n", " 17/17\n", " 100%\n", " 0/0\n", " 0%\n", - " 102/102\n", + " 104/104\n", " 100%\n", " 1.000000\n", - " 1.00000\n", + " 1.000000\n", " \n", " \n", - " Tactile sensory seeking [HP:5200065]\n", + " Abnormality of the nervous system [HP:0000707]\n", " 16/16\n", " 100%\n", " 0/0\n", " 0%\n", - " 12/12\n", + " 19/19\n", " 100%\n", " 1.000000\n", - " 1.00000\n", + " 1.000000\n", " \n", " \n", - " Abnormal repetitive mannerisms [HP:0000733]\n", - " 16/16\n", + " Visual impairment [HP:0000505]\n", + " 17/17\n", " 100%\n", " 0/0\n", " 0%\n", - " 12/12\n", + " 97/97\n", " 100%\n", " 1.000000\n", - " 1.00000\n", + " 1.000000\n", " \n", " \n", - " Abnormal nervous system physiology [HP:0012638]\n", - " 16/16\n", + " High hypermetropia [HP:0008499]\n", + " 3/3\n", " 100%\n", " 0/0\n", " 0%\n", - " 19/19\n", + " 6/6\n", " 100%\n", " 1.000000\n", - " 1.00000\n", + " 1.000000\n", " \n", " \n", - " Abnormal electroretinogram [HP:0000512]\n", + " Moderately reduced visual acuity [HP:0030515]\n", " 0/0\n", " 0%\n", " 0/0\n", " 0%\n", - " 27/28\n", - " 96%\n", + " 24/24\n", + " 100%\n", + " 1.000000\n", " 1.000000\n", - " 1.00000\n", " \n", " \n", - " Phenotypic abnormality [HP:0000118]\n", - " 17/17\n", + " Mildly reduced visual acuity [HP:0032037]\n", + " 0/0\n", + " 0%\n", + " 0/0\n", + " 0%\n", + " 3/3\n", " 100%\n", + " 1.000000\n", + " 1.000000\n", + " \n", + " \n", + " Visual field defect [HP:0001123]\n", " 0/0\n", " 0%\n", - " 104/104\n", + " 0/0\n", + " 0%\n", + " 10/10\n", " 100%\n", " 1.000000\n", - " 1.00000\n", + " 1.000000\n", " \n", " \n", - " Abnormality of vision [HP:0000504]\n", + " Reduced visual acuity [HP:0007663]\n", " 17/17\n", " 100%\n", " 0/0\n", @@ -2340,32 +2611,32 @@ " 97/97\n", " 100%\n", " 1.000000\n", - " 1.00000\n", + " 1.000000\n", " \n", " \n", - " Constriction of peripheral visual field [HP:0001133]\n", - " 0/0\n", - " 0%\n", + " Sensory seeking [HP:4000079]\n", + " 16/16\n", + " 100%\n", " 0/0\n", " 0%\n", - " 10/10\n", + " 12/12\n", " 100%\n", " 1.000000\n", - " 1.00000\n", + " 1.000000\n", " \n", " \n", - " Moderate hypermetropia [HP:0031729]\n", - " 0/0\n", - " 0%\n", + " Neurodevelopmental delay [HP:0012758]\n", + " 4/4\n", + " 100%\n", " 0/0\n", " 0%\n", - " 12/12\n", + " 7/7\n", " 100%\n", " 1.000000\n", - " 1.00000\n", + " 1.000000\n", " \n", " \n", - " Abnormality of mental function [HP:0011446]\n", + " Self-injurious behavior [HP:0100716]\n", " 16/16\n", " 100%\n", " 0/0\n", @@ -2373,18 +2644,18 @@ " 12/12\n", " 100%\n", " 1.000000\n", - " 1.00000\n", + " 1.000000\n", " \n", " \n", - " Abnormality of refraction [HP:0000539]\n", - " 3/3\n", + " Phenotypic abnormality [HP:0000118]\n", + " 17/17\n", " 100%\n", " 0/0\n", " 0%\n", - " 25/25\n", + " 104/104\n", " 100%\n", " 1.000000\n", - " 1.00000\n", + " 1.000000\n", " \n", " \n", " Neurodevelopmental abnormality [HP:0012759]\n", @@ -2395,168 +2666,102 @@ " 7/7\n", " 100%\n", " 1.000000\n", - " 1.00000\n", + " 1.000000\n", " \n", " \n", - " Mild hypermetropia [HP:0031728]\n", + " Moderate hypermetropia [HP:0031729]\n", " 0/0\n", " 0%\n", " 0/0\n", " 0%\n", - " 2/2\n", + " 12/12\n", " 100%\n", " 1.000000\n", - " 1.00000\n", + " 1.000000\n", " \n", " \n", - " Neurodevelopmental delay [HP:0012758]\n", - " 4/4\n", + " Atypical behavior [HP:0000708]\n", + " 16/16\n", " 100%\n", " 0/0\n", " 0%\n", - " 7/7\n", + " 12/12\n", " 100%\n", " 1.000000\n", - " 1.00000\n", + " 1.000000\n", " \n", " \n", - " High myopia [HP:0011003]\n", + " Abnormal visual electrophysiology [HP:0030453]\n", " 0/0\n", " 0%\n", " 0/0\n", " 0%\n", - " 4/4\n", + " 27/27\n", + " 100%\n", + " 1.000000\n", + " 1.000000\n", + " \n", + " \n", + " Abnormality of vision [HP:0000504]\n", + " 17/17\n", + " 100%\n", + " 0/0\n", + " 0%\n", + " 97/97\n", " 100%\n", " 1.000000\n", - " 1.00000\n", + " 1.000000\n", " \n", " \n", "\n", "" ], "text/plain": [ - "variant has ID of 14_21312457_21312458_GA_G Both One \\\n", - " Count Percent Count \n", - "Eye poking [HP:0001483] 16/16 100% 0/0 \n", - "Very low visual acuity [HP:0032122] 17/17 100% 0/0 \n", - "Restricted or repetitive behaviors or interests... 16/16 100% 0/0 \n", - "Sensory behavioral abnormality [HP:5200046] 16/16 100% 0/0 \n", - "Abnormality of the eye [HP:0000478] 17/17 100% 0/0 \n", - "Mildly reduced visual acuity [HP:0032037] 0/0 0% 0/0 \n", - "All [HP:0000001] 17/17 100% 0/0 \n", - "Recurrent maladaptive behavior [HP:5200241] 16/16 100% 0/0 \n", - "Self-injurious behavior [HP:0100716] 16/16 100% 0/0 \n", - "Myopia [HP:0000545] 0/0 0% 0/0 \n", - "Reduced visual acuity [HP:0007663] 17/17 100% 0/0 \n", - "Moderately reduced visual acuity [HP:0030515] 0/0 0% 0/0 \n", - "Atypical behavior [HP:0000708] 16/16 100% 0/0 \n", - "Hypermetropia [HP:0000540] 3/3 100% 0/0 \n", - "Visual field defect [HP:0001123] 0/0 0% 0/0 \n", - "Sensory seeking [HP:4000079] 16/16 100% 0/0 \n", - "High hypermetropia [HP:0008499] 3/3 100% 0/0 \n", - "Abnormality of the nervous system [HP:0000707] 16/16 100% 0/0 \n", - "Visual impairment [HP:0000505] 17/17 100% 0/0 \n", - "Severely reduced visual acuity [HP:0001141] 0/0 0% 0/0 \n", - "Abnormal movements of face and head [HP:5200017] 16/16 100% 0/0 \n", - "Abnormal visual electrophysiology [HP:0030453] 0/0 0% 0/0 \n", - "Abnormal eye physiology [HP:0012373] 17/17 100% 0/0 \n", - "Tactile sensory seeking [HP:5200065] 16/16 100% 0/0 \n", - "Abnormal repetitive mannerisms [HP:0000733] 16/16 100% 0/0 \n", - "Abnormal nervous system physiology [HP:0012638] 16/16 100% 0/0 \n", - "Abnormal electroretinogram [HP:0000512] 0/0 0% 0/0 \n", - "Phenotypic abnormality [HP:0000118] 17/17 100% 0/0 \n", - "Abnormality of vision [HP:0000504] 17/17 100% 0/0 \n", - "Constriction of peripheral visual field [HP:000... 0/0 0% 0/0 \n", - "Moderate hypermetropia [HP:0031729] 0/0 0% 0/0 \n", - "Abnormality of mental function [HP:0011446] 16/16 100% 0/0 \n", - "Abnormality of refraction [HP:0000539] 3/3 100% 0/0 \n", - "Neurodevelopmental abnormality [HP:0012759] 4/4 100% 0/0 \n", - "Mild hypermetropia [HP:0031728] 0/0 0% 0/0 \n", - "Neurodevelopmental delay [HP:0012758] 4/4 100% 0/0 \n", - "High myopia [HP:0011003] 0/0 0% 0/0 \n", + "variant has ID of 14_21312457_21312458_GA_G Both ... \n", + " Count ... Corrected p value\n", + "Eye poking [HP:0001483] 16/16 ... 0.034930\n", + "Global developmental delay [HP:0001263] 4/25 ... 0.947524\n", + "Restricted or repetitive behaviors or interests... 16/16 ... 1.000000\n", + "Recurrent maladaptive behavior [HP:5200241] 16/16 ... 1.000000\n", + "Abnormality of the eye [HP:0000478] 17/17 ... 1.000000\n", + "Myopia [HP:0000545] 0/0 ... 1.000000\n", + "Abnormality of mental function [HP:0011446] 16/16 ... 1.000000\n", + "Tactile sensory seeking [HP:5200065] 16/16 ... 1.000000\n", + "Abnormal movements of face and head [HP:5200017] 16/16 ... 1.000000\n", + "Mild hypermetropia [HP:0031728] 0/0 ... 1.000000\n", + "Abnormal nervous system physiology [HP:0012638] 16/16 ... 1.000000\n", + "Severely reduced visual acuity [HP:0001141] 0/0 ... 1.000000\n", + "Sensory behavioral abnormality [HP:5200046] 16/16 ... 1.000000\n", + "Constriction of peripheral visual field [HP:000... 0/0 ... 1.000000\n", + "Abnormality of refraction [HP:0000539] 3/3 ... 1.000000\n", + "Hypermetropia [HP:0000540] 3/3 ... 1.000000\n", + "Abnormal electroretinogram [HP:0000512] 0/0 ... 1.000000\n", + "Abnormal eye physiology [HP:0012373] 17/17 ... 1.000000\n", + "Abnormal repetitive mannerisms [HP:0000733] 16/16 ... 1.000000\n", + "Very low visual acuity [HP:0032122] 17/17 ... 1.000000\n", + "High myopia [HP:0011003] 0/0 ... 1.000000\n", + "All [HP:0000001] 17/17 ... 1.000000\n", + "Abnormality of the nervous system [HP:0000707] 16/16 ... 1.000000\n", + "Visual impairment [HP:0000505] 17/17 ... 1.000000\n", + "High hypermetropia [HP:0008499] 3/3 ... 1.000000\n", + "Moderately reduced visual acuity [HP:0030515] 0/0 ... 1.000000\n", + "Mildly reduced visual acuity [HP:0032037] 0/0 ... 1.000000\n", + "Visual field defect [HP:0001123] 0/0 ... 1.000000\n", + "Reduced visual acuity [HP:0007663] 17/17 ... 1.000000\n", + "Sensory seeking [HP:4000079] 16/16 ... 1.000000\n", + "Neurodevelopmental delay [HP:0012758] 4/4 ... 1.000000\n", + "Self-injurious behavior [HP:0100716] 16/16 ... 1.000000\n", + "Phenotypic abnormality [HP:0000118] 17/17 ... 1.000000\n", + "Neurodevelopmental abnormality [HP:0012759] 4/4 ... 1.000000\n", + "Moderate hypermetropia [HP:0031729] 0/0 ... 1.000000\n", + "Atypical behavior [HP:0000708] 16/16 ... 1.000000\n", + "Abnormal visual electrophysiology [HP:0030453] 0/0 ... 1.000000\n", + "Abnormality of vision [HP:0000504] 17/17 ... 1.000000\n", "\n", - "variant has ID of 14_21312457_21312458_GA_G Neither \\\n", - " Percent Count Percent \n", - "Eye poking [HP:0001483] 0% 12/23 52% \n", - "Very low visual acuity [HP:0032122] 0% 64/64 100% \n", - "Restricted or repetitive behaviors or interests... 0% 12/12 100% \n", - "Sensory behavioral abnormality [HP:5200046] 0% 12/12 100% \n", - "Abnormality of the eye [HP:0000478] 0% 102/102 100% \n", - "Mildly reduced visual acuity [HP:0032037] 0% 3/3 100% \n", - "All [HP:0000001] 0% 104/104 100% \n", - "Recurrent maladaptive behavior [HP:5200241] 0% 12/12 100% \n", - "Self-injurious behavior [HP:0100716] 0% 12/12 100% \n", - "Myopia [HP:0000545] 0% 4/4 100% \n", - "Reduced visual acuity [HP:0007663] 0% 97/97 100% \n", - "Moderately reduced visual acuity [HP:0030515] 0% 24/24 100% \n", - "Atypical behavior [HP:0000708] 0% 12/12 100% \n", - "Hypermetropia [HP:0000540] 0% 21/21 100% \n", - "Visual field defect [HP:0001123] 0% 10/10 100% \n", - "Sensory seeking [HP:4000079] 0% 12/12 100% \n", - "High hypermetropia [HP:0008499] 0% 6/6 100% \n", - "Abnormality of the nervous system [HP:0000707] 0% 19/19 100% \n", - "Visual impairment [HP:0000505] 0% 97/97 100% \n", - "Severely reduced visual acuity [HP:0001141] 0% 6/6 100% \n", - "Abnormal movements of face and head [HP:5200017] 0% 12/12 100% \n", - "Abnormal visual electrophysiology [HP:0030453] 0% 27/27 100% \n", - "Abnormal eye physiology [HP:0012373] 0% 102/102 100% \n", - "Tactile sensory seeking [HP:5200065] 0% 12/12 100% \n", - "Abnormal repetitive mannerisms [HP:0000733] 0% 12/12 100% \n", - "Abnormal nervous system physiology [HP:0012638] 0% 19/19 100% \n", - "Abnormal electroretinogram [HP:0000512] 0% 27/28 96% \n", - "Phenotypic abnormality [HP:0000118] 0% 104/104 100% \n", - "Abnormality of vision [HP:0000504] 0% 97/97 100% \n", - "Constriction of peripheral visual field [HP:000... 0% 10/10 100% \n", - "Moderate hypermetropia [HP:0031729] 0% 12/12 100% \n", - "Abnormality of mental function [HP:0011446] 0% 12/12 100% \n", - "Abnormality of refraction [HP:0000539] 0% 25/25 100% \n", - "Neurodevelopmental abnormality [HP:0012759] 0% 7/7 100% \n", - "Mild hypermetropia [HP:0031728] 0% 2/2 100% \n", - "Neurodevelopmental delay [HP:0012758] 0% 7/7 100% \n", - "High myopia [HP:0011003] 0% 4/4 100% \n", - "\n", - "variant has ID of 14_21312457_21312458_GA_G \n", - " p value Corrected p value \n", - "Eye poking [HP:0001483] 0.000919 0.03401 \n", - "Very low visual acuity [HP:0032122] 1.000000 1.00000 \n", - "Restricted or repetitive behaviors or interests... 1.000000 1.00000 \n", - "Sensory behavioral abnormality [HP:5200046] 1.000000 1.00000 \n", - "Abnormality of the eye [HP:0000478] 1.000000 1.00000 \n", - "Mildly reduced visual acuity [HP:0032037] 1.000000 1.00000 \n", - "All [HP:0000001] 1.000000 1.00000 \n", - "Recurrent maladaptive behavior [HP:5200241] 1.000000 1.00000 \n", - "Self-injurious behavior [HP:0100716] 1.000000 1.00000 \n", - "Myopia [HP:0000545] 1.000000 1.00000 \n", - "Reduced visual acuity [HP:0007663] 1.000000 1.00000 \n", - "Moderately reduced visual acuity [HP:0030515] 1.000000 1.00000 \n", - "Atypical behavior [HP:0000708] 1.000000 1.00000 \n", - "Hypermetropia [HP:0000540] 1.000000 1.00000 \n", - "Visual field defect [HP:0001123] 1.000000 1.00000 \n", - "Sensory seeking [HP:4000079] 1.000000 1.00000 \n", - "High hypermetropia [HP:0008499] 1.000000 1.00000 \n", - "Abnormality of the nervous system [HP:0000707] 1.000000 1.00000 \n", - "Visual impairment [HP:0000505] 1.000000 1.00000 \n", - "Severely reduced visual acuity [HP:0001141] 1.000000 1.00000 \n", - "Abnormal movements of face and head [HP:5200017] 1.000000 1.00000 \n", - "Abnormal visual electrophysiology [HP:0030453] 1.000000 1.00000 \n", - "Abnormal eye physiology [HP:0012373] 1.000000 1.00000 \n", - "Tactile sensory seeking [HP:5200065] 1.000000 1.00000 \n", - "Abnormal repetitive mannerisms [HP:0000733] 1.000000 1.00000 \n", - "Abnormal nervous system physiology [HP:0012638] 1.000000 1.00000 \n", - "Abnormal electroretinogram [HP:0000512] 1.000000 1.00000 \n", - "Phenotypic abnormality [HP:0000118] 1.000000 1.00000 \n", - "Abnormality of vision [HP:0000504] 1.000000 1.00000 \n", - "Constriction of peripheral visual field [HP:000... 1.000000 1.00000 \n", - "Moderate hypermetropia [HP:0031729] 1.000000 1.00000 \n", - "Abnormality of mental function [HP:0011446] 1.000000 1.00000 \n", - "Abnormality of refraction [HP:0000539] 1.000000 1.00000 \n", - "Neurodevelopmental abnormality [HP:0012759] 1.000000 1.00000 \n", - "Mild hypermetropia [HP:0031728] 1.000000 1.00000 \n", - "Neurodevelopmental delay [HP:0012758] 1.000000 1.00000 \n", - "High myopia [HP:0011003] 1.000000 1.00000 " + "[38 rows x 8 columns]" ] }, - "execution_count": 15, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -2591,7 +2796,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.3" + "version": "3.10.10" } }, "nbformat": 4, diff --git a/notebooks/STXBP1/STXBP1.ipynb b/notebooks/STXBP1/STXBP1.ipynb index 94e27e78..b39865d6 100644 --- a/notebooks/STXBP1/STXBP1.ipynb +++ b/notebooks/STXBP1/STXBP1.ipynb @@ -19,35 +19,19 @@ "name": "stdout", "output_type": "stream", "text": [ - "Using genophenocorr version 0.1.1dev\n" - ] - } - ], - "source": [ - "import genophenocorr\n", - "print(f\"Using genophenocorr version {genophenocorr.__version__}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "c0692465", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loaded HPO v2023-10-09\n" + "Loaded HPO v2023-10-09\n", + "Using gpsea version 0.2.1.dev0\n" ] } ], "source": [ + "import gpsea\n", "import hpotk\n", "\n", "store = hpotk.configure_ontology_store()\n", "hpo = store.load_minimal_hpo(release='v2023-10-09')\n", - "print(f'Loaded HPO v{hpo.version}')" + "print(f'Loaded HPO v{hpo.version}')\n", + "print(f\"Using gpsea version {gpsea.__version__}\")" ] }, { @@ -62,29 +46,18 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "id": "b3095872", "metadata": {}, "outputs": [], "source": [ - "tx_id = 'NM_003165.6'" + "STXBP1_MANE_transcript = 'NM_003165.6'\n", + "STXBP1_protein_id = 'NP_003156.1'" ] }, { "cell_type": "code", - "execution_count": 4, - "id": "77660956-d30f-4c49-a4a0-7fdae1e75b9f", - "metadata": {}, - "outputs": [], - "source": [ - "from genophenocorr.preprocessing import configure_caching_cohort_creator\n", - "\n", - "cohort_creator = configure_caching_cohort_creator(hpo, timeout=20)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, + "execution_count": 3, "id": "76967087-8fde-4388-95d2-dba1776bfa08", "metadata": {}, "outputs": [ @@ -92,7 +65,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Patients Created: 41%|\u001b[32m████▏ \u001b[0m| 192/463 [00:00<00:00, 516.95it/s]" + "Patients Created: 17%|█▋ | 81/463 [02:28<09:44, 1.53s/it]" ] }, { @@ -107,45 +80,40 @@ "name": "stdout", "output_type": "stream", "text": [ - "Patients Created: 100%|\u001b[32m██████████\u001b[0m| 463/463 [00:01<00:00, 335.33it/s]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ + "Patients Created: 100%|██████████| 463/463 [11:05<00:00, 1.44s/it]\n", "Validated under none policy\n", "Showing errors and warnings\n", - "Load phenopackets\n", - " patient #29\n", - " phenotype-features\n", - " warnings:\n", - " ·Triggered by physical trauma [HP:0031135] is not a descendant of Phenotypic abnormality [HP:0000118]\n", - " patient #227\n", + "Phenopackets\n", + " patient #71\n", " phenotype-features\n", " warnings:\n", - " ·Death in childhood [HP:0003819] is not a descendant of Phenotypic abnormality [HP:0000118]\n", - " patient #239\n", + "\n", + " patient #81\n", " variants\n", " warnings:\n", - " ·Patient STX_23708187_T23289 has no variants to work with\n", + "\n", " #0\n", " warnings:\n", " ·Patient STX_23708187_T23289 has an error with variant 9_127684373_127684373_A_A. Try again or remove variant form testing... Expected a result but got an Error. See log for details.\n", - " patient #359\n", + " patient #219\n", " phenotype-features\n", " warnings:\n", - " ·Death in adolescence [HP:0011421] is not a descendant of Phenotypic abnormality [HP:0000118]\n" + "\n", + " patient #382\n", + " phenotype-features\n", + " warnings:\n", + "\n" ] } ], "source": [ "from ppktstore.registry import configure_phenopacket_registry\n", - "from genophenocorr.preprocessing import load_phenopackets\n", + "from gpsea.preprocessing import load_phenopackets, configure_caching_cohort_creator\n", "\n", "cohort_name = 'STXBP1'\n", - "phenopacket_store_release = '0.1.18'\n", + "phenopacket_store_release = '0.1.19'\n", "\n", + "cohort_creator = configure_caching_cohort_creator(hpo)\n", "registry = configure_phenopacket_registry()\n", "with registry.open_phenopacket_store(phenopacket_store_release) as ps:\n", " phenopackets = tuple(ps.iter_cohort_phenopackets(cohort_name))\n", @@ -170,7 +138,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 4, "id": "a41be80d-0678-4dc3-97b6-2876d69648e0", "metadata": {}, "outputs": [ @@ -242,10 +210,10 @@ "\n", "\n", "\n", - "

genophenocorr cohort analysis

\n", - "

Successfully ingested 463 phenopackets.

\n", + "

GPSEA cohort analysis

\n", + "

Successfully loaded 463 individuals.

\n", " \n", - "

Not able to ingest 1 individuals.

\n", + "

Not able to load 1 individuals.

\n", " \n", "\n", " \n", @@ -257,7 +225,7 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -330,92 +298,92 @@ " \n", " \n", " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", " \n", " \n", "
HPO TermIDAnnotation CountSeen in n individuals
VariantCountVariant keyVariant NameProtein VariantVariant ClassVariant Count
199_127675910_127675910_G_Ac.1217G>Ap.Arg406HisMISSENSE_VARIANT19
199_127675909_127675909_C_Tc.1216C>Tp.Arg406CysMISSENSE_VARIANT19
189_127682509_127682509_C_Tc.1651C>Tp.Arg551CysMISSENSE_VARIANT18
189_127668160_127668160_G_Ac.875G>Ap.Arg292HisMISSENSE_VARIANT18
129_127661192_127661192_C_Tc.416C>Tp.Pro139LeuMISSENSE_VARIANT12
119_127663343_127663343_C_Tc.568C>Tp.Arg190TrpMISSENSE_VARIANT11
9_127675855_127675855_C_Tc.1162C>Tp.Arg388TerSTOP_GAINED10
9_127668159_127668159_C_Tc.874C>Tp.Arg292CysMISSENSE_VARIANT
109_127678510_127678510_C_Tc.1439C>Tp.Pro480LeuMISSENSE_VARIANT10
9_127668159_127668159_C_Tc.874C>Tp.Arg292CysMISSENSE_VARIANT10
9_127675855_127675855_C_Tc.1162C>Tp.Arg388TerSTOP_GAINED
99_127661140_127661140_C_Tc.364C>Tp.Arg122TerSTOP_GAINED9
\n", @@ -450,13 +418,13 @@ " \n", " \n", " \n", - " STOP_GAINED\n", - " 71\n", + " MISSENSE_VARIANT\n", + " 250\n", " \n", " \n", " \n", - " MISSENSE_VARIANT\n", - " 250\n", + " SPLICE_ACCEPTOR_VARIANT\n", + " 22\n", " \n", " \n", " \n", @@ -470,28 +438,28 @@ " \n", " \n", " \n", - " SPLICE_ACCEPTOR_VARIANT\n", - " 22\n", + " STOP_GAINED\n", + " 71\n", " \n", " \n", " \n", - " SPLICE_REGION_VARIANT\n", - " 18\n", + " SPLICE_POLYPYRIMIDINE_TRACT_VARIANT\n", + " 1\n", " \n", " \n", " \n", - " INFRAME_DELETION\n", - " 8\n", + " INTRON_VARIANT\n", + " 1\n", " \n", " \n", " \n", - " SPLICE_POLYPYRIMIDINE_TRACT_VARIANT\n", - " 1\n", + " SPLICE_REGION_VARIANT\n", + " 18\n", " \n", " \n", " \n", - " INTRON_VARIANT\n", - " 1\n", + " INFRAME_DELETION\n", + " 8\n", " \n", " \n", " \n", @@ -522,16 +490,16 @@ ], "source": [ "from IPython.display import display, HTML\n", - "from genophenocorr.view import CohortViewable\n", + "from gpsea.view import CohortViewable\n", "\n", "viewer = CohortViewable(hpo=hpo)\n", - "html = viewer.process(cohort=cohort, transcript_id=tx_id)\n", + "html = viewer.process(cohort=cohort, transcript_id=STXBP1_MANE_transcript)\n", "display(HTML(html))" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 5, "id": "cb573d37", "metadata": {}, "outputs": [ @@ -541,13 +509,13 @@ "" ] }, - "execution_count": 7, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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" ] @@ -557,20 +525,123 @@ } ], "source": [ - "from genophenocorr.preprocessing import UniprotProteinMetadataService\n", - "from genophenocorr.model.genome import GRCh38\n", - "from genophenocorr.preprocessing import VVMultiCoordinateService\n", + "from gpsea.preprocessing import UniprotProteinMetadataService\n", + "from gpsea.model.genome import GRCh38\n", + "from gpsea.preprocessing import VVMultiCoordinateService\n", "txc_service = VVMultiCoordinateService(genome_build=GRCh38)\n", - "tx_coordinates = txc_service.fetch(tx_id)\n", - "from genophenocorr.preprocessing import UniprotProteinMetadataService\n", + "tx_coordinates = txc_service.fetch(STXBP1_MANE_transcript)\n", + "from gpsea.preprocessing import UniprotProteinMetadataService\n", "pms = UniprotProteinMetadataService()\n", - "protein_meta = pms.annotate('NP_003156.1')\n", - "from genophenocorr.view import ProteinVisualizable, ProteinVisualizer\n", + "protein_meta = pms.annotate(STXBP1_protein_id)\n", + "from gpsea.view import ProteinVisualizable, ProteinVisualizer, ProteinViewable\n", "pvis = ProteinVisualizable(tx_coordinates=tx_coordinates, protein_meta=protein_meta, cohort=cohort)\n", "drawer = ProteinVisualizer()\n", "drawer.draw_fig(pvis=pvis)" ] }, + { + "cell_type": "code", + "execution_count": 6, + "id": "a054c7c8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " \n", + " Cohort\n", + " \n", + "\n", + "\n", + "\n", + "

GPSEA protein analysis

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The UniProt API successfully returned protein information for ID: NP_003156.1

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Protein Name: Syntaxin-binding protein 1

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Protein Features

\n", + "
Feature NameFeature TypeFeature CoordinatesVariants in Feature
\n", + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "viewer = ProteinViewable()\n", + "html_prot = viewer.process(cohort, pvis)\n", + "display(HTML(html_prot))" + ] + }, { "cell_type": "markdown", "id": "b1ed6106", @@ -581,22 +652,22 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "id": "cc3cc692", "metadata": {}, "outputs": [], "source": [ - "from genophenocorr.analysis import configure_cohort_analysis, CohortAnalysisConfiguration\n", + "from gpsea.analysis import configure_cohort_analysis, CohortAnalysisConfiguration\n", "\n", "config = CohortAnalysisConfiguration()\n", "config.pval_correction = 'fdr_bh'\n", - "config.heuristic_strategy()\n", + "config.hpo_mtc_strategy()\n", "analysis = configure_cohort_analysis(cohort, hpo, config=config)" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "id": "c7ab8b6b", "metadata": {}, "outputs": [ @@ -691,11 +762,11 @@ " ...\n", " \n", " \n", - " Abnormal basal ganglia morphology [HP:0002134]\n", - " 2/8\n", - " 25%\n", - " 1/3\n", - " 33%\n", + " Abnormal cerebral cortex morphology [HP:0002538]\n", + " 15/21\n", + " 71%\n", + " 7/9\n", + " 78%\n", " 1.000000\n", " 1.0\n", " \n", @@ -709,7 +780,16 @@ " 1.0\n", " \n", " \n", - " Abnormal metencephalon morphology [HP:0011283]\n", + " Increased head circumference [HP:0040194]\n", + " 3/3\n", + " 100%\n", + " 4/5\n", + " 80%\n", + " 1.000000\n", + " 1.0\n", + " \n", + " \n", + " Abnormal hindbrain morphology [HP:0011282]\n", " 1/3\n", " 33%\n", " 2/4\n", @@ -726,77 +806,40 @@ " 1.000000\n", " 1.0\n", " \n", - " \n", - " Abnormal hindbrain morphology [HP:0011282]\n", - " 1/3\n", - " 33%\n", - " 2/4\n", - " 50%\n", - " 1.000000\n", - " 1.0\n", - " \n", " \n", "\n", "

102 rows × 6 columns

\n", "" ], "text/plain": [ - "MISSENSE_VARIANT on NM_003165.6 Yes No \\\n", - " Count Percent Count \n", - "Febrile seizure (within the age range of 3 mont... 15/18 83% 3/7 \n", - "Seizure precipitated by febrile infection [HP:0... 15/18 83% 3/7 \n", - "Infection-related seizure [HP:0032892] 15/18 83% 3/7 \n", - "Atonic seizure [HP:0010819] 12/15 80% 3/7 \n", - "Focal impaired awareness autonomic seizure [HP:... 4/7 57% 0/4 \n", - "... ... ... ... \n", - "Abnormal basal ganglia morphology [HP:0002134] 2/8 25% 1/3 \n", - "Leukoencephalopathy [HP:0002352] 3/9 33% 2/4 \n", - "Abnormal metencephalon morphology [HP:0011283] 1/3 33% 2/4 \n", - "Atrophy/Degeneration affecting the cerebrum [HP... 19/25 76% 10/12 \n", - "Abnormal hindbrain morphology [HP:0011282] 1/3 33% 2/4 \n", - "\n", - "MISSENSE_VARIANT on NM_003165.6 \\\n", - " Percent p value \n", - "Febrile seizure (within the age range of 3 mont... 43% 0.066362 \n", - "Seizure precipitated by febrile infection [HP:0... 43% 0.066362 \n", - "Infection-related seizure [HP:0032892] 43% 0.066362 \n", - "Atonic seizure [HP:0010819] 43% 0.144661 \n", - "Focal impaired awareness autonomic seizure [HP:... 0% 0.193939 \n", - "... ... ... \n", - "Abnormal basal ganglia morphology [HP:0002134] 33% 1.000000 \n", - "Leukoencephalopathy [HP:0002352] 50% 1.000000 \n", - "Abnormal metencephalon morphology [HP:0011283] 50% 1.000000 \n", - "Atrophy/Degeneration affecting the cerebrum [HP... 83% 1.000000 \n", - "Abnormal hindbrain morphology [HP:0011282] 50% 1.000000 \n", - "\n", - "MISSENSE_VARIANT on NM_003165.6 \n", - " Corrected p value \n", - "Febrile seizure (within the age range of 3 mont... 1.0 \n", - "Seizure precipitated by febrile infection [HP:0... 1.0 \n", - "Infection-related seizure [HP:0032892] 1.0 \n", - "Atonic seizure [HP:0010819] 1.0 \n", - "Focal impaired awareness autonomic seizure [HP:... 1.0 \n", - "... ... \n", - "Abnormal basal ganglia morphology [HP:0002134] 1.0 \n", - "Leukoencephalopathy [HP:0002352] 1.0 \n", - "Abnormal metencephalon morphology [HP:0011283] 1.0 \n", - "Atrophy/Degeneration affecting the cerebrum [HP... 1.0 \n", - "Abnormal hindbrain morphology [HP:0011282] 1.0 \n", + "MISSENSE_VARIANT on NM_003165.6 Yes ... \n", + " Count ... Corrected p value\n", + "Febrile seizure (within the age range of 3 mont... 15/18 ... 1.0\n", + "Seizure precipitated by febrile infection [HP:0... 15/18 ... 1.0\n", + "Infection-related seizure [HP:0032892] 15/18 ... 1.0\n", + "Atonic seizure [HP:0010819] 12/15 ... 1.0\n", + "Focal impaired awareness autonomic seizure [HP:... 4/7 ... 1.0\n", + "... ... ... ...\n", + "Abnormal cerebral cortex morphology [HP:0002538] 15/21 ... 1.0\n", + "Leukoencephalopathy [HP:0002352] 3/9 ... 1.0\n", + "Increased head circumference [HP:0040194] 3/3 ... 1.0\n", + "Abnormal hindbrain morphology [HP:0011282] 1/3 ... 1.0\n", + "Atrophy/Degeneration affecting the cerebrum [HP... 19/25 ... 1.0\n", "\n", "[102 rows x 6 columns]" ] }, - "execution_count": 9, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "from genophenocorr.analysis.predicate import PatientCategories\n", - "from genophenocorr.model import VariantEffect\n", - "from genophenocorr.analysis.predicate.genotype import VariantPredicates\n", + "from gpsea.analysis.predicate import PatientCategories\n", + "from gpsea.model import VariantEffect\n", + "from gpsea.analysis.predicate.genotype import VariantPredicates\n", "\n", - "is_missense = VariantPredicates.variant_effect(VariantEffect.MISSENSE_VARIANT, tx_id=tx_id)\n", + "is_missense = VariantPredicates.variant_effect(VariantEffect.MISSENSE_VARIANT, tx_id=STXBP1_MANE_transcript)\n", "missense = analysis.compare_hpo_vs_genotype(is_missense)\n", "missense.summarize(hpo, category=PatientCategories.YES)" ] @@ -826,7 +869,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.3" + "version": "3.10.10" } }, "nbformat": 4, diff --git a/notebooks/SUOX/SUOX.ipynb b/notebooks/SUOX/SUOX.ipynb index 4a0bee57..29f87378 100644 --- a/notebooks/SUOX/SUOX.ipynb +++ b/notebooks/SUOX/SUOX.ipynb @@ -24,18 +24,18 @@ "output_type": "stream", "text": [ "Loaded HPO v2023-10-09\n", - "Using genophenocorr version 0.1.1dev\n" + "Using gpsea version 0.2.1.dev0\n" ] } ], "source": [ + "import gpsea\n", "import hpotk\n", - "import genophenocorr\n", "\n", "store = hpotk.configure_ontology_store()\n", "hpo = store.load_minimal_hpo(release='v2023-10-09')\n", "print(f'Loaded HPO v{hpo.version}')\n", - "print(f\"Using genophenocorr version {genophenocorr.__version__}\")" + "print(f\"Using gpsea version {gpsea.__version__}\")" ] }, { @@ -77,19 +77,18 @@ "name": "stdout", "output_type": "stream", "text": [ - "Patients Created: 100%|██████████| 35/35 [00:00<00:00, 335.92it/s]\n", + "Patients Created: 100%|██████████| 35/35 [01:26<00:00, 2.46s/it]\n", "Validated under none policy\n", - "No errors or warnings were found\n", - "\n" + "No errors or warnings were found\n" ] } ], "source": [ "from ppktstore.registry import configure_phenopacket_registry\n", - "from genophenocorr.preprocessing import configure_caching_cohort_creator, load_phenopackets\n", + "from gpsea.preprocessing import configure_caching_cohort_creator, load_phenopackets\n", "\n", "cohort_name = 'SUOX'\n", - "phenopacket_store_release = '0.1.18'\n", + "phenopacket_store_release = '0.1.19'\n", "\n", "registry = configure_phenopacket_registry()\n", "with registry.open_phenopacket_store(phenopacket_store_release) as ps:\n", @@ -178,8 +177,8 @@ "\n", "\n", "\n", - "

genophenocorr cohort analysis

\n", - "

Successfully ingested 35 phenopackets.

\n", + "

GPSEA cohort analysis

\n", + "

Successfully loaded 35 individuals.

\n", " \n", "

No errors encountered.

\n", " \n", @@ -193,7 +192,7 @@ " \n", " HPO Term\n", " ID\n", - " Annotation Count\n", + " Seen in n individuals\n", " \n", " \n", " \n", @@ -227,14 +226,14 @@ " \n", " \n", " \n", - " Microcephaly\n", - " HP:0000252\n", + " Hypohomocysteinemia\n", + " HP:0020222\n", " 10\n", " \n", " \n", " \n", - " Hypohomocysteinemia\n", - " HP:0020222\n", + " Microcephaly\n", + " HP:0000252\n", " 10\n", " \n", " \n", @@ -266,92 +265,92 @@ " \n", " \n", " \n", - " Variant\n", + " Count\n", + " Variant key\n", " Variant Name\n", " Protein Variant\n", " Variant Class\n", - " Variant Count\n", " \n", " \n", " \n", + " 7\n", " 12_56004589_56004589_C_G\n", " c.1200C>G\n", " p.Tyr400Ter\n", " STOP_GAINED\n", - " 7\n", - " \n", + " \n", " \n", " \n", - " 12_56004039_56004039_G_A\n", - " c.650G>A\n", - " p.Arg217Gln\n", - " MISSENSE_VARIANT\n", " 3\n", - " \n", - " \n", - " \n", " 12_56004485_56004485_C_T\n", " c.1096C>T\n", " p.Arg366Cys\n", " MISSENSE_VARIANT\n", - " 3\n", - " \n", + " \n", " \n", " \n", + " 3\n", " 12_56004765_56004765_G_A\n", " c.1376G>A\n", " p.Arg459Gln\n", " MISSENSE_VARIANT\n", - " 3\n", - " \n", + " \n", " \n", " \n", - " 12_56004273_56004273_G_A\n", - " c.884G>A\n", - " p.Gly295Glu\n", + " 3\n", + " 12_56004039_56004039_G_A\n", + " c.650G>A\n", + " p.Arg217Gln\n", " MISSENSE_VARIANT\n", + " \n", + " \n", + " \n", " 2\n", - " \n", + " 12_56004933_56004933_A_ACAATGTGCAGCCAGACACCGTGGCCC\n", + " c.1549_1574dup\n", + " p.Ile525MetfsTer102\n", + " FRAMESHIFT_VARIANT\n", + " \n", " \n", " \n", + " 2\n", " 12_56004771_56004771_A_T\n", " c.1382A>T\n", " p.Asp461Val\n", " MISSENSE_VARIANT\n", - " 2\n", - " \n", + " \n", " \n", " \n", + " 2\n", " 12_56004905_56004909_ATTGT_A\n", " c.1521_1524del\n", " p.Cys508ArgfsTer109\n", " FRAMESHIFT_VARIANT\n", - " 2\n", - " \n", + " \n", " \n", " \n", - " 12_56004933_56004933_A_ACAATGTGCAGCCAGACACCGTGGCCC\n", - " c.1549_1574dup\n", - " p.Ile525MetfsTer102\n", - " FRAMESHIFT_VARIANT\n", " 2\n", - " \n", + " 12_56004273_56004273_G_A\n", + " c.884G>A\n", + " p.Gly295Glu\n", + " MISSENSE_VARIANT\n", + " \n", " \n", " \n", - " 12_56003864_56003864_G_T\n", - " c.475G>T\n", - " p.Glu159Ter\n", - " STOP_GAINED\n", " 1\n", - " \n", + " 12_56004183_56004183_C_A\n", + " c.794C>A\n", + " p.Ala265Asp\n", + " MISSENSE_VARIANT\n", + " \n", " \n", " \n", - " 12_56004590_56004590_A_G\n", - " c.1201A>G\n", - " p.Lys401Glu\n", - " MISSENSE_VARIANT\n", " 1\n", - " \n", + " 12_56004669_56004669_C_A\n", + " c.1280C>A\n", + " p.Ser427Ter\n", + " STOP_GAINED\n", + " \n", " \n", " \n", " \n", @@ -391,13 +390,13 @@ " \n", " \n", " \n", - " FRAMESHIFT_VARIANT\n", - " 9\n", + " STOP_GAINED\n", + " 10\n", " \n", " \n", " \n", - " STOP_GAINED\n", - " 10\n", + " FRAMESHIFT_VARIANT\n", + " 9\n", " \n", " \n", " \n", @@ -417,7 +416,7 @@ } ], "source": [ - "from genophenocorr.view import CohortViewable\n", + "from gpsea.view import CohortViewable\n", "from IPython.display import HTML, display\n", "\n", "viewer = CohortViewable(hpo)\n", @@ -431,8 +430,8 @@ "metadata": {}, "outputs": [], "source": [ - "from genophenocorr.model.genome import GRCh38\n", - "from genophenocorr.preprocessing import configure_protein_metadata_service, VVMultiCoordinateService\n", + "from gpsea.model.genome import GRCh38\n", + "from gpsea.preprocessing import configure_protein_metadata_service, VVMultiCoordinateService\n", "\n", "txc_service = VVMultiCoordinateService(genome_build=GRCh38)\n", "tx_coordinates = txc_service.fetch(SUOX_transcript_id) \n", @@ -459,7 +458,7 @@ }, { "data": { - "image/png": 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" ] @@ -469,13 +468,144 @@ } ], "source": [ - "from genophenocorr.view import ProteinVisualizable, ProteinVisualizer\n", + "from gpsea.view import ProteinVisualizable, ProteinVisualizer, ProteinViewable\n", "\n", "pvis = ProteinVisualizable(tx_coordinates=tx_coordinates, protein_meta=protein_meta, cohort=cohort)\n", "drawer = ProteinVisualizer()\n", "drawer.draw_fig(pvis=pvis)" ] }, + { + "cell_type": "code", + "execution_count": 7, + "id": "cbb0ea2f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " \n", + " Cohort\n", + " \n", + "\n", + "\n", + "\n", + "

GPSEA protein analysis

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The UniProt API successfully returned protein information for ID: NP_001027558.1

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Protein Name: Sulfite oxidase, mitochondrial

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Protein Features

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Feature NameFeature TypeFeature CoordinatesVariants in Feature
Cytochrome b5 heme-bindingdomain82 - 1616
Hingeregion165 - 1741
Moco domainregion175 - 40126
Homodimerizationregion402 - 53813
\n", + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "viewer = ProteinViewable()\n", + "html_prot = viewer.process(cohort, pvis)\n", + "display(HTML(html_prot))" + ] + }, { "cell_type": "markdown", "id": "8357f23c", @@ -486,17 +616,17 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 14, "id": "531e077d", "metadata": {}, "outputs": [], "source": [ - "from genophenocorr.analysis import configure_cohort_analysis, CohortAnalysisConfiguration\n", + "from gpsea.analysis import configure_cohort_analysis, CohortAnalysisConfiguration\n", "\n", "analysis_config = CohortAnalysisConfiguration()\n", "analysis_config.missing_implies_excluded = True\n", "analysis_config.pval_correction = 'fdr_bh'\n", - "#analysis_config.heuristic_strategy()\n", + "#analysis_config.hpo_mtc_strategy()\n", "analysis = configure_cohort_analysis(cohort, hpo, config=analysis_config)" ] }, @@ -518,7 +648,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 15, "id": "f4fb138e", "metadata": {}, "outputs": [ @@ -628,51 +758,51 @@ " ...\n", " \n", " \n", - " Abnormal muscle physiology [HP:0011804]\n", - " 11/11\n", + " Decreased head circumference [HP:0040195]\n", + " 4/4\n", " 100%\n", - " 3/3\n", + " 2/2\n", " 100%\n", - " 5/5\n", + " 4/4\n", " 100%\n", " 1.000000\n", " 1.0\n", " \n", " \n", - " Abnormal anterior eye segment morphology [HP:0004328]\n", - " 3/3\n", + " Abnormality of the head [HP:0000234]\n", + " 4/4\n", " 100%\n", - " 3/3\n", + " 2/2\n", " 100%\n", - " 1/1\n", + " 4/4\n", " 100%\n", " 1.000000\n", " 1.0\n", " \n", " \n", - " Abnormality of the nervous system [HP:0000707]\n", - " 17/17\n", + " Abnormal circulating homocysteine concentration [HP:0010919]\n", + " 7/7\n", " 100%\n", - " 5/5\n", + " 2/2\n", " 100%\n", - " 11/11\n", + " 1/1\n", " 100%\n", " 1.000000\n", " 1.0\n", " \n", " \n", - " Phenotypic abnormality [HP:0000118]\n", - " 17/17\n", + " Abnormal urine metabolite level [HP:0033354]\n", + " 9/9\n", " 100%\n", - " 6/6\n", + " 3/3\n", " 100%\n", - " 11/11\n", + " 2/2\n", " 100%\n", " 1.000000\n", " 1.0\n", " \n", " \n", - " Abnormal circulating carboxylic acid concentration [HP:0004354]\n", + " Abnormal circulating amino acid concentration [HP:0003112]\n", " 8/8\n", " 100%\n", " 2/2\n", @@ -696,11 +826,11 @@ "Abnormality of extrapyramidal motor function [H... 8/12 67% 1/5 \n", "Neurodevelopmental delay [HP:0012758] 4/12 33% 0/5 \n", "... ... ... ... \n", - "Abnormal muscle physiology [HP:0011804] 11/11 100% 3/3 \n", - "Abnormal anterior eye segment morphology [HP:00... 3/3 100% 3/3 \n", - "Abnormality of the nervous system [HP:0000707] 17/17 100% 5/5 \n", - "Phenotypic abnormality [HP:0000118] 17/17 100% 6/6 \n", - "Abnormal circulating carboxylic acid concentrat... 8/8 100% 2/2 \n", + "Decreased head circumference [HP:0040195] 4/4 100% 2/2 \n", + "Abnormality of the head [HP:0000234] 4/4 100% 2/2 \n", + "Abnormal circulating homocysteine concentration... 7/7 100% 2/2 \n", + "Abnormal urine metabolite level [HP:0033354] 9/9 100% 3/3 \n", + "Abnormal circulating amino acid concentration [... 8/8 100% 2/2 \n", "\n", "MISSENSE_VARIANT on NM_001032386.2 Neither \\\n", " Percent Count Percent \n", @@ -710,11 +840,11 @@ "Abnormality of extrapyramidal motor function [H... 20% 2/8 25% \n", "Neurodevelopmental delay [HP:0012758] 0% 4/8 50% \n", "... ... ... ... \n", - "Abnormal muscle physiology [HP:0011804] 100% 5/5 100% \n", - "Abnormal anterior eye segment morphology [HP:00... 100% 1/1 100% \n", - "Abnormality of the nervous system [HP:0000707] 100% 11/11 100% \n", - "Phenotypic abnormality [HP:0000118] 100% 11/11 100% \n", - "Abnormal circulating carboxylic acid concentrat... 100% 5/5 100% \n", + "Decreased head circumference [HP:0040195] 100% 4/4 100% \n", + "Abnormality of the head [HP:0000234] 100% 4/4 100% \n", + "Abnormal circulating homocysteine concentration... 100% 1/1 100% \n", + "Abnormal urine metabolite level [HP:0033354] 100% 2/2 100% \n", + "Abnormal circulating amino acid concentration [... 100% 5/5 100% \n", "\n", "MISSENSE_VARIANT on NM_001032386.2 \n", " p value Corrected p value \n", @@ -724,24 +854,24 @@ "Abnormality of extrapyramidal motor function [H... 0.139377 1.0 \n", "Neurodevelopmental delay [HP:0012758] 0.151874 1.0 \n", "... ... ... \n", - "Abnormal muscle physiology [HP:0011804] 1.000000 1.0 \n", - "Abnormal anterior eye segment morphology [HP:00... 1.000000 1.0 \n", - "Abnormality of the nervous system [HP:0000707] 1.000000 1.0 \n", - "Phenotypic abnormality [HP:0000118] 1.000000 1.0 \n", - "Abnormal circulating carboxylic acid concentrat... 1.000000 1.0 \n", + "Decreased head circumference [HP:0040195] 1.000000 1.0 \n", + "Abnormality of the head [HP:0000234] 1.000000 1.0 \n", + "Abnormal circulating homocysteine concentration... 1.000000 1.0 \n", + "Abnormal urine metabolite level [HP:0033354] 1.000000 1.0 \n", + "Abnormal circulating amino acid concentration [... 1.000000 1.0 \n", "\n", "[64 rows x 8 columns]" ] }, - "execution_count": 8, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "from genophenocorr.model import VariantEffect\n", - "from genophenocorr.analysis.predicate import PatientCategories\n", - "from genophenocorr.analysis.predicate.genotype import VariantPredicates\n", + "from gpsea.model import VariantEffect\n", + "from gpsea.analysis.predicate import PatientCategories\n", + "from gpsea.analysis.predicate.genotype import VariantPredicates\n", "\n", "is_missense = VariantPredicates.variant_effect(VariantEffect.MISSENSE_VARIANT, SUOX_transcript_id)\n", "missense = analysis.compare_hpo_vs_recessive_genotype(is_missense)\n", @@ -758,7 +888,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 16, "id": "743954bd", "metadata": {}, "outputs": [ @@ -868,51 +998,51 @@ " ...\n", " \n", " \n", - " Abnormal muscle physiology [HP:0011804]\n", + " Abnormality of the head [HP:0000234]\n", " 1/1\n", " 100%\n", " 1/1\n", " 100%\n", - " 17/17\n", + " 8/8\n", " 100%\n", " 1.000000\n", " 1.0\n", " \n", " \n", - " Abnormal anterior eye segment morphology [HP:0004328]\n", - " 0/0\n", - " 0%\n", + " Seizure [HP:0001250]\n", " 2/2\n", " 100%\n", - " 5/5\n", - " 100%\n", + " 4/5\n", + " 80%\n", + " 22/28\n", + " 79%\n", " 1.000000\n", " 1.0\n", " \n", " \n", - " Abnormality of the nervous system [HP:0000707]\n", + " Abnormal circulating homocysteine concentration [HP:0010919]\n", + " 0/0\n", + " 0%\n", " 2/2\n", " 100%\n", - " 4/4\n", - " 100%\n", - " 27/27\n", + " 8/8\n", " 100%\n", " 1.000000\n", " 1.0\n", " \n", " \n", - " Phenotypic abnormality [HP:0000118]\n", + " Abnormal urine metabolite level [HP:0033354]\n", + " 0/0\n", + " 0%\n", " 2/2\n", " 100%\n", - " 5/5\n", - " 100%\n", - " 27/27\n", + " 12/12\n", " 100%\n", " 1.000000\n", " 1.0\n", " \n", " \n", - " Abnormal circulating carboxylic acid concentration [HP:0004354]\n", + " Abnormal circulating amino acid concentration [HP:0003112]\n", " 1/1\n", " 100%\n", " 2/2\n", @@ -936,11 +1066,11 @@ "Abnormality of extrapyramidal motor function [H... 0/1 0% 1/5 \n", "Cognitive regression [HP:0034332] 0/1 0% 0/5 \n", "... ... ... ... \n", - "Abnormal muscle physiology [HP:0011804] 1/1 100% 1/1 \n", - "Abnormal anterior eye segment morphology [HP:00... 0/0 0% 2/2 \n", - "Abnormality of the nervous system [HP:0000707] 2/2 100% 4/4 \n", - "Phenotypic abnormality [HP:0000118] 2/2 100% 5/5 \n", - "Abnormal circulating carboxylic acid concentrat... 1/1 100% 2/2 \n", + "Abnormality of the head [HP:0000234] 1/1 100% 1/1 \n", + "Seizure [HP:0001250] 2/2 100% 4/5 \n", + "Abnormal circulating homocysteine concentration... 0/0 0% 2/2 \n", + "Abnormal urine metabolite level [HP:0033354] 0/0 0% 2/2 \n", + "Abnormal circulating amino acid concentration [... 1/1 100% 2/2 \n", "\n", "variant has ID of 12_56004589_56004589_C_G Neither \\\n", " Percent Count Percent \n", @@ -950,11 +1080,11 @@ "Abnormality of extrapyramidal motor function [H... 20% 10/19 53% \n", "Cognitive regression [HP:0034332] 0% 6/19 32% \n", "... ... ... ... \n", - "Abnormal muscle physiology [HP:0011804] 100% 17/17 100% \n", - "Abnormal anterior eye segment morphology [HP:00... 100% 5/5 100% \n", - "Abnormality of the nervous system [HP:0000707] 100% 27/27 100% \n", - "Phenotypic abnormality [HP:0000118] 100% 27/27 100% \n", - "Abnormal circulating carboxylic acid concentrat... 100% 12/12 100% \n", + "Abnormality of the head [HP:0000234] 100% 8/8 100% \n", + "Seizure [HP:0001250] 80% 22/28 79% \n", + "Abnormal circulating homocysteine concentration... 100% 8/8 100% \n", + "Abnormal urine metabolite level [HP:0033354] 100% 12/12 100% \n", + "Abnormal circulating amino acid concentration [... 100% 12/12 100% \n", "\n", "variant has ID of 12_56004589_56004589_C_G \n", " p value Corrected p value \n", @@ -964,16 +1094,16 @@ "Abnormality of extrapyramidal motor function [H... 0.340580 1.0 \n", "Cognitive regression [HP:0034332] 0.452851 1.0 \n", "... ... ... \n", - "Abnormal muscle physiology [HP:0011804] 1.000000 1.0 \n", - "Abnormal anterior eye segment morphology [HP:00... 1.000000 1.0 \n", - "Abnormality of the nervous system [HP:0000707] 1.000000 1.0 \n", - "Phenotypic abnormality [HP:0000118] 1.000000 1.0 \n", - "Abnormal circulating carboxylic acid concentrat... 1.000000 1.0 \n", + "Abnormality of the head [HP:0000234] 1.000000 1.0 \n", + "Seizure [HP:0001250] 1.000000 1.0 \n", + "Abnormal circulating homocysteine concentration... 1.000000 1.0 \n", + "Abnormal urine metabolite level [HP:0033354] 1.000000 1.0 \n", + "Abnormal circulating amino acid concentration [... 1.000000 1.0 \n", "\n", "[64 rows x 8 columns]" ] }, - "execution_count": 9, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -1010,7 +1140,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.3" + "version": "3.10.10" } }, "nbformat": 4, diff --git a/src/gpsea/analysis/_gp_impl.py b/src/gpsea/analysis/_gp_impl.py index c6251f8b..1d01045c 100644 --- a/src/gpsea/analysis/_gp_impl.py +++ b/src/gpsea/analysis/_gp_impl.py @@ -105,7 +105,7 @@ def compare_disease_vs_genotype( # This can be updated to any genotype poly predicate in future, if necessary. genotype_predicate = wrap_as_boolean_predicate(predicate) - return self.compare_genotype_vs_phenotypes(pheno_predicates, genotype_predicate) + return self.compare_genotype_vs_phenotypes(genotype_predicate, pheno_predicates) def _prepare_disease_predicates( self, From 3f38f152fce9322aa5c86b65db17d490d6468c91 Mon Sep 17 00:00:00 2001 From: Daniel Danis Date: Tue, 27 Aug 2024 21:03:52 +0200 Subject: [PATCH 08/47] Configure the protein metadata service in the `preprocessing` module. --- src/gpsea/analysis/_config.py | 53 ++++++++----------------- src/gpsea/preprocessing/__init__.py | 4 +- src/gpsea/preprocessing/_config.py | 61 ++++++++++++++++++++++++++--- 3 files changed, 74 insertions(+), 44 deletions(-) diff --git a/src/gpsea/analysis/_config.py b/src/gpsea/analysis/_config.py index 3fb67fc7..dd723347 100644 --- a/src/gpsea/analysis/_config.py +++ b/src/gpsea/analysis/_config.py @@ -1,13 +1,15 @@ +import enum import logging import os import typing -import enum +import warnings + import hpotk from gpsea.config import get_cache_dir_path from gpsea.model import Cohort -from gpsea.preprocessing import ProteinMetadataService, UniprotProteinMetadataService, ProteinAnnotationCache, \ - ProtCachingMetadataService +from gpsea.preprocessing import ProteinMetadataService +from gpsea.preprocessing import configure_default_protein_metadata_service as backup_pms from .mtc_filter import PhenotypeMtcFilter, UseAllTermsMtcFilter, SpecifiedTermsMtcFilter, HpoMtcFilter from ._api import CohortAnalysis from ._gp_analysis import FisherExactAnalyzer @@ -264,7 +266,11 @@ def configure_cohort_analysis( """ if config is None: config = CohortAnalysisConfiguration() # Use the default config - cache_dir = _configure_cache_dir(cache_dir) + + cache_path = get_cache_dir_path(cache_dir) + os.makedirs(cache_path, exist_ok=True) + + cache_dir = str(cache_path) protein_metadata_service = configure_default_protein_metadata_service(protein_source, cache_dir) mtc_filter: PhenotypeMtcFilter @@ -338,36 +344,9 @@ def configure_default_protein_metadata_service( in current working directory under `.gpsea_cache/protein_cache` and reach out to UNIPROT REST API if a cache entry is missing. """ - cache_dir = _configure_cache_dir(cache_dir) - return _configure_protein_service(protein_fallback=protein_source, cache_dir=cache_dir) - - -def _configure_protein_service( - protein_fallback: str, - cache_dir: str, -) -> ProteinMetadataService: - # (1) ProteinMetadataService - # Setup fallback - protein_fallback = _configure_fallback_protein_service(protein_fallback) - # Setup protein metadata cache - prot_cache_dir = os.path.join(cache_dir, 'protein_cache') - os.makedirs(prot_cache_dir, exist_ok=True) - prot_cache = ProteinAnnotationCache(prot_cache_dir) - # Assemble the final protein metadata service - protein_metadata_service = ProtCachingMetadataService(prot_cache, protein_fallback) - return protein_metadata_service - - -def _configure_fallback_protein_service(protein_fallback: str) -> ProteinMetadataService: - if protein_fallback == 'UNIPROT': - fallback1 = UniprotProteinMetadataService() - else: - raise ValueError(f'Unknown protein fallback annotator type {protein_fallback}') - return fallback1 - - -def _configure_cache_dir(cache_dir: typing.Optional[str]) -> str: - cache_path = get_cache_dir_path(cache_dir) - os.makedirs(cache_path, exist_ok=True) - - return str(cache_path) + # TODO: remove at some point. + warnings.warn( + 'Use gpsea.preprocessing.configure_default_protein_metadata_service` instead', + DeprecationWarning, stacklevel=2, + ) + return backup_pms(protein_source=protein_source, cache_dir=cache_dir) diff --git a/src/gpsea/preprocessing/__init__.py b/src/gpsea/preprocessing/__init__.py index b4367167..1551b4d6 100644 --- a/src/gpsea/preprocessing/__init__.py +++ b/src/gpsea/preprocessing/__init__.py @@ -4,7 +4,7 @@ from ._config import configure_caching_patient_creator, configure_patient_creator from ._config import load_phenopacket_folder, load_phenopackets from ._config import configure_caching_cohort_creator, configure_cohort_creator -from ._config import configure_protein_metadata_service +from ._config import configure_default_protein_metadata_service, configure_protein_metadata_service from ._generic import DefaultImpreciseSvFunctionalAnnotator from ._patient import PatientCreator, CohortCreator from ._phenopacket import PhenopacketVariantCoordinateFinder, PhenopacketPatientCreator @@ -18,7 +18,7 @@ __all__ = [ 'configure_caching_patient_creator', 'configure_patient_creator', 'configure_caching_cohort_creator', 'configure_cohort_creator', - 'configure_protein_metadata_service', + 'configure_default_protein_metadata_service', 'configure_protein_metadata_service', 'VariantCoordinateFinder', 'FunctionalAnnotator', 'ImpreciseSvFunctionalAnnotator', 'ProteinMetadataService', 'PatientCreator', 'CohortCreator', 'PhenopacketVariantCoordinateFinder', 'PhenopacketPatientCreator', 'load_phenopacket_folder', 'load_phenopackets', diff --git a/src/gpsea/preprocessing/_config.py b/src/gpsea/preprocessing/_config.py index 67c14eab..348bd5b1 100644 --- a/src/gpsea/preprocessing/_config.py +++ b/src/gpsea/preprocessing/_config.py @@ -225,14 +225,65 @@ def configure_protein_metadata_service( In any case, the directory will be created if it does not exist (including any non-existing parents). :param timeout: timeout in seconds for the REST APIs. """ + warnings.warn('Use `configure_default_protein_metadata_service` instead', DeprecationWarning, stacklevel=2) + return configure_default_protein_metadata_service( + cache_dir=cache_dir, + timeout=timeout, + ) + + +def configure_default_protein_metadata_service( + protein_source: typing.Literal['UNIPROT'] = 'UNIPROT', + cache_dir: typing.Optional[str] = None, + timeout: float = 30., +) -> ProteinMetadataService: + """ + Create default protein metadata service that will cache the protein metadata + in current working directory under `.gpsea_cache/protein_cache` + and reach out to UNIPROT REST API if a cache entry is missing. + + :param protein_source: a `str` with the code of the protein data sources (currently accepting just `UNIPROT`). + :param cache_dir: path to the folder where we will cache the results fetched from the remote APIs or `None` + if the data should be cached as described by :func:`~gpsea.config.get_cache_dir_path` function. + In any case, the directory will be created if it does not exist (including any non-existing parents). + :param timeout: timeout in seconds for the REST APIs. + """ cache_dir = _configure_cache_dir(cache_dir) + return _configure_protein_service( + protein_fallback=protein_source, + cache_dir=cache_dir, + timeout=timeout, + ) - protein_cache_dir = os.path.join(cache_dir, "protein_cache") - os.makedirs(protein_cache_dir, exist_ok=True) - cache = ProteinAnnotationCache(protein_cache_dir) - fallback = UniprotProteinMetadataService(timeout=timeout) - return ProtCachingMetadataService(cache=cache, fallback=fallback) +def _configure_protein_service( + protein_fallback: str, + cache_dir: str, + timeout: float, +) -> ProteinMetadataService: + # (1) ProteinMetadataService + # Setup fallback + protein_fallback = _configure_fallback_protein_service( + protein_fallback, + timeout, + ) + # Setup protein metadata cache + prot_cache_dir = os.path.join(cache_dir, 'protein_cache') + os.makedirs(prot_cache_dir, exist_ok=True) + prot_cache = ProteinAnnotationCache(prot_cache_dir) + # Assemble the final protein metadata service + protein_metadata_service = ProtCachingMetadataService(prot_cache, protein_fallback) + return protein_metadata_service + + +def _configure_fallback_protein_service( + protein_fallback: str, + timeout: float, +) -> ProteinMetadataService: + if protein_fallback == 'UNIPROT': + return UniprotProteinMetadataService(timeout) + else: + raise ValueError(f'Unknown protein fallback annotator type {protein_fallback}') def _configure_cache_dir( From d641c0835dc0e5f9c7af055179c1f510d876c648 Mon Sep 17 00:00:00 2001 From: Daniel Danis Date: Wed, 28 Aug 2024 10:47:06 +0200 Subject: [PATCH 09/47] Add `KaryotypicSex`. --- src/gpsea/model/__init__.py | 10 +++--- src/gpsea/model/_base.py | 63 +++++++++++++++++++++++++++++++++++++ 2 files changed, 69 insertions(+), 4 deletions(-) diff --git a/src/gpsea/model/__init__.py b/src/gpsea/model/__init__.py index 2530b498..d1b8e892 100644 --- a/src/gpsea/model/__init__.py +++ b/src/gpsea/model/__init__.py @@ -5,19 +5,21 @@ and we follow with data classes for phenotype, genotype, transcript, and protein info. """ -from ._base import SampleLabels +from ._base import SampleLabels, KaryotypicSex from ._cohort import Cohort, Patient from ._gt import Genotype, Genotypes, Genotyped from ._phenotype import Phenotype, Disease from ._protein import FeatureInfo, FeatureType, ProteinFeature, ProteinMetadata from ._tx import TranscriptCoordinates -from ._variant import VariantCoordinates, ImpreciseSvInfo, VariantInfo, VariantInfoAware, TranscriptAnnotation, TranscriptInfoAware, VariantClass, Variant +from ._variant import VariantCoordinates, ImpreciseSvInfo, VariantInfo, VariantInfoAware, VariantClass, Variant +from ._variant import TranscriptAnnotation, TranscriptInfoAware from ._variant_effects import VariantEffect __all__ = [ - 'Cohort', 'Patient', 'SampleLabels', + 'Cohort', 'Patient', 'SampleLabels', 'KaryotypicSex', 'Phenotype', 'Disease', - 'Variant', 'VariantClass', 'VariantCoordinates', 'ImpreciseSvInfo', 'VariantInfo', 'VariantInfoAware', 'Genotype', 'Genotypes', 'Genotyped', + 'Variant', 'VariantClass', 'VariantCoordinates', 'ImpreciseSvInfo', 'VariantInfo', 'VariantInfoAware', + 'Genotype', 'Genotypes', 'Genotyped', 'TranscriptAnnotation', 'VariantEffect', 'TranscriptInfoAware', 'TranscriptCoordinates', 'ProteinMetadata', 'ProteinFeature', 'FeatureInfo', 'FeatureType', ] diff --git a/src/gpsea/model/_base.py b/src/gpsea/model/_base.py index 36716831..31ca49c8 100644 --- a/src/gpsea/model/_base.py +++ b/src/gpsea/model/_base.py @@ -1,8 +1,71 @@ +import enum import typing import hpotk +class KaryotypicSex(enum.Enum): + """ + `KaryotypicSex`, as included in + `Phenopacket Schema `_ + """ + + UNKNOWN_KARYOTYPE = 0 + """ + Untyped or inconclusive karyotyping. + """ + + XX = 1 + """ + Female. + """ + + XY = 2 + """ + Male. + """ + + XO = 3 + """ + Single X chromosome. + """ + + XXY = 4 + """ + Two X and one Y chromosome. + """ + + XXX = 5 + """ + Three X chromosomes + """ + + XXYY = 6 + """ + Two X chromosomes and two Y chromosomes. + """ + + XXXY = 7 + """ + Three X chromosomes and one Y chromosome. + """ + + XXXX = 8 + """ + Four X chromosomes. + """ + + XYY = 9 + """ + One X and two Y chromosomes. + """ + + OTHER_KARYOTYPE = 10 + """ + None of the above types. + """ + + class SampleLabels: """ A data model for subject identifiers. From 30f052179017ff5f84dd39b8cd43dd76fec35bdb Mon Sep 17 00:00:00 2001 From: Lauren Rekerle Date: Wed, 28 Aug 2024 13:11:04 -0500 Subject: [PATCH 10/47] Added an error if there were multiple of the same patient_ids --- src/gpsea/preprocessing/_patient.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/src/gpsea/preprocessing/_patient.py b/src/gpsea/preprocessing/_patient.py index 902851e3..6de31219 100644 --- a/src/gpsea/preprocessing/_patient.py +++ b/src/gpsea/preprocessing/_patient.py @@ -38,10 +38,14 @@ def __init__(self, patient_creator: PatientCreator[T]): def process(self, inputs: typing.Iterable[T], notepad: Notepad) -> Cohort: patients = [] + patient_ids = set() for i, pp in enumerate(inputs): sub = notepad.add_subsection(f'patient #{i}') patient = self._pc.process(pp, sub) + if patient.patient_id in patient_ids: + raise ValueError(f"Patient ID ({patient.patient_id}) is being used more than once. Please verify every ID is different.") + patient_ids.add(patient.patient_id) patients.append(patient) # What happens if a sample has From 82779917ff84fbe59227ed25996fbc482c9c65ac Mon Sep 17 00:00:00 2001 From: Lauren Rekerle Date: Wed, 28 Aug 2024 13:23:40 -0500 Subject: [PATCH 11/47] Updated so it will check every patient before incase there is more than one. --- src/gpsea/preprocessing/_patient.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/src/gpsea/preprocessing/_patient.py b/src/gpsea/preprocessing/_patient.py index 6de31219..56cbda66 100644 --- a/src/gpsea/preprocessing/_patient.py +++ b/src/gpsea/preprocessing/_patient.py @@ -39,16 +39,19 @@ def __init__(self, patient_creator: PatientCreator[T]): def process(self, inputs: typing.Iterable[T], notepad: Notepad) -> Cohort: patients = [] patient_ids = set() + duplicate_pat_ids = set() for i, pp in enumerate(inputs): sub = notepad.add_subsection(f'patient #{i}') patient = self._pc.process(pp, sub) if patient.patient_id in patient_ids: - raise ValueError(f"Patient ID ({patient.patient_id}) is being used more than once. Please verify every ID is different.") + duplicate_pat_ids.add(patient.patient_id) patient_ids.add(patient.patient_id) patients.append(patient) # What happens if a sample has + if len(duplicate_pat_ids) > 0: + raise ValueError(f"Patient ID/s {", ".join(duplicate_pat_ids)} have a duplicate. Please verify every patient has an unique ID.") # We should have >1 patients in the cohort, right? if len(patients) <= 1: From 566f80f3902423090807cf9472fa3a14043bae56 Mon Sep 17 00:00:00 2001 From: Lauren Rekerle Date: Wed, 28 Aug 2024 13:27:11 -0500 Subject: [PATCH 12/47] Fixed error --- src/gpsea/preprocessing/_patient.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/gpsea/preprocessing/_patient.py b/src/gpsea/preprocessing/_patient.py index 56cbda66..10a95aaa 100644 --- a/src/gpsea/preprocessing/_patient.py +++ b/src/gpsea/preprocessing/_patient.py @@ -51,7 +51,7 @@ def process(self, inputs: typing.Iterable[T], notepad: Notepad) -> Cohort: # What happens if a sample has if len(duplicate_pat_ids) > 0: - raise ValueError(f"Patient ID/s {", ".join(duplicate_pat_ids)} have a duplicate. Please verify every patient has an unique ID.") + raise ValueError(f"Patient ID/s {', '.join(duplicate_pat_ids)} have a duplicate. Please verify every patient has an unique ID.") # We should have >1 patients in the cohort, right? if len(patients) <= 1: From 9613f9416bc4fde27c5898e4e0593798540ac578 Mon Sep 17 00:00:00 2001 From: Daniel Danis Date: Wed, 28 Aug 2024 21:18:30 +0200 Subject: [PATCH 13/47] Add `Sex` to `Patient`. --- src/gpsea/analysis/_config.py | 2 +- src/gpsea/data/_toy.py | 462 +++++++++++------- src/gpsea/io.py | 8 +- src/gpsea/model/__init__.py | 4 +- src/gpsea/model/_base.py | 57 +-- src/gpsea/model/_cohort.py | 48 +- src/gpsea/preprocessing/_audit.py | 2 +- src/gpsea/preprocessing/_phenopacket.py | 220 ++++++--- tests/analysis/conftest.py | 6 +- tests/analysis/predicate/genotype/conftest.py | 6 +- .../predicate/genotype/test_allele_counter.py | 3 +- tests/analysis/test_pscore.py | 5 +- tests/conftest.py | 83 ++-- tests/test_data/SUOX.json | 35 ++ tests/test_io.py | 1 - 15 files changed, 593 insertions(+), 349 deletions(-) diff --git a/src/gpsea/analysis/_config.py b/src/gpsea/analysis/_config.py index dd723347..2c6d68c6 100644 --- a/src/gpsea/analysis/_config.py +++ b/src/gpsea/analysis/_config.py @@ -271,7 +271,7 @@ def configure_cohort_analysis( os.makedirs(cache_path, exist_ok=True) cache_dir = str(cache_path) - protein_metadata_service = configure_default_protein_metadata_service(protein_source, cache_dir) + protein_metadata_service = backup_pms(protein_source, cache_dir) mtc_filter: PhenotypeMtcFilter if config.mtc_strategy == MtcStrategy.HPO_MTC: diff --git a/src/gpsea/data/_toy.py b/src/gpsea/data/_toy.py index e9b80234..24a3b9c4 100644 --- a/src/gpsea/data/_toy.py +++ b/src/gpsea/data/_toy.py @@ -3,7 +3,7 @@ from gpsea.model import * from gpsea.model.genome import Contig, GenomicRegion, Strand -CONTIG = Contig('1', 'GB_ACC', 'REFSEQ_NAME', 'UCSC_NAME', 1_000) +CONTIG = Contig("1", "GB_ACC", "REFSEQ_NAME", "UCSC_NAME", 1_000) def make_region(start: int, end: int) -> GenomicRegion: @@ -22,189 +22,293 @@ def get_toy_cohort() -> Cohort: # - Spasticity HP:0001257 # - Chronic pancreatitis HP:0006280 - arachnodactyly_T = Phenotype(TermId.from_curie('HP:0001166'), True) - focal_clonic_seizure_T = Phenotype(TermId.from_curie('HP:0002266'), True) - seizure_T = Phenotype(TermId.from_curie('HP:0001250'), True) - spasticity_T = Phenotype(TermId.from_curie('HP:0001257'), True) - arachnodactyly_F = Phenotype(TermId.from_curie('HP:0001166'), False) - focal_clonic_seizure_F = Phenotype(TermId.from_curie('HP:0002266'), False) - seizure_F = Phenotype(TermId.from_curie('HP:0001250'), False) - spasticity_F = Phenotype(TermId.from_curie('HP:0001257'), False) - - Disease_T = Disease(TermId.from_curie('OMIM:001234'), "Test present disease", True) - Disease_F = Disease(TermId.from_curie('OMIM:001234'), "Test absent disease", False) + arachnodactyly_T = Phenotype(TermId.from_curie("HP:0001166"), True) + focal_clonic_seizure_T = Phenotype(TermId.from_curie("HP:0002266"), True) + seizure_T = Phenotype(TermId.from_curie("HP:0001250"), True) + spasticity_T = Phenotype(TermId.from_curie("HP:0001257"), True) + arachnodactyly_F = Phenotype(TermId.from_curie("HP:0001166"), False) + focal_clonic_seizure_F = Phenotype(TermId.from_curie("HP:0002266"), False) + seizure_F = Phenotype(TermId.from_curie("HP:0001250"), False) + spasticity_F = Phenotype(TermId.from_curie("HP:0001257"), False) - snv = Variant.create_variant_from_scratch(VariantCoordinates(make_region(280, 281), 'A', 'G', 0), 'FakeGene', - 'NM_1234.5', 'NM_1234.5:c.180A>G', False, [VariantEffect.MISSENSE_VARIANT], [1], - 'NP_09876.5', 'NP_09876.5:p.Unk200', 60, 60, - Genotypes.from_mapping({ - SampleLabels('A'): Genotype.HETEROZYGOUS, SampleLabels('B'): Genotype.HETEROZYGOUS, - SampleLabels('C'): Genotype.HOMOZYGOUS_ALTERNATE, - SampleLabels('D'): Genotype.HETEROZYGOUS, SampleLabels('E'): Genotype.HETEROZYGOUS, - SampleLabels('G'): Genotype.HETEROZYGOUS, SampleLabels('J'): Genotype.HETEROZYGOUS, - SampleLabels('K'): Genotype.HETEROZYGOUS, SampleLabels('M'): Genotype.HETEROZYGOUS, - SampleLabels('N'): Genotype.HOMOZYGOUS_ALTERNATE, - SampleLabels('P'): Genotype.HETEROZYGOUS, - SampleLabels('Q'): Genotype.HOMOZYGOUS_ALTERNATE, - SampleLabels('R'): Genotype.HETEROZYGOUS, SampleLabels('T'): Genotype.HETEROZYGOUS, - SampleLabels('V'): Genotype.HETEROZYGOUS, SampleLabels('Y'): Genotype.HETEROZYGOUS, - }) - ) - deletion = Variant.create_variant_from_scratch(VariantCoordinates(make_region(360, 363), 'TTC', 'T', -2), - 'FakeGene', 'NM_1234.5', 'NM_1234.5:c.261_263del', - False, [VariantEffect.FRAMESHIFT_VARIANT], - [2], 'NP_09876.5','NP_09876.5:p.Unk200', 86, 87, - Genotypes.from_mapping({ - SampleLabels('D'): Genotype.HETEROZYGOUS, SampleLabels('F'): Genotype.HETEROZYGOUS, - SampleLabels('G'): Genotype.HETEROZYGOUS, SampleLabels('H'): Genotype.HETEROZYGOUS, - SampleLabels('I'): Genotype.HOMOZYGOUS_ALTERNATE, - SampleLabels('L'): Genotype.HETEROZYGOUS, SampleLabels('O'): Genotype.HETEROZYGOUS, - SampleLabels('R'): Genotype.HETEROZYGOUS, - SampleLabels('S'): Genotype.HOMOZYGOUS_ALTERNATE, - SampleLabels('U'): Genotype.HETEROZYGOUS, SampleLabels('W'): Genotype.HETEROZYGOUS, - SampleLabels('X'): Genotype.HETEROZYGOUS, SampleLabels('Z'): Genotype.HETEROZYGOUS, - }) - ) + Disease_T = Disease(TermId.from_curie("OMIM:001234"), "Test present disease", True) + Disease_F = Disease(TermId.from_curie("OMIM:001234"), "Test absent disease", False) + + snv = Variant.create_variant_from_scratch( + VariantCoordinates(make_region(280, 281), "A", "G", 0), + "FakeGene", + "NM_1234.5", + "NM_1234.5:c.180A>G", + False, + [VariantEffect.MISSENSE_VARIANT], + [1], + "NP_09876.5", + "NP_09876.5:p.Unk200", + 60, + 60, + Genotypes.from_mapping( + { + SampleLabels("A"): Genotype.HETEROZYGOUS, + SampleLabels("B"): Genotype.HETEROZYGOUS, + SampleLabels("C"): Genotype.HOMOZYGOUS_ALTERNATE, + SampleLabels("D"): Genotype.HETEROZYGOUS, + SampleLabels("E"): Genotype.HETEROZYGOUS, + SampleLabels("G"): Genotype.HETEROZYGOUS, + SampleLabels("J"): Genotype.HETEROZYGOUS, + SampleLabels("K"): Genotype.HETEROZYGOUS, + SampleLabels("M"): Genotype.HETEROZYGOUS, + SampleLabels("N"): Genotype.HOMOZYGOUS_ALTERNATE, + SampleLabels("P"): Genotype.HETEROZYGOUS, + SampleLabels("Q"): Genotype.HOMOZYGOUS_ALTERNATE, + SampleLabels("R"): Genotype.HETEROZYGOUS, + SampleLabels("T"): Genotype.HETEROZYGOUS, + SampleLabels("V"): Genotype.HETEROZYGOUS, + SampleLabels("Y"): Genotype.HETEROZYGOUS, + } + ), + ) + deletion = Variant.create_variant_from_scratch( + VariantCoordinates(make_region(360, 363), "TTC", "T", -2), + "FakeGene", + "NM_1234.5", + "NM_1234.5:c.261_263del", + False, + [VariantEffect.FRAMESHIFT_VARIANT], + [2], + "NP_09876.5", + "NP_09876.5:p.Unk200", + 86, + 87, + Genotypes.from_mapping( + { + SampleLabels("D"): Genotype.HETEROZYGOUS, + SampleLabels("F"): Genotype.HETEROZYGOUS, + SampleLabels("G"): Genotype.HETEROZYGOUS, + SampleLabels("H"): Genotype.HETEROZYGOUS, + SampleLabels("I"): Genotype.HOMOZYGOUS_ALTERNATE, + SampleLabels("L"): Genotype.HETEROZYGOUS, + SampleLabels("O"): Genotype.HETEROZYGOUS, + SampleLabels("R"): Genotype.HETEROZYGOUS, + SampleLabels("S"): Genotype.HOMOZYGOUS_ALTERNATE, + SampleLabels("U"): Genotype.HETEROZYGOUS, + SampleLabels("W"): Genotype.HETEROZYGOUS, + SampleLabels("X"): Genotype.HETEROZYGOUS, + SampleLabels("Z"): Genotype.HETEROZYGOUS, + } + ), + ) het_dup = Variant.create_variant_from_scratch( - VariantCoordinates(make_region(175, 176), 'T', 'TG', 1), 'FakeGene', 'NM_1234.5', - 'NM_1234.5:c.75A>G', False, [VariantEffect.FRAMESHIFT_VARIANT], [1], 'NP_09876.5', 'NP_09876.5:p.Unk200', 25, 25, - Genotypes.empty()) # Not used in the patients below, hence `empty()`. + VariantCoordinates(make_region(175, 176), "T", "TG", 1), + "FakeGene", + "NM_1234.5", + "NM_1234.5:c.75A>G", + False, + [VariantEffect.FRAMESHIFT_VARIANT], + [1], + "NP_09876.5", + "NP_09876.5:p.Unk200", + 25, + 25, + Genotypes.empty(), + ) # Not used in the patients below, hence `empty()`. hom_dup = Variant.create_variant_from_scratch( - VariantCoordinates(make_region(175, 176), 'T', 'TG', 1),'FakeGene', 'NM_1234.5', - 'NM_1234.5:c.75A>G', False, [VariantEffect.FRAMESHIFT_VARIANT], [1], 'NP_09876.5', 'NP_09876.5:p.Unk200', 25, 25, - Genotypes.empty()) # Not used in the patients below, hence `empty()`. + VariantCoordinates(make_region(175, 176), "T", "TG", 1), + "FakeGene", + "NM_1234.5", + "NM_1234.5:c.75A>G", + False, + [VariantEffect.FRAMESHIFT_VARIANT], + [1], + "NP_09876.5", + "NP_09876.5:p.Unk200", + 25, + 25, + Genotypes.empty(), + ) # Not used in the patients below, hence `empty()`. patients = ( - Patient(SampleLabels('A'), - phenotypes=(arachnodactyly_T, spasticity_F, seizure_T), - variants=[snv], - diseases=[Disease_T] - ), - Patient(SampleLabels('B'), - phenotypes=(arachnodactyly_T, seizure_T, spasticity_T), - variants=[snv], - diseases=[Disease_T] - ), - Patient(SampleLabels('C'), - phenotypes=(arachnodactyly_F, spasticity_T, seizure_T), - variants=[snv], - diseases=[Disease_T] - ), - Patient(SampleLabels('D'), - phenotypes=(arachnodactyly_T, spasticity_T, seizure_T), - variants=[snv, deletion], - diseases=[Disease_T] - ), - Patient(SampleLabels('E'), - phenotypes=(arachnodactyly_T, spasticity_T, seizure_F), - variants=[snv], - diseases=[Disease_T] - ), - Patient(SampleLabels('F'), - phenotypes=(arachnodactyly_F, spasticity_F, seizure_T), - variants=[deletion], - diseases=[Disease_T] - ), - Patient(SampleLabels('G'), - phenotypes=(arachnodactyly_T, seizure_T, spasticity_T), - variants=[snv, deletion], - diseases=[Disease_T] - ), - Patient(SampleLabels('H'), - phenotypes=(arachnodactyly_T, seizure_T, spasticity_F), - variants=[deletion], - diseases=[Disease_T] - ), - Patient(SampleLabels('I'), - phenotypes=(arachnodactyly_F, spasticity_F, seizure_T), - variants=[deletion], - diseases=[Disease_T] - ), - Patient(SampleLabels('J'), - phenotypes=(arachnodactyly_T, seizure_T, spasticity_T), - variants=[snv], - diseases=[Disease_T] - ), - Patient(SampleLabels('K'), - phenotypes=(arachnodactyly_F, spasticity_T, seizure_T), - variants=[snv], - diseases=[Disease_T] - ), - Patient(SampleLabels('L'), - phenotypes=(arachnodactyly_F, seizure_F, spasticity_F), - variants=[deletion], - diseases=[Disease_T] - ), - Patient(SampleLabels('M'), - phenotypes=(arachnodactyly_T, seizure_F, spasticity_T), - variants=[snv], - diseases=[Disease_T] - ), - Patient(SampleLabels('N'), - phenotypes=(arachnodactyly_F, seizure_T, spasticity_F), - variants=[snv], - diseases=[Disease_T] - ), - Patient(SampleLabels('O'), - phenotypes=(arachnodactyly_F, seizure_F, spasticity_T), - variants=[deletion], - diseases=[Disease_T] - ), - Patient(SampleLabels('P'), - phenotypes=(arachnodactyly_T, seizure_T, spasticity_F), - variants=[snv], - diseases=[Disease_T] - ), - Patient(SampleLabels('Q'), - phenotypes=(arachnodactyly_T, seizure_F, spasticity_F), - variants=[snv], - diseases=[Disease_T] - ), - Patient(SampleLabels('R'), - phenotypes=(arachnodactyly_T, seizure_T, spasticity_F), - variants=[snv, deletion], - diseases=[Disease_T] - ), - Patient(SampleLabels('S'), - phenotypes=(arachnodactyly_F, seizure_T, spasticity_T), - variants=[deletion], - diseases=[Disease_T] - ), - Patient(SampleLabels('T'), - phenotypes=(arachnodactyly_T, seizure_F, spasticity_T), - variants=[snv], - diseases=[Disease_T] - ), - Patient(SampleLabels('U'), - phenotypes=(arachnodactyly_F, seizure_T, spasticity_T), - variants=[deletion], - diseases=[Disease_T] - ), - Patient(SampleLabels('V'), - phenotypes=(arachnodactyly_T, seizure_T, spasticity_T), - variants=[snv], - diseases=[Disease_T] - ), - Patient(SampleLabels('W'), - phenotypes=(arachnodactyly_F, seizure_T, spasticity_T), - variants=[deletion], - diseases=[Disease_T] - ), - Patient(SampleLabels('X'), - phenotypes=(arachnodactyly_F, seizure_T, spasticity_T), - variants=[deletion], - diseases=[Disease_T] - ), - Patient(SampleLabels('Y'), - phenotypes=(arachnodactyly_T, seizure_T, spasticity_T), - variants=[snv], - diseases=[Disease_T] - ), - Patient(SampleLabels('Z'), - phenotypes=(arachnodactyly_F, seizure_T, spasticity_T), - variants=[deletion], - diseases=[Disease_T] - ), + Patient.from_raw_parts( + SampleLabels("A"), + sex=None, + phenotypes=(arachnodactyly_T, spasticity_F, seizure_T), + variants=[snv], + diseases=[Disease_T], + ), + Patient.from_raw_parts( + SampleLabels("B"), + sex=None, + phenotypes=(arachnodactyly_T, seizure_T, spasticity_T), + variants=[snv], + diseases=[Disease_T], + ), + Patient.from_raw_parts( + SampleLabels("C"), + sex=None, + phenotypes=(arachnodactyly_F, spasticity_T, seizure_T), + variants=[snv], + diseases=[Disease_T], + ), + Patient.from_raw_parts( + SampleLabels("D"), + sex=None, + phenotypes=(arachnodactyly_T, spasticity_T, seizure_T), + variants=[snv, deletion], + diseases=[Disease_T], + ), + Patient.from_raw_parts( + SampleLabels("E"), + sex=None, + phenotypes=(arachnodactyly_T, spasticity_T, seizure_F), + variants=[snv], + diseases=[Disease_T], + ), + Patient.from_raw_parts( + SampleLabels("F"), + sex=None, + phenotypes=(arachnodactyly_F, spasticity_F, seizure_T), + variants=[deletion], + diseases=[Disease_T], + ), + Patient.from_raw_parts( + SampleLabels("G"), + sex=None, + phenotypes=(arachnodactyly_T, seizure_T, spasticity_T), + variants=[snv, deletion], + diseases=[Disease_T], + ), + Patient.from_raw_parts( + SampleLabels("H"), + sex=None, + phenotypes=(arachnodactyly_T, seizure_T, spasticity_F), + variants=[deletion], + diseases=[Disease_T], + ), + Patient.from_raw_parts( + SampleLabels("I"), + sex=None, + phenotypes=(arachnodactyly_F, spasticity_F, seizure_T), + variants=[deletion], + diseases=[Disease_T], + ), + Patient.from_raw_parts( + SampleLabels("J"), + sex=None, + phenotypes=(arachnodactyly_T, seizure_T, spasticity_T), + variants=[snv], + diseases=[Disease_T], + ), + Patient.from_raw_parts( + SampleLabels("K"), + sex=None, + phenotypes=(arachnodactyly_F, spasticity_T, seizure_T), + variants=[snv], + diseases=[Disease_T], + ), + Patient.from_raw_parts( + SampleLabels("L"), + sex=None, + phenotypes=(arachnodactyly_F, seizure_F, spasticity_F), + variants=[deletion], + diseases=[Disease_T], + ), + Patient.from_raw_parts( + SampleLabels("M"), + sex=None, + phenotypes=(arachnodactyly_T, seizure_F, spasticity_T), + variants=[snv], + diseases=[Disease_T], + ), + Patient.from_raw_parts( + SampleLabels("N"), + sex=None, + phenotypes=(arachnodactyly_F, seizure_T, spasticity_F), + variants=[snv], + diseases=[Disease_T], + ), + Patient.from_raw_parts( + SampleLabels("O"), + sex=None, + phenotypes=(arachnodactyly_F, seizure_F, spasticity_T), + variants=[deletion], + diseases=[Disease_T], + ), + Patient.from_raw_parts( + SampleLabels("P"), + sex=None, + phenotypes=(arachnodactyly_T, seizure_T, spasticity_F), + variants=[snv], + diseases=[Disease_T], + ), + Patient.from_raw_parts( + SampleLabels("Q"), + sex=None, + phenotypes=(arachnodactyly_T, seizure_F, spasticity_F), + variants=[snv], + diseases=[Disease_T], + ), + Patient.from_raw_parts( + SampleLabels("R"), + sex=None, + phenotypes=(arachnodactyly_T, seizure_T, spasticity_F), + variants=[snv, deletion], + diseases=[Disease_T], + ), + Patient.from_raw_parts( + SampleLabels("S"), + sex=None, + phenotypes=(arachnodactyly_F, seizure_T, spasticity_T), + variants=[deletion], + diseases=[Disease_T], + ), + Patient.from_raw_parts( + SampleLabels("T"), + sex=None, + phenotypes=(arachnodactyly_T, seizure_F, spasticity_T), + variants=[snv], + diseases=[Disease_T], + ), + Patient.from_raw_parts( + SampleLabels("U"), + sex=None, + phenotypes=(arachnodactyly_F, seizure_T, spasticity_T), + variants=[deletion], + diseases=[Disease_T], + ), + Patient.from_raw_parts( + SampleLabels("V"), + sex=None, + phenotypes=(arachnodactyly_T, seizure_T, spasticity_T), + variants=[snv], + diseases=[Disease_T], + ), + Patient.from_raw_parts( + SampleLabels("W"), + sex=None, + phenotypes=(arachnodactyly_F, seizure_T, spasticity_T), + variants=[deletion], + diseases=[Disease_T], + ), + Patient.from_raw_parts( + SampleLabels("X"), + sex=None, + phenotypes=(arachnodactyly_F, seizure_T, spasticity_T), + variants=[deletion], + diseases=[Disease_T], + ), + Patient.from_raw_parts( + SampleLabels("Y"), + sex=None, + phenotypes=(arachnodactyly_T, seizure_T, spasticity_T), + variants=[snv], + diseases=[Disease_T], + ), + Patient.from_raw_parts( + SampleLabels("Z"), + sex=None, + phenotypes=(arachnodactyly_F, seizure_T, spasticity_T), + variants=[deletion], + diseases=[Disease_T], + ), ) return Cohort.from_patients(patients) diff --git a/src/gpsea/io.py b/src/gpsea/io.py index a9b30078..c5b3aea1 100644 --- a/src/gpsea/io.py +++ b/src/gpsea/io.py @@ -23,7 +23,7 @@ def default(self, o): 'tx_annotations': o.tx_annotations, 'genotypes': o.genotypes, } - elif isinstance(o, VariantInfo): + elif isinstance(o, VariantInfo): return { 'variant_coordinates': o.variant_coordinates, 'sv_info': o.sv_info, @@ -87,7 +87,7 @@ def default(self, o): 'label': o.label, 'meta_label': o.meta_label, } - elif isinstance(o, (Genotype, VariantEffect, Strand, VariantClass)): + elif isinstance(o, (Sex, Genotype, VariantEffect, Strand, VariantClass)): # enums return o.name elif isinstance(o, Phenotype): @@ -104,6 +104,7 @@ def default(self, o): elif isinstance(o, Patient): return { 'labels': o.labels, + 'sex': o.sex, 'phenotypes': o.phenotypes, 'diseases': o.diseases, 'variants': o.variants, @@ -161,7 +162,7 @@ def default(self, o): _FEATURE_INFO = ('name', 'region') _PHENOTYPE_FIELDS = ('term_id', 'is_present') _DISEASE_FIELDS = ('term_id', 'name', 'is_observed') -_PATIENT_FIELDS = ('labels', 'phenotypes', 'diseases', 'variants') +_PATIENT_FIELDS = ('labels', 'sex', 'phenotypes', 'diseases', 'variants') _COHORT_FIELDS = ('members', 'excluded_patient_count') @@ -281,6 +282,7 @@ def object_hook(obj: typing.Dict[typing.Any, typing.Any]) -> typing.Any: elif GpseaJSONDecoder._has_all_fields(obj, _PATIENT_FIELDS): return Patient( labels=obj['labels'], + sex=Sex[obj['sex']], phenotypes=obj['phenotypes'], diseases=obj['diseases'], variants=obj['variants'], diff --git a/src/gpsea/model/__init__.py b/src/gpsea/model/__init__.py index d1b8e892..1d17138c 100644 --- a/src/gpsea/model/__init__.py +++ b/src/gpsea/model/__init__.py @@ -5,7 +5,7 @@ and we follow with data classes for phenotype, genotype, transcript, and protein info. """ -from ._base import SampleLabels, KaryotypicSex +from ._base import SampleLabels, Sex from ._cohort import Cohort, Patient from ._gt import Genotype, Genotypes, Genotyped from ._phenotype import Phenotype, Disease @@ -16,7 +16,7 @@ from ._variant_effects import VariantEffect __all__ = [ - 'Cohort', 'Patient', 'SampleLabels', 'KaryotypicSex', + 'Cohort', 'Patient', 'SampleLabels', 'Sex', 'Phenotype', 'Disease', 'Variant', 'VariantClass', 'VariantCoordinates', 'ImpreciseSvInfo', 'VariantInfo', 'VariantInfoAware', 'Genotype', 'Genotypes', 'Genotyped', diff --git a/src/gpsea/model/_base.py b/src/gpsea/model/_base.py index 31ca49c8..0effeab0 100644 --- a/src/gpsea/model/_base.py +++ b/src/gpsea/model/_base.py @@ -4,65 +4,26 @@ import hpotk -class KaryotypicSex(enum.Enum): - """ - `KaryotypicSex`, as included in - `Phenopacket Schema `_ - """ - - UNKNOWN_KARYOTYPE = 0 - """ - Untyped or inconclusive karyotyping. - """ - - XX = 1 - """ - Female. - """ - - XY = 2 - """ - Male. +class Sex(enum.Enum): """ + `Sex` represents typical “phenotypic sex”, as would be determined by a midwife or physician at birth. - XO = 3 - """ - Single X chromosome. + The definition is aligned with `Phenopacket Schema `_ """ - XXY = 4 + UNKNOWN_SEX = 0 """ - Two X and one Y chromosome. + Not assessed or not available. Maps to `NCIT:C17998`. """ - XXX = 5 - """ - Three X chromosomes - """ - - XXYY = 6 - """ - Two X chromosomes and two Y chromosomes. - """ - - XXXY = 7 - """ - Three X chromosomes and one Y chromosome. - """ - - XXXX = 8 - """ - Four X chromosomes. - """ - - XYY = 9 + FEMALE = 1 """ - One X and two Y chromosomes. + Female sex. Maps to `NCIT:C46113`. """ - OTHER_KARYOTYPE = 10 + MALE = 2 """ - None of the above types. + Male sex. Maps to `NCIT:C46112`. """ diff --git a/src/gpsea/model/_cohort.py b/src/gpsea/model/_cohort.py index 3209748e..03fcf08e 100644 --- a/src/gpsea/model/_cohort.py +++ b/src/gpsea/model/_cohort.py @@ -5,7 +5,7 @@ import hpotk -from ._base import SampleLabels +from ._base import SampleLabels, Sex from ._phenotype import Phenotype, Disease from ._variant import Variant @@ -13,16 +13,49 @@ class Patient: """ `Patient` represents a single investigated individual. + + .. note:: + + We strongly recommend using the :func:`from_raw_parts` static constructor + instead of `__init__`. """ + @staticmethod + def from_raw_parts( + labels: SampleLabels, + sex: typing.Optional[Sex], + phenotypes: typing.Iterable[Phenotype], + diseases: typing.Iterable[Disease], + variants: typing.Iterable[Variant] + ) -> "Patient": + """ + Create `Patient` from the primary data. + """ + if sex is None: + sex = Sex.UNKNOWN_SEX + + return Patient( + labels=labels, + sex=sex, + phenotypes=phenotypes, + diseases=diseases, + variants=variants, + ) + def __init__( self, labels: SampleLabels, + sex: Sex, phenotypes: typing.Iterable[Phenotype], diseases: typing.Iterable[Disease], variants: typing.Iterable[Variant] ): + assert isinstance(labels, SampleLabels) self._labels = labels + + assert isinstance(sex, Sex) + self._sex = sex + self._phenotypes = tuple(phenotypes) self._diseases = tuple(diseases) self._variants = tuple(variants) @@ -41,6 +74,13 @@ def labels(self) -> SampleLabels: """ return self._labels + @property + def sex(self) -> Sex: + """ + Get the "phenotype sex" of the sample. + """ + return self._sex + @property def phenotypes(self) -> typing.Sequence[Phenotype]: """ @@ -89,6 +129,7 @@ def excluded_diseases(self) -> typing.Iterator[Disease]: def __str__(self) -> str: return (f"Patient(" f"labels:{self._labels}, " + f"sex:{self._sex}, " f"variants:{self.variants}, " f"phenotypes:{[pheno.identifier for pheno in self.phenotypes]}, " f"diseases:{[dis.identifier for dis in self.diseases]}") @@ -99,12 +140,13 @@ def __repr__(self) -> str: def __eq__(self, other) -> bool: return (isinstance(other, Patient) and self._labels == other._labels + and self._sex == other._sex and self._variants == other._variants and self._phenotypes == other._phenotypes and self._diseases == other._diseases) def __hash__(self) -> int: - return hash((self._labels, self._variants, self._phenotypes, self._diseases)) + return hash((self._labels, self._sex, self._variants, self._phenotypes, self._diseases)) class Cohort(typing.Sized, typing.Iterable[Patient]): @@ -201,7 +243,7 @@ def list_present_phenotypes( Otherwise, lists only the `top` highest counts Returns: - typing.Sequence[typing.Tuple[str, int]]: A sequence of tuples, formatted (phenotype CURIE, + typing.Sequence[typing.Tuple[str, int]]: A sequence of tuples, formatted (phenotype CURIE, number of patients with that phenotype) """ counter = Counter() diff --git a/src/gpsea/preprocessing/_audit.py b/src/gpsea/preprocessing/_audit.py index f4ab6597..3bdafe97 100644 --- a/src/gpsea/preprocessing/_audit.py +++ b/src/gpsea/preprocessing/_audit.py @@ -165,7 +165,7 @@ def __init__(self, label: str): @abc.abstractmethod - def add_subsection(self, label: str): + def add_subsection(self, label: str) -> "Notepad": """ Add a labeled subsection. diff --git a/src/gpsea/preprocessing/_phenopacket.py b/src/gpsea/preprocessing/_phenopacket.py index abe83fbc..136825bd 100644 --- a/src/gpsea/preprocessing/_phenopacket.py +++ b/src/gpsea/preprocessing/_phenopacket.py @@ -4,15 +4,28 @@ import hpotk from phenopackets.schema.v2.phenopackets_pb2 import Phenopacket +import phenopackets.schema.v2.core.individual_pb2 as ppi from phenopackets.schema.v2.core.disease_pb2 import Disease as PPDisease from phenopackets.schema.v2.core.interpretation_pb2 import GenomicInterpretation from phenopackets.vrsatile.v1.vrsatile_pb2 import VcfRecord, VariationDescriptor from phenopackets.vrs.v1.vrs_pb2 import Variation -from gpsea.model import Patient, SampleLabels, Disease -from gpsea.model import VariantClass, VariantCoordinates, ImpreciseSvInfo, VariantInfo, Variant, Genotype, Genotypes +from gpsea.model import SampleLabels, Patient, Sex, Disease +from gpsea.model import ( + VariantClass, + VariantCoordinates, + ImpreciseSvInfo, + VariantInfo, + Variant, + Genotype, + Genotypes, +) from gpsea.model.genome import GenomeBuild, GenomicRegion, Strand -from ._api import VariantCoordinateFinder, FunctionalAnnotator, ImpreciseSvFunctionalAnnotator +from ._api import ( + VariantCoordinateFinder, + FunctionalAnnotator, + ImpreciseSvFunctionalAnnotator, +) from ._audit import Notepad from ._patient import PatientCreator from ._phenotype import PhenotypeCreator @@ -27,14 +40,14 @@ def find_genotype( self, item: GenomicInterpretation, ) -> typing.Optional[Genotype]: - if item.HasField('variant_interpretation'): + if item.HasField("variant_interpretation"): variant_interpretation = item.variant_interpretation - if variant_interpretation.HasField('variation_descriptor'): + if variant_interpretation.HasField("variation_descriptor"): variation_descriptor = variant_interpretation.variation_descriptor - if variation_descriptor.HasField('allelic_state'): + if variation_descriptor.HasField("allelic_state"): genotype = variation_descriptor.allelic_state.label return self._map_geno_genotype_label(genotype) - + return None @staticmethod @@ -85,7 +98,7 @@ def find_coordinates( Args: item (GenomicInterpretation): a genomic interpretation element from Phenopacket Schema - + Returns: typing.Optional[VariantCoordinates]: variant coordinates """ @@ -102,8 +115,9 @@ def find_coordinates( variation_descriptor.vcf_record.genome_assembly ): raise ValueError( - f"Variant id {variation_descriptor.id} for patient {item.subject_or_biosample_id} has a different Genome Assembly than what was given. " - + f"{variation_descriptor.vcf_record.genome_assembly} is not {self._build.identifier}." + f"Variant id {variation_descriptor.id} for patient {item.subject_or_biosample_id} " + "has a different Genome Assembly than what was given. " + f"{variation_descriptor.vcf_record.genome_assembly} is not {self._build.identifier}." ) contig = self._build.contig_by_name(variation_descriptor.vcf_record.chrom) start = int(variation_descriptor.vcf_record.pos) - 1 @@ -134,7 +148,9 @@ def find_coordinates( alt = "" change_length = end - start else: - raise ValueError(f"The copy number of {number} is not supported. Supported values: {{1, 3}}") + raise ValueError( + f"The copy number of {number} is not supported. Supported values: {{1, 3}}" + ) region = GenomicRegion(contig, start, end, Strand.POSITIVE) return VariantCoordinates(region, ref, alt, change_length) @@ -197,7 +213,7 @@ def _looks_like_large_sv( else None ) - # If we have these fields, we seem to have all information + # If we have these fields, we seem to have all information # to parse the variation descriptor elsewhere. return structural_type is not None and gene_context is not None @@ -214,7 +230,19 @@ def __init__( functional_annotator: FunctionalAnnotator, imprecise_sv_functional_annotator: ImpreciseSvFunctionalAnnotator, hgvs_coordinate_finder: VariantCoordinateFinder[str], + assume_karyotypic_sex: bool = True, ): + """ + Create an instance. + + :param build: the genome build to use for variants. + :param phenotype_creator: a phenotype creator for creating phenotypes. + :param functional_annotator: for computing functional annotations. + :param imprecise_sv_functional_annotator: for getting info about imprecise variants. + :param hgvs_coordinate_finder: for finding chromosomal coordinates for HGVS variant descriptions. + :param assume_karyotypic_sex: `True` if it is OK to assume that `FEMALE` has the `XX` karyotype + and `MALE` has `XY`. + """ self._logger = logging.getLogger(__name__) # Violates DI, but it is specific to this class, so I'll leave it "as is". self._coord_finder = PhenopacketVariantCoordinateFinder( @@ -228,49 +256,64 @@ def __init__( functional_annotator, FunctionalAnnotator, "functional_annotator" ) self._imprecise_sv_functional_annotator = hpotk.util.validate_instance( - imprecise_sv_functional_annotator, ImpreciseSvFunctionalAnnotator, "imprecise_sv_functional_annotator" + imprecise_sv_functional_annotator, + ImpreciseSvFunctionalAnnotator, + "imprecise_sv_functional_annotator", ) - + # Set of sequence ontology IDs that we will treat as a deletion (`DEL`) # for the purpose of assigning imprecise SV info with a variant class. self._so_deletions = { - '1000029', # chromosomal deletion: An incomplete chromosome. - '0001893', # transcript ablation: A feature ablation whereby the deleted region includes a transcript feature. - '0001879', # feature_ablation: A sequence variant, caused by an alteration of the genomic sequence, where the deletion, is greater than the extent of the underlying genomic features. + "1000029", # chromosomal deletion: An incomplete chromosome. + # transcript ablation: A feature ablation whereby the deleted region includes a transcript feature. + "0001893", + # feature_ablation: A sequence variant, caused by an alteration of the genomic sequence, + # where the deletion, is greater than the extent of the underlying genomic features. + "0001879", } self._so_duplications = { - '1000037', # chromosomal_duplication + "1000037", # chromosomal_duplication } + self._assume_karyotypic_sex = assume_karyotypic_sex - def process(self, inputs: Phenopacket, notepad: Notepad) -> Patient: + def process(self, pp: Phenopacket, notepad: Notepad) -> Patient: """Creates a Patient from the data in a given Phenopacket Args: - inputs (Phenopacket): A Phenopacket object + pp (Phenopacket): A Phenopacket object notepad (Notepad): notepad to write down the issues Returns: Patient: A Patient object """ sample_id = SampleLabels( - label=inputs.subject.id, - meta_label=inputs.id if len(inputs.id) > 0 else None, + label=pp.subject.id, + meta_label=pp.id if len(pp.id) > 0 else None, ) + # Extract karyotypic sex + indi = notepad.add_subsection("individual") + sex = self._extract_sex(pp.subject, indi) + # Check phenotypes pfs = notepad.add_subsection("phenotype-features") phenotypes = self._phenotype_creator.process( - ((pf.type.id, not pf.excluded) for pf in inputs.phenotypic_features), pfs + ((pf.type.id, not pf.excluded) for pf in pp.phenotypic_features), pfs ) # Check diseases - diseases = self._add_diseases([dis for dis in inputs.diseases], pfs) + dip = notepad.add_subsection("diseases") + diseases = self._add_diseases(pp.diseases, dip) # Check variants vs = notepad.add_subsection("variants") - variants = self._add_variants(sample_id, inputs, vs) - - return Patient( - sample_id, phenotypes=phenotypes, variants=variants, diseases=diseases + variants = self._add_variants(sample_id, pp, vs) + + return Patient.from_raw_parts( + sample_id, + sex=sex, + phenotypes=phenotypes, + variants=variants, + diseases=diseases, ) def _add_diseases( @@ -285,21 +328,43 @@ def _add_diseases( Sequence[Dis]: A list of Disease objects """ if len(diseases) == 0: - notepad.add_warning(f"No diseases found.") - return [] + notepad.add_warning("No diseases found") + return () + final_diseases = [] for i, dis in enumerate(diseases): if not dis.HasField("term"): - raise ValueError("Could not find term in Disease.") - term_id = hpotk.TermId.from_curie(dis.term.id) + notepad.add_error(f"#{i} has no `term`") + continue + else: + term_id = hpotk.TermId.from_curie(dis.term.id) + # Do not include excluded diseases if we decide to assume excluded if not included final_diseases.append(Disease(term_id, dis.term.label, not dis.excluded)) + return final_diseases + def _extract_sex( + self, + individual: ppi.Individual, + notepad: Notepad, + ) -> typing.Optional[Sex]: + # Let's use the phenotypic sex as fallback + sex = individual.sex + if sex == ppi.FEMALE: + return Sex.FEMALE + elif sex == ppi.MALE: + return Sex.MALE + elif sex == ppi.OTHER_SEX or sex == ppi.UNKNOWN_SEX: + return Sex.UNKNOWN_SEX + else: + notepad.add_warning(f'Unknown sex type: {sex}') + return Sex.UNKNOWN_SEX + def _add_variants( - self, - sample_id: SampleLabels, - pp: Phenopacket, + self, + sample_id: SampleLabels, + pp: Phenopacket, notepad: Notepad, ) -> typing.Sequence[Variant]: """Creates a list of Variant objects from the data in a given Phenopacket @@ -311,24 +376,28 @@ def _add_variants( Sequence[Variant]: A list of Variant objects """ variants = [] - + for i, interpretation in enumerate(pp.interpretations): - sub_note = notepad.add_subsection(f'#{i}') - if interpretation.HasField('diagnosis'): - for genomic_interpretation in interpretation.diagnosis.genomic_interpretations: + sub_note = notepad.add_subsection(f"#{i}") + if interpretation.HasField("diagnosis"): + for ( + genomic_interpretation + ) in interpretation.diagnosis.genomic_interpretations: gt = self._gt_parser.find_genotype(genomic_interpretation) if gt is None: sub_note.add_warning( - f"Could not extract genotype from genomic interpretation", + "Could not extract genotype from genomic interpretation", "Remove variant from testing", ) continue - - variant_info = self._extract_variant_info(sample_id, genomic_interpretation, sub_note) + + variant_info = self._extract_variant_info( + sample_id, genomic_interpretation, sub_note + ) if variant_info is None: # We already complained in the extract function continue - + if variant_info.has_variant_coordinates(): try: tx_annotations = self._functional_annotator.annotate( @@ -342,8 +411,10 @@ def _add_variants( continue elif variant_info.has_sv_info(): try: - tx_annotations = self._imprecise_sv_functional_annotator.annotate( - item=variant_info.sv_info, + tx_annotations = ( + self._imprecise_sv_functional_annotator.annotate( + item=variant_info.sv_info, + ) ) except ValueError as error: sub_note.add_warning( @@ -352,23 +423,25 @@ def _add_variants( ) continue else: - raise ValueError('VariantInfo should have either the coordinates or the SV info, but had neither!') + raise ValueError( + "VariantInfo should have either the coordinates or the SV info, but had neither!" + ) if len(tx_annotations) == 0: sub_note.add_warning( f"Patient {pp.id} has an error with variant {variant_info.variant_key}", - f"Remove variant from testing... tx_anno == 0", + "Remove variant from testing... tx_anno == 0", ) continue - genotype = Genotypes.single(sample_id, gt) variants.append( Variant( - variant_info=variant_info, - tx_annotations=tx_annotations, + variant_info=variant_info, + tx_annotations=tx_annotations, genotypes=genotype, - )) + ) + ) if len(variants) == 0: notepad.add_warning(f"Patient {pp.id} has no variants to work with") @@ -385,39 +458,50 @@ def _extract_variant_info( sv_info = None try: - variant_coordinates = self._coord_finder.find_coordinates(genomic_interpretation) + variant_coordinates = self._coord_finder.find_coordinates( + genomic_interpretation + ) except ValueError: notepad.add_warning( - f"Expected a VCF record, a VRS CNV, or an expression with `hgvs.c` but had an error retrieving any from patient {sample_id}", + "Expected a VCF record, a VRS CNV, or an expression with `hgvs.c`" + f"but had an error retrieving any from patient {sample_id}", "Remove variant from testing", ) return None - + if variant_coordinates is None: sv_info = self._map_to_imprecise_sv(genomic_interpretation) if sv_info is None: notepad.add_warning( - f"Could not extract the information for large SV annotation", + "Could not extract the information for large SV annotation", "Remove variant from testing", ) return None - + return VariantInfo( variant_coordinates=variant_coordinates, sv_info=sv_info, ) def _map_to_imprecise_sv( - self, + self, genomic_interpretation: GenomicInterpretation, ) -> typing.Optional[ImpreciseSvInfo]: - if genomic_interpretation.HasField('variant_interpretation'): + if genomic_interpretation.HasField("variant_interpretation"): variant_interpretation = genomic_interpretation.variant_interpretation - if variant_interpretation.HasField('variation_descriptor'): + if variant_interpretation.HasField("variation_descriptor"): variation_descriptor = variant_interpretation.variation_descriptor - - structural_type = variation_descriptor.structural_type if variation_descriptor.HasField('structural_type') else None - gene_context = variation_descriptor.gene_context if variation_descriptor.HasField('gene_context') else None + + structural_type = ( + variation_descriptor.structural_type + if variation_descriptor.HasField("structural_type") + else None + ) + gene_context = ( + variation_descriptor.gene_context + if variation_descriptor.HasField("gene_context") + else None + ) if structural_type is not None and gene_context is not None: st = hpotk.TermId.from_curie(curie=structural_type.id) @@ -428,23 +512,23 @@ def _map_to_imprecise_sv( gene_id=gene_context.value_id, gene_symbol=gene_context.symbol, ) - + return None - + def _map_structural_type_to_variant_class( self, structural_type: hpotk.TermId, ) -> VariantClass: # This method is most likely incomplete. - # Please open a ticket if you receive a `ValueError` + # Please open a ticket if you receive a `ValueError` # for a structural type, that is not mapped at the moment, # to help us enhance the mapping. - if structural_type.prefix == 'SO': + if structural_type.prefix == "SO": if structural_type.id in self._so_deletions: return VariantClass.DEL elif structural_type.id in self._so_duplications: return VariantClass.DUP else: - raise ValueError(f'Unknown structural type {structural_type}') + raise ValueError(f"Unknown structural type {structural_type}") else: - raise ValueError(f'Unknown structural type {structural_type}') + raise ValueError(f"Unknown structural type {structural_type}") diff --git a/tests/analysis/conftest.py b/tests/analysis/conftest.py index 86dd01c4..90b46c8d 100644 --- a/tests/analysis/conftest.py +++ b/tests/analysis/conftest.py @@ -35,8 +35,9 @@ def degenerated_cohort( return Cohort.from_patients( members=( - Patient( + Patient.from_raw_parts( labels=labels_a, + sex=Sex.UNKNOWN_SEX, phenotypes=( Phenotype( term_id=hpotk.TermId.from_curie("HP:0000118"), @@ -69,8 +70,9 @@ def degenerated_cohort( ), ), ), - Patient( + Patient.from_raw_parts( labels=labels_b, + sex=Sex.UNKNOWN_SEX, phenotypes=( Phenotype( term_id=hpotk.TermId.from_curie("HP:0000118"), diff --git a/tests/analysis/predicate/genotype/conftest.py b/tests/analysis/predicate/genotype/conftest.py index 22b6d588..3043d341 100644 --- a/tests/analysis/predicate/genotype/conftest.py +++ b/tests/analysis/predicate/genotype/conftest.py @@ -187,8 +187,9 @@ def patient_w_missense( sample_labels: SampleLabels, missense_variant: Variant, ) -> Patient: - return Patient( + return Patient.from_raw_parts( labels=sample_labels, + sex=Sex.UNKNOWN_SEX, phenotypes=(), diseases=(), variants=( @@ -202,8 +203,9 @@ def patient_w_frameshift( sample_labels: SampleLabels, frameshift_variant: Variant, ) -> Patient: - return Patient( + return Patient.from_raw_parts( labels=sample_labels, + sex=Sex.UNKNOWN_SEX, phenotypes=(), diseases=(), variants=( diff --git a/tests/analysis/predicate/genotype/test_allele_counter.py b/tests/analysis/predicate/genotype/test_allele_counter.py index 0b435501..506ec302 100644 --- a/tests/analysis/predicate/genotype/test_allele_counter.py +++ b/tests/analysis/predicate/genotype/test_allele_counter.py @@ -175,8 +175,9 @@ def patient( hom_alt_lmna: Variant, hemi_dmd: Variant, ) -> Patient: - return Patient( + return Patient.from_raw_parts( labels=sample_labels, + sex=Sex.UNKNOWN_SEX, variants=( het_lmna, hom_alt_lmna, diff --git a/tests/analysis/test_pscore.py b/tests/analysis/test_pscore.py index 9b632902..a4c1c24b 100644 --- a/tests/analysis/test_pscore.py +++ b/tests/analysis/test_pscore.py @@ -3,7 +3,7 @@ import hpotk import pytest -from gpsea.model import Patient, Phenotype, SampleLabels +from gpsea.model import Patient, Phenotype, SampleLabels, Sex from gpsea.analysis.pscore import CountingPhenotypeScorer @@ -50,8 +50,9 @@ def test_a_patient( expected: int, counting_scorer: CountingPhenotypeScorer, ): - patient = Patient( + patient = Patient.from_raw_parts( labels=SampleLabels("test"), + sex=Sex.UNKNOWN_SEX, phenotypes=( Phenotype( hpotk.TermId.from_curie(curie), diff --git a/tests/conftest.py b/tests/conftest.py index 54f012ec..b66b55e7 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -268,42 +268,53 @@ def toy_cohort( genotypes=Genotypes.from_mapping({SampleLabels('LargeCNV'): Genotype.HETEROZYGOUS})) patients = ( - Patient(SampleLabels('HetSingleVar'), - phenotypes=( - test_phenotypes['arachnodactyly_T'], - test_phenotypes['spasticity_F'], - test_phenotypes['focal_clonic_seizure_T']), - variants=(dup,), - diseases=(test_diseases['KBG_T'],) - ), - Patient(SampleLabels('HetDoubleVar1'), - phenotypes=( - test_phenotypes['arachnodactyly_T'], test_phenotypes['seizure_T'], test_phenotypes['spasticity_T'], - ), - variants=(indel, snv_stop_gain), - diseases=(test_diseases['KBG_T'],) - ), - Patient(SampleLabels('HetDoubleVar2'), - phenotypes=( - test_phenotypes['arachnodactyly_F'], test_phenotypes['spasticity_T'], test_phenotypes['seizure_T'], - ), - variants=(snv_missense, del_frameshift), - diseases=(test_diseases['KBG_T'],) - ), - Patient(SampleLabels('HomoVar'), - phenotypes=( - test_phenotypes['arachnodactyly_T'], test_phenotypes['spasticity_T'], test_phenotypes['seizure_T'], - ), - variants=(del_small,), - diseases=() - ), - Patient(SampleLabels('LargeCNV'), - phenotypes=( - test_phenotypes['arachnodactyly_T'], test_phenotypes['spasticity_T'], test_phenotypes['seizure_F'], - ), - variants=(del_large,), - diseases=() - ), + Patient.from_raw_parts( + SampleLabels('HetSingleVar'), + sex=Sex.UNKNOWN_SEX, + phenotypes=( + test_phenotypes['arachnodactyly_T'], + test_phenotypes['spasticity_F'], + test_phenotypes['focal_clonic_seizure_T'] + ), + variants=(dup,), + diseases=(test_diseases['KBG_T'],) + ), + Patient.from_raw_parts( + SampleLabels('HetDoubleVar1'), + sex=Sex.UNKNOWN_SEX, + phenotypes=( + test_phenotypes['arachnodactyly_T'], test_phenotypes['seizure_T'], test_phenotypes['spasticity_T'], + ), + variants=(indel, snv_stop_gain), + diseases=(test_diseases['KBG_T'],) + ), + Patient.from_raw_parts( + SampleLabels('HetDoubleVar2'), + sex=Sex.UNKNOWN_SEX, + phenotypes=( + test_phenotypes['arachnodactyly_F'], test_phenotypes['spasticity_T'], test_phenotypes['seizure_T'], + ), + variants=(snv_missense, del_frameshift), + diseases=(test_diseases['KBG_T'],) + ), + Patient.from_raw_parts( + SampleLabels('HomoVar'), + sex=Sex.UNKNOWN_SEX, + phenotypes=( + test_phenotypes['arachnodactyly_T'], test_phenotypes['spasticity_T'], test_phenotypes['seizure_T'], + ), + variants=(del_small,), + diseases=() + ), + Patient.from_raw_parts( + SampleLabels('LargeCNV'), + sex=Sex.UNKNOWN_SEX, + phenotypes=( + test_phenotypes['arachnodactyly_T'], test_phenotypes['spasticity_T'], test_phenotypes['seizure_F'], + ), + variants=(del_large,), + diseases=() + ), ) return Cohort.from_patients(patients) diff --git a/tests/test_data/SUOX.json b/tests/test_data/SUOX.json index d85873ad..083f77dd 100644 --- a/tests/test_data/SUOX.json +++ b/tests/test_data/SUOX.json @@ -5,6 +5,7 @@ "label": "individual_10_PMID_12112661", "meta_label": "PMID_36303223_individual_10_PMID_12112661" }, + "sex": "UNKNOWN_SEX", "phenotypes": [ { "term_id": "HP:0001250", @@ -141,6 +142,7 @@ "label": "individual_11_PMID_12112661", "meta_label": "PMID_36303223_individual_11_PMID_12112661" }, + "sex": "UNKNOWN_SEX", "phenotypes": [ { "term_id": "HP:0001250", @@ -392,6 +394,7 @@ "label": "individual_12_PMID_12112661", "meta_label": "PMID_36303223_individual_12_PMID_12112661" }, + "sex": "UNKNOWN_SEX", "phenotypes": [ { "term_id": "HP:0001250", @@ -528,6 +531,7 @@ "label": "individual_13_PMID_12112661", "meta_label": "PMID_36303223_individual_13_PMID_12112661" }, + "sex": "UNKNOWN_SEX", "phenotypes": [ { "term_id": "HP:0001250", @@ -664,6 +668,7 @@ "label": "individual_14_PMID_11825068", "meta_label": "PMID_36303223_individual_14_PMID_11825068" }, + "sex": "FEMALE", "phenotypes": [ { "term_id": "HP:0034332", @@ -840,6 +845,7 @@ "label": "individual_15_PMID_12001203", "meta_label": "PMID_36303223_individual_15_PMID_12001203" }, + "sex": "FEMALE", "phenotypes": [ { "term_id": "HP:0001250", @@ -1000,6 +1006,7 @@ "label": "individual_16_PMID_12368985", "meta_label": "PMID_36303223_individual_16_PMID_12368985" }, + "sex": "MALE", "phenotypes": [ { "term_id": "HP:0001250", @@ -1283,6 +1290,7 @@ "label": "individual_17_PMID_15952210", "meta_label": "PMID_36303223_individual_17_PMID_15952210" }, + "sex": "MALE", "phenotypes": [ { "term_id": "HP:0012758", @@ -1459,6 +1467,7 @@ "label": "individual_18_PMID_23414711", "meta_label": "PMID_36303223_individual_18_PMID_23414711" }, + "sex": "FEMALE", "phenotypes": [ { "term_id": "HP:0012758", @@ -1639,6 +1648,7 @@ "label": "individual_19_PMID_23452914", "meta_label": "PMID_36303223_individual_19_PMID_23452914" }, + "sex": "FEMALE", "phenotypes": [ { "term_id": "HP:0002071", @@ -1827,6 +1837,7 @@ "label": "individual_1_PMID_9050047", "meta_label": "PMID_36303223_individual_1_PMID_9050047" }, + "sex": "MALE", "phenotypes": [ { "term_id": "HP:0012758", @@ -1999,6 +2010,7 @@ "label": "individual_20_PMID_24938149", "meta_label": "PMID_36303223_individual_20_PMID_24938149" }, + "sex": "FEMALE", "phenotypes": [ { "term_id": "HP:0001250", @@ -2270,6 +2282,7 @@ "label": "individual_21_PMID_27289259", "meta_label": "PMID_36303223_individual_21_PMID_27289259" }, + "sex": "MALE", "phenotypes": [ { "term_id": "HP:0012758", @@ -2442,6 +2455,7 @@ "label": "individual_22_PMID_27289259", "meta_label": "PMID_36303223_individual_22_PMID_27289259" }, + "sex": "FEMALE", "phenotypes": [ { "term_id": "HP:0012758", @@ -2614,6 +2628,7 @@ "label": "individual_23_PMID_28725568", "meta_label": "PMID_36303223_individual_23_PMID_28725568" }, + "sex": "MALE", "phenotypes": [ { "term_id": "HP:0001250", @@ -2897,6 +2912,7 @@ "label": "individual_24_PMID_28629418", "meta_label": "PMID_36303223_individual_24_PMID_28629418" }, + "sex": "FEMALE", "phenotypes": [ { "term_id": "HP:0001250", @@ -3065,6 +3081,7 @@ "label": "individual_25_PMID_31870341", "meta_label": "PMID_36303223_individual_25_PMID_31870341" }, + "sex": "MALE", "phenotypes": [ { "term_id": "HP:0034332", @@ -3348,6 +3365,7 @@ "label": "individual_26_PMID_31870341", "meta_label": "PMID_36303223_individual_26_PMID_31870341" }, + "sex": "MALE", "phenotypes": [ { "term_id": "HP:0034332", @@ -3631,6 +3649,7 @@ "label": "individual_27_PMID_31870341", "meta_label": "PMID_36303223_individual_27_PMID_31870341" }, + "sex": "FEMALE", "phenotypes": [ { "term_id": "HP:0034332", @@ -3914,6 +3933,7 @@ "label": "individual_28_PMID_33335014", "meta_label": "PMID_36303223_individual_28_PMID_33335014" }, + "sex": "FEMALE", "phenotypes": [ { "term_id": "HP:0012758", @@ -4086,6 +4106,7 @@ "label": "individual_29_PMID_33335014", "meta_label": "PMID_36303223_individual_29_PMID_33335014" }, + "sex": "MALE", "phenotypes": [ { "term_id": "HP:0012758", @@ -4262,6 +4283,7 @@ "label": "individual_2_PMID_9600976", "meta_label": "PMID_36303223_individual_2_PMID_9600976" }, + "sex": "FEMALE", "phenotypes": [ { "term_id": "HP:0012758", @@ -4434,6 +4456,7 @@ "label": "individual_30_PMID_31806255", "meta_label": "PMID_36303223_individual_30_PMID_31806255" }, + "sex": "MALE", "phenotypes": [ { "term_id": "HP:0034332", @@ -4622,6 +4645,7 @@ "label": "individual_31_PMID_31806255", "meta_label": "PMID_36303223_individual_31_PMID_31806255" }, + "sex": "MALE", "phenotypes": [ { "term_id": "HP:0034332", @@ -4810,6 +4834,7 @@ "label": "individual_32_PMID_34025712", "meta_label": "PMID_36303223_individual_32_PMID_34025712" }, + "sex": "MALE", "phenotypes": [ { "term_id": "HP:0001250", @@ -5097,6 +5122,7 @@ "label": "individual_33_PMID_33405344", "meta_label": "PMID_36303223_individual_33_PMID_33405344" }, + "sex": "FEMALE", "phenotypes": [ { "term_id": "HP:0001250", @@ -5368,6 +5394,7 @@ "label": "individual_34_PMID_33405344", "meta_label": "PMID_36303223_individual_34_PMID_33405344" }, + "sex": "MALE", "phenotypes": [ { "term_id": "HP:0001250", @@ -5639,6 +5666,7 @@ "label": "individual_35_PMID_36303223", "meta_label": "PMID_36303223_individual_35_PMID_36303223" }, + "sex": "MALE", "phenotypes": [ { "term_id": "HP:0001083", @@ -5938,6 +5966,7 @@ "label": "individual_3_PMID_10519592", "meta_label": "PMID_36303223_individual_3_PMID_10519592" }, + "sex": "MALE", "phenotypes": [ { "term_id": "HP:0001250", @@ -6229,6 +6258,7 @@ "label": "individual_4_PMID_12112661", "meta_label": "PMID_36303223_individual_4_PMID_12112661" }, + "sex": "UNKNOWN_SEX", "phenotypes": [ { "term_id": "HP:0001250", @@ -6365,6 +6395,7 @@ "label": "individual_5_PMID_12112661", "meta_label": "PMID_36303223_individual_5_PMID_12112661" }, + "sex": "UNKNOWN_SEX", "phenotypes": [ { "term_id": "HP:0001250", @@ -6616,6 +6647,7 @@ "label": "individual_6_PMID_12112661", "meta_label": "PMID_36303223_individual_6_PMID_12112661" }, + "sex": "UNKNOWN_SEX", "phenotypes": [ { "term_id": "HP:0001250", @@ -6752,6 +6784,7 @@ "label": "individual_7_PMID_12112661", "meta_label": "PMID_36303223_individual_7_PMID_12112661" }, + "sex": "UNKNOWN_SEX", "phenotypes": [ { "term_id": "HP:0001250", @@ -6888,6 +6921,7 @@ "label": "individual_8_PMID_12112661", "meta_label": "PMID_36303223_individual_8_PMID_12112661" }, + "sex": "UNKNOWN_SEX", "phenotypes": [ { "term_id": "HP:0001250", @@ -7024,6 +7058,7 @@ "label": "individual_9_PMID_12112661", "meta_label": "PMID_36303223_individual_9_PMID_12112661" }, + "sex": "UNKNOWN_SEX", "phenotypes": [ { "term_id": "HP:0001250", diff --git a/tests/test_io.py b/tests/test_io.py index a479dcee..7d45cabd 100644 --- a/tests/test_io.py +++ b/tests/test_io.py @@ -24,7 +24,6 @@ def test_regenerate_cohort( ): """ The test for regenerating the `SUOX.json` file based on a cohort of phenopackets. - The test needs path to a folder with phenopacket JSON files (empty `str` below). Note, the test may need to be run multiple times if the ENSEMBL API times out. """ From 1e3c4635ca9538fc44f7ead67995baed2de1dfca Mon Sep 17 00:00:00 2001 From: Peter Robinson Date: Thu, 29 Aug 2024 08:26:56 +0200 Subject: [PATCH 14/47] WIP, de Vries score and documentation --- docs/user-guide/devries.rst | 139 +++++++++++++++++++++++ docs/user-guide/index.rst | 1 + docs/user-guide/phenotype_predicates.rst | 18 +++ src/gpsea/analysis/pscore/_impl.py | 75 ++++++++++-- 4 files changed, 225 insertions(+), 8 deletions(-) create mode 100644 docs/user-guide/devries.rst create mode 100644 docs/user-guide/phenotype_predicates.rst diff --git a/docs/user-guide/devries.rst b/docs/user-guide/devries.rst new file mode 100644 index 00000000..dd62d13d --- /dev/null +++ b/docs/user-guide/devries.rst @@ -0,0 +1,139 @@ +.. _devries: + +============== +De Vries Score +============== + + +The De Vries score was developed as a relatively simple phenotypic severity +score for individuals with intellectual disability in which points are given +for (severity of) intellectual disability, +growth abnormalities (prenatal and postnatal), facial dysmorphisms, +nonfacial dysmorphisms, and other congenital anomalies +(`Dingemans et al. (2022) `_). +Statistical significance of a difference in the De Vries score between groups can be +determined using the Mann–Whitney-U test. + +We refer to `Feenstra et al. (2011) `_ for +te original description of the adjusted De Vries score. Here we offer a version of the +score that leverages the structure of the Human Phenotype Ontology to assess the phenotype. + +The De Vries score has several sections, each of which is scored on a point system. The +final score is obtained as the sum of the points of each of the sections. + +Developmental delay +~~~~~~~~~~~~~~~~~~~ + +The original score assigns one point for mild or moderate developmental delay +and two points for severe developmental delay. + ++----------------------------------------------------------------------------------------------------------+-----------+ +| HPO term | Score | ++==========================================================================================================+===========+ +| `Mild global developmental delay (HP:0011342) `_ | 1 | ++----------------------------------------------------------------------------------------------------------+-----------+ +| `Moderate global developmental delay (HP:0011343) `_ | 1 | ++----------------------------------------------------------------------------------------------------------+-----------+ +| `Severe global developmental delay (HP:0011344) `_ | 2 | ++----------------------------------------------------------------------------------------------------------+-----------+ +| `Profound global developmental delay (HP:0011344) `_ | 2 | ++----------------------------------------------------------------------------------------------------------+-----------+ +| `Global developmental delay (HP:0001263) `_ | 1 | ++----------------------------------------------------------------------------------------------------------+-----------+ + +Note that one point is assigned for the term Global developmental delay (HP:0001263), which is the +parent of the other terms, because no information was provided about the degree of delay. + +If none of the above terms is found, then the scorer assigns terms based on the Intellectual Disability terms. + ++----------------------------------------------------------------------------------------------------------+-----------+ +| HPO term | Score | ++==========================================================================================================+===========+ +| `Intellectual disability, borderline (HP:0006889) `_ | 0.5 | ++----------------------------------------------------------------------------------------------------------+-----------+ +| `Intellectual disability, mild (HP:0001256) `_ | 1 | ++----------------------------------------------------------------------------------------------------------+-----------+ +| `Intellectual disability, moderate (HP:0002342) `_ | 1 | ++----------------------------------------------------------------------------------------------------------+-----------+ +| `Intellectual disability, severe (HP:0010864) `_ | 2 | ++----------------------------------------------------------------------------------------------------------+-----------+ +| `Intellectual disability, profound (HP:0002187) `_ | 2 | ++----------------------------------------------------------------------------------------------------------+-----------+ +| `Intellectual disability (HP:0001249) `_ | 1 | ++----------------------------------------------------------------------------------------------------------+-----------+ + +If none of these terms is found, a score of zero is assigned for this section. + + +Postnatal growth abnormalities +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +In the original score, one point is assigned for each of Microcephaly, Short stature, Macrocephaly, and Tall stature, +with the maximum score for the section being limited to 2 points. We implement this as adding one point for either the +corresponding HPO terms or any of their descendents (for instance, `Disproportionate short stature (HP:0003498) `_ would +be counted for `Short stature (HP:0004322) `_). + ++----------------------------------------------------------------------------------------------------------+-----------+ +| HPO term | Score | ++==========================================================================================================+===========+ +| `Microcephaly (HP:0000252) `_ | 1 | ++----------------------------------------------------------------------------------------------------------+-----------+ +| `Short stature (HP:0004322) `_ | 1 | ++----------------------------------------------------------------------------------------------------------+-----------+ +| `Macrocephaly (HP:0000256) `_ | 1 | ++----------------------------------------------------------------------------------------------------------+-----------+ +| `Tall stature (HP:0000098) `_ | 1 | ++----------------------------------------------------------------------------------------------------------+-----------+ + +If none of these terms is found, a score of zero is assigned for this section. Logically, the maximum score obtainable +is 2 because the same individual cannot have both tall and short stature or both micro- and macrocephaly. + + +Facial dysmorphic features +~~~~~~~~~~~~~~~~~~~~~~~~~~ + +This section assigns two points if two or more anomalies are identified in the following +categories: hypertelorism, nasal anomalies and ear anomalies. Our implementation of this feature counts the total +number of terms or descendents of the following HPO terms. + ++----------------------------------------------------------------------------------------------------------+-----------+ +| HPO term | Score | ++==========================================================================================================+===========+ +| `Hypertelorism (HP:0000316) `_ | 1 | ++----------------------------------------------------------------------------------------------------------+-----------+ +| `Abnormal external nose morphology (HP:0010938) `_ | 1 each | ++----------------------------------------------------------------------------------------------------------+-----------+ +| `Abnormal pinna morphology (HP:0000377) `_ | 1 each | ++----------------------------------------------------------------------------------------------------------+-----------+ + +If two or more terms are found, the score is 2, otherwise a score of zero is assigned. + + +Non-facial dysmorphism and congenital abnormalities +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +One point is assigned for for either the +corresponding HPO terms or any of their descendents up to a maximum of two points. + ++----------------------------------------------------------------------------------------------------------+-----------+ +| HPO term | Score | ++==========================================================================================================+===========+ +| `Abnormal hand morphology (HP:0005922) `_ | 1 each | ++----------------------------------------------------------------------------------------------------------+-----------+ +| `Abnormal heart morphology (HP:0001627) `_ | 1 each | ++----------------------------------------------------------------------------------------------------------+-----------+ +| `Hypospadias (HP:0000047) `_ | 1 | ++----------------------------------------------------------------------------------------------------------+-----------+ + +Final score +~~~~~~~~~~~ + +The final score is obtained by summing the scores from each of the sections. The final score ranges from 0 to 10, with +higher scores being considered a proxy for higher clinical severity. + + +Using the De Vries Scorer in code +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +TODO + + diff --git a/docs/user-guide/index.rst b/docs/user-guide/index.rst index 9f7933ee..f3e0569a 100644 --- a/docs/user-guide/index.rst +++ b/docs/user-guide/index.rst @@ -13,6 +13,7 @@ TODO - write high level overview and bridge to individual sections. input-data exploratory predicates + phenotype_predicates stats mtc glossary diff --git a/docs/user-guide/phenotype_predicates.rst b/docs/user-guide/phenotype_predicates.rst new file mode 100644 index 00000000..86572657 --- /dev/null +++ b/docs/user-guide/phenotype_predicates.rst @@ -0,0 +1,18 @@ +.. _phenotype_predicates: + +==================== +Phenotype Predicates +==================== + +GPSEA offers several phenotype predicates. + +TODO -- separate explanations for HPO (Fisher), scores (Mann Whitney) and survival (log rank test). + + + + +.. toctree:: + :maxdepth: 1 + :caption: Contents: + + devries diff --git a/src/gpsea/analysis/pscore/_impl.py b/src/gpsea/analysis/pscore/_impl.py index b6bd6d5b..8a2807f7 100644 --- a/src/gpsea/analysis/pscore/_impl.py +++ b/src/gpsea/analysis/pscore/_impl.py @@ -20,8 +20,8 @@ class CountingPhenotypeScorer(PhenotypeScorer): @staticmethod def from_query_curies( - hpo: hpotk.MinimalOntology, - query: typing.Iterable[typing.Union[str, hpotk.TermId]], + hpo: hpotk.MinimalOntology, + query: typing.Iterable[typing.Union[str, hpotk.TermId]], ): """ Create a scorer to test for the number of phenotype terms that fall into the phenotype groups. @@ -65,9 +65,9 @@ def from_query_curies( ) def __init__( - self, - hpo: hpotk.MinimalOntology, - query: typing.Iterable[hpotk.TermId], + self, + hpo: hpotk.MinimalOntology, + query: typing.Iterable[hpotk.TermId], ): self._hpo = hpo self._query = set(query) @@ -76,8 +76,8 @@ def get_question(self) -> str: return "How many of the query HPO terms (or their descendants) does the individual display" def score( - self, - patient: Patient, + self, + patient: Patient, ) -> float: """ Get the count (number) of terms in the query set @@ -118,8 +118,67 @@ class DeVriesPhenotypeScorer(PhenotypeScorer): as described in `Feenstra et al `_. """ + def __init__( + self, + hpo: hpotk.MinimalOntology, + ): + self._hpo = hpo + + def _developmental_delay_score(self, observed_term_ids: typing.List[str]) -> float: + """ + calculate the dev delay component of the score + Args: + observed_term_ids: terms observed in patient + + Returns: a score between 0 and 2 + """ + gdd_tids = {'HP:0011344': 2, 'HP:0011344': 2, + 'HP:0011342': 1, 'HP:0011343': 1, 'HP:0001263': 1} # severe and profound GDD + idd_tids = {'HP:0010864': 2, 'HP:0002187': 2,'HP:0001256': 1, 'HP:0002342': 1, 'HP:0001249': 1, + 'HP:0006889': 0.5} # mild, moderate, and unspecified GDD (borderline has 0.5) + # check GDD terms with higher priority than ID terms + for t in observed_term_ids: + if t in gdd_tids: + return gdd_tids.get(t) + for t in observed_term_ids: + if t in idd_tids: + return idd_tids.get(t) + return 0 + + + def _postnatal_growth_score(self, observed_term_ids: typing.List[str]) -> float: + """ + calculate the postnatal growth component of the score + Args: + observed_term_ids: + + Returns: + + """ + microcephaly = 'HP:0000252' + short_stature = 'HP:0004322' + macrocephaly = 'HP: 0000256' + tall_stature = 'HP:0000098' + # to do -- implement me + + def _calculate_score(self, observed_term_ids: typing.List[str]) -> float: + """ + calculate score based on list of strings with term identifiers or observed HPO terms. + Args: + observed_term_ids: list of strings with term identifiers or observed HPO terms + + Returns: de Vries score between 0 and 10 + + """ + delay_score = self._developmental_delay_score(observed_term_ids) + growth_score = self._postnatal_growth_score(observed_term_ids) + ## todo -- complete + return delay_score + growth_score + def score(self, patient: Patient) -> float: """ Compute the score for the `patient`. """ - raise NotImplementedError() + # collect term identifiers as strings for all observed phenotypes + observed_term_ids = [tid.identifier.value for tid in patient.present_phenotypes()] + return self._calculate_score(observed_term_ids) From 57a2a7d422eb5fa59d9246c176c125395342aa3f Mon Sep 17 00:00:00 2001 From: Peter Robinson Date: Thu, 29 Aug 2024 14:13:21 +0200 Subject: [PATCH 15/47] de Vries score and documentation and unit tests --- docs/user-guide/devries.rst | 16 ++++ src/gpsea/analysis/pscore/_impl.py | 125 ++++++++++++++++++++++--- tests/analysis/test_de_vries_scorer.py | 85 +++++++++++++++++ 3 files changed, 215 insertions(+), 11 deletions(-) create mode 100644 tests/analysis/test_de_vries_scorer.py diff --git a/docs/user-guide/devries.rst b/docs/user-guide/devries.rst index dd62d13d..60585b5c 100644 --- a/docs/user-guide/devries.rst +++ b/docs/user-guide/devries.rst @@ -65,6 +65,22 @@ If none of the above terms is found, then the scorer assigns terms based on the If none of these terms is found, a score of zero is assigned for this section. +Prenatal-onset growth retardation +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +In the original score, two points are assigned if Prenatal-onset growth retardation is present. In our implementation, +we assign two points if either of the following terms is present (the score is thus either zero or two). + ++----------------------------------------------------------------------------------------------------------+-----------+ +| HPO term | Score | ++==========================================================================================================+===========+ +| `Small for gestational age (HP:0001518) `_ | 2 | ++----------------------------------------------------------------------------------------------------------+-----------+ +| `Intrauterine growth retardation (HP:0001511) `_ | 2 | ++----------------------------------------------------------------------------------------------------------+-----------+ + + + + Postnatal growth abnormalities ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ diff --git a/src/gpsea/analysis/pscore/_impl.py b/src/gpsea/analysis/pscore/_impl.py index 8a2807f7..6c7248b9 100644 --- a/src/gpsea/analysis/pscore/_impl.py +++ b/src/gpsea/analysis/pscore/_impl.py @@ -12,7 +12,7 @@ class CountingPhenotypeScorer(PhenotypeScorer): that is equivalent to the count of present phenotypes that are either an exact match to the `query` terms or their descendants. - For instance, we may want to count whether an individual has brain, liver, kidney, and skin abormalities. + For instance, we may want to count whether an individual has brain, liver, kidney, and skin abnormalities. In the case, the query would include the corresponding terms (e.g., Abnormal brain morphology HP:0012443). An individual can then have between 0 and 4 phenotype group abnormalities. This predicate is intended to be used with the Mann Whitney U test. @@ -42,7 +42,7 @@ def from_query_curies( f"query argument must be iterable of hpotk TermId's or strings but we found {type(q)}" ) - # Now chack that the term IDs are HPO term IDs. + # Now check that the term IDs are HPO term IDs. if q not in hpo: raise ValueError(f"The query {q} was not found in the HPO") query_term_ids.add(q) @@ -115,7 +115,7 @@ def score( class DeVriesPhenotypeScorer(PhenotypeScorer): """ `DeVriesPhenotypeScorer` computes "adapted De Vries Score" - as described in `Feenstra et al `_. + as described in `Feenstra et al. `_. """ def __init__( @@ -132,10 +132,10 @@ def _developmental_delay_score(self, observed_term_ids: typing.List[str]) -> flo Returns: a score between 0 and 2 """ - gdd_tids = {'HP:0011344': 2, 'HP:0011344': 2, + gdd_tids = {'HP:0011344': 2, 'HP:0012736': 2, 'HP:0011342': 1, 'HP:0011343': 1, 'HP:0001263': 1} # severe and profound GDD - idd_tids = {'HP:0010864': 2, 'HP:0002187': 2,'HP:0001256': 1, 'HP:0002342': 1, 'HP:0001249': 1, - 'HP:0006889': 0.5} # mild, moderate, and unspecified GDD (borderline has 0.5) + idd_tids = {'HP:0010864': 2, 'HP:0002187': 2, 'HP:0001256': 1, 'HP:0002342': 1, 'HP:0001249': 1, + 'HP:0006889': 0.5} # mild, moderate, and unspecified GDD (borderline has 0.5) # check GDD terms with higher priority than ID terms for t in observed_term_ids: if t in gdd_tids: @@ -145,21 +145,122 @@ def _developmental_delay_score(self, observed_term_ids: typing.List[str]) -> flo return idd_tids.get(t) return 0 + def _term_or_descendant(self, + target_tid: str, + observed_term_ids: typing.List[str]): + """ + Args: + target_tid: term of interest + observed_term_ids: all terms observed in patient + + Returns: + 1 if the term or any descendant is present in the patient, otherwise 0 + """ + for term_id in observed_term_ids: + for desc_tid in self._hpo.graph.get_ancestors(term_id, include_source=True): + if desc_tid.value == target_tid: + return 1 + return 0 + + def _term_or_descendant_count(self, + target_tid: str, + observed_term_ids: typing.List[str]): + """ + Args: + target_tid: term of interest + observed_term_ids: all terms observed in patient + + Returns: + 1 if the term or any descendant is present in the patient, otherwise 0 + """ + total_count = 0 + for term_id in observed_term_ids: + for desc_tid in self._hpo.graph.get_ancestors(term_id, include_source=True): + if desc_tid.value == target_tid: + total_count += 1 + return total_count def _postnatal_growth_score(self, observed_term_ids: typing.List[str]) -> float: """ calculate the postnatal growth component of the score Args: - observed_term_ids: + observed_term_ids: terms observed in patient Returns: """ microcephaly = 'HP:0000252' short_stature = 'HP:0004322' - macrocephaly = 'HP: 0000256' + macrocephaly = 'HP:0000256' tall_stature = 'HP:0000098' - # to do -- implement me + total_count = 0 + for tid in [microcephaly, short_stature, macrocephaly, tall_stature]: + total_count += self._term_or_descendant(tid, observed_term_ids) + if total_count > 2: + raise ValueError(f"Inconsistent annotations for postnatal growth score {total_count}: {observed_term_ids}") + return total_count + + def _facial_dysmorphism_score(self, observed_term_ids: typing.List[str]) -> float: + """ + This section assigns two points if two or more anomalies are identified in the following + categories: hypertelorism, nasal anomalies and ear anomalies. Our implementation counts the total + number of terms or descendents of the hypertelorism, Abnormal external nose morphology, and + Abnormal pinna morphology. + + Args: + observed_term_ids: terms observed in patient + + Returns: facial dysmorphism score (between 0 and 2) + + """ + hypertelorism = 'HP:0000316' + external_nose = 'HP:0010938' + pinna_morphology = 'HP:0000377' + total_count = len([t for t in observed_term_ids if t == hypertelorism]) + total_count += self._term_or_descendant_count(target_tid=external_nose, observed_term_ids=observed_term_ids) + total_count += self._term_or_descendant_count(target_tid=pinna_morphology, observed_term_ids=observed_term_ids) + if total_count > 1: + return 2 + else: + return 0 + + def _congenital_score(self, observed_term_ids: typing.List[str]) -> float: + """ + Non-facial dysmorphism and congenital abnormalities component + One point is assigned for either the corresponding HPO terms or any of their descendents up to a maximum of 2. + Args: + observed_term_ids: terms observed in patient + + Returns: Non-facial dysmorphism and congenital abnormalities score (between 0 and 2) + + """ + hypospadias = 'HP:0000047' + abnormal_hand_morphology = 'HP:0005922' + abnormal_heart_morphology = 'HP:0001627' + total_count = len([t for t in observed_term_ids if t == hypospadias]) + total_count += self._term_or_descendant_count(target_tid=abnormal_hand_morphology, + observed_term_ids=observed_term_ids) + total_count += self._term_or_descendant_count(target_tid=abnormal_heart_morphology, + observed_term_ids=observed_term_ids) + return min(2, total_count) + + def _prenatal_growth_score(self, observed_term_ids: typing.List[str]) -> float: + """ + two points are assigned if Prenatal-onset growth retardation is present + + Args: + observed_term_ids: list of strings with term identifiers or observed HPO terms + + Returns: score between 0 and 2 + + """ + small_for_gestational_age = 'HP:0001518' + intrauterine_growth_retardation = 'HP:0001511' + targets = {small_for_gestational_age, intrauterine_growth_retardation} + for tid in observed_term_ids: + if tid in targets: + return 2 + return 0 def _calculate_score(self, observed_term_ids: typing.List[str]) -> float: """ @@ -172,8 +273,10 @@ def _calculate_score(self, observed_term_ids: typing.List[str]) -> float: """ delay_score = self._developmental_delay_score(observed_term_ids) growth_score = self._postnatal_growth_score(observed_term_ids) - ## todo -- complete - return delay_score + growth_score + facial_score = self._facial_dysmorphism_score(observed_term_ids) + congen_score = self._congenital_score(observed_term_ids) + prenatal_score = self._prenatal_growth_score(observed_term_ids) + return delay_score + growth_score + facial_score + congen_score + prenatal_score def score(self, patient: Patient) -> float: """ diff --git a/tests/analysis/test_de_vries_scorer.py b/tests/analysis/test_de_vries_scorer.py new file mode 100644 index 00000000..6057c247 --- /dev/null +++ b/tests/analysis/test_de_vries_scorer.py @@ -0,0 +1,85 @@ +import typing + +import hpotk +import pytest + +from gpsea.analysis.pscore import DeVriesPhenotypeScorer +from gpsea.model import Patient, SampleLabels, Phenotype + +intrauterine_growth_retardation = 'HP:0001511' +small_for_gestational_age = 'HP:0001518' +arachnodactyly = "HP:0001166" +seizure = "HP:0001250" +sensorineural_hearing_impairment = 'HP:0000407' +intellectual_disability_mild = 'HP:0001256' +intellectual_disability_profound = 'HP:0002187' +microcephaly = 'HP:0000252' +short_stature = 'HP:0004322' +hypertelorism = 'HP:0000316' +posteriorly_rotated_ears = 'HP:0000358' +underdeveloped_crus_of_the_helix = 'HP:0009898' # external ear morphology +ventricular_septal_defect = 'HP:0001629' +metacarpal_synostosis = 'HP:0009701' # hand morphology +hypospadias = 'HP:0000047' + + +class TestDeVriesScorer: + + @pytest.fixture + def devries_scorer( + self, + hpo: hpotk.MinimalOntology, + ) -> DeVriesPhenotypeScorer: + return DeVriesPhenotypeScorer(hpo=hpo) + + @pytest.mark.parametrize( + "term_set, expected", + [ + ([intrauterine_growth_retardation], 2,), + ([intrauterine_growth_retardation, small_for_gestational_age], 2,), # superfluous, still should be 2 + ([sensorineural_hearing_impairment, ], 0,), # Unrelated + ([seizure, arachnodactyly], 0,), # Unrelated + ([intrauterine_growth_retardation, intellectual_disability_mild], 3,), + ([intrauterine_growth_retardation, intellectual_disability_profound], 4,), + ([intrauterine_growth_retardation, intellectual_disability_profound, microcephaly], 5,), + ([intrauterine_growth_retardation, intellectual_disability_profound, microcephaly, short_stature], 6,), + ([intrauterine_growth_retardation, intellectual_disability_profound, microcephaly, short_stature, + hypertelorism], 6,), + ([intrauterine_growth_retardation, intellectual_disability_profound, microcephaly, short_stature, + hypertelorism, posteriorly_rotated_ears], 8,), + ([intrauterine_growth_retardation, intellectual_disability_profound, microcephaly, short_stature, + hypertelorism, posteriorly_rotated_ears, underdeveloped_crus_of_the_helix], 8,), + ([intrauterine_growth_retardation, intellectual_disability_profound, microcephaly, short_stature, + hypertelorism, posteriorly_rotated_ears, underdeveloped_crus_of_the_helix, + ventricular_septal_defect], 9,), + ([intrauterine_growth_retardation, intellectual_disability_profound, microcephaly, short_stature, + hypertelorism, posteriorly_rotated_ears, underdeveloped_crus_of_the_helix, + ventricular_septal_defect, hypospadias], 10,), + ([intrauterine_growth_retardation, intellectual_disability_profound, microcephaly, short_stature, + hypertelorism, posteriorly_rotated_ears, underdeveloped_crus_of_the_helix, + ventricular_septal_defect, hypospadias, metacarpal_synostosis], 10,), + + ], + ) + def test_a_patient( + self, + term_set: typing.Sequence[str], + expected: int, + devries_scorer: DeVriesPhenotypeScorer, + ): + patient = Patient( + labels=SampleLabels("test"), + phenotypes=( + Phenotype( + hpotk.TermId.from_curie(curie), + is_observed=True, + ) + for curie in term_set + ), + diseases=(), + variants=(), + ) + + actual = devries_scorer.score(patient) + + assert actual == expected From 202be91a8acf4141f79f5e413b32054a125dfd20 Mon Sep 17 00:00:00 2001 From: Daniel Danis Date: Thu, 29 Aug 2024 15:10:08 +0200 Subject: [PATCH 16/47] Implement `ModeOfInheritanceParedicate`. --- src/gpsea/analysis/predicate/_api.py | 31 +- .../analysis/predicate/genotype/__init__.py | 2 + .../predicate/genotype/_gt_predicates.py | 424 ++++++++++++++++-- src/gpsea/model/_base.py | 24 + tests/analysis/predicate/genotype/conftest.py | 400 ++++++++++++++++- .../predicate/genotype/test_gt_predicates.py | 104 ++++- 6 files changed, 941 insertions(+), 44 deletions(-) diff --git a/src/gpsea/analysis/predicate/_api.py b/src/gpsea/analysis/predicate/_api.py index 8c62e191..da446a54 100644 --- a/src/gpsea/analysis/predicate/_api.py +++ b/src/gpsea/analysis/predicate/_api.py @@ -1,6 +1,8 @@ import abc import typing +from collections import Counter + import hpotk.util from gpsea.model import Patient @@ -136,9 +138,7 @@ class PolyPredicate(typing.Generic[C], metaclass=abc.ABCMeta): and *exhaustive* - the groups must cover all possible scenarios. However, if the patient cannot be assigned into any meaningful category, `None` can be returned. - - Note, that `PolyPredicate` must be used on a happy path - testing a patient must inherently make sense. - Predicate will *not* check if, for instance, the patient variants are compatible with a certain mode of inheritance. + As a rule of thumb, returning `None` will exclude the patient from the analysis. """ @abc.abstractmethod @@ -210,3 +210,28 @@ def _check_patient(patient: Patient): """ if not isinstance(patient, Patient): raise ValueError(f"patient must be type Patient but was type {type(patient)}") + + @staticmethod + def _check_categorizations( + categorizations: typing.Sequence[Categorization], + ) -> typing.Sequence[str]: + """ + Check that the categorizations meet the requirements. + + The requirements include: + + * the `cat_id` must be unique within the predicate + """ + issues = [] + # There must be at least one category + + # `cat_id` must be unique for a predicate! + cat_id_counts = Counter() + for c in categorizations: + cat_id_counts[c.category.cat_id] += 1 + + for cat_id, count in cat_id_counts.items(): + if count > 1: + issues.append(f'`cat_id` {cat_id} is present {count}>1 times') + + return issues diff --git a/src/gpsea/analysis/predicate/genotype/__init__.py b/src/gpsea/analysis/predicate/genotype/__init__.py index a8a2ba91..ff8d16e1 100644 --- a/src/gpsea/analysis/predicate/genotype/__init__.py +++ b/src/gpsea/analysis/predicate/genotype/__init__.py @@ -2,11 +2,13 @@ from ._api import VariantPredicate from ._counter import AlleleCounter from ._gt_predicates import boolean_predicate, groups_predicate, recessive_predicate +from ._gt_predicates import ModeOfInheritancePredicate from ._variant import VariantPredicates, ProteinPredicates __all__ = [ 'GenotypePolyPredicate', 'boolean_predicate', 'groups_predicate', 'recessive_predicate', + 'ModeOfInheritancePredicate', 'AlleleCounter', 'VariantPredicate', 'VariantPredicates', 'ProteinPredicates', ] diff --git a/src/gpsea/analysis/predicate/genotype/_gt_predicates.py b/src/gpsea/analysis/predicate/genotype/_gt_predicates.py index 3f54a7e0..601aa70b 100644 --- a/src/gpsea/analysis/predicate/genotype/_gt_predicates.py +++ b/src/gpsea/analysis/predicate/genotype/_gt_predicates.py @@ -1,8 +1,12 @@ +import dataclasses +import enum import typing -from gpsea.model import Patient +from collections import defaultdict -from .._api import Categorization, PatientCategories +from gpsea.model import Patient, Sex + +from .._api import Categorization, PatientCategory, PatientCategories from ._api import GenotypePolyPredicate, RecessiveGroupingPredicate from ._api import VariantPredicate from ._counter import AlleleCounter @@ -30,7 +34,10 @@ def get_categorizations(self) -> typing.Sequence[Categorization]: :attr:`AlleleCountingGenotypeBooleanPredicate.NO` or :class:`AlleleCountingGenotypeBooleanPredicate.YES` category. """ - return AlleleCountingGenotypeBooleanPredicate.YES, AlleleCountingGenotypeBooleanPredicate.NO + return ( + AlleleCountingGenotypeBooleanPredicate.YES, + AlleleCountingGenotypeBooleanPredicate.NO, + ) def get_question(self) -> str: return self._allele_counter.get_question() @@ -49,15 +56,17 @@ def test(self, patient: Patient) -> typing.Optional[Categorization]: ) def __eq__(self, value: object) -> bool: - return isinstance(value, AlleleCountingGenotypeBooleanPredicate) \ + return ( + isinstance(value, AlleleCountingGenotypeBooleanPredicate) and self._allele_counter == value._allele_counter - + ) + def __hash__(self) -> int: return hash((self._allele_counter,)) - + def __str__(self) -> str: - return f'AlleleCountingGenotypeBooleanPredicate(allele_counter={self._allele_counter})' - + return f"AlleleCountingGenotypeBooleanPredicate(allele_counter={self._allele_counter})" + def __repr__(self) -> str: return str(self) @@ -83,8 +92,8 @@ def __init__( ): self._counters = tuple(counters) self._categorizations = tuple(categorizations) - group_names = ', '.join(c.category.name for c in self._categorizations) - self._question = f'Genotype group: {group_names}' + group_names = ", ".join(c.category.name for c in self._categorizations) + self._question = f"Genotype group: {group_names}" def get_categorizations(self) -> typing.Sequence[Categorization]: return self._categorizations @@ -108,20 +117,29 @@ def test(self, patient: Patient) -> typing.Optional[Categorization]: return None def __eq__(self, value: object) -> bool: - return isinstance(value, AlleleCountingGroupsPredicate) \ - and self._counters == value._counters \ + return ( + isinstance(value, AlleleCountingGroupsPredicate) + and self._counters == value._counters and self._categorizations == value._categorizations + ) def __hash__(self) -> int: - return hash((self._counters, self._categorizations,)) + return hash( + ( + self._counters, + self._categorizations, + ) + ) def __str__(self) -> str: return self.get_question() def __repr__(self) -> str: - return 'AlleleCountingGroupsPredicate(' \ - + 'counters={self._counters}, ' \ - + 'categorizations={self._categorizations})' + return ( + "AlleleCountingGroupsPredicate(" + + "counters={self._counters}, " + + "categorizations={self._categorizations})" + ) def groups_predicate( @@ -141,9 +159,10 @@ def groups_predicate( predicates = tuple(predicates) group_names = tuple(group_names) - assert len(predicates) >= 2, f'We need at least 2 predicates: {len(predicates)}' - assert len(predicates) == len(group_names), \ - f'The number of group names must match the number of predicates: {len(group_names)}!={len(predicates)}' + assert len(predicates) >= 2, f"We need at least 2 predicates: {len(predicates)}" + assert len(predicates) == len( + group_names + ), f"The number of group names must match the number of predicates: {len(group_names)}!={len(predicates)}" # Then, prepare the counters and categorizations. counters = [AlleleCounter(predicate=predicate) for predicate in predicates] @@ -170,7 +189,7 @@ class AlleleCountingRecessivePredicate(RecessiveGroupingPredicate): # Therefore, I do not write any tests at this point. def __init__( - self, + self, allele_counter: AlleleCounter, ): self._allele_counter = allele_counter @@ -190,16 +209,21 @@ def test(self, patient: Patient) -> typing.Optional[Categorization]: return RecessiveGroupingPredicate.BOTH else: return None - + def __eq__(self, value: object) -> bool: - return isinstance(value, AlleleCountingRecessivePredicate) and self._allele_counter == value._allele_counter - + return ( + isinstance(value, AlleleCountingRecessivePredicate) + and self._allele_counter == value._allele_counter + ) + def __hash__(self) -> int: return hash((self._allele_counter,)) - + def __str__(self) -> str: - return f'AlleleCountingRecessivePredicate(allele_counter={self._allele_counter})' - + return ( + f"AlleleCountingRecessivePredicate(allele_counter={self._allele_counter})" + ) + def __repr__(self) -> str: return str(self) @@ -209,8 +233,8 @@ def recessive_predicate( ) -> GenotypePolyPredicate: """ Create a recessive grouping predicate from given `variant_predicate` - to bin the patient into :class:`RecessiveGroupingPredicate.NEITHER`, - :class:`RecessiveGroupingPredicate.ONE`, or :class:`RecessiveGroupingPredicate.BOTH`, + to bin the patient into :class:`RecessiveGroupingPredicate.NEITHER`, + :class:`RecessiveGroupingPredicate.ONE`, or :class:`RecessiveGroupingPredicate.BOTH`, depending on the number of variant alleles matching the variant predicate. The patient is assigned into a group in the following manner: @@ -221,3 +245,347 @@ def recessive_predicate( """ allele_counter = AlleleCounter(predicate=variant_predicate) return AlleleCountingRecessivePredicate(allele_counter=allele_counter) + + +@dataclasses.dataclass(eq=True, frozen=True) +class GenotypeGroup: + allele_count: int + sex: typing.Optional[Sex] + categorization: Categorization + + +class MendelianInheritanceAspect(enum.Enum): + AUTOSOMAL = 0 + """ + Related to chromosomes that do *not* determine the sex of an individual. + """ + + GONOSOMAL = 1 + """ + Related to chromosomes that determine the sex of an individual. + """ + + MITOCHONDRIAL = 2 + """ + Related to mitochondrial DNA. + """ + + +class ModeOfInheritanceInfo: + + @staticmethod + def autosomal_dominant() -> "ModeOfInheritanceInfo": + groups = ( + GenotypeGroup( + allele_count=0, + sex=None, + categorization=Categorization( + PatientCategory( + cat_id=0, name="0/0", description="Homozygous reference" + ), + ), + ), + GenotypeGroup( + allele_count=1, + sex=None, + categorization=Categorization( + PatientCategory(cat_id=1, name="0/1", description="Heterozygous"), + ), + ), + ) + return ModeOfInheritanceInfo( + mendelian_inheritance_aspect=MendelianInheritanceAspect.AUTOSOMAL, + groups=groups, + ) + + @staticmethod + def autosomal_recessive() -> "ModeOfInheritanceInfo": + groups = ( + GenotypeGroup( + allele_count=0, + sex=None, + categorization=Categorization( + PatientCategory( + cat_id=0, name="0/0", description="Homozygous reference" + ), + ), + ), + GenotypeGroup( + allele_count=1, + sex=None, + categorization=Categorization( + PatientCategory(cat_id=1, name="0/1", description="Heterozygous"), + ), + ), + GenotypeGroup( + allele_count=2, + sex=None, + categorization=Categorization( + PatientCategory(cat_id=2, name="1/1", description="Homozygous alternate"), + ), + ), + ) + return ModeOfInheritanceInfo( + mendelian_inheritance_aspect=MendelianInheritanceAspect.AUTOSOMAL, + groups=groups, + ) + + @staticmethod + def x_dominant() -> "ModeOfInheritanceInfo": + groups = ( + GenotypeGroup( + allele_count=0, + sex=None, + categorization=Categorization( + PatientCategory( + cat_id=0, name="0", description="Homozygous reference" + ), + ), + ), + GenotypeGroup( + allele_count=1, + sex=Sex.FEMALE, + categorization=Categorization( + PatientCategory(cat_id=1, name="0/1", description="Heterozygous"), + ), + ), + GenotypeGroup( + allele_count=1, + sex=Sex.MALE, + categorization=Categorization( + PatientCategory(cat_id=2, name="1", description="Hemizygous"), + ), + ), + ) + return ModeOfInheritanceInfo( + mendelian_inheritance_aspect=MendelianInheritanceAspect.GONOSOMAL, + groups=groups, + ) + + @staticmethod + def x_recessive() -> "ModeOfInheritanceInfo": + groups = ( + GenotypeGroup( + allele_count=0, + sex=None, + categorization=Categorization( + PatientCategory( + cat_id=0, name="0/0", description="Homozygous reference" + ), + ), + ), + GenotypeGroup( + allele_count=1, + sex=Sex.FEMALE, + categorization=Categorization( + PatientCategory(cat_id=1, name="0/1", description="Heterozygous"), + ), + ), + GenotypeGroup( + allele_count=1, + sex=Sex.MALE, + categorization=Categorization( + PatientCategory(cat_id=2, name="1", description="Hemizygous"), + ), + ), + ) + + return ModeOfInheritanceInfo( + mendelian_inheritance_aspect=MendelianInheritanceAspect.GONOSOMAL, + groups=groups, + ) + + def __init__( + self, + mendelian_inheritance_aspect: MendelianInheritanceAspect, + groups: typing.Iterable[GenotypeGroup], + ): + # We pre-compute the hash manually. + # The correctness depends on two default dicts with same keys and values + # comparing equal. + hash_value = 17 + assert isinstance(mendelian_inheritance_aspect, MendelianInheritanceAspect) + self._aspect = mendelian_inheritance_aspect + + hash_value += 31 * hash(self._aspect) + + self._groups = defaultdict(list) + for group in groups: + assert isinstance(group, GenotypeGroup) + self._groups[group.allele_count].append(group) + hash_value += 13 * hash(group) + + self._hash = hash_value + + @property + def groups(self) -> typing.Iterator[GenotypeGroup]: + # Flatten `values()` which is an iterable of lists. + return (group for meta_group in self._groups.values() for group in meta_group) + + @property + def mendelian_inheritance_aspect(self) -> MendelianInheritanceAspect: + return self._aspect + + def get_groups_for_allele_count( + self, + allele_count: int, + ) -> typing.Sequence[GenotypeGroup]: + try: + return self._groups[allele_count] + except KeyError: + # No group for this allele count is OK + return () + + def is_autosomal(self) -> bool: + return self._aspect == MendelianInheritanceAspect.AUTOSOMAL + + def is_gonosomal(self) -> bool: + return self._aspect == MendelianInheritanceAspect.GONOSOMAL + + def is_mitochondrial(self) -> bool: + return self._aspect == MendelianInheritanceAspect.MITOCHONDRIAL + + def __eq__(self, value: object) -> bool: + return ( + isinstance(value, ModeOfInheritanceInfo) + and self._aspect == value._aspect + and self._groups == value._groups + ) + + def __hash__(self) -> int: + return self._hash + + def __str__(self) -> str: + return f"ModeOfInheritanceInfo(aspect={self._aspect}, groups={self._groups})" + + def __repr__(self) -> str: + return str(self) + + +class ModeOfInheritancePredicate(GenotypePolyPredicate): + + @staticmethod + def autosomal_dominant( + variant_predicate: VariantPredicate, + ) -> "ModeOfInheritancePredicate": + return ModeOfInheritancePredicate.from_moi_info( + variant_predicate=variant_predicate, + mode_of_inheritance_info=ModeOfInheritanceInfo.autosomal_dominant(), + ) + + @staticmethod + def autosomal_recessive( + variant_predicate: VariantPredicate, + ) -> "ModeOfInheritancePredicate": + return ModeOfInheritancePredicate.from_moi_info( + variant_predicate=variant_predicate, + mode_of_inheritance_info=ModeOfInheritanceInfo.autosomal_recessive(), + ) + + @staticmethod + def x_dominant( + variant_predicate: VariantPredicate, + ) -> "ModeOfInheritancePredicate": + return ModeOfInheritancePredicate.from_moi_info( + variant_predicate=variant_predicate, + mode_of_inheritance_info=ModeOfInheritanceInfo.x_dominant(), + ) + + @staticmethod + def x_recessive( + variant_predicate: VariantPredicate, + ) -> "ModeOfInheritancePredicate": + return ModeOfInheritancePredicate.from_moi_info( + variant_predicate=variant_predicate, + mode_of_inheritance_info=ModeOfInheritanceInfo.x_recessive(), + ) + + @staticmethod + def from_moi_info( + variant_predicate: VariantPredicate, + mode_of_inheritance_info: ModeOfInheritanceInfo, + ) -> "ModeOfInheritancePredicate": + allele_counter = AlleleCounter(predicate=variant_predicate) + return ModeOfInheritancePredicate( + allele_counter=allele_counter, + mode_of_inheritance_info=mode_of_inheritance_info, + ) + + def __init__( + self, + allele_counter: AlleleCounter, + mode_of_inheritance_info: ModeOfInheritanceInfo, + ): + assert isinstance(allele_counter, AlleleCounter) + self._allele_counter = allele_counter + + assert isinstance(mode_of_inheritance_info, ModeOfInheritanceInfo) + self._moi_info = mode_of_inheritance_info + + self._categorizations = tuple(group.categorization for group in mode_of_inheritance_info.groups) + issues = ModeOfInheritancePredicate._check_categorizations(self._categorizations) + if issues: + raise ValueError('Cannot create predicate: {}'.format(', '.join(issues))) + self._question = 'Which genotype group does the patient fit in: {}'.format( + ', '.join(cat.category.name for cat in self._categorizations), + ) + + def get_categorizations(self) -> typing.Sequence[Categorization]: + return self._categorizations + + def get_question(self) -> str: + return self._question + + def test( + self, + patient: Patient, + ) -> typing.Optional[Categorization]: + self._check_patient(patient) + + if self._moi_info.is_autosomal(): + allele_count = self._allele_counter.count(patient) + groups = self._moi_info.get_groups_for_allele_count(allele_count) + if len(groups) == 1: + return groups[0].categorization + else: + return None + elif self._moi_info.is_gonosomal(): + if patient.sex.is_provided(): + allele_count = self._allele_counter.count(patient) + groups = self._moi_info.get_groups_for_allele_count(allele_count) + for group in groups: + if group.sex is not None and group.sex == patient.sex: + return group.categorization + return None + else: + # We must have patient's sex + # to do any meaningful analysis + # in the non-autosomal scenario. + return None + + elif self._moi_info.is_mitochondrial(): + # Cannot deal with mitochondrial inheritance right now. + return None + else: + # Bug, please report to the developers + raise ValueError("Unexpected mode of inheritance condition") + + def __eq__(self, value: object) -> bool: + return ( + isinstance(value, ModeOfInheritancePredicate) + and self._allele_counter == value._allele_counter + and self._moi_info == value._moi_info + ) + + def __hash__(self) -> int: + return hash((self._allele_counter, self._moi_info,)) + + def __str__(self) -> str: + return ( + "ModeOfInheritancePredicate(" + f"allele_counter={self._allele_counter}, " + f"moi_info={self._moi_info})" + ) + + def __repr__(self) -> str: + return str(self) diff --git a/src/gpsea/model/_base.py b/src/gpsea/model/_base.py index 0effeab0..0d541a65 100644 --- a/src/gpsea/model/_base.py +++ b/src/gpsea/model/_base.py @@ -26,6 +26,30 @@ class Sex(enum.Enum): Male sex. Maps to `NCIT:C46112`. """ + def is_provided(self) -> bool: + """ + Return `True` if the sex is a known value, such as `FEMALE` or `MALE`. + """ + return self != Sex.UNKNOWN_SEX + + def is_unknown(self) -> bool: + """ + Return `True` if this is an `UNKNOWN_SEX`. + """ + return self == Sex.UNKNOWN_SEX + + def is_female(self) -> bool: + """ + Return `True` if the sex represents a `FEMALE`. + """ + return self == Sex.MALE + + def is_male(self) -> bool: + """ + Return `True` if the sex represents a `MALE`. + """ + return self == Sex.MALE + class SampleLabels: """ diff --git a/tests/analysis/predicate/genotype/conftest.py b/tests/analysis/predicate/genotype/conftest.py index 3043d341..c44a214b 100644 --- a/tests/analysis/predicate/genotype/conftest.py +++ b/tests/analysis/predicate/genotype/conftest.py @@ -192,9 +192,7 @@ def patient_w_missense( sex=Sex.UNKNOWN_SEX, phenotypes=(), diseases=(), - variants=( - missense_variant, - ), + variants=(missense_variant,), ) @@ -208,7 +206,399 @@ def patient_w_frameshift( sex=Sex.UNKNOWN_SEX, phenotypes=(), diseases=(), - variants=( - frameshift_variant, + variants=(frameshift_variant,), + ) + + +""" +Genesis family - Autosomal dominant + +* Adam - father, affected +* Eve - mother, unaffected +* Cain - son, affected +""" + + +@pytest.fixture(scope="package") +def genesis_mutation( + genome_build: GenomeBuild, + adam_label: SampleLabels, + eve_label: SampleLabels, + cain_label: SampleLabels, +) -> Variant: + chr22 = genome_build.contig_by_name("chr22") + assert chr22 is not None + return Variant( + variant_info=VariantInfo( + variant_coordinates=VariantCoordinates( + region=GenomicRegion( + contig=chr22, + start=100, + end=101, + strand=Strand.POSITIVE, + ), + ref="C", + alt="G", + change_length=0, + ) + ), + tx_annotations=( + TranscriptAnnotation( + gene_id="a_gene", + tx_id="tx:xyz", + hgvs_cdna=None, + is_preferred=False, + variant_effects=( + VariantEffect.MISSENSE_VARIANT, + VariantEffect.SPLICE_DONOR_VARIANT, + ), + affected_exons=(4,), + protein_id="pt:xyz", + hgvsp=None, + protein_effect_coordinates=Region(40, 41), + ), + ), + genotypes=Genotypes.from_mapping( + { + adam_label: Genotype.HETEROZYGOUS, + eve_label: Genotype.HOMOZYGOUS_REFERENCE, + cain_label: Genotype.HETEROZYGOUS, + } + ), + ) + + +@pytest.fixture(scope="package") +def adam_label() -> SampleLabels: + return SampleLabels("Adam") + + +@pytest.fixture(scope="package") +def adam( + adam_label: SampleLabels, + genesis_mutation: Variant, +) -> Patient: + return Patient( + adam_label, + sex=Sex.MALE, + phenotypes=(), + diseases=(), + variants=(genesis_mutation,), + ) + + +@pytest.fixture(scope="package") +def eve_label() -> SampleLabels: + return SampleLabels("Eve") + + +@pytest.fixture(scope="package") +def eve( + eve_label: SampleLabels, + genesis_mutation: Variant, +) -> Patient: + return Patient( + eve_label, + sex=Sex.FEMALE, + phenotypes=(), + diseases=(), + variants=(genesis_mutation,), + ) + + +@pytest.fixture(scope="package") +def cain_label() -> SampleLabels: + return SampleLabels("Cain") + + +@pytest.fixture(scope="package") +def cain( + cain_label: SampleLabels, + genesis_mutation: Variant, +) -> Patient: + return Patient( + cain_label, + sex=Sex.MALE, + phenotypes=(), + diseases=(), + variants=(genesis_mutation,), + ) + + +""" +White family - Autosomal recessive + +* Walt - father, HET +* Skyler - mother, HET +* Flynn - son, HOM_ALT +* Holly - daughter, HOM_REF +""" + + +@pytest.fixture(scope="package") +def white_mutation( + genome_build: GenomeBuild, + walt_label: SampleLabels, + skyler_label: SampleLabels, + flynn_label: SampleLabels, + holly_label: SampleLabels, +) -> Variant: + chr22 = genome_build.contig_by_name("chr22") + assert chr22 is not None + return Variant( + variant_info=VariantInfo( + variant_coordinates=VariantCoordinates( + region=GenomicRegion( + contig=chr22, + start=100, + end=101, + strand=Strand.POSITIVE, + ), + ref="C", + alt="G", + change_length=0, + ) + ), + tx_annotations=( + TranscriptAnnotation( + gene_id="a_gene", + tx_id="tx:xyz", + hgvs_cdna=None, + is_preferred=False, + variant_effects=( + VariantEffect.MISSENSE_VARIANT, + VariantEffect.SPLICE_DONOR_VARIANT, + ), + affected_exons=(4,), + protein_id="pt:xyz", + hgvsp=None, + protein_effect_coordinates=Region(40, 41), + ), + ), + genotypes=Genotypes.from_mapping( + { + walt_label: Genotype.HETEROZYGOUS, + skyler_label: Genotype.HETEROZYGOUS, + flynn_label: Genotype.HOMOZYGOUS_ALTERNATE, + holly_label: Genotype.HOMOZYGOUS_REFERENCE, + } ), ) + + +@pytest.fixture(scope="package") +def walt_label() -> SampleLabels: + return SampleLabels("Walt") + + +@pytest.fixture(scope="package") +def walt( + walt_label: SampleLabels, + white_mutation: Variant, +) -> Patient: + return Patient( + walt_label, + sex=Sex.MALE, + phenotypes=(), + diseases=(), + variants=(white_mutation,), + ) + + +@pytest.fixture(scope="package") +def skyler_label() -> SampleLabels: + return SampleLabels("Skyler") + + +@pytest.fixture(scope="package") +def skyler( + skyler_label: SampleLabels, + white_mutation: Variant, +) -> Patient: + return Patient( + skyler_label, + sex=Sex.FEMALE, + phenotypes=(), + diseases=(), + variants=(white_mutation,), + ) + + +@pytest.fixture(scope="package") +def flynn_label() -> SampleLabels: + return SampleLabels("Flynn") + + +@pytest.fixture(scope="package") +def flynn( + flynn_label: SampleLabels, + white_mutation: Variant, +) -> Patient: + return Patient( + flynn_label, + sex=Sex.MALE, + phenotypes=(), + diseases=(), + variants=(white_mutation,), + ) + + +@pytest.fixture(scope="package") +def holly_label() -> SampleLabels: + return SampleLabels("Holly") + + +@pytest.fixture(scope="package") +def holly( + holly_label: SampleLabels, + white_mutation: Variant, +) -> Patient: + return Patient( + holly_label, + sex=Sex.FEMALE, + phenotypes=(), + diseases=(), + variants=(white_mutation,), + ) + + +""" +Skywalker family - X-linked recessive + +* Anakin - father, homozygous reference (possibly hemizygous reference?) +* Padme - mother, heterozygous +* Luke - son, hemizygous +* Leia - daughter, heterozygous +""" + + +@pytest.fixture(scope="package") +def skywalker_mutation( + genome_build: GenomeBuild, + anakin_label: SampleLabels, + padme_label: SampleLabels, + luke_label: SampleLabels, + leia_label: SampleLabels, +) -> Variant: + chrX = genome_build.contig_by_name("chrX") + assert chrX is not None + return Variant( + variant_info=VariantInfo( + variant_coordinates=VariantCoordinates( + region=GenomicRegion( + contig=chrX, + start=100, + end=101, + strand=Strand.POSITIVE, + ), + ref="C", + alt="G", + change_length=0, + ) + ), + tx_annotations=( + TranscriptAnnotation( + gene_id="a_gene", + tx_id="tx:xyz", + hgvs_cdna=None, + is_preferred=False, + variant_effects=( + VariantEffect.MISSENSE_VARIANT, + VariantEffect.SPLICE_DONOR_VARIANT, + ), + affected_exons=(4,), + protein_id="pt:xyz", + hgvsp=None, + protein_effect_coordinates=Region(40, 41), + ), + ), + genotypes=Genotypes.from_mapping( + { + anakin_label: Genotype.HOMOZYGOUS_REFERENCE, + padme_label: Genotype.HETEROZYGOUS, + luke_label: Genotype.HEMIZYGOUS, + leia_label: Genotype.HETEROZYGOUS, + } + ), + ) + + +@pytest.fixture(scope="package") +def anakin_label() -> SampleLabels: + return SampleLabels("Anakin") + + +@pytest.fixture(scope="package") +def anakin( + anakin_label: SampleLabels, + skywalker_mutation: Variant, +) -> Patient: + return Patient( + anakin_label, + sex=Sex.MALE, + phenotypes=(), + diseases=(), + variants=(skywalker_mutation,), + ) + + +@pytest.fixture(scope="package") +def padme_label() -> SampleLabels: + return SampleLabels("Padme") + + +@pytest.fixture(scope="package") +def padme( + padme_label: SampleLabels, + skywalker_mutation: Variant, +) -> Patient: + return Patient( + padme_label, + sex=Sex.FEMALE, + phenotypes=(), + diseases=(), + variants=(skywalker_mutation,), + ) + + +@pytest.fixture(scope="package") +def luke_label() -> SampleLabels: + return SampleLabels("Luke") + + +@pytest.fixture(scope="package") +def luke( + luke_label: SampleLabels, + skywalker_mutation: Variant, +) -> Patient: + return Patient( + luke_label, + sex=Sex.MALE, + phenotypes=(), + diseases=(), + variants=(skywalker_mutation,), + ) + + +@pytest.fixture(scope="package") +def leia_label() -> SampleLabels: + return SampleLabels("Leia") + + +@pytest.fixture(scope="package") +def leia( + leia_label: SampleLabels, + skywalker_mutation: Variant, +) -> Patient: + return Patient( + leia_label, + sex=Sex.FEMALE, + phenotypes=(), + diseases=(), + variants=(skywalker_mutation,), + ) + + +""" +XR family +""" diff --git a/tests/analysis/predicate/genotype/test_gt_predicates.py b/tests/analysis/predicate/genotype/test_gt_predicates.py index 3e71283b..ac6977c8 100644 --- a/tests/analysis/predicate/genotype/test_gt_predicates.py +++ b/tests/analysis/predicate/genotype/test_gt_predicates.py @@ -5,22 +5,23 @@ GenotypePolyPredicate, groups_predicate, VariantPredicates, + VariantPredicate, + ModeOfInheritancePredicate, ) -class TestGroupsPredicate: +TX_ID = "tx:xyz" + - TX_ID = "tx:xyz" +class TestGroupsPredicate: @pytest.fixture(scope="class") def predicate(self) -> GenotypePolyPredicate: return groups_predicate( predicates=( + VariantPredicates.variant_effect(VariantEffect.MISSENSE_VARIANT, TX_ID), VariantPredicates.variant_effect( - VariantEffect.MISSENSE_VARIANT, TestGroupsPredicate.TX_ID - ), - VariantPredicates.variant_effect( - VariantEffect.FRAMESHIFT_VARIANT, TestGroupsPredicate.TX_ID + VariantEffect.FRAMESHIFT_VARIANT, TX_ID ), ), group_names=( @@ -53,7 +54,7 @@ def test_get_categorizations( def test_test__missense( self, - patient_w_missense: Variant, + patient_w_missense: Patient, predicate: GenotypePolyPredicate, ): cat = predicate.test(patient_w_missense) @@ -65,7 +66,7 @@ def test_test__missense( def test_test__frameshift( self, - patient_w_frameshift: Variant, + patient_w_frameshift: Patient, predicate: GenotypePolyPredicate, ): cat = predicate.test(patient_w_frameshift) @@ -74,3 +75,90 @@ def test_test__frameshift( assert cat.category.cat_id == 1 assert cat.category.name == "LoF" assert cat.category.description == "FRAMESHIFT_VARIANT on tx:xyz" + + +class TestModeOfInheritancePredicate: + + @pytest.fixture(scope="class") + def variant_predicate(self) -> VariantPredicate: + return VariantPredicates.variant_effect(VariantEffect.MISSENSE_VARIANT, TX_ID) + + @pytest.mark.parametrize( + "patient_name,cat_id,name", + [ + ("adam", 1, "0/1"), + ("eve", 0, "0/0"), + ("cain", 1, "0/1"), + ], + ) + def test_autosomal_dominant( + self, + patient_name: str, + cat_id: int, + name: str, + variant_predicate: VariantPredicate, + request: pytest.FixtureRequest, + ): + patient = request.getfixturevalue(patient_name) + predicate = ModeOfInheritancePredicate.autosomal_dominant(variant_predicate) + + categorization = predicate.test(patient) + + assert categorization is not None + + assert categorization.category.cat_id == cat_id + assert categorization.category.name == name + + @pytest.mark.parametrize( + "patient_name,cat_id,name", + [ + ("walt", 1, "0/1"), + ("skyler", 1, "0/1"), + ("flynn", 2, "1/1"), + ("holly", 0, "0/0"), + ], + ) + def test_autosomal_recessive( + self, + patient_name: str, + cat_id: int, + name: str, + variant_predicate: VariantPredicate, + request: pytest.FixtureRequest, + ): + patient = request.getfixturevalue(patient_name) + predicate = ModeOfInheritancePredicate.autosomal_recessive(variant_predicate) + + categorization = predicate.test(patient) + + assert categorization is not None + + assert categorization.category.cat_id == cat_id + assert categorization.category.name == name + + @pytest.mark.parametrize( + "patient_name,cat_id,name", + [ + ("anakin", 0, "0/0"), + ("padme", 1, "0/1"), + ("luke", 2, "1"), + ("leia", 1, "0/1"), + ], + ) + def test_x_recessive( + self, + patient_name: str, + cat_id: int, + name: str, + variant_predicate: VariantPredicate, + request: pytest.FixtureRequest, + ): + patient = request.getfixturevalue(patient_name) + predicate = ModeOfInheritancePredicate.x_recessive(variant_predicate) + + categorization = predicate.test(patient) + + assert categorization is not None + + assert categorization.category.cat_id == cat_id + assert categorization.category.name == name From 1b9ed118a40e77826673d32a0641e7883e38d0bf Mon Sep 17 00:00:00 2001 From: Daniel Danis Date: Thu, 29 Aug 2024 16:40:36 +0200 Subject: [PATCH 17/47] Fix typos, rearrange few things. --- docs/user-guide/devries.rst | 12 +-- src/gpsea/analysis/pscore/_impl.py | 140 +++++++++++++++---------- src/gpsea/model/_phenotype.py | 25 ++++- tests/analysis/test_de_vries_scorer.py | 8 +- 4 files changed, 116 insertions(+), 69 deletions(-) diff --git a/docs/user-guide/devries.rst b/docs/user-guide/devries.rst index 60585b5c..74e5cd26 100644 --- a/docs/user-guide/devries.rst +++ b/docs/user-guide/devries.rst @@ -15,7 +15,7 @@ Statistical significance of a difference in the De Vries score between groups ca determined using the Mann–Whitney-U test. We refer to `Feenstra et al. (2011) `_ for -te original description of the adjusted De Vries score. Here we offer a version of the +the original description of the adjusted De Vries score. Here we offer a version of the score that leverages the structure of the Human Phenotype Ontology to assess the phenotype. The De Vries score has several sections, each of which is scored on a point system. The @@ -119,7 +119,7 @@ number of terms or descendents of the following HPO terms. +----------------------------------------------------------------------------------------------------------+-----------+ | `Abnormal external nose morphology (HP:0010938) `_ | 1 each | +----------------------------------------------------------------------------------------------------------+-----------+ -| `Abnormal pinna morphology (HP:0000377) `_ | 1 each | +| `Abnormal pinna morphology (HP:0000377) `_ | 1 each | +----------------------------------------------------------------------------------------------------------+-----------+ If two or more terms are found, the score is 2, otherwise a score of zero is assigned. @@ -127,17 +127,17 @@ If two or more terms are found, the score is 2, otherwise a score of zero is ass Non-facial dysmorphism and congenital abnormalities ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -One point is assigned for for either the +One point is assigned for either the corresponding HPO terms or any of their descendents up to a maximum of two points. +----------------------------------------------------------------------------------------------------------+-----------+ | HPO term | Score | +==========================================================================================================+===========+ -| `Abnormal hand morphology (HP:0005922) `_ | 1 each | +| `Abnormal hand morphology (HP:0005922) `_ | 1 each | +----------------------------------------------------------------------------------------------------------+-----------+ -| `Abnormal heart morphology (HP:0001627) `_ | 1 each | +| `Abnormal heart morphology (HP:0001627) `_ | 1 each | +----------------------------------------------------------------------------------------------------------+-----------+ -| `Hypospadias (HP:0000047) `_ | 1 | +| `Hypospadias (HP:0000047) `_ | 1 | +----------------------------------------------------------------------------------------------------------+-----------+ Final score diff --git a/src/gpsea/analysis/pscore/_impl.py b/src/gpsea/analysis/pscore/_impl.py index 6c7248b9..0520cd2f 100644 --- a/src/gpsea/analysis/pscore/_impl.py +++ b/src/gpsea/analysis/pscore/_impl.py @@ -101,16 +101,6 @@ def score( return count - # def __call__( - # self, - # *args: typing.Any, - # **kwds: typing.Any, - # ) -> float: - # # TODO: move to `PhenotypeScorer` API. - # assert len(args) == 1 and isinstance(args[0], Patient), 'The first argument must be an instance of `Patient`' - # assert len(kwds) == 0, 'We do not take any key-word arguments' - # return self.score(args[0]) - class DeVriesPhenotypeScorer(PhenotypeScorer): """ @@ -119,35 +109,54 @@ class DeVriesPhenotypeScorer(PhenotypeScorer): """ def __init__( - self, - hpo: hpotk.MinimalOntology, + self, + hpo: hpotk.MinimalOntology, ): self._hpo = hpo - def _developmental_delay_score(self, observed_term_ids: typing.List[str]) -> float: + # severe and profound GDD + self._gdd_tids = { + 'HP:0011344': 2, 'HP:0012736': 2, + 'HP:0011342': 1, 'HP:0011343': 1, 'HP:0001263': 1, + } + + # mild, moderate, and unspecified GDD (borderline has 0.5) + self._idd_tids = { + 'HP:0010864': 2, 'HP:0002187': 2, + 'HP:0001256': 1, 'HP:0002342': 1, 'HP:0001249': 1, + 'HP:0006889': 0.5, + } + + def _developmental_delay_score( + self, + observed_term_ids: typing.Iterable[str], + ) -> float: """ - calculate the dev delay component of the score + Calculate the dev delay component of the score + Args: observed_term_ids: terms observed in patient Returns: a score between 0 and 2 """ - gdd_tids = {'HP:0011344': 2, 'HP:0012736': 2, - 'HP:0011342': 1, 'HP:0011343': 1, 'HP:0001263': 1} # severe and profound GDD - idd_tids = {'HP:0010864': 2, 'HP:0002187': 2, 'HP:0001256': 1, 'HP:0002342': 1, 'HP:0001249': 1, - 'HP:0006889': 0.5} # mild, moderate, and unspecified GDD (borderline has 0.5) - # check GDD terms with higher priority than ID terms + # Check GDD terms with higher priority than ID terms. + # Global developmental delay for t in observed_term_ids: - if t in gdd_tids: - return gdd_tids.get(t) + if t in self._gdd_tids: + return self._gdd_tids[t] + + # Intellectual disability for t in observed_term_ids: - if t in idd_tids: - return idd_tids.get(t) + if t in self._idd_tids: + return self._idd_tids[t] + return 0 - def _term_or_descendant(self, - target_tid: str, - observed_term_ids: typing.List[str]): + def _term_or_descendant( + self, + target_tid: str, + observed_term_ids: typing.Iterable[str], + ) -> int: """ Args: target_tid: term of interest @@ -157,14 +166,17 @@ def _term_or_descendant(self, 1 if the term or any descendant is present in the patient, otherwise 0 """ for term_id in observed_term_ids: - for desc_tid in self._hpo.graph.get_ancestors(term_id, include_source=True): - if desc_tid.value == target_tid: - return 1 + if term_id == target_tid \ + or any(ancestor == target_tid for ancestor in self._hpo.graph.get_ancestors(term_id)): + return 1 + return 0 - def _term_or_descendant_count(self, - target_tid: str, - observed_term_ids: typing.List[str]): + def _term_or_descendant_count( + self, + target_tid: str, + observed_term_ids: typing.Iterable[str], + ) -> int: """ Args: target_tid: term of interest @@ -180,31 +192,37 @@ def _term_or_descendant_count(self, total_count += 1 return total_count - def _postnatal_growth_score(self, observed_term_ids: typing.List[str]) -> float: + def _postnatal_growth_score( + self, + observed_term_ids: typing.Iterable[str], + ) -> int: """ - calculate the postnatal growth component of the score + Calculate the postnatal growth component of the score. + Args: observed_term_ids: terms observed in patient - Returns: - + Returns: an `int` (between 0 and 2) """ microcephaly = 'HP:0000252' short_stature = 'HP:0004322' macrocephaly = 'HP:0000256' tall_stature = 'HP:0000098' total_count = 0 - for tid in [microcephaly, short_stature, macrocephaly, tall_stature]: + for tid in (microcephaly, short_stature, macrocephaly, tall_stature): total_count += self._term_or_descendant(tid, observed_term_ids) if total_count > 2: raise ValueError(f"Inconsistent annotations for postnatal growth score {total_count}: {observed_term_ids}") return total_count - def _facial_dysmorphism_score(self, observed_term_ids: typing.List[str]) -> float: + def _facial_dysmorphism_score( + self, + observed_term_ids: typing.Collection[str], + ) -> int: """ This section assigns two points if two or more anomalies are identified in the following categories: hypertelorism, nasal anomalies and ear anomalies. Our implementation counts the total - number of terms or descendents of the hypertelorism, Abnormal external nose morphology, and + number of terms or descendants of the hypertelorism, Abnormal external nose morphology, and Abnormal pinna morphology. Args: @@ -216,7 +234,7 @@ def _facial_dysmorphism_score(self, observed_term_ids: typing.List[str]) -> floa hypertelorism = 'HP:0000316' external_nose = 'HP:0010938' pinna_morphology = 'HP:0000377' - total_count = len([t for t in observed_term_ids if t == hypertelorism]) + total_count = self._term_or_descendant_count(target_tid=hypertelorism, observed_term_ids=observed_term_ids) total_count += self._term_or_descendant_count(target_tid=external_nose, observed_term_ids=observed_term_ids) total_count += self._term_or_descendant_count(target_tid=pinna_morphology, observed_term_ids=observed_term_ids) if total_count > 1: @@ -224,10 +242,14 @@ def _facial_dysmorphism_score(self, observed_term_ids: typing.List[str]) -> floa else: return 0 - def _congenital_score(self, observed_term_ids: typing.List[str]) -> float: + def _congenital_score( + self, + observed_term_ids: typing.Iterable[str], + ) -> int: """ - Non-facial dysmorphism and congenital abnormalities component + Non-facial dysmorphism and congenital abnormalities component. One point is assigned for either the corresponding HPO terms or any of their descendents up to a maximum of 2. + Args: observed_term_ids: terms observed in patient @@ -237,51 +259,53 @@ def _congenital_score(self, observed_term_ids: typing.List[str]) -> float: hypospadias = 'HP:0000047' abnormal_hand_morphology = 'HP:0005922' abnormal_heart_morphology = 'HP:0001627' - total_count = len([t for t in observed_term_ids if t == hypospadias]) + # total_count = len([t for t in observed_term_ids if t == hypospadias]) + total_count = self._term_or_descendant_count( + target_tid=hypospadias, observed_term_ids=observed_term_ids, + ) total_count += self._term_or_descendant_count(target_tid=abnormal_hand_morphology, observed_term_ids=observed_term_ids) total_count += self._term_or_descendant_count(target_tid=abnormal_heart_morphology, observed_term_ids=observed_term_ids) return min(2, total_count) - def _prenatal_growth_score(self, observed_term_ids: typing.List[str]) -> float: + def _prenatal_growth_score( + self, + observed_term_ids: typing.Iterable[str], + ) -> int: """ - two points are assigned if Prenatal-onset growth retardation is present + Two points are assigned if Prenatal-onset growth retardation is present. Args: - observed_term_ids: list of strings with term identifiers or observed HPO terms + observed_term_ids: list of strings with term identifiers or observed HPO terms Returns: score between 0 and 2 """ small_for_gestational_age = 'HP:0001518' intrauterine_growth_retardation = 'HP:0001511' - targets = {small_for_gestational_age, intrauterine_growth_retardation} + targets = (small_for_gestational_age, intrauterine_growth_retardation) for tid in observed_term_ids: if tid in targets: return 2 return 0 - def _calculate_score(self, observed_term_ids: typing.List[str]) -> float: + def score(self, patient: Patient) -> float: """ - calculate score based on list of strings with term identifiers or observed HPO terms. + Calculate score based on list of strings with term identifiers or observed HPO terms. + Args: - observed_term_ids: list of strings with term identifiers or observed HPO terms + patient: list of strings with term identifiers or observed HPO terms Returns: de Vries score between 0 and 10 """ + observed_term_ids = tuple(tid.identifier.value for tid in patient.present_phenotypes()) + delay_score = self._developmental_delay_score(observed_term_ids) growth_score = self._postnatal_growth_score(observed_term_ids) facial_score = self._facial_dysmorphism_score(observed_term_ids) congen_score = self._congenital_score(observed_term_ids) prenatal_score = self._prenatal_growth_score(observed_term_ids) + return delay_score + growth_score + facial_score + congen_score + prenatal_score - - def score(self, patient: Patient) -> float: - """ - Compute the score for the `patient`. - """ - # collect term identifiers as strings for all observed phenotypes - observed_term_ids = [tid.identifier.value for tid in patient.present_phenotypes()] - return self._calculate_score(observed_term_ids) diff --git a/src/gpsea/model/_phenotype.py b/src/gpsea/model/_phenotype.py index def0ec9c..267f0ec4 100644 --- a/src/gpsea/model/_phenotype.py +++ b/src/gpsea/model/_phenotype.py @@ -13,7 +13,30 @@ class Phenotype(hpotk.model.Identified, hpotk.model.ObservableFeature): @staticmethod def from_term(term: hpotk.model.MinimalTerm, is_observed: bool): - return Phenotype(term.identifier, is_observed) + return Phenotype.from_raw_parts(term.identifier, is_observed) + + @staticmethod + def from_raw_parts( + term_id: typing.Union[str, hpotk.TermId], + is_observed: bool, + ) -> "Phenotype": + """ + Create `Phenotype` from a term ID and observation state. + + :param term_id: a `str` with CURIE (e.g. `HP:0001250`) or a :class:`~hpotk.TermId`. + :param is_observed: `True` if the term ID was observed in patient or `False` if it was explicitly excluded. + """ + if isinstance(term_id, str): + term_id = hpotk.TermId.from_curie(term_id) + elif isinstance(term_id, hpotk.TermId): + pass + else: + raise ValueError('`term_id` must be either a `str` or a `hpotk.TermId`') + + return Phenotype( + term_id, + is_observed, + ) def __init__( self, diff --git a/tests/analysis/test_de_vries_scorer.py b/tests/analysis/test_de_vries_scorer.py index 6057c247..220dd18b 100644 --- a/tests/analysis/test_de_vries_scorer.py +++ b/tests/analysis/test_de_vries_scorer.py @@ -27,8 +27,8 @@ class TestDeVriesScorer: @pytest.fixture def devries_scorer( - self, - hpo: hpotk.MinimalOntology, + self, + hpo: hpotk.MinimalOntology, ) -> DeVriesPhenotypeScorer: return DeVriesPhenotypeScorer(hpo=hpo) @@ -70,8 +70,8 @@ def test_a_patient( patient = Patient( labels=SampleLabels("test"), phenotypes=( - Phenotype( - hpotk.TermId.from_curie(curie), + Phenotype.from_raw_parts( + term_id=curie, is_observed=True, ) for curie in term_set From eb08b3040a6608800007419f3b41c3311c987ba9 Mon Sep 17 00:00:00 2001 From: Peter Robinson Date: Thu, 29 Aug 2024 17:05:28 +0200 Subject: [PATCH 18/47] foxing documentation --- src/gpsea/analysis/pscore/_impl.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/gpsea/analysis/pscore/_impl.py b/src/gpsea/analysis/pscore/_impl.py index 6c7248b9..aba4546f 100644 --- a/src/gpsea/analysis/pscore/_impl.py +++ b/src/gpsea/analysis/pscore/_impl.py @@ -171,7 +171,7 @@ def _term_or_descendant_count(self, observed_term_ids: all terms observed in patient Returns: - 1 if the term or any descendant is present in the patient, otherwise 0 + the total count of the terms equal to or descending from the target_tid """ total_count = 0 for term_id in observed_term_ids: From 900922f42810fb767d29011065a8fb3fb56622b2 Mon Sep 17 00:00:00 2001 From: Daniel Danis Date: Thu, 29 Aug 2024 17:19:24 +0200 Subject: [PATCH 19/47] Revert the way of testing for hypertelorism. --- src/gpsea/analysis/pscore/_impl.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/src/gpsea/analysis/pscore/_impl.py b/src/gpsea/analysis/pscore/_impl.py index 3e40287d..16931c39 100644 --- a/src/gpsea/analysis/pscore/_impl.py +++ b/src/gpsea/analysis/pscore/_impl.py @@ -234,7 +234,9 @@ def _facial_dysmorphism_score( hypertelorism = 'HP:0000316' external_nose = 'HP:0010938' pinna_morphology = 'HP:0000377' - total_count = self._term_or_descendant_count(target_tid=hypertelorism, observed_term_ids=observed_term_ids) + + # No need to inspect descendants since Hypertelorism has none. + total_count = 1 if hypertelorism in observed_term_ids else 0 total_count += self._term_or_descendant_count(target_tid=external_nose, observed_term_ids=observed_term_ids) total_count += self._term_or_descendant_count(target_tid=pinna_morphology, observed_term_ids=observed_term_ids) if total_count > 1: From 600955d6c373c6e19ea6d5de8dc52c954da5213b Mon Sep 17 00:00:00 2001 From: Lauren Rekerle Date: Thu, 29 Aug 2024 12:53:43 -0500 Subject: [PATCH 20/47] Made changes to the cohort creator - duplicate patient ID verifier and added test --- src/gpsea/preprocessing/_patient.py | 15 +++--- .../data/dup_id_test_data/pp1.json | 54 +++++++++++++++++++ .../data/dup_id_test_data/pp2.json | 54 +++++++++++++++++++ .../data/dup_id_test_data/pp3.json | 54 +++++++++++++++++++ .../data/dup_id_test_data/pp4.json | 54 +++++++++++++++++++ .../test_patient_and_cohort_creator.py | 11 ++++ 6 files changed, 235 insertions(+), 7 deletions(-) create mode 100644 tests/preprocessing/data/dup_id_test_data/pp1.json create mode 100644 tests/preprocessing/data/dup_id_test_data/pp2.json create mode 100644 tests/preprocessing/data/dup_id_test_data/pp3.json create mode 100644 tests/preprocessing/data/dup_id_test_data/pp4.json diff --git a/src/gpsea/preprocessing/_patient.py b/src/gpsea/preprocessing/_patient.py index 10a95aaa..d4786350 100644 --- a/src/gpsea/preprocessing/_patient.py +++ b/src/gpsea/preprocessing/_patient.py @@ -38,20 +38,21 @@ def __init__(self, patient_creator: PatientCreator[T]): def process(self, inputs: typing.Iterable[T], notepad: Notepad) -> Cohort: patients = [] - patient_ids = set() - duplicate_pat_ids = set() + patient_labels = set() + duplicate_pat_labels = set() for i, pp in enumerate(inputs): sub = notepad.add_subsection(f'patient #{i}') patient = self._pc.process(pp, sub) - if patient.patient_id in patient_ids: - duplicate_pat_ids.add(patient.patient_id) - patient_ids.add(patient.patient_id) + if patient.labels in patient_labels: + duplicate_pat_labels.add(patient.labels) + patient_labels.add(patient.labels) patients.append(patient) # What happens if a sample has - if len(duplicate_pat_ids) > 0: - raise ValueError(f"Patient ID/s {', '.join(duplicate_pat_ids)} have a duplicate. Please verify every patient has an unique ID.") + if len(duplicate_pat_labels) > 0: + notepad.add_error(f"Patient ID/s {', '.join([d.label_summary() for d in duplicate_pat_labels])} have a duplicate", + "Please verify every patient has an unique ID.") # We should have >1 patients in the cohort, right? if len(patients) <= 1: diff --git a/tests/preprocessing/data/dup_id_test_data/pp1.json b/tests/preprocessing/data/dup_id_test_data/pp1.json new file mode 100644 index 00000000..6f81c2e4 --- /dev/null +++ b/tests/preprocessing/data/dup_id_test_data/pp1.json @@ -0,0 +1,54 @@ +{ + "id": "PMID_12345", + "subject": { + "id": "Pat_1" + }, + "phenotypicFeatures": [ + { + "type": { + "id": "HP:5200338", + "label": "Excessive fire setting" + } + } + ], + "interpretations": [ + { + "id": "Pat_1", + "progressStatus": "SOLVED", + "diagnosis": { + "genomicInterpretations": [ + { + "subjectOrBiosampleId": "Pat_1", + "interpretationStatus": "CAUSATIVE", + "variantInterpretation": { + "variationDescriptor": { + "geneContext": { + "valueId": "HGNC:6138", + "symbol": "ITGA2B" + }, + "expressions": [ + { + "syntax": "hgvs.c", + "value": "NM_000419.3:c.3077G>A" + }, + { + "syntax": "hgvs.g", + "value": "NC_000017.11:g.44372407C>T" + } + ], + "vcfRecord": { + "genomeAssembly": "hg38", + "chrom": "chr17", + "pos": "44372407", + "ref": "C", + "alt": "T" + }, + "moleculeContext": "genomic" + } + } + } + ] + } + } + ] +} \ No newline at end of file diff --git a/tests/preprocessing/data/dup_id_test_data/pp2.json b/tests/preprocessing/data/dup_id_test_data/pp2.json new file mode 100644 index 00000000..6f81c2e4 --- /dev/null +++ b/tests/preprocessing/data/dup_id_test_data/pp2.json @@ -0,0 +1,54 @@ +{ + "id": "PMID_12345", + "subject": { + "id": "Pat_1" + }, + "phenotypicFeatures": [ + { + "type": { + "id": "HP:5200338", + "label": "Excessive fire setting" + } + } + ], + "interpretations": [ + { + "id": "Pat_1", + "progressStatus": "SOLVED", + "diagnosis": { + "genomicInterpretations": [ + { + "subjectOrBiosampleId": "Pat_1", + "interpretationStatus": "CAUSATIVE", + "variantInterpretation": { + "variationDescriptor": { + "geneContext": { + "valueId": "HGNC:6138", + "symbol": "ITGA2B" + }, + "expressions": [ + { + "syntax": "hgvs.c", + "value": "NM_000419.3:c.3077G>A" + }, + { + "syntax": "hgvs.g", + "value": "NC_000017.11:g.44372407C>T" + } + ], + "vcfRecord": { + "genomeAssembly": "hg38", + "chrom": "chr17", + "pos": "44372407", + "ref": "C", + "alt": "T" + }, + "moleculeContext": "genomic" + } + } + } + ] + } + } + ] +} \ No newline at end of file diff --git a/tests/preprocessing/data/dup_id_test_data/pp3.json b/tests/preprocessing/data/dup_id_test_data/pp3.json new file mode 100644 index 00000000..5ace5173 --- /dev/null +++ b/tests/preprocessing/data/dup_id_test_data/pp3.json @@ -0,0 +1,54 @@ +{ + "id": "PMID_67890", + "subject": { + "id": "Pat_2" + }, + "phenotypicFeatures": [ + { + "type": { + "id": "HP:5200338", + "label": "Excessive fire setting" + } + } + ], + "interpretations": [ + { + "id": "Pat_2", + "progressStatus": "SOLVED", + "diagnosis": { + "genomicInterpretations": [ + { + "subjectOrBiosampleId": "Pat_2", + "interpretationStatus": "CAUSATIVE", + "variantInterpretation": { + "variationDescriptor": { + "geneContext": { + "valueId": "HGNC:6138", + "symbol": "ITGA2B" + }, + "expressions": [ + { + "syntax": "hgvs.c", + "value": "NM_000419.3:c.3077G>A" + }, + { + "syntax": "hgvs.g", + "value": "NC_000017.11:g.44372407C>T" + } + ], + "vcfRecord": { + "genomeAssembly": "hg38", + "chrom": "chr17", + "pos": "44372407", + "ref": "C", + "alt": "T" + }, + "moleculeContext": "genomic" + } + } + } + ] + } + } + ] +} \ No newline at end of file diff --git a/tests/preprocessing/data/dup_id_test_data/pp4.json b/tests/preprocessing/data/dup_id_test_data/pp4.json new file mode 100644 index 00000000..5ace5173 --- /dev/null +++ b/tests/preprocessing/data/dup_id_test_data/pp4.json @@ -0,0 +1,54 @@ +{ + "id": "PMID_67890", + "subject": { + "id": "Pat_2" + }, + "phenotypicFeatures": [ + { + "type": { + "id": "HP:5200338", + "label": "Excessive fire setting" + } + } + ], + "interpretations": [ + { + "id": "Pat_2", + "progressStatus": "SOLVED", + "diagnosis": { + "genomicInterpretations": [ + { + "subjectOrBiosampleId": "Pat_2", + "interpretationStatus": "CAUSATIVE", + "variantInterpretation": { + "variationDescriptor": { + "geneContext": { + "valueId": "HGNC:6138", + "symbol": "ITGA2B" + }, + "expressions": [ + { + "syntax": "hgvs.c", + "value": "NM_000419.3:c.3077G>A" + }, + { + "syntax": "hgvs.g", + "value": "NC_000017.11:g.44372407C>T" + } + ], + "vcfRecord": { + "genomeAssembly": "hg38", + "chrom": "chr17", + "pos": "44372407", + "ref": "C", + "alt": "T" + }, + "moleculeContext": "genomic" + } + } + } + ] + } + } + ] +} \ No newline at end of file diff --git a/tests/preprocessing/test_patient_and_cohort_creator.py b/tests/preprocessing/test_patient_and_cohort_creator.py index d4564bfe..5d15061a 100644 --- a/tests/preprocessing/test_patient_and_cohort_creator.py +++ b/tests/preprocessing/test_patient_and_cohort_creator.py @@ -1,4 +1,5 @@ import os +import io import hpotk import pytest @@ -92,3 +93,13 @@ def test_load_phenopacket( cohort_creator=phenopacket_cohort_creator, ) print(cohort) + + def test_cohort_creator( + self, + phenopacket_cohort_creator: CohortCreator, + ): + folder = os.path.join(os.getcwd(), 'tests', 'preprocessing', 'data', 'dup_id_test_data') + cohort, results = load_phenopacket_folder(folder, phenopacket_cohort_creator) + outfile = io.StringIO() + results.summarize(outfile) + assert " Patient ID/s Pat_1[PMID_12345], Pat_2[PMID_67890] have a duplicate. Please verify every patient has an unique ID." in outfile.getvalue().split('\n') From 1ca62b1aa0ef7a77f5ee70aa4a63061ab83414e1 Mon Sep 17 00:00:00 2001 From: Lauren Rekerle Date: Thu, 29 Aug 2024 13:00:21 -0500 Subject: [PATCH 21/47] Sorting so we get same output every time --- src/gpsea/preprocessing/_patient.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/gpsea/preprocessing/_patient.py b/src/gpsea/preprocessing/_patient.py index d4786350..11ab810d 100644 --- a/src/gpsea/preprocessing/_patient.py +++ b/src/gpsea/preprocessing/_patient.py @@ -51,7 +51,7 @@ def process(self, inputs: typing.Iterable[T], notepad: Notepad) -> Cohort: # What happens if a sample has if len(duplicate_pat_labels) > 0: - notepad.add_error(f"Patient ID/s {', '.join([d.label_summary() for d in duplicate_pat_labels])} have a duplicate", + notepad.add_error(f"Patient ID/s {', '.join([d.label_summary() for d in duplicate_pat_labels].sort())} have a duplicate", "Please verify every patient has an unique ID.") # We should have >1 patients in the cohort, right? From f27f8a588f3cd0247263910552be8967c0fb8bee Mon Sep 17 00:00:00 2001 From: Lauren Rekerle Date: Thu, 29 Aug 2024 13:09:42 -0500 Subject: [PATCH 22/47] sort didn't work in line. --- src/gpsea/preprocessing/_patient.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/src/gpsea/preprocessing/_patient.py b/src/gpsea/preprocessing/_patient.py index 11ab810d..ae198511 100644 --- a/src/gpsea/preprocessing/_patient.py +++ b/src/gpsea/preprocessing/_patient.py @@ -51,7 +51,9 @@ def process(self, inputs: typing.Iterable[T], notepad: Notepad) -> Cohort: # What happens if a sample has if len(duplicate_pat_labels) > 0: - notepad.add_error(f"Patient ID/s {', '.join([d.label_summary() for d in duplicate_pat_labels].sort())} have a duplicate", + list = [d.label_summary() for d in duplicate_pat_labels] + list.sort() + notepad.add_error(f"Patient ID/s {', '.join(list)} have a duplicate", "Please verify every patient has an unique ID.") # We should have >1 patients in the cohort, right? From 4bc175289f25d79547a8a84b2995c2aa6fbe672e Mon Sep 17 00:00:00 2001 From: Daniel Danis Date: Thu, 29 Aug 2024 20:53:31 +0200 Subject: [PATCH 23/47] Minor test enhancements. --- .../preprocessing/test_patient_and_cohort_creator.py | 12 +++++++++--- 1 file changed, 9 insertions(+), 3 deletions(-) diff --git a/tests/preprocessing/test_patient_and_cohort_creator.py b/tests/preprocessing/test_patient_and_cohort_creator.py index 5d15061a..5e5e84a2 100644 --- a/tests/preprocessing/test_patient_and_cohort_creator.py +++ b/tests/preprocessing/test_patient_and_cohort_creator.py @@ -96,10 +96,16 @@ def test_load_phenopacket( def test_cohort_creator( self, + fpath_test_dir: str, phenopacket_cohort_creator: CohortCreator, ): - folder = os.path.join(os.getcwd(), 'tests', 'preprocessing', 'data', 'dup_id_test_data') - cohort, results = load_phenopacket_folder(folder, phenopacket_cohort_creator) + folder = os.path.join(fpath_test_dir, 'preprocessing', 'data', 'dup_id_test_data') + _, results = load_phenopacket_folder(folder, phenopacket_cohort_creator) + outfile = io.StringIO() results.summarize(outfile) - assert " Patient ID/s Pat_1[PMID_12345], Pat_2[PMID_67890] have a duplicate. Please verify every patient has an unique ID." in outfile.getvalue().split('\n') + + actual_lines = outfile.getvalue().split(os.linesep) + + expected = " Patient ID/s Pat_1[PMID_12345], Pat_2[PMID_67890] have a duplicate. Please verify every patient has an unique ID." + assert expected in actual_lines From b67a709357f208df6529efdae82df23934669168 Mon Sep 17 00:00:00 2001 From: Daniel Danis Date: Thu, 29 Aug 2024 20:54:07 +0200 Subject: [PATCH 24/47] Rename variable from `list` (Python keyword) to something else. --- src/gpsea/preprocessing/_patient.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/src/gpsea/preprocessing/_patient.py b/src/gpsea/preprocessing/_patient.py index ae198511..7a9ed95c 100644 --- a/src/gpsea/preprocessing/_patient.py +++ b/src/gpsea/preprocessing/_patient.py @@ -51,9 +51,9 @@ def process(self, inputs: typing.Iterable[T], notepad: Notepad) -> Cohort: # What happens if a sample has if len(duplicate_pat_labels) > 0: - list = [d.label_summary() for d in duplicate_pat_labels] - list.sort() - notepad.add_error(f"Patient ID/s {', '.join(list)} have a duplicate", + label_summaries = [d.label_summary() for d in duplicate_pat_labels] + label_summaries.sort() + notepad.add_error(f"Patient ID/s {', '.join(label_summaries)} have a duplicate", "Please verify every patient has an unique ID.") # We should have >1 patients in the cohort, right? From ecf589c2220bb2e14d56806c1f8fbff0c0701eb5 Mon Sep 17 00:00:00 2001 From: Daniel Danis Date: Fri, 30 Aug 2024 10:28:48 +0200 Subject: [PATCH 25/47] Move `DeVriesScorer` test elsewhere. --- tests/analysis/{ => pscore}/test_de_vries_scorer.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) rename tests/analysis/{ => pscore}/test_de_vries_scorer.py (97%) diff --git a/tests/analysis/test_de_vries_scorer.py b/tests/analysis/pscore/test_de_vries_scorer.py similarity index 97% rename from tests/analysis/test_de_vries_scorer.py rename to tests/analysis/pscore/test_de_vries_scorer.py index 220dd18b..89679a37 100644 --- a/tests/analysis/test_de_vries_scorer.py +++ b/tests/analysis/pscore/test_de_vries_scorer.py @@ -4,7 +4,7 @@ import pytest from gpsea.analysis.pscore import DeVriesPhenotypeScorer -from gpsea.model import Patient, SampleLabels, Phenotype +from gpsea.model import Patient, SampleLabels, Phenotype, Sex intrauterine_growth_retardation = 'HP:0001511' small_for_gestational_age = 'HP:0001518' @@ -69,6 +69,7 @@ def test_a_patient( ): patient = Patient( labels=SampleLabels("test"), + sex=Sex.UNKNOWN_SEX, phenotypes=( Phenotype.from_raw_parts( term_id=curie, From 0eb40a6d711dd1dfd16ab028441a5b432b78de43 Mon Sep 17 00:00:00 2001 From: Daniel Danis Date: Fri, 30 Aug 2024 10:32:55 +0200 Subject: [PATCH 26/47] Fine tune the inheritance mode assignment. --- .../analysis/predicate/genotype/_gt_predicates.py | 14 +++++++++++--- 1 file changed, 11 insertions(+), 3 deletions(-) diff --git a/src/gpsea/analysis/predicate/genotype/_gt_predicates.py b/src/gpsea/analysis/predicate/genotype/_gt_predicates.py index 601aa70b..1cd1a657 100644 --- a/src/gpsea/analysis/predicate/genotype/_gt_predicates.py +++ b/src/gpsea/analysis/predicate/genotype/_gt_predicates.py @@ -553,9 +553,17 @@ def test( if patient.sex.is_provided(): allele_count = self._allele_counter.count(patient) groups = self._moi_info.get_groups_for_allele_count(allele_count) - for group in groups: - if group.sex is not None and group.sex == patient.sex: - return group.categorization + if len(groups) == 0: + # Unable to assign the individual. + return None + elif len(groups) == 1: + # We can only assign into one category no matter what the individual's sex is. + return groups[0].categorization + else: + # We choose depending on the sex. + for group in groups: + if group.sex is not None and group.sex == patient.sex: + return group.categorization return None else: # We must have patient's sex From 29d126551ecc3b50f47d9a99a9d4cc40fdc3a9e3 Mon Sep 17 00:00:00 2001 From: Daniel Danis Date: Fri, 30 Aug 2024 16:00:53 +0200 Subject: [PATCH 27/47] Improve documentation for predicates. --- docs/report/tbx5_frameshift_vs_missense.csv | 244 +------------- docs/tutorial.rst | 6 +- docs/user-guide/predicates.rst | 315 ++++++++++++++++-- docs/user-guide/report/tbx5_frameshift.csv | 282 ++-------------- docs/user-guide/stats.rst | 45 ++- .../predicate/genotype/_gt_predicates.py | 89 +++-- src/gpsea/model/_base.py | 6 +- tests/analysis/predicate/genotype/conftest.py | 15 +- .../predicate/genotype/test_gt_predicates.py | 58 ++-- 9 files changed, 445 insertions(+), 615 deletions(-) diff --git a/docs/report/tbx5_frameshift_vs_missense.csv b/docs/report/tbx5_frameshift_vs_missense.csv index 421e6927..07245a44 100644 --- a/docs/report/tbx5_frameshift_vs_missense.csv +++ b/docs/report/tbx5_frameshift_vs_missense.csv @@ -16,247 +16,7 @@ Hypoplasia of the radius [HP:0002984],30/62,48%,6/14,43%,0.9520604334894502,0.77 Atrial septal defect [HP:0001631],42/44,95%,20/20,100%,1.0,1.0 Absent radius [HP:0003974],7/32,22%,6/25,24%,1.0,1.0 Short humerus [HP:0005792],7/17,41%,4/9,44%,1.0,1.0 -Abnormal carpal morphology [HP:0001191],30/32,94%,0/0,0%,, -Abnormal hand morphology [HP:0005922],53/53,100%,20/20,100%,, -Abnormality of the hand [HP:0001155],60/60,100%,31/31,100%,, +Forearm undergrowth [HP:0009821],30/30,100%,7/7,100%,, +Abnormal upper limb bone morphology [HP:0040070],40/40,100%,14/14,100%,, Abnormality of the upper limb [HP:0002817],73/73,100%,34/34,100%,, Abnormality of limbs [HP:0040064],73/73,100%,34/34,100%,, -Phenotypic abnormality [HP:0000118],82/82,100%,38/38,100%,, -All [HP:0000001],82/82,100%,38/38,100%,, -Abnormality of the wrist [HP:0003019],30/30,100%,0/0,0%,, -Abnormality of upper limb joint [HP:0009810],30/30,100%,6/6,100%,, -Abnormal joint morphology [HP:0001367],31/31,100%,6/6,100%,, -Abnormal skeletal morphology [HP:0011842],73/73,100%,35/35,100%,, -Abnormality of the skeletal system [HP:0000924],73/73,100%,35/35,100%,, -Abnormality of the musculoskeletal system [HP:0033127],74/74,100%,35/35,100%,, -Short thumb [HP:0009778],11/41,27%,8/30,27%,, -Aplasia/Hypoplasia of the thumb [HP:0009601],20/20,100%,19/19,100%,, -Aplasia/Hypoplasia of fingers [HP:0006265],22/22,100%,19/19,100%,, -Aplasia/hypoplasia involving bones of the hand [HP:0005927],22/22,100%,19/19,100%,, -Aplasia/hypoplasia involving bones of the upper limbs [HP:0006496],55/55,100%,22/22,100%,, -Aplasia/hypoplasia involving bones of the extremities [HP:0045060],55/55,100%,22/22,100%,, -Aplasia/hypoplasia of the extremities [HP:0009815],55/55,100%,22/22,100%,, -Aplasia/hypoplasia involving the skeleton [HP:0009115],56/56,100%,23/23,100%,, -Abnormal limb bone morphology [HP:0002813],63/63,100%,34/34,100%,, -Abnormality of limb bone [HP:0040068],63/63,100%,34/34,100%,, -Abnormal appendicular skeleton morphology [HP:0011844],64/64,100%,34/34,100%,, -Abnormal finger morphology [HP:0001167],36/36,100%,31/31,100%,, -Abnormal digit morphology [HP:0011297],38/38,100%,33/33,100%,, -Abnormal thumb morphology [HP:0001172],30/30,100%,31/31,100%,, -Short finger [HP:0009381],11/11,100%,8/8,100%,, -Short digit [HP:0011927],11/11,100%,10/10,100%,, -Complete atrioventricular canal defect [HP:0001674],5/37,14%,3/36,8%,, -Atrioventricular canal defect [HP:0006695],5/5,100%,3/3,100%,, -Abnormal cardiac septum morphology [HP:0001671],62/62,100%,28/28,100%,, -Abnormal heart morphology [HP:0001627],62/62,100%,30/30,100%,, -Abnormal cardiovascular system morphology [HP:0030680],63/63,100%,30/30,100%,, -Abnormality of the cardiovascular system [HP:0001626],65/65,100%,32/32,100%,, -11 pairs of ribs [HP:0000878],1/1,100%,0/0,0%,, -Missing ribs [HP:0000921],1/1,100%,0/0,0%,, -Aplasia/Hypoplasia of the ribs [HP:0006712],1/1,100%,0/0,0%,, -Aplasia/Hypoplasia involving bones of the thorax [HP:0006711],1/1,100%,2/2,100%,, -Aplasia/hypoplasia affecting bones of the axial skeleton [HP:0009122],2/2,100%,2/2,100%,, -Abnormal axial skeleton morphology [HP:0009121],8/8,100%,5/5,100%,, -Abnormal thorax morphology [HP:0000765],6/6,100%,5/5,100%,, -Abnormal rib morphology [HP:0000772],1/1,100%,0/0,0%,, -Abnormal rib cage morphology [HP:0001547],4/4,100%,0/0,0%,, -Abnormal cardiac ventricle morphology [HP:0001713],31/31,100%,19/19,100%,, -Abnormal atrial septum morphology [HP:0011994],43/43,100%,20/20,100%,, -Abnormal cardiac atrium morphology [HP:0005120],43/43,100%,20/20,100%,, -Finger aplasia [HP:0009380],15/15,100%,14/14,100%,, -Aplasia involving forearm bones [HP:0009822],7/7,100%,6/6,100%,, -Aplasia/hypoplasia involving forearm bones [HP:0006503],37/37,100%,12/12,100%,, -Abnormal forearm bone morphology [HP:0040072],37/37,100%,14/14,100%,, -Abnormal upper limb bone morphology [HP:0040070],40/40,100%,14/14,100%,, -Abnormal forearm morphology [HP:0002973],37/37,100%,14/14,100%,, -Aplasia/Hypoplasia of the radius [HP:0006501],37/37,100%,11/11,100%,, -Abnormal morphology of the radius [HP:0002818],37/37,100%,13/13,100%,, -Absent forearm bone [HP:0003953],7/7,100%,6/6,100%,, -Forearm undergrowth [HP:0009821],30/30,100%,7/7,100%,, -Upper limb undergrowth [HP:0009824],33/33,100%,7/7,100%,, -Limb undergrowth [HP:0009826],33/33,100%,7/7,100%,, -Short long bone [HP:0003026],35/35,100%,9/9,100%,, -Abnormal long bone morphology [HP:0011314],44/44,100%,13/13,100%,, -Abnormal ventricular septum morphology [HP:0010438],31/31,100%,19/19,100%,, -Abnormality of thumb phalanx [HP:0009602],13/13,100%,13/13,100%,, -Limited pronation/supination of forearm [HP:0006394],0/0,0%,3/3,100%,, -Limited elbow movement [HP:0002996],0/0,0%,4/4,100%,, -Abnormality of the elbow [HP:0009811],0/0,0%,5/5,100%,, -Limitation of joint mobility [HP:0001376],0/0,0%,4/4,100%,, -Abnormality of joint mobility [HP:0011729],1/1,100%,5/5,100%,, -Abnormal joint physiology [HP:0034430],1/1,100%,5/5,100%,, -Abnormal musculoskeletal physiology [HP:0011843],1/1,100%,5/5,100%,, -Abnormality of cardiovascular system electrophysiology [HP:0030956],15/15,100%,3/3,100%,, -Abnormal cardiovascular system physiology [HP:0011025],23/23,100%,5/5,100%,, -Pre-capillary pulmonary hypertension [HP:0033578],4/4,100%,0/0,0%,, -Elevated pulmonary artery pressure [HP:0004890],4/4,100%,0/0,0%,, -Abnormality of pulmonary circulation [HP:0030875],4/4,100%,0/0,0%,, -Abnormal vascular physiology [HP:0030163],4/4,100%,0/0,0%,, -Abnormality of the vasculature [HP:0002597],10/10,100%,2/2,100%,, -Abnormal respiratory system physiology [HP:0002795],4/4,100%,0/0,0%,, -Abnormality of the respiratory system [HP:0002086],4/4,100%,0/0,0%,, -Tricuspid regurgitation [HP:0005180],3/3,100%,0/0,0%,, -Atrioventricular valve regurgitation [HP:0034376],4/4,100%,2/2,100%,, -Abnormal atrioventricular valve physiology [HP:0031650],4/4,100%,2/2,100%,, -Abnormal heart valve physiology [HP:0031653],4/4,100%,2/2,100%,, -Abnormal tricuspid valve physiology [HP:0031651],3/3,100%,0/0,0%,, -Aplasia of the 1st metacarpal [HP:0010035],0/0,0%,0/0,0%,, -Aplasia of the proximal phalanges of the hand [HP:0010242],0/0,0%,0/0,0%,, -Aplasia/Hypoplasia of the proximal phalanges of the hand [HP:0009851],0/0,0%,0/0,0%,, -Abnormal proximal phalanx morphology of the hand [HP:0009834],0/0,0%,0/0,0%,, -Abnormal finger phalanx morphology [HP:0005918],3/3,100%,0/0,0%,, -Aplasia/Hypoplasia of the phalanges of the hand [HP:0009767],0/0,0%,0/0,0%,, -Aplasia of the phalanges of the hand [HP:0009802],0/0,0%,0/0,0%,, -Aplasia involving bones of the upper limbs [HP:0009823],0/0,0%,0/0,0%,, -Aplasia involving bones of the extremities [HP:0009825],0/0,0%,0/0,0%,, -Aplasia of metacarpal bones [HP:0010048],0/0,0%,0/0,0%,, -Aplasia/Hypoplasia involving the metacarpal bones [HP:0005914],0/0,0%,0/0,0%,, -Abnormal metacarpal morphology [HP:0005916],0/0,0%,0/0,0%,, -Aplasia/Hypoplasia of the 1st metacarpal [HP:0010026],0/0,0%,0/0,0%,, -Abnormal 1st metacarpal morphology [HP:0010009],0/0,0%,0/0,0%,, -Aplasia/Hypoplasia of the phalanges of the thumb [HP:0009658],0/0,0%,0/0,0%,, -Partial absence of thumb [HP:0009659],0/0,0%,0/0,0%,, -1-2 finger syndactyly [HP:0010704],3/3,100%,1/1,100%,, -Finger syndactyly [HP:0006101],4/4,100%,2/2,100%,, -Syndactyly [HP:0001159],4/4,100%,2/2,100%,, -Upper limb phocomelia [HP:0009813],8/85,9%,2/37,5%,, -Phocomelia [HP:0009829],8/8,100%,2/2,100%,, -Clinodactyly of the 5th finger [HP:0004209],1/1,100%,0/0,0%,, -Finger clinodactyly [HP:0040019],1/1,100%,0/0,0%,, -Clinodactyly [HP:0030084],1/1,100%,0/0,0%,, -Deviation of finger [HP:0004097],1/1,100%,2/2,100%,, -Deviation of the hand or of fingers of the hand [HP:0009484],2/2,100%,2/2,100%,, -Deviation of the 5th finger [HP:0009179],1/1,100%,0/0,0%,, -Abnormal 5th finger morphology [HP:0004207],4/4,100%,0/0,0%,, -Patent foramen ovale [HP:0001655],4/40,10%,0/36,0%,, -Small hypothenar eminence [HP:0010487],2/2,100%,0/0,0%,, -Abnormality of the hypothenar eminence [HP:0010486],2/2,100%,0/0,0%,, -Abnormality of the musculature of the hand [HP:0001421],2/2,100%,0/0,0%,, -Abnormality of the musculature of the upper limbs [HP:0001446],2/2,100%,0/0,0%,, -Abnormality of the musculature of the limbs [HP:0009127],2/2,100%,0/0,0%,, -Abnormal skeletal muscle morphology [HP:0011805],2/2,100%,0/0,0%,, -Abnormality of the musculature [HP:0003011],2/2,100%,0/0,0%,, -Perimembranous ventricular septal defect [HP:0011682],3/59,5%,3/25,12%,, -Deviation of the thumb [HP:0009603],0/0,0%,2/2,100%,, -Aplasia/hypoplasia of the humerus [HP:0006507],7/7,100%,4/4,100%,, -Abnormality of the humerus [HP:0003063],7/7,100%,4/4,100%,, -Abnormality of the upper arm [HP:0001454],7/7,100%,4/4,100%,, -Right atrial enlargement [HP:0030718],4/4,100%,0/0,0%,, -Abnormal right atrium morphology [HP:0025580],4/4,100%,0/0,0%,, -Atrial septal dilatation [HP:0011995],4/4,100%,0/0,0%,, -Pectus excavatum [HP:0000767],3/4,75%,2/2,100%,, -Abnormal sternum morphology [HP:0000766],3/3,100%,2/2,100%,, -Postaxial hand polydactyly [HP:0001162],3/4,75%,0/0,0%,, -Postaxial polydactyly [HP:0100259],3/3,100%,0/0,0%,, -Polydactyly [HP:0010442],3/3,100%,0/0,0%,, -Hand polydactyly [HP:0001161],3/3,100%,0/0,0%,, -Duplication of phalanx of hand [HP:0009997],3/3,100%,0/0,0%,, -Duplication of hand bones [HP:0004275],3/3,100%,0/0,0%,, -Duplication of bones involving the upper extremities [HP:0009142],3/3,100%,0/0,0%,, -High palate [HP:0000218],3/3,100%,0/0,0%,, -Abnormal palate morphology [HP:0000174],5/5,100%,0/0,0%,, -Abnormal oral cavity morphology [HP:0000163],5/5,100%,1/1,100%,, -Abnormal oral morphology [HP:0031816],5/5,100%,1/1,100%,, -Abnormality of the mouth [HP:0000153],5/5,100%,1/1,100%,, -Abnormality of the face [HP:0000271],5/5,100%,1/1,100%,, -Abnormality of the head [HP:0000234],5/5,100%,2/2,100%,, -Abnormality of head or neck [HP:0000152],5/5,100%,2/2,100%,, -Short neck [HP:0000470],3/3,100%,0/0,0%,, -Abnormal neck morphology [HP:0025668],3/3,100%,0/0,0%,, -Abnormality of the neck [HP:0000464],3/3,100%,0/0,0%,, -Abnormality of the cervical spine [HP:0003319],3/3,100%,0/0,0%,, -Abnormality of the vertebral column [HP:0000925],4/4,100%,1/1,100%,, -Shield chest [HP:0000914],3/3,100%,0/0,0%,, -Enlarged thorax [HP:0100625],3/3,100%,0/0,0%,, -Y-shaped metatarsals [HP:0010567],3/3,100%,0/0,0%,, -Abnormal metatarsal morphology [HP:0001832],3/3,100%,0/0,0%,, -Abnormal lower limb bone morphology [HP:0040069],3/3,100%,0/0,0%,, -Abnormality of the lower limb [HP:0002814],3/3,100%,0/0,0%,, -Abnormal foot morphology [HP:0001760],3/3,100%,0/0,0%,, -Mitral regurgitation [HP:0001653],1/1,100%,2/2,100%,, -Abnormal mitral valve physiology [HP:0031481],1/1,100%,2/2,100%,, -First degree atrioventricular block [HP:0011705],0/22,0%,1/1,100%,, -Congenital malformation of the great arteries [HP:0011603],4/4,100%,2/2,100%,, -Abnormal morphology of the great vessels [HP:0030962],6/6,100%,2/2,100%,, -Abnormal blood vessel morphology [HP:0033353],6/6,100%,2/2,100%,, -Abnormal vascular morphology [HP:0025015],6/6,100%,2/2,100%,, -Abnormal skull morphology [HP:0000929],1/1,100%,2/2,100%,, -Sinus bradycardia [HP:0001688],0/0,0%,1/1,100%,, -Abnormal electrophysiology of sinoatrial node origin [HP:0011702],0/0,0%,1/1,100%,, -Arrhythmia [HP:0011675],1/1,100%,1/1,100%,, -Bradycardia [HP:0001662],0/0,0%,1/1,100%,, -Abnormal skin morphology [HP:0011121],0/0,0%,1/1,100%,, -Abnormality of the skin [HP:0000951],0/0,0%,1/1,100%,, -Abnormality of the integument [HP:0001574],0/0,0%,1/1,100%,, -Sinus venosus atrial septal defect [HP:0011567],0/2,0%,1/1,100%,, -Abnormal morphology of ulna [HP:0040071],2/2,100%,4/4,100%,, -Aplasia/Hypoplasia of the ulna [HP:0006495],2/2,100%,2/2,100%,, -Persistent left superior vena cava [HP:0005301],2/37,5%,0/0,0%,, -Abnormal superior vena cava morphology [HP:0025575],2/2,100%,0/0,0%,, -Abnormal vena cava morphology [HP:0005345],2/2,100%,0/0,0%,, -Abnormal venous morphology [HP:0002624],2/2,100%,0/0,0%,, -Abnormal shoulder morphology [HP:0003043],1/1,100%,1/1,100%,, -Hypoplasia of deltoid muscle [HP:0030241],0/0,0%,0/0,0%,, -Shoulder muscle hypoplasia [HP:0008952],0/0,0%,0/0,0%,, -Hypoplasia of the musculature [HP:0009004],0/0,0%,0/0,0%,, -Aplasia/Hypoplasia involving the skeletal musculature [HP:0001460],0/0,0%,0/0,0%,, -Abnormality of muscle size [HP:0030236],0/0,0%,0/0,0%,, -Aplasia/Hypoplasia involving the shoulder musculature [HP:0001464],0/0,0%,0/0,0%,, -Aplasia/Hypoplasia involving the musculature of the upper limbs [HP:0001467],0/0,0%,0/0,0%,, -Aplasia/Hypoplasia involving the musculature of the extremities [HP:0009128],0/0,0%,0/0,0%,, -Abnormality of the shoulder girdle musculature [HP:0001435],0/0,0%,0/0,0%,, -Common atrium [HP:0011565],0/83,0%,0/38,0%,, -Unroofed coronary sinus [HP:0031297],0/85,0%,0/38,0%,, -Atrioventricular dissociation [HP:0011709],0/22,0%,1/1,100%,, -Bowed forearm bones [HP:0003956],0/0,0%,1/1,100%,, -Bowing of the arm [HP:0006488],0/0,0%,1/1,100%,, -Bowing of the long bones [HP:0006487],0/0,0%,1/1,100%,, -Abnormal diaphysis morphology [HP:0000940],0/0,0%,1/1,100%,, -Proximal placement of thumb [HP:0009623],0/0,0%,0/0,0%,, -Short middle phalanx of the 5th finger [HP:0004220],0/0,0%,0/0,0%,, -Type A brachydactyly [HP:0009370],0/0,0%,0/0,0%,, -Brachydactyly [HP:0001156],0/0,0%,0/0,0%,, -Short 5th finger [HP:0009237],0/0,0%,0/0,0%,, -Aplasia/Hypoplasia of the 5th finger [HP:0006262],0/0,0%,0/0,0%,, -Aplasia/Hypoplasia of the middle phalanx of the 5th finger [HP:0009161],0/0,0%,0/0,0%,, -Aplasia/Hypoplasia of the phalanges of the 5th finger [HP:0009376],0/0,0%,0/0,0%,, -Abnormal 5th finger phalanx morphology [HP:0004213],0/0,0%,0/0,0%,, -Abnormality of the middle phalanx of the 5th finger [HP:0004219],0/0,0%,0/0,0%,, -Short middle phalanx of finger [HP:0005819],0/0,0%,0/0,0%,, -Aplasia/Hypoplasia of the middle phalanges of the hand [HP:0009843],0/0,0%,0/0,0%,, -Abnormal middle phalanx morphology of the hand [HP:0009833],0/0,0%,0/0,0%,, -Short phalanx of finger [HP:0009803],0/0,0%,0/0,0%,, -Micrognathia [HP:0000347],1/1,100%,1/1,100%,, -Aplasia/Hypoplasia of the mandible [HP:0009118],1/1,100%,1/1,100%,, -Aplasia/Hypoplasia involving bones of the skull [HP:0009116],1/1,100%,1/1,100%,, -Abnormal mandible morphology [HP:0000277],1/1,100%,1/1,100%,, -Abnormal jaw morphology [HP:0030791],1/1,100%,1/1,100%,, -Abnormal facial skeleton morphology [HP:0011821],1/1,100%,1/1,100%,, -Cleft soft palate [HP:0000185],2/2,100%,0/0,0%,, -Abnormal soft palate morphology [HP:0100736],2/2,100%,0/0,0%,, -Cleft palate [HP:0000175],2/2,100%,0/0,0%,, -Orofacial cleft [HP:0000202],2/2,100%,0/0,0%,, -Craniofacial cleft [HP:5201015],2/2,100%,0/0,0%,, -Hypoplastic scapulae [HP:0000882],1/1,100%,1/1,100%,, -Aplasia/Hypoplasia of the scapulae [HP:0006713],1/1,100%,1/1,100%,, -Abnormal scapula morphology [HP:0000782],1/1,100%,1/1,100%,, -Amelia involving the upper limbs [HP:0009812],0/83,0%,1/37,3%,, -Third degree atrioventricular block [HP:0001709],0/22,0%,1/1,100%,, -Upper extremity joint dislocation [HP:0030310],0/0,0%,2/2,100%,, -Joint dislocation [HP:0001373],0/0,0%,2/2,100%,, -Abnormal toe morphology [HP:0001780],0/0,0%,0/0,0%,, -Abnormal toe phalanx morphology [HP:0010161],0/0,0%,0/0,0%,, -Abnormality of the distal phalanges of the toes [HP:0010182],0/0,0%,0/0,0%,, -Aplasia/Hypoplasia of the 2nd finger [HP:0006264],1/1,100%,0/0,0%,, -Abnormal 2nd finger morphology [HP:0004100],1/1,100%,0/0,0%,, -Synostosis of joints [HP:0100240],1/1,100%,1/1,100%,, -Left ventricular noncompaction cardiomyopathy [HP:0011664],0/1,0%,1/5,20%,, -Noncompaction cardiomyopathy [HP:0012817],0/0,0%,1/1,100%,, -Cardiomyopathy [HP:0001638],0/0,0%,1/1,100%,, -Abnormal myocardium morphology [HP:0001637],0/0,0%,1/1,100%,, -Mitral valve prolapse [HP:0001634],0/0,0%,1/1,100%,, -Abnormal mitral valve morphology [HP:0001633],0/0,0%,1/1,100%,, -Abnormal atrioventricular valve morphology [HP:0006705],0/0,0%,1/1,100%,, -Abnormal heart valve morphology [HP:0001654],0/0,0%,1/1,100%,, -Short 1st metacarpal [HP:0010034],0/30,0%,0/22,0%,, -Short phalanx of the thumb [HP:0009660],0/30,0%,0/22,0%,, diff --git a/docs/tutorial.rst b/docs/tutorial.rst index 10ce6a23..8d1d4726 100644 --- a/docs/tutorial.rst +++ b/docs/tutorial.rst @@ -266,11 +266,11 @@ by exploring the phenotype MTC filtering report. .. raw:: html :file: report/tbx5_frameshift_vs_missense.mtc_report.html -and these are the HPO terms ordered by the p value corrected with the Benjamini-Hochberg procedure: +and these are the top 20 HPO terms ordered by the p value corrected with the Benjamini-Hochberg procedure: >>> from gpsea.analysis.predicate import PatientCategories >>> summary_df = result.summarize(hpo, PatientCategories.YES) ->>> summary_df.to_csv('docs/report/tbx5_frameshift_vs_missense.csv') # doctest: +SKIP +>>> summary_df.head(20).to_csv('docs/report/tbx5_frameshift_vs_missense.csv') # doctest: +SKIP .. csv-table:: *TBX5* frameshift vs missense :file: report/tbx5_frameshift_vs_missense.csv @@ -283,4 +283,4 @@ was observed in 31/60 (52%) patients with a missense variant but it was observed in 19/19 (100%) patients with a frameshift variant. Fisher exact test computed a p value of `~0.0000562` and the p value corrected by Benjamini-Hochberg procedure -is `~0.00112`. +is `~0.000899`. diff --git a/docs/user-guide/predicates.rst b/docs/user-guide/predicates.rst index 00647f1f..908afa14 100644 --- a/docs/user-guide/predicates.rst +++ b/docs/user-guide/predicates.rst @@ -4,23 +4,80 @@ Predicates ========== -GPSEA uses predicates to test if variant, patient, or any tested item -meets a condition. Based on the test results, the items are assigned into groups. +Searching for genotype-phenotype associations usually requires to partition +the individuals into several discrete groups to allow testing for the inter-group differences. +GPSEA reflects these requirements with its predicate API. +Perhaps unsurprisingly, a predicate must be capable of partitioning the individuals into two or more groups. +The groups must be *exclusive* - each individual must be assigned at most into one group. +Moreover, the groups should be *exhaustive* and cover maximum of the possible states. +However, the predicate is allowed to return `None` if the individual cannot be assigned. +In result, the individual will be omitted from the downstream analysis. + +Predicates serve both *genotype* and *phenotype* prongs of the analysis. +Genotype predicates (:class:`~gpsea.analysis.predicate.genotype.GenotypePolyPredicate`) +assign the :class:`~gpsea.model.Patient` +into a group (mostly) based on the variant information, while the +phenotype predicates (:class:`~gpsea.analysis.predicate.phenotype.PhenotypePolyPredicate`) +use the HPO terms to assign a group. + +It is literally impossible to use GPSEA without the predicates +because all analyses need at least one predicate (typically a *genotype* predicate). +Luckily, the purpose of this guide is to show all that is to know about predicates. +We will first discuss the genotype predicates and end with phenotype predicates. + +.. _genotype-predicates: + +******************* +Genotype predicates +******************* + +A genotype predicate seeks to divide the individuals along an axis that is orthogonal to phenotypes. +Typically, this includes using the genotype data, such as presence of a missense variant +in a heterozygous genotype. However, other categorical variables, +such as diagnoses (TODO - add link to disease predicate) or cluster ids can also be used. + +The genotype predicates test the individual for a presence of variants that meet certain inclusion criteria. +The testing is done in two steps. First, we count the alleles +of the matching variants and then we interpret the count, possibly including factors +such as the expected mode of inheritance and sex, to assign the individual into a group. +Finding the matching variants is what +the :class:`~gpsea.analysis.predicate.genotype.VariantPredicate` is all about. + + +TODO: wordsmith +We must first create the variant predicate and then wrap it in genotype predicate. -As described in the :class:`~gpsea.analysis.predicate.PolyPredicate` API, -the groups must be *exclusive* - the item can be assigned with one and only one group, -and *exhaustive* - the groups must cover all possible scenarios. -However, if the item cannot be assigned into any meaningful category, -the predicate can return `None`, and the item will be omitted from the analysis. +Variant predicates +================== -The predicates can be chained to test for more complex conditions. -For instance, "test if a patient has a missense or synonymous variant located in exon 6 of transcript `NM_013275.6`". +GPSEA uses the :class:`~gpsea.analysis.predicate.genotype.VariantPredicate` class +to test if a :class:`~gpsea.model.Variant` meets the inclusion criteria. +The variant predicate can leverage multiple primary data: -Let's demonstrate this on an example with a :class:`~gpsea.analysis.predicate.genotype.VariantPredicate`. -We will load a cohort of 5 subjects with variants in *ANKRD11*, leading to KBG syndrome. -The the clinical signs and symptoms of the subjects were encoded into HPO terms -along with the pathogenic *ANKRD11* variant. ++------------------------+-------------------------------------------------------------------------------------------------+ +| Primary data source | Example | ++========================+=================================================================================================+ +| Allele | the variant being a deletion or a single nucleotide variant (SNV) | ++------------------------+-------------------------------------------------------------------------------------------------+ +| Genome | overlaps of a target genomic region | ++------------------------+-------------------------------------------------------------------------------------------------+ +| Functional annotation | variant is predicted to lead to a missense change or affect an exon of certain transcript | ++------------------------+-------------------------------------------------------------------------------------------------+ +| Protein data | variant is located in a region encoding a protein domain, protein feature type | ++------------------------+-------------------------------------------------------------------------------------------------+ + +which demands a considerable amount of flexibility for creating the predicate. + +As a rule of thumb, the predicates for testing basic conditions are available off the shelf, +and they can be used as building block for testing for more complex conditions, +such as testing if the variant is "a missense or synonymous variant located in exon 6 of transcript `NM_013275.6`". + +Let's demonstrate this on few examples. +We will load a cohort of 19 subjects with variants in *RERE*, +leading to `Holt-Oram syndrome MIM:142900 `_. +The the clinical signs and symptoms of the subjects were encoded into HPO terms +along with the pathogenic *RERE* variant. Let's load the phenopackets, as previously described in greater detail the :ref:`input-data` section. Briefly, we first load HPO: @@ -36,7 +93,7 @@ then, we configure the cohort creator: which we use to create a :class:`~gpsea.model.Cohort` from a bunch of phenopackets from the release `0.1.18` of `Phenopacket Store `_. -This time, however, we will load 19 individuals with mutations in *RERE* gene: +We will load 19 individuals with mutations in *RERE* gene: >>> from ppktstore.registry import configure_phenopacket_registry >>> registry = configure_phenopacket_registry() @@ -58,8 +115,8 @@ that replaces the histidine encoded by the 1435th codon of `NM_001042681.2` with >>> variant_key_of_interest = '1_8358231_8358231_T_C' >>> variant = cohort.get_variant_by_key(variant_key_of_interest) -Simple predicates -***************** +Building blocks +--------------- We can check that the variant overlaps with *RERE*: @@ -92,10 +149,10 @@ See :class:`~gpsea.analysis.predicate.genotype.VariantPredicates` for more info on the predicates available off the shelf. -Compound predicates -******************* +Complex conditions +------------------ -The simple predicates can be combined to test for more elaborate conditions. +We can combine the building blocks to test for more elaborate conditions. For instance, we can test if the variant meets *any* or several conditions: >>> nonsense = VariantPredicates.variant_effect(VariantEffect.STOP_GAINED, tx_id=rere_mane_tx_id) @@ -121,8 +178,8 @@ such as testing if the variant is a *"chromosomal deletion" or a deletion which '(structural type is SO:1000029 OR (variant class is DEL AND change length <= -50))' -Inverted predicate -****************** +Inverting conditions +-------------------- Sometimes we may want to test the variant for a condition that must *not* be met. For instance, we may want to test if the variant is a deletion @@ -151,9 +208,219 @@ This is how we can use the predicate inversion to build the predicate for non-fr Note the presence of a tilde ``~`` before the variant effect predicate and resulting ``NOT`` in the predicate question. +The variant predicate offers a flexible API for testing if variants meet a condition. +However, the genotype phenotype correlations are done on the individual level +and the variant predicates are used as a component of the genotype predicate. +The next sections show how to use variant predicates to assign individuals into groups. + + +Mode of inheritance predicate +============================= + +The :class:`~gpsea.analysis.predicate.genotype.ModeOfInheritancePredicate` +assigns the individual into a group based on the number of alleles +that match a condition specified by a :class:`~gpsea.analysis.predicate.genotype.VariantPredicate`. +The :class:`~gpsea.analysis.predicate.genotype.ModeOfInheritancePredicate` supports +the following Mendelian modes of inheritance (MoI): + + ++-----------------------+-----------------------------------+------------------+------------------------+ +| Mode of inheritance | Sex | Allele count | Genotype category | ++=======================+===================================+==================+========================+ +| Autosomal dominant | `*` | 0 | `HOM_REF` | ++ +-----------------------------------+------------------+------------------------+ +| | `*` | 1 | `HET` | ++ +-----------------------------------+------------------+------------------------+ +| | `*` | :math:`\ge 2` | ``None`` | ++-----------------------+-----------------------------------+------------------+------------------------+ +| Autosomal recessive | `*` | 0 | `HOM_REF` | ++ +-----------------------------------+------------------+------------------------+ +| | `*` | 1 | `HET` | ++ +-----------------------------------+------------------+------------------------+ +| | `*` | 2 | `BIALLELIC_ALT` | ++ +-----------------------------------+------------------+------------------------+ +| | `*` | :math:`\ge 3` | ``None`` | ++-----------------------+-----------------------------------+------------------+------------------------+ +| X-linked dominant | `*` | 0 | `HOM_REF` | ++ +-----------------------------------+------------------+------------------------+ +| | `*` | 1 | `HET` | ++ +-----------------------------------+------------------+------------------------+ +| | `*` | :math:`\ge 2` | ``None`` | ++-----------------------+-----------------------------------+------------------+------------------------+ +| X-linked recessive | `*` | 0 | `HOM_REF` | ++ +-----------------------------------+------------------+------------------------+ +| | :class:`~gpsea.model.Sex.FEMALE` | 1 | `HET` | ++ + +------------------+------------------------+ +| | | 2 | `BIALLELIC_ALT` | ++ + +------------------+------------------------+ +| | | :math:`\ge 3` | ``None`` | ++ +-----------------------------------+------------------+------------------------+ +| | :class:`~gpsea.model.Sex.MALE` | 1 | `HEMI` | ++ + +------------------+------------------------+ +| | | :math:`\ge 2` | ``None`` | ++-----------------------+-----------------------------------+------------------+------------------------+ + +.. note:: + + `BIALLELIC_ALT` includes both homozygous and compound heterozygous genotypes. + +Clinical judgment should be used to choose the MoI for the cohort analysis. +Then a predicate for the desired MoI can be created by one of +:class:`~gpsea.analysis.predicate.genotype.ModeOfInheritancePredicate` static constructors: + +* :func:`~gpsea.analysis.predicate.genotype.ModeOfInheritancePredicate.autosomal_dominant` +* :func:`~gpsea.analysis.predicate.genotype.ModeOfInheritancePredicate.autosomal_recessive` +* :func:`~gpsea.analysis.predicate.genotype.ModeOfInheritancePredicate.x_dominant` +* :func:`~gpsea.analysis.predicate.genotype.ModeOfInheritancePredicate.x_recessive` + +All constructors take an instance +of :class:`~gpsea.analysis.predicate.genotype.VariantPredicate` as an argument. + + +Example +------- + +Here we show seting up a predicate for grouping individuals based on +having a variant that leads to a frameshift or to a stop gain to a fictional transcript ``NM_1234.5`` +to test differences between the genotypes of a disease with an autosomal recessive MoI. + +First, we set up a :class:`~gpsea.analysis.predicate.genotype.VariantPredicate` +for testing if a variant meets the condition: + +>>> from gpsea.model import VariantEffect +>>> from gpsea.analysis.predicate.genotype import VariantPredicates +>>> tx_id = 'NM_1234.5' +>>> is_frameshift_or_stop_gain = VariantPredicates.variant_effect(VariantEffect.FRAMESHIFT_VARIANT, tx_id) \ +... | VariantPredicates.variant_effect(VariantEffect.STOP_GAINED, tx_id) +>>> is_frameshift_or_stop_gain.get_question() +'(FRAMESHIFT_VARIANT on NM_1234.5 OR STOP_GAINED on NM_1234.5)' + +Next, we use :class:`~gpsea.analysis.predicate.genotype.ModeOfInheritancePredicate.autosomal_recessive` +for assigning a patient into a genotype group: + +>>> from gpsea.analysis.predicate.genotype import ModeOfInheritancePredicate +>>> gt_predicate = ModeOfInheritancePredicate.autosomal_recessive(is_frameshift_or_stop_gain) +>>> gt_predicate.get_question() +'Which genotype group does the patient fit in: HOM_REF, HET, BIALLELIC_ALT' + +The `gt_predicate` can be used in downstream analysis, such as in :class: + + +Groups predicate +================ + +Sometimes, all we want is to compare if there is a difference between individuals +who include one or more alleles of variant $X$ vs. individuals with variants $Y$, +vs. individuals with variants $Z$, where $X$, $Y$ and $Z$ are variant predicates. +We can do this with a *groups* predicate. + +The :func:`~gpsea.analysis.predicate.genotype.groups_predicate` +takes *n* variant predicates and *n* group labels, and it will assign the patients +into the respective groups if one or more matching allele is found. +However, only one predicate is allowed to return a non-zero allele count. +Otherwise, the patient is assigned with ``None`` and excluded from the analysis. + +Example +------- + +Here we show how to build a :class:`~gpsea.analysis.predicate.genotype.GenotypePolyPredicate` +for testing if the individual has at least one missense vs. frameshift vs. synonymous variant. + +>>> from gpsea.model import VariantEffect +>>> from gpsea.analysis.predicate.genotype import VariantPredicates, groups_predicate +>>> tx_id = 'NM_1234.5' +>>> gt_predicate = groups_predicate( +... predicates=( +... VariantPredicates.variant_effect(VariantEffect.MISSENSE_VARIANT, tx_id), +... VariantPredicates.variant_effect(VariantEffect.FRAMESHIFT_VARIANT, tx_id), +... VariantPredicates.variant_effect(VariantEffect.SYNONYMOUS_VARIANT, tx_id), +... ), +... group_names=('Missense', 'Frameshift', 'Synonymous'), +... ) +>>> gt_predicate.get_question() +'Genotype group: Missense, Frameshift, Synonymous' + + +.. _phenotype-predicates: + +******************** +Phenotype predicates +******************** + +The phenotype predicate assigns the individual into a group with respect to tested phenotype. +Typically, the phenotype corresponds to a clinical sign or symptom encoded into an HPO term. + + +Propagating phenotype predicate +=============================== + +When testing for presence or absence of an HPO term, the propagating phenotype predicate +leverages the :ref:`true-path-rule` to take advantage of the HPO hierarchy. +In result, an individual annotated with a term is implicitly annotated with all its ancestors. +For instance, an individual annotated with `Ectopia lentis `_ +is also annotated with `Abnormal lens morphology `_, +`Abnormal anterior eye segment morphology `_, +`Abnormal eye morphology `_, ... + +Similarly, all descendants of a term, whose presence was specifically excluded in an individual, +are implicitly excluded. + +:class:`~gpsea.analysis.predicate.phenotype.PropagatingPhenotypePredicate` implements this logic. + +Example +------- + +Here we show how to set up :class:`~gpsea.analysis.predicate.phenotype.PropagatingPhenotypePredicate` +to test for a presence of `Abnormal lens morphology `_. + + +>>> from gpsea.analysis.predicate.phenotype import PropagatingPhenotypePredicate +>>> query = hpotk.TermId.from_curie('HP:0000517') +>>> pheno_predicate = PropagatingPhenotypePredicate( +... hpo=hpo, +... query=query, +... ) +>>> pheno_predicate.get_question() +'Is Abnormal lens morphology present in the patient?' + + +TODO: explain ``missing_implies_phenotype_excluded`` + + +Predicates for all cohort phenotypes +==================================== + +Constructing phenotype predicates for all HPO terms of a cohort sounds a bit tedious. +The :func:`~gpsea.analysis.predicate.phenotype.prepare_predicates_for_terms_of_interest` +function cuts down the tedium: + +>>> from gpsea.analysis.predicate.phenotype import prepare_predicates_for_terms_of_interest +>>> pheno_predicates = prepare_predicates_for_terms_of_interest( +... cohort=cohort, +... hpo=hpo, +... ) +>>> len(pheno_predicates) +301 + +and prepares predicates for testing 301 HPO terms of the *RERE* cohort. + + +******* +Gallery +******* + +Here we show examples of predicates used in some of our analyses. + +TODO + + +********** +Need more? +********** -That's it for predicates. Please see :class:`~gpsea.analysis.predicate.genotype.VariantPredicates` +Please see :class:`~gpsea.analysis.predicate.genotype.VariantPredicates` and :class:`~gpsea.analysis.predicate.genotype.ProteinPredicates` -for a comprehensive list of the predicates available off the shelf. +for a list of the predicates available off the shelf. -Please open an issue on our `GitHub tracker `_ if a predicate seems to be missing. +However, feel free to open an issue on our `GitHub tracker `_ +if a predicate seems to be missing. diff --git a/docs/user-guide/report/tbx5_frameshift.csv b/docs/user-guide/report/tbx5_frameshift.csv index 6abe64be..a96700cf 100644 --- a/docs/user-guide/report/tbx5_frameshift.csv +++ b/docs/user-guide/report/tbx5_frameshift.csv @@ -1,262 +1,22 @@ -FRAMESHIFT_VARIANT on NM_181486.4,Yes,Yes,No,No,, +"Which genotype group does the patient fit in: HOM_REF, HET",HOM_REF,HOM_REF,HET,HET,, ,Count,Percent,Count,Percent,Corrected p values,p values -Ventricular septal defect [HP:0001629],19/19,100%,42/71,59%,0.004112753923262261,0.00024192670136836828 -Abnormal atrioventricular conduction [HP:0005150],3/3,100%,1/23,4%,0.013076923076923076,0.0015384615384615385 -Absent thumb [HP:0009777],14/31,45%,18/100,18%,0.021044138590779585,0.0037136715160199273 -Atrioventricular block [HP:0001678],2/2,100%,1/23,4%,0.034,0.010000000000000002 -Heart block [HP:0012722],2/2,100%,1/23,4%,0.034,0.010000000000000002 -Patent ductus arteriosus [HP:0001643],2/2,100%,6/40,15%,0.09214092140921408,0.03252032520325203 -Secundum atrial septal defect [HP:0001684],4/22,18%,23/55,42%,0.1440020479198931,0.06544319142266644 -Triphalangeal thumb [HP:0001199],13/32,41%,23/99,23%,0.1440020479198931,0.06932119159387057 -Cardiac conduction abnormality [HP:0031546],3/3,100%,15/37,41%,0.1440020479198931,0.08259109311740892 -Muscular ventricular septal defect [HP:0011623],6/25,24%,8/84,10%,0.1440020479198931,0.08470708701170182 -Pulmonary arterial hypertension [HP:0002092],0/2,0%,8/14,57%,0.6899307928951144,0.4666666666666667 -Short thumb [HP:0009778],8/30,27%,25/69,36%,0.6899307928951144,0.4870099714553749 -Absent radius [HP:0003974],6/25,24%,9/43,21%,1.0,0.7703831604944444 -Hypoplasia of the radius [HP:0002984],6/14,43%,34/75,45%,1.0,1.0 -Atrial septal defect [HP:0001631],20/20,100%,63/65,97%,1.0,1.0 -Hypoplasia of the ulna [HP:0003022],2/10,20%,3/17,18%,1.0,1.0 -Short humerus [HP:0005792],4/9,44%,8/21,38%,1.0,1.0 -Abnormal ventricular septum morphology [HP:0010438],19/19,100%,42/42,100%,, -Abnormal cardiac ventricle morphology [HP:0001713],19/19,100%,43/43,100%,, -Abnormal heart morphology [HP:0001627],30/30,100%,89/89,100%,, -Abnormal cardiovascular system morphology [HP:0030680],30/30,100%,92/92,100%,, -Abnormality of the cardiovascular system [HP:0001626],32/32,100%,94/94,100%,, -Phenotypic abnormality [HP:0000118],38/38,100%,114/114,100%,, -All [HP:0000001],38/38,100%,114/114,100%,, -Abnormal cardiac septum morphology [HP:0001671],28/28,100%,89/89,100%,, -Forearm undergrowth [HP:0009821],7/7,100%,35/35,100%,, -Abnormal upper limb bone morphology [HP:0040070],14/14,100%,50/50,100%,, -Abnormality of the upper limb [HP:0002817],34/34,100%,102/102,100%,, -Abnormality of limbs [HP:0040064],34/34,100%,102/102,100%,, -Abnormal limb bone morphology [HP:0002813],34/34,100%,92/92,100%,, -Abnormality of limb bone [HP:0040068],34/34,100%,92/92,100%,, -Abnormality of the skeletal system [HP:0000924],35/35,100%,103/103,100%,, -Abnormality of the musculoskeletal system [HP:0033127],35/35,100%,104/104,100%,, -Abnormal appendicular skeleton morphology [HP:0011844],34/34,100%,93/93,100%,, -Abnormal skeletal morphology [HP:0011842],35/35,100%,103/103,100%,, -Upper limb undergrowth [HP:0009824],7/7,100%,38/38,100%,, -Limb undergrowth [HP:0009826],7/7,100%,38/38,100%,, -Aplasia/hypoplasia of the extremities [HP:0009815],22/22,100%,78/78,100%,, -Aplasia/hypoplasia involving the skeleton [HP:0009115],23/23,100%,80/80,100%,, -Aplasia/hypoplasia involving bones of the upper limbs [HP:0006496],22/22,100%,78/78,100%,, -Aplasia/hypoplasia involving bones of the extremities [HP:0045060],22/22,100%,78/78,100%,, -Aplasia/hypoplasia involving forearm bones [HP:0006503],12/12,100%,43/43,100%,, -Abnormal forearm bone morphology [HP:0040072],14/14,100%,43/43,100%,, -Abnormal forearm morphology [HP:0002973],14/14,100%,43/43,100%,, -Short long bone [HP:0003026],9/9,100%,41/41,100%,, -Abnormal long bone morphology [HP:0011314],13/13,100%,50/50,100%,, -Aplasia/Hypoplasia of the radius [HP:0006501],11/11,100%,43/43,100%,, -Abnormal morphology of the radius [HP:0002818],13/13,100%,43/43,100%,, -1-2 finger syndactyly [HP:0010704],1/1,100%,4/4,100%,, -Finger syndactyly [HP:0006101],2/2,100%,5/5,100%,, -Syndactyly [HP:0001159],2/2,100%,5/5,100%,, -Abnormal digit morphology [HP:0011297],33/33,100%,67/67,100%,, -Abnormal atrial septum morphology [HP:0011994],20/20,100%,64/64,100%,, -Abnormal cardiac atrium morphology [HP:0005120],20/20,100%,64/64,100%,, -Perimembranous ventricular septal defect [HP:0011682],3/25,12%,6/84,7%,, -Aplasia/Hypoplasia of the thumb [HP:0009601],19/19,100%,40/40,100%,, -Aplasia/Hypoplasia of fingers [HP:0006265],19/19,100%,44/44,100%,, -Aplasia/hypoplasia involving bones of the hand [HP:0005927],19/19,100%,44/44,100%,, -Abnormal hand morphology [HP:0005922],20/20,100%,75/75,100%,, -Abnormality of the hand [HP:0001155],31/31,100%,88/88,100%,, -Abnormal finger morphology [HP:0001167],31/31,100%,64/64,100%,, -Abnormal thumb morphology [HP:0001172],31/31,100%,58/58,100%,, -Finger aplasia [HP:0009380],14/14,100%,23/23,100%,, -Aplasia involving forearm bones [HP:0009822],6/6,100%,9/9,100%,, -Absent forearm bone [HP:0003953],6/6,100%,9/9,100%,, -Aplasia/Hypoplasia of the ulna [HP:0006495],2/2,100%,4/4,100%,, -Abnormal morphology of ulna [HP:0040071],4/4,100%,4/4,100%,, -Abnormal carpal morphology [HP:0001191],0/0,0%,30/32,94%,, -Abnormality of the wrist [HP:0003019],0/0,0%,30/30,100%,, -Abnormality of upper limb joint [HP:0009810],6/6,100%,32/32,100%,, -Abnormal joint morphology [HP:0001367],6/6,100%,33/33,100%,, -Abnormality of cardiovascular system electrophysiology [HP:0030956],3/3,100%,18/18,100%,, -Abnormal cardiovascular system physiology [HP:0011025],5/5,100%,30/30,100%,, -Abnormality of thumb phalanx [HP:0009602],13/13,100%,26/26,100%,, -Upper limb phocomelia [HP:0009813],2/37,5%,8/116,7%,, -Phocomelia [HP:0009829],2/2,100%,8/8,100%,, -Short finger [HP:0009381],8/8,100%,27/27,100%,, -Short digit [HP:0011927],10/10,100%,28/28,100%,, -Pre-capillary pulmonary hypertension [HP:0033578],0/0,0%,8/8,100%,, -Elevated pulmonary artery pressure [HP:0004890],0/0,0%,8/8,100%,, -Abnormality of pulmonary circulation [HP:0030875],0/0,0%,8/8,100%,, -Abnormal vascular physiology [HP:0030163],0/0,0%,8/8,100%,, -Abnormality of the vasculature [HP:0002597],2/2,100%,17/17,100%,, -Abnormal respiratory system physiology [HP:0002795],0/0,0%,8/8,100%,, -Abnormality of the respiratory system [HP:0002086],0/0,0%,8/8,100%,, -Congenital malformation of the great arteries [HP:0011603],2/2,100%,7/7,100%,, -Abnormal morphology of the great vessels [HP:0030962],2/2,100%,10/10,100%,, -Abnormal blood vessel morphology [HP:0033353],2/2,100%,11/11,100%,, -Abnormal vascular morphology [HP:0025015],2/2,100%,11/11,100%,, -Patent foramen ovale [HP:0001655],0/36,0%,4/69,6%,, -Synostosis of joints [HP:0100240],1/1,100%,1/1,100%,, -Abnormality of joint mobility [HP:0011729],5/5,100%,3/3,100%,, -Abnormal joint physiology [HP:0034430],5/5,100%,3/3,100%,, -Abnormal musculoskeletal physiology [HP:0011843],5/5,100%,3/3,100%,, -Abnormality of the vertebral column [HP:0000925],1/1,100%,4/4,100%,, -Abnormal axial skeleton morphology [HP:0009121],5/5,100%,9/9,100%,, -Complete atrioventricular canal defect [HP:0001674],3/36,8%,6/67,9%,, -Atrioventricular canal defect [HP:0006695],3/3,100%,6/6,100%,, -Hypoplasia of deltoid muscle [HP:0030241],0/0,0%,6/6,100%,, -Shoulder muscle hypoplasia [HP:0008952],0/0,0%,6/6,100%,, -Hypoplasia of the musculature [HP:0009004],0/0,0%,6/6,100%,, -Aplasia/Hypoplasia involving the skeletal musculature [HP:0001460],0/0,0%,6/6,100%,, -Abnormality of muscle size [HP:0030236],0/0,0%,6/6,100%,, -Abnormal skeletal muscle morphology [HP:0011805],0/0,0%,8/8,100%,, -Abnormality of the musculature [HP:0003011],0/0,0%,8/8,100%,, -Aplasia/Hypoplasia involving the shoulder musculature [HP:0001464],0/0,0%,6/6,100%,, -Aplasia/Hypoplasia involving the musculature of the upper limbs [HP:0001467],0/0,0%,6/6,100%,, -Aplasia/Hypoplasia involving the musculature of the extremities [HP:0009128],0/0,0%,6/6,100%,, -Abnormality of the musculature of the limbs [HP:0009127],0/0,0%,8/8,100%,, -Abnormality of the musculature of the upper limbs [HP:0001446],0/0,0%,8/8,100%,, -Abnormality of the shoulder girdle musculature [HP:0001435],0/0,0%,6/6,100%,, -Bowed forearm bones [HP:0003956],1/1,100%,0/0,0%,, -Bowing of the arm [HP:0006488],1/1,100%,0/0,0%,, -Bowing of the long bones [HP:0006487],1/1,100%,0/0,0%,, -Abnormal diaphysis morphology [HP:0000940],1/1,100%,0/0,0%,, -Micrognathia [HP:0000347],1/1,100%,1/1,100%,, -Aplasia/Hypoplasia of the mandible [HP:0009118],1/1,100%,1/1,100%,, -Aplasia/Hypoplasia involving bones of the skull [HP:0009116],1/1,100%,1/1,100%,, -Aplasia/hypoplasia affecting bones of the axial skeleton [HP:0009122],2/2,100%,3/3,100%,, -Abnormal skull morphology [HP:0000929],2/2,100%,1/1,100%,, -Abnormality of the head [HP:0000234],2/2,100%,5/5,100%,, -Abnormality of head or neck [HP:0000152],2/2,100%,5/5,100%,, -Abnormal mandible morphology [HP:0000277],1/1,100%,1/1,100%,, -Abnormal jaw morphology [HP:0030791],1/1,100%,1/1,100%,, -Abnormal facial skeleton morphology [HP:0011821],1/1,100%,1/1,100%,, -Abnormal oral cavity morphology [HP:0000163],1/1,100%,5/5,100%,, -Abnormal oral morphology [HP:0031816],1/1,100%,5/5,100%,, -Abnormality of the mouth [HP:0000153],1/1,100%,5/5,100%,, -Abnormality of the face [HP:0000271],1/1,100%,5/5,100%,, -Aplasia/Hypoplasia involving bones of the thorax [HP:0006711],2/2,100%,2/2,100%,, -Abnormal thorax morphology [HP:0000765],5/5,100%,7/7,100%,, -Persistent left superior vena cava [HP:0005301],0/0,0%,4/39,10%,, -Abnormal superior vena cava morphology [HP:0025575],0/0,0%,4/4,100%,, -Abnormal vena cava morphology [HP:0005345],0/0,0%,4/4,100%,, -Abnormal venous morphology [HP:0002624],0/0,0%,4/4,100%,, -Aplasia/hypoplasia of the humerus [HP:0006507],4/4,100%,8/8,100%,, -Abnormality of the humerus [HP:0003063],4/4,100%,8/8,100%,, -Abnormality of the upper arm [HP:0001454],4/4,100%,8/8,100%,, -Right atrial enlargement [HP:0030718],0/0,0%,4/4,100%,, -Abnormal right atrium morphology [HP:0025580],0/0,0%,4/4,100%,, -Atrial septal dilatation [HP:0011995],0/0,0%,4/4,100%,, -Left ventricular noncompaction cardiomyopathy [HP:0011664],1/5,20%,1/7,14%,, -Noncompaction cardiomyopathy [HP:0012817],1/1,100%,1/1,100%,, -Cardiomyopathy [HP:0001638],1/1,100%,1/1,100%,, -Abnormal myocardium morphology [HP:0001637],1/1,100%,1/1,100%,, -Hypoplastic scapulae [HP:0000882],1/1,100%,1/1,100%,, -Aplasia/Hypoplasia of the scapulae [HP:0006713],1/1,100%,1/1,100%,, -Abnormal scapula morphology [HP:0000782],1/1,100%,1/1,100%,, -Abnormal toe morphology [HP:0001780],0/0,0%,1/1,100%,, -Abnormal foot morphology [HP:0001760],0/0,0%,4/4,100%,, -Abnormality of the lower limb [HP:0002814],0/0,0%,4/4,100%,, -Abnormal toe phalanx morphology [HP:0010161],0/0,0%,1/1,100%,, -Abnormality of the distal phalanges of the toes [HP:0010182],0/0,0%,1/1,100%,, -Abnormal lower limb bone morphology [HP:0040069],0/0,0%,4/4,100%,, -Aplasia involving bones of the extremities [HP:0009825],0/0,0%,3/3,100%,, -Aplasia of the 1st metacarpal [HP:0010035],0/0,0%,3/3,100%,, -Aplasia of the proximal phalanges of the hand [HP:0010242],0/0,0%,3/3,100%,, -Aplasia/Hypoplasia of the proximal phalanges of the hand [HP:0009851],0/0,0%,3/3,100%,, -Abnormal proximal phalanx morphology of the hand [HP:0009834],0/0,0%,3/3,100%,, -Abnormal finger phalanx morphology [HP:0005918],0/0,0%,9/9,100%,, -Aplasia/Hypoplasia of the phalanges of the hand [HP:0009767],0/0,0%,6/6,100%,, -Aplasia of the phalanges of the hand [HP:0009802],0/0,0%,3/3,100%,, -Aplasia involving bones of the upper limbs [HP:0009823],0/0,0%,3/3,100%,, -Aplasia of metacarpal bones [HP:0010048],0/0,0%,3/3,100%,, -Aplasia/Hypoplasia involving the metacarpal bones [HP:0005914],0/0,0%,4/4,100%,, -Abnormal metacarpal morphology [HP:0005916],0/0,0%,4/4,100%,, -Aplasia/Hypoplasia of the 1st metacarpal [HP:0010026],0/0,0%,4/4,100%,, -Abnormal 1st metacarpal morphology [HP:0010009],0/0,0%,4/4,100%,, -Aplasia/Hypoplasia of the phalanges of the thumb [HP:0009658],0/0,0%,4/4,100%,, -Partial absence of thumb [HP:0009659],0/0,0%,3/3,100%,, -Limited pronation/supination of forearm [HP:0006394],3/3,100%,2/2,100%,, -Limited elbow movement [HP:0002996],4/4,100%,2/2,100%,, -Abnormality of the elbow [HP:0009811],5/5,100%,2/2,100%,, -Limitation of joint mobility [HP:0001376],4/4,100%,2/2,100%,, -Mitral regurgitation [HP:0001653],2/2,100%,2/2,100%,, -Atrioventricular valve regurgitation [HP:0034376],2/2,100%,7/7,100%,, -Abnormal atrioventricular valve physiology [HP:0031650],2/2,100%,7/7,100%,, -Abnormal heart valve physiology [HP:0031653],2/2,100%,7/7,100%,, -Abnormal mitral valve physiology [HP:0031481],2/2,100%,2/2,100%,, -Tricuspid regurgitation [HP:0005180],0/0,0%,5/5,100%,, -Abnormal tricuspid valve physiology [HP:0031651],0/0,0%,5/5,100%,, -Cleft soft palate [HP:0000185],0/0,0%,2/2,100%,, -Abnormal soft palate morphology [HP:0100736],0/0,0%,2/2,100%,, -Abnormal palate morphology [HP:0000174],0/0,0%,5/5,100%,, -Cleft palate [HP:0000175],0/0,0%,2/2,100%,, -Orofacial cleft [HP:0000202],0/0,0%,2/2,100%,, -Craniofacial cleft [HP:5201015],0/0,0%,2/2,100%,, -Sinus bradycardia [HP:0001688],1/1,100%,2/2,100%,, -Abnormal electrophysiology of sinoatrial node origin [HP:0011702],1/1,100%,2/2,100%,, -Arrhythmia [HP:0011675],1/1,100%,3/3,100%,, -Bradycardia [HP:0001662],1/1,100%,2/2,100%,, -Pectus excavatum [HP:0000767],2/2,100%,3/4,75%,, -Abnormal sternum morphology [HP:0000766],2/2,100%,3/3,100%,, -Deviation of the thumb [HP:0009603],2/2,100%,3/3,100%,, -Deviation of finger [HP:0004097],2/2,100%,4/4,100%,, -Deviation of the hand or of fingers of the hand [HP:0009484],2/2,100%,5/5,100%,, -Abnormal skin morphology [HP:0011121],1/1,100%,0/0,0%,, -Abnormality of the skin [HP:0000951],1/1,100%,0/0,0%,, -Abnormality of the integument [HP:0001574],1/1,100%,0/0,0%,, -Sinus venosus atrial septal defect [HP:0011567],1/1,100%,0/2,0%,, -First degree atrioventricular block [HP:0011705],1/1,100%,1/23,4%,, -Postaxial hand polydactyly [HP:0001162],0/0,0%,3/4,75%,, -Postaxial polydactyly [HP:0100259],0/0,0%,3/3,100%,, -Polydactyly [HP:0010442],0/0,0%,3/3,100%,, -Abnormal 5th finger morphology [HP:0004207],0/0,0%,6/6,100%,, -Hand polydactyly [HP:0001161],0/0,0%,3/3,100%,, -Duplication of phalanx of hand [HP:0009997],0/0,0%,3/3,100%,, -Duplication of hand bones [HP:0004275],0/0,0%,3/3,100%,, -Duplication of bones involving the upper extremities [HP:0009142],0/0,0%,3/3,100%,, -High palate [HP:0000218],0/0,0%,3/3,100%,, -Short neck [HP:0000470],0/0,0%,3/3,100%,, -Abnormal neck morphology [HP:0025668],0/0,0%,3/3,100%,, -Abnormality of the neck [HP:0000464],0/0,0%,3/3,100%,, -Abnormality of the cervical spine [HP:0003319],0/0,0%,3/3,100%,, -Shield chest [HP:0000914],0/0,0%,3/3,100%,, -Enlarged thorax [HP:0100625],0/0,0%,3/3,100%,, -Abnormal rib cage morphology [HP:0001547],0/0,0%,5/5,100%,, -Y-shaped metatarsals [HP:0010567],0/0,0%,3/3,100%,, -Abnormal metatarsal morphology [HP:0001832],0/0,0%,3/3,100%,, -11 pairs of ribs [HP:0000878],0/0,0%,2/2,100%,, -Missing ribs [HP:0000921],0/0,0%,2/2,100%,, -Aplasia/Hypoplasia of the ribs [HP:0006712],0/0,0%,2/2,100%,, -Abnormal rib morphology [HP:0000772],0/0,0%,2/2,100%,, -Aplasia/Hypoplasia of the 2nd finger [HP:0006264],0/0,0%,2/2,100%,, -Abnormal 2nd finger morphology [HP:0004100],0/0,0%,2/2,100%,, -Small hypothenar eminence [HP:0010487],0/0,0%,2/2,100%,, -Abnormality of the hypothenar eminence [HP:0010486],0/0,0%,2/2,100%,, -Abnormality of the musculature of the hand [HP:0001421],0/0,0%,2/2,100%,, -Upper extremity joint dislocation [HP:0030310],2/2,100%,0/0,0%,, -Joint dislocation [HP:0001373],2/2,100%,0/0,0%,, -Amelia involving the upper limbs [HP:0009812],1/37,3%,0/114,0%,, -Third degree atrioventricular block [HP:0001709],1/1,100%,0/22,0%,, -Atrioventricular dissociation [HP:0011709],1/1,100%,0/22,0%,, -Abnormal shoulder morphology [HP:0003043],1/1,100%,1/1,100%,, -Proximal placement of thumb [HP:0009623],0/0,0%,3/3,100%,, -Short middle phalanx of the 5th finger [HP:0004220],0/0,0%,2/2,100%,, -Type A brachydactyly [HP:0009370],0/0,0%,2/2,100%,, -Brachydactyly [HP:0001156],0/0,0%,2/2,100%,, -Short 5th finger [HP:0009237],0/0,0%,2/2,100%,, -Aplasia/Hypoplasia of the 5th finger [HP:0006262],0/0,0%,2/2,100%,, -Aplasia/Hypoplasia of the middle phalanx of the 5th finger [HP:0009161],0/0,0%,2/2,100%,, -Aplasia/Hypoplasia of the phalanges of the 5th finger [HP:0009376],0/0,0%,2/2,100%,, -Abnormal 5th finger phalanx morphology [HP:0004213],0/0,0%,2/2,100%,, -Abnormality of the middle phalanx of the 5th finger [HP:0004219],0/0,0%,2/2,100%,, -Short middle phalanx of finger [HP:0005819],0/0,0%,2/2,100%,, -Aplasia/Hypoplasia of the middle phalanges of the hand [HP:0009843],0/0,0%,2/2,100%,, -Abnormal middle phalanx morphology of the hand [HP:0009833],0/0,0%,2/2,100%,, -Short phalanx of finger [HP:0009803],0/0,0%,2/2,100%,, -Common atrium [HP:0011565],0/38,0%,1/115,1%,, -Unroofed coronary sinus [HP:0031297],0/38,0%,1/117,1%,, -Clinodactyly of the 5th finger [HP:0004209],0/0,0%,2/2,100%,, -Finger clinodactyly [HP:0040019],0/0,0%,2/2,100%,, -Clinodactyly [HP:0030084],0/0,0%,2/2,100%,, -Deviation of the 5th finger [HP:0009179],0/0,0%,2/2,100%,, -Mitral valve prolapse [HP:0001634],1/1,100%,1/1,100%,, -Abnormal mitral valve morphology [HP:0001633],1/1,100%,1/1,100%,, -Abnormal atrioventricular valve morphology [HP:0006705],1/1,100%,1/1,100%,, -Abnormal heart valve morphology [HP:0001654],1/1,100%,1/1,100%,, -Short 1st metacarpal [HP:0010034],0/22,0%,1/45,2%,, -Short phalanx of the thumb [HP:0009660],0/22,0%,1/45,2%,, +Ventricular septal defect [HP:0001629],42/71,59%,19/19,100%,0.00411275392326226,0.00024192670136836825 +Abnormal atrioventricular conduction [HP:0005150],1/23,4%,3/3,100%,0.01307692307692308,0.0015384615384615387 +Absent thumb [HP:0009777],18/100,18%,14/31,45%,0.021044138590779585,0.003713671516019927 +Atrioventricular block [HP:0001678],1/23,4%,2/2,100%,0.034,0.01 +Heart block [HP:0012722],1/23,4%,2/2,100%,0.034,0.01 +Patent ductus arteriosus [HP:0001643],6/40,15%,2/2,100%,0.09214092140921408,0.03252032520325203 +Secundum atrial septal defect [HP:0001684],23/55,42%,4/22,18%,0.1440020479198931,0.06544319142266644 +Triphalangeal thumb [HP:0001199],23/99,23%,13/32,41%,0.1440020479198931,0.06932119159387057 +Cardiac conduction abnormality [HP:0031546],15/37,41%,3/3,100%,0.1440020479198931,0.08259109311740892 +Muscular ventricular septal defect [HP:0011623],8/84,10%,6/25,24%,0.1440020479198931,0.08470708701170182 +Pulmonary arterial hypertension [HP:0002092],8/14,57%,0/2,0%,0.6899307928951143,0.4666666666666667 +Short thumb [HP:0009778],25/69,36%,8/30,27%,0.6899307928951143,0.48700997145537483 +Absent radius [HP:0003974],9/43,21%,6/25,24%,1.0,0.7703831604944444 +Atrial septal defect [HP:0001631],63/65,97%,20/20,100%,1.0,1.0 +Hypoplasia of the radius [HP:0002984],34/75,45%,6/14,43%,1.0,1.0 +Hypoplasia of the ulna [HP:0003022],3/17,18%,2/10,20%,1.0,1.0 +Short humerus [HP:0005792],8/21,38%,4/9,44%,1.0,1.0 +Abnormal atrial septum morphology [HP:0011994],64/64,100%,20/20,100%,, +Abnormal cardiac septum morphology [HP:0001671],89/89,100%,28/28,100%,, +Abnormal heart morphology [HP:0001627],89/89,100%,30/30,100%,, diff --git a/docs/user-guide/stats.rst b/docs/user-guide/stats.rst index 3eeca2bf..96e9376e 100644 --- a/docs/user-guide/stats.rst +++ b/docs/user-guide/stats.rst @@ -122,13 +122,34 @@ We want to separate the patients into two groups: a group *with* a frameshift va and a group *without* a frameshift variant, based on the functional annotation. We will use the *MANE* transcript for the analysis: +Building a genotype predicate is a two step process. +First, we create a :class:`~gpsea.analysis.predicate.genotype.VariantPredicate` +to test if the variant leads to a frameshift (in this case): + >>> from gpsea.model import VariantEffect >>> from gpsea.analysis.predicate.genotype import VariantPredicates, boolean_predicate >>> tx_id = 'NM_181486.4' ->>> gt_predicate = boolean_predicate(VariantPredicates.variant_effect(VariantEffect.FRAMESHIFT_VARIANT, tx_id)) ->>> gt_predicate.get_question() +>>> is_frameshift = VariantPredicates.variant_effect(VariantEffect.FRAMESHIFT_VARIANT, tx_id) +>>> is_frameshift.get_question() 'FRAMESHIFT_VARIANT on NM_181486.4' +and then we choose the expected mode of inheritance to test. In case of *TBX5*, +we expect the autosomal dominant mode of inheritance: + +>>> from gpsea.analysis.predicate.genotype import ModeOfInheritancePredicate +>>> gt_predicate = ModeOfInheritancePredicate.autosomal_dominant(is_frameshift) +>>> gt_predicate.get_question() +'Which genotype group does the patient fit in: HOM_REF, HET' + +`gt_predicate` will assign the patients with no frameshift variant allele into `HOM_REF` group +and the patients with one frameshift allele will be assigned into `HET` group. +Note, any patient with 2 or more alleles will be *omitted* from the analysis. + +.. note:: + + Mode of inheritance testing is not the only way to dissect by a genotype. + See the :ref:`genotype-predicates` section for more info. + **Phenotype predicates** @@ -229,18 +250,32 @@ We can learn more by showing the MTC filter report: Genotype phenotype associations ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -Last, let's explore the associations. This is a table of the tested HPO terms -ordered by the corrected p value (Benjamini-Hochberg FDR): +Last, let's explore the associations. The results include a table with all tested HPO terms +ordered by the corrected p value (Benjamini-Hochberg FDR). +Here we show the top 20 table rows: >>> from gpsea.analysis.predicate import PatientCategories >>> summary_df = result.summarize(hpo, PatientCategories.YES) ->>> summary_df.to_csv('docs/user-guide/report/tbx5_frameshift.csv') # doctest: +SKIP +>>> summary_df.head(20).to_csv('docs/user-guide/report/tbx5_frameshift.csv') # doctest: +SKIP .. csv-table:: *TBX5* frameshift vs rest :file: report/tbx5_frameshift.csv :header-rows: 2 +The table shows that several HPO terms are significantly associated +with presence of a heterozygous (`HET`) frameshift variant in *TBX5*. +For example, `Ventricular septal defect `_ +was observed in 31/60 (52%) patients with a missense variant +but it was observed in 19/19 (100%) patients with a frameshift variant. +Fisher exact test computed a p value of `~0.000242` +and the p value corrected by Benjamini-Hochberg procedure +is `~0.00411`. + +The table includes all HPO terms of the cohort, including the terms that were not selected for testing +and thus have no associated p value. + + .. _phenotype-score-stats: *************** diff --git a/src/gpsea/analysis/predicate/genotype/_gt_predicates.py b/src/gpsea/analysis/predicate/genotype/_gt_predicates.py index 1cd1a657..a032aae9 100644 --- a/src/gpsea/analysis/predicate/genotype/_gt_predicates.py +++ b/src/gpsea/analysis/predicate/genotype/_gt_predicates.py @@ -273,24 +273,42 @@ class MendelianInheritanceAspect(enum.Enum): class ModeOfInheritanceInfo: + # NOT PART OF THE PUBLIC API!!! + + HOM_REF = Categorization( + PatientCategory( + cat_id=0, name="HOM_REF", description="Homozygous reference", + ), + ) + HET = Categorization( + PatientCategory( + cat_id=1, name="HET", description="Heterozygous", + ), + ) + BIALLELIC_ALT = Categorization( + PatientCategory( + cat_id=2, name="BIALLELIC_ALT", + description="Homozygous alternate or compound heterozygous", + ), + ) + HEMI = Categorization( + PatientCategory( + cat_id=3, name="HEMI", description="Hemizygous", + ), + ) + @staticmethod def autosomal_dominant() -> "ModeOfInheritanceInfo": groups = ( GenotypeGroup( allele_count=0, sex=None, - categorization=Categorization( - PatientCategory( - cat_id=0, name="0/0", description="Homozygous reference" - ), - ), + categorization=ModeOfInheritanceInfo.HOM_REF, ), GenotypeGroup( allele_count=1, sex=None, - categorization=Categorization( - PatientCategory(cat_id=1, name="0/1", description="Heterozygous"), - ), + categorization=ModeOfInheritanceInfo.HET, ), ) return ModeOfInheritanceInfo( @@ -304,25 +322,17 @@ def autosomal_recessive() -> "ModeOfInheritanceInfo": GenotypeGroup( allele_count=0, sex=None, - categorization=Categorization( - PatientCategory( - cat_id=0, name="0/0", description="Homozygous reference" - ), - ), + categorization=ModeOfInheritanceInfo.HOM_REF, ), GenotypeGroup( allele_count=1, sex=None, - categorization=Categorization( - PatientCategory(cat_id=1, name="0/1", description="Heterozygous"), - ), + categorization=ModeOfInheritanceInfo.HET, ), GenotypeGroup( allele_count=2, sex=None, - categorization=Categorization( - PatientCategory(cat_id=2, name="1/1", description="Homozygous alternate"), - ), + categorization=ModeOfInheritanceInfo.BIALLELIC_ALT, ), ) return ModeOfInheritanceInfo( @@ -336,25 +346,12 @@ def x_dominant() -> "ModeOfInheritanceInfo": GenotypeGroup( allele_count=0, sex=None, - categorization=Categorization( - PatientCategory( - cat_id=0, name="0", description="Homozygous reference" - ), - ), - ), - GenotypeGroup( - allele_count=1, - sex=Sex.FEMALE, - categorization=Categorization( - PatientCategory(cat_id=1, name="0/1", description="Heterozygous"), - ), + categorization=ModeOfInheritanceInfo.HOM_REF, ), GenotypeGroup( allele_count=1, - sex=Sex.MALE, - categorization=Categorization( - PatientCategory(cat_id=2, name="1", description="Hemizygous"), - ), + sex=None, + categorization=ModeOfInheritanceInfo.HET, ), ) return ModeOfInheritanceInfo( @@ -368,25 +365,22 @@ def x_recessive() -> "ModeOfInheritanceInfo": GenotypeGroup( allele_count=0, sex=None, - categorization=Categorization( - PatientCategory( - cat_id=0, name="0/0", description="Homozygous reference" - ), - ), + categorization=ModeOfInheritanceInfo.HOM_REF, ), GenotypeGroup( allele_count=1, sex=Sex.FEMALE, - categorization=Categorization( - PatientCategory(cat_id=1, name="0/1", description="Heterozygous"), - ), + categorization=ModeOfInheritanceInfo.HET, + ), + GenotypeGroup( + allele_count=2, + sex=Sex.FEMALE, + categorization=ModeOfInheritanceInfo.BIALLELIC_ALT, ), GenotypeGroup( allele_count=1, sex=Sex.MALE, - categorization=Categorization( - PatientCategory(cat_id=2, name="1", description="Hemizygous"), - ), + categorization=ModeOfInheritanceInfo.HEMI, ), ) @@ -400,7 +394,8 @@ def __init__( mendelian_inheritance_aspect: MendelianInheritanceAspect, groups: typing.Iterable[GenotypeGroup], ): - # We pre-compute the hash manually. + # We want this to be hashable but also keep a non-hashable dict + # as a field. Therefore, we pre-compute the hash manually. # The correctness depends on two default dicts with same keys and values # comparing equal. hash_value = 17 diff --git a/src/gpsea/model/_base.py b/src/gpsea/model/_base.py index 0d541a65..09a1c913 100644 --- a/src/gpsea/model/_base.py +++ b/src/gpsea/model/_base.py @@ -13,17 +13,17 @@ class Sex(enum.Enum): UNKNOWN_SEX = 0 """ - Not assessed or not available. Maps to `NCIT:C17998`. + Not assessed or not available. Maps to ``NCIT:C17998``. """ FEMALE = 1 """ - Female sex. Maps to `NCIT:C46113`. + Female sex. Maps to ``NCIT:C46113``. """ MALE = 2 """ - Male sex. Maps to `NCIT:C46112`. + Male sex. Maps to ``NCIT:C46112``. """ def is_provided(self) -> bool: diff --git a/tests/analysis/predicate/genotype/conftest.py b/tests/analysis/predicate/genotype/conftest.py index c44a214b..7138e93f 100644 --- a/tests/analysis/predicate/genotype/conftest.py +++ b/tests/analysis/predicate/genotype/conftest.py @@ -211,10 +211,10 @@ def patient_w_frameshift( """ -Genesis family - Autosomal dominant +Genesis family - Autosomal dominant but can also be used as X dominant. -* Adam - father, affected -* Eve - mother, unaffected +* Adam - father, unaffected +* Eve - mother, affected * Cain - son, affected """ @@ -260,8 +260,8 @@ def genesis_mutation( ), genotypes=Genotypes.from_mapping( { - adam_label: Genotype.HETEROZYGOUS, - eve_label: Genotype.HOMOZYGOUS_REFERENCE, + adam_label: Genotype.HOMOZYGOUS_REFERENCE, + eve_label: Genotype.HETEROZYGOUS, cain_label: Genotype.HETEROZYGOUS, } ), @@ -597,8 +597,3 @@ def leia( diseases=(), variants=(skywalker_mutation,), ) - - -""" -XR family -""" diff --git a/tests/analysis/predicate/genotype/test_gt_predicates.py b/tests/analysis/predicate/genotype/test_gt_predicates.py index ac6977c8..36904159 100644 --- a/tests/analysis/predicate/genotype/test_gt_predicates.py +++ b/tests/analysis/predicate/genotype/test_gt_predicates.py @@ -84,17 +84,16 @@ def variant_predicate(self) -> VariantPredicate: return VariantPredicates.variant_effect(VariantEffect.MISSENSE_VARIANT, TX_ID) @pytest.mark.parametrize( - "patient_name,cat_id,name", + "patient_name,name", [ - ("adam", 1, "0/1"), - ("eve", 0, "0/0"), - ("cain", 1, "0/1"), + ("adam", "HOM_REF"), + ("eve", "HET"), + ("cain", "HET"), ], ) def test_autosomal_dominant( self, patient_name: str, - cat_id: int, name: str, variant_predicate: VariantPredicate, request: pytest.FixtureRequest, @@ -106,22 +105,20 @@ def test_autosomal_dominant( assert categorization is not None - assert categorization.category.cat_id == cat_id assert categorization.category.name == name @pytest.mark.parametrize( - "patient_name,cat_id,name", + "patient_name,name", [ - ("walt", 1, "0/1"), - ("skyler", 1, "0/1"), - ("flynn", 2, "1/1"), - ("holly", 0, "0/0"), + ("walt", "HET"), + ("skyler", "HET"), + ("flynn", "BIALLELIC_ALT"), + ("holly", "HOM_REF"), ], ) def test_autosomal_recessive( self, patient_name: str, - cat_id: int, name: str, variant_predicate: VariantPredicate, request: pytest.FixtureRequest, @@ -133,22 +130,44 @@ def test_autosomal_recessive( assert categorization is not None - assert categorization.category.cat_id == cat_id assert categorization.category.name == name @pytest.mark.parametrize( - "patient_name,cat_id,name", + "patient_name,name", [ - ("anakin", 0, "0/0"), - ("padme", 1, "0/1"), - ("luke", 2, "1"), - ("leia", 1, "0/1"), + ("adam", "HOM_REF"), + ("eve", "HET"), + ("cain", "HET"), + ], + ) + def test_x_dominant( + self, + patient_name: str, + name: str, + variant_predicate: VariantPredicate, + request: pytest.FixtureRequest, + ): + patient = request.getfixturevalue(patient_name) + predicate = ModeOfInheritancePredicate.x_dominant(variant_predicate) + + categorization = predicate.test(patient) + + assert categorization is not None + + assert categorization.category.name == name + + @pytest.mark.parametrize( + "patient_name,name", + [ + ("anakin", "HOM_REF"), + ("padme", "HET"), + ("luke", "HEMI"), + ("leia", "HET"), ], ) def test_x_recessive( self, patient_name: str, - cat_id: int, name: str, variant_predicate: VariantPredicate, request: pytest.FixtureRequest, @@ -160,5 +179,4 @@ def test_x_recessive( assert categorization is not None - assert categorization.category.cat_id == cat_id assert categorization.category.name == name From 7acfc72dbaace44ad12e1107b869e47294b4ca08 Mon Sep 17 00:00:00 2001 From: Daniel Danis Date: Sun, 1 Sep 2024 23:24:19 +0200 Subject: [PATCH 28/47] Check compatibility between the predicates and the count statistic. --- docs/tutorial.rst | 4 +- docs/user-guide/mtc.rst | 4 +- docs/user-guide/stats.rst | 4 +- src/gpsea/analysis/pcats/_impl.py | 74 ++++++++++++++- src/gpsea/analysis/pcats/stats/__init__.py | 4 +- src/gpsea/analysis/pcats/stats/_stats.py | 90 ++++++++++++------- .../analysis/pcats/stats/_test__stats.py | 9 +- tests/analysis/pcats/test_disease.py | 4 +- .../analysis/pcats/test_hpo_term_analysis.py | 4 +- tests/test_tutorial.py | 4 +- 10 files changed, 147 insertions(+), 54 deletions(-) diff --git a/docs/tutorial.rst b/docs/tutorial.rst index 8d1d4726..e047943c 100644 --- a/docs/tutorial.rst +++ b/docs/tutorial.rst @@ -224,8 +224,8 @@ with a false discovery control level at (``mtc_alpha=0.05``): Choosing the statistical procedure for assessment of association between genotype and phenotype groups is the last missing piece of the analysis. We will use Fisher Exact Test: ->>> from gpsea.analysis.pcats.stats import ScipyFisherExact ->>> count_statistic = ScipyFisherExact() +>>> from gpsea.analysis.pcats.stats import FisherExactTest +>>> count_statistic = FisherExactTest() and we finalize the analysis setup by putting all components together into :class:`~gpsea.analysis.pcats.HpoTermAnalysis`: diff --git a/docs/user-guide/mtc.rst b/docs/user-guide/mtc.rst index f8958b8a..070539d1 100644 --- a/docs/user-guide/mtc.rst +++ b/docs/user-guide/mtc.rst @@ -96,9 +96,9 @@ when creating an instance of :class:`~gpsea.analysis.pcats.HpoTermAnalysis`: >>> from gpsea.analysis.mtc_filter import UseAllTermsMtcFilter >>> from gpsea.analysis.pcats import HpoTermAnalysis ->>> from gpsea.analysis.pcats.stats import ScipyFisherExact +>>> from gpsea.analysis.pcats.stats import FisherExactTest >>> analysis = HpoTermAnalysis( -... count_statistic=ScipyFisherExact(), +... count_statistic=FisherExactTest(), ... mtc_filter=UseAllTermsMtcFilter(), ... mtc_correction='bonferroni', # <--- The MTC correction setup ... ) diff --git a/docs/user-guide/stats.rst b/docs/user-guide/stats.rst index 96e9376e..16f0060b 100644 --- a/docs/user-guide/stats.rst +++ b/docs/user-guide/stats.rst @@ -176,8 +176,8 @@ including the *indirect* annotations whose presence is implied by the true path We will use :ref: to test the association between genotype and phenotype groups, as described previously. ->>> from gpsea.analysis.pcats.stats import ScipyFisherExact ->>> count_statistic = ScipyFisherExact() +>>> from gpsea.analysis.pcats.stats import FisherExactTest +>>> count_statistic = FisherExactTest() FET will compute a p value for each genotype phenotype group. diff --git a/src/gpsea/analysis/pcats/_impl.py b/src/gpsea/analysis/pcats/_impl.py index e4513fa0..6600dab4 100644 --- a/src/gpsea/analysis/pcats/_impl.py +++ b/src/gpsea/analysis/pcats/_impl.py @@ -1,5 +1,6 @@ import abc import math +import os import typing from collections import Counter @@ -273,20 +274,87 @@ def __init__( (e.g. Bonferroni MTC) or false discovery rate for the FDR procedures (e.g. Benjamini-Hochberg). """ assert isinstance(count_statistic, CountStatistic) + assert len(count_statistic.supports_shape) == 2, "The statistic must support 2D contingency tables" self._count_statistic = count_statistic self._mtc_correction = mtc_correction assert isinstance(mtc_alpha, float) and 0. <= mtc_alpha <= 1. self._mtc_alpha = mtc_alpha - @abc.abstractmethod def compare_genotype_vs_phenotypes( self, cohort: typing.Iterable[Patient], gt_predicate: GenotypePolyPredicate, pheno_predicates: typing.Iterable[PhenotypePolyPredicate[P]], + ) -> MultiPhenotypeAnalysisResult[P]: + # Check compatibility between the count statistic and predicate. + issues = MultiPhenotypeAnalysis._check_compatibility( + count_statistic=self._count_statistic, + gt_predicate=gt_predicate, + pheno_predicates=pheno_predicates, + ) + if len(issues) != 0: + msg = os.linesep.join(issues) + raise ValueError(f'Cannot execute the analysis: {msg}') + + return self._compute_result( + cohort=cohort, + gt_predicate=gt_predicate, + pheno_predicates=pheno_predicates, + ) + + @abc.abstractmethod + def _compute_result( + self, + cohort: typing.Iterable[Patient], + gt_predicate: GenotypePolyPredicate, + pheno_predicates: typing.Iterable[PhenotypePolyPredicate[P]], ) -> MultiPhenotypeAnalysisResult[P]: pass + @staticmethod + def _check_compatibility( + count_statistic: CountStatistic, + gt_predicate: GenotypePolyPredicate, + pheno_predicates: typing.Iterable[PhenotypePolyPredicate[P]], + ) -> typing.Collection[str]: + # There should be 2 items due to check in `__init__`. + (pheno, geno) = count_statistic.supports_shape + + issues = [] + # Check phenotype + if isinstance(pheno, int): + pheno_accepted = (pheno,) + elif isinstance(pheno, typing.Sequence): + pheno_accepted = pheno + else: + issues.append('Cannot use a count statistic that does not check phenotypes') + + pheno_failed = [] + for i, ph_predicate in enumerate(pheno_predicates): + if ph_predicate.n_categorizations not in pheno_accepted: + pheno_failed.append(i) + if len(pheno_failed) != 0: + issues.append( + 'Phenotype predicates {} are incompatible with the count statistic'.format( + ', '.join(str(i) for i in pheno_failed) + ) + ) + + # Check genotype + if isinstance(geno, int): + geno_accepted = (geno,) + elif isinstance(geno, typing.Sequence): + geno_accepted = geno + elif pheno is None: + raise ValueError('Cannot use a count statistic that does not check genotypes') + else: + raise ValueError(f'Cannot use a count statistic that supports shape {pheno, geno}') + + if gt_predicate.n_categorizations not in geno_accepted: + issues.append('Genotype predicate is incompatible with the count statistic') + + return issues + def _compute_nominal_pvals( self, n_usable: typing.Iterable[int], @@ -380,7 +448,7 @@ def __hash__(self): class DiseaseAnalysis(MultiPhenotypeAnalysis[hpotk.TermId]): - def compare_genotype_vs_phenotypes( + def _compute_result( self, cohort: typing.Iterable[Patient], gt_predicate: GenotypePolyPredicate, @@ -513,7 +581,7 @@ def __init__( assert isinstance(mtc_filter, PhenotypeMtcFilter) self._mtc_filter = mtc_filter - def compare_genotype_vs_phenotypes( + def _compute_result( self, cohort: typing.Iterable[Patient], gt_predicate: GenotypePolyPredicate, diff --git a/src/gpsea/analysis/pcats/stats/__init__.py b/src/gpsea/analysis/pcats/stats/__init__.py index 4d7e0543..a535129d 100644 --- a/src/gpsea/analysis/pcats/stats/__init__.py +++ b/src/gpsea/analysis/pcats/stats/__init__.py @@ -1,5 +1,5 @@ -from ._stats import CountStatistic, ScipyFisherExact, PythonMultiFisherExact +from ._stats import CountStatistic, FisherExactTest __all__ = [ - 'CountStatistic', 'ScipyFisherExact', 'PythonMultiFisherExact', + 'CountStatistic', 'FisherExactTest', ] diff --git a/src/gpsea/analysis/pcats/stats/_stats.py b/src/gpsea/analysis/pcats/stats/_stats.py index 8df07fdc..981176ee 100644 --- a/src/gpsea/analysis/pcats/stats/_stats.py +++ b/src/gpsea/analysis/pcats/stats/_stats.py @@ -1,5 +1,7 @@ import abc import math +import typing + from decimal import Decimal @@ -14,9 +16,45 @@ class CountStatistic(metaclass=abc.ABCMeta): `CountStatistic` calculates a p value for a contingency table produced by a pair of discrete random variables. - The `counts` table is usually `2x2` or `2x3`. + + Supports shape + ^^^^^^^^^^^^^^ + + `CountStatistic` takes the counts in form of a data frame, + and some statistics impose additional requirements on the frame shape. + For instance, GPSEA's implementation of the Fisher exact test + can compare counts in a ``(2, 2)`` or ``(2, 3)`` arrays + but χ2 test can test an ``(m, n)`` array. + + It is important to check that a genotype/phenotype predicate produces + the number of groups which the statistic can test. + + The :attr:`supports_shape` returns a sequence with requirements + on the shape of the data array/frame. The sequence includes + the number of + + Examples + ******** + + +------------------------+-------------------------+------------------+ + | Test | Array shape | `supports_shape` | + +========================+=========================+==================+ + | Fisher Exact Test | ``(2, [2, 3])`` | ``(2, [2,3])`` | + +------------------------+-------------------------+------------------+ + | χ2 | ``(*, *)`` | ``(None, None)`` | + +------------------------+-------------------------+------------------+ """ + @property + @abc.abstractmethod + def supports_shape( + self, + ) -> typing.Sequence[typing.Union[int, typing.Sequence[int], None]]: + """ + Get a sequence of the supported shapes. + """ + pass + @abc.abstractmethod def compute_pval( self, @@ -25,47 +63,35 @@ def compute_pval( pass -class ScipyFisherExact(CountStatistic): +class FisherExactTest(CountStatistic): """ - `ScipyFisherExact` performs Fisher Exact Test on a `2x2` contingency table. + `FisherExactTest` performs Fisher Exact Test on a `2x2` or `2x3` contingency table. - The class is a thin wrapper around Scipy :func:`~scipy.stats.fisher_exact` function. - The two-sided :math:`H_1` is considered. + The `2x2` version is a thin wrapper around Scipy :func:`~scipy.stats.fisher_exact` function, + while the `2x3` variant is implemented in Python. + In both variants, the two-sided :math:`H_1` is considered. """ + + def __init__(self): + self._shape = (2, (2, 3)) - def compute_pval( + @property + def supports_shape( self, - counts: pd.DataFrame, - ) -> float: - assert counts.shape == ( - 2, - 2, - ), f"Cannot run Fisher exact test on an array with {counts.shape} shape" - _, pval = scipy.stats.fisher_exact(counts.values, alternative="two-sided") - return pval - - -class PythonMultiFisherExact(CountStatistic): - """ - `PythonMultiFisherExact` is a Python implementation of Fisher Exact Test to compute - p value for a `2x3` contingency table. - """ + ) -> typing.Sequence[typing.Union[int, typing.Sequence[int], None]]: + return self._shape def compute_pval( self, counts: pd.DataFrame, ) -> float: - PythonMultiFisherExact._check_input(counts) - return self._fisher_exact(counts.values) - - @staticmethod - def _check_input(a: pd.DataFrame): - if not isinstance(a, pd.DataFrame): - raise ValueError(f"Expected a pandas DataFrame but got {type(a)}") - if not a.shape == (2, 3): - raise ValueError(f"Shape of the array must be (2, 3) but got {a.shape}") - if np.array_equal(a.values, np.zeros_like(a)): - raise ValueError("Data frame is all zeros, cannot run analysis") + if counts.shape == (2, 2): + _, pval = scipy.stats.fisher_exact(counts.values, alternative="two-sided") + return pval + elif counts.shape == (2, 3): + return self._fisher_exact(counts.values) + else: + raise ValueError(f'Unsupported counts shape {counts.shape}') def _fisher_exact( self, diff --git a/src/gpsea/analysis/pcats/stats/_test__stats.py b/src/gpsea/analysis/pcats/stats/_test__stats.py index aa254306..c8db0b16 100644 --- a/src/gpsea/analysis/pcats/stats/_test__stats.py +++ b/src/gpsea/analysis/pcats/stats/_test__stats.py @@ -2,14 +2,14 @@ import pandas as pd import pytest -from ._stats import PythonMultiFisherExact +from ._stats import FisherExactTest class TestPythonMultiFisherExact: @pytest.fixture - def fisher_exact(self) -> PythonMultiFisherExact: - return PythonMultiFisherExact() + def fisher_exact(self) -> FisherExactTest: + return FisherExactTest() @pytest.mark.parametrize( "counts, expected", @@ -24,10 +24,9 @@ def test_compute_pval( self, counts, expected, - fisher_exact: PythonMultiFisherExact, + fisher_exact: FisherExactTest, ): contingency_matrix = pd.DataFrame(np.array(counts, dtype=np.int64)) final_pval = fisher_exact.compute_pval(contingency_matrix) assert final_pval == pytest.approx(expected) - diff --git a/tests/analysis/pcats/test_disease.py b/tests/analysis/pcats/test_disease.py index 48acece3..9e16b300 100644 --- a/tests/analysis/pcats/test_disease.py +++ b/tests/analysis/pcats/test_disease.py @@ -4,7 +4,7 @@ from gpsea.model import Cohort from gpsea.analysis.pcats import DiseaseAnalysis -from gpsea.analysis.pcats.stats import CountStatistic, ScipyFisherExact +from gpsea.analysis.pcats.stats import CountStatistic, FisherExactTest from gpsea.analysis.predicate.genotype import GenotypePolyPredicate from gpsea.analysis.predicate.phenotype import DiseasePresencePredicate @@ -13,7 +13,7 @@ class TestDiseaseAnalysis: @pytest.fixture(scope='class') def count_statistic(self) -> CountStatistic: - return ScipyFisherExact() + return FisherExactTest() @pytest.fixture(scope='class') def suox_disease(self) -> DiseasePresencePredicate: diff --git a/tests/analysis/pcats/test_hpo_term_analysis.py b/tests/analysis/pcats/test_hpo_term_analysis.py index 516bf9fc..690e9dd1 100644 --- a/tests/analysis/pcats/test_hpo_term_analysis.py +++ b/tests/analysis/pcats/test_hpo_term_analysis.py @@ -7,7 +7,7 @@ from gpsea.analysis.mtc_filter import PhenotypeMtcFilter, HpoMtcFilter from gpsea.analysis.pcats import HpoTermAnalysis -from gpsea.analysis.pcats.stats import CountStatistic, ScipyFisherExact +from gpsea.analysis.pcats.stats import CountStatistic, FisherExactTest from gpsea.analysis.predicate.genotype import GenotypePolyPredicate from gpsea.analysis.predicate.phenotype import PhenotypePolyPredicate @@ -16,7 +16,7 @@ class TestHpoTermAnalysis: @pytest.fixture(scope='class') def count_statistic(self) -> CountStatistic: - return ScipyFisherExact() + return FisherExactTest() @pytest.fixture(scope='class') def phenotype_mtc_filter( diff --git a/tests/test_tutorial.py b/tests/test_tutorial.py index 52924eff..b592f1f8 100644 --- a/tests/test_tutorial.py +++ b/tests/test_tutorial.py @@ -6,7 +6,7 @@ from gpsea.preprocessing import configure_caching_cohort_creator, CohortCreator, load_phenopackets from gpsea.analysis.pcats import HpoTermAnalysis from gpsea.analysis.mtc_filter import HpoMtcFilter -from gpsea.analysis.pcats.stats import ScipyFisherExact +from gpsea.analysis.pcats.stats import FisherExactTest from gpsea.analysis.predicate.genotype import VariantPredicates, groups_predicate from gpsea.analysis.predicate.phenotype import prepare_predicates_for_terms_of_interest @@ -53,7 +53,7 @@ def hpo_term_analysis( mtc_filter, ) -> HpoTermAnalysis: return HpoTermAnalysis( - count_statistic=ScipyFisherExact(), + count_statistic=FisherExactTest(), mtc_filter=mtc_filter, mtc_correction='fdr_bh', mtc_alpha=0.05, From ae2e8d00166e292d2c0d53a6fc3a9b5c41db1619 Mon Sep 17 00:00:00 2001 From: Daniel Danis Date: Mon, 2 Sep 2024 10:03:10 +0200 Subject: [PATCH 29/47] Add docs to `ModeOfInheritancePredicate`. --- .../predicate/genotype/_gt_predicates.py | 49 ++++++++++++++++--- .../analysis/predicate/genotype/_variant.py | 2 +- 2 files changed, 44 insertions(+), 7 deletions(-) diff --git a/src/gpsea/analysis/predicate/genotype/_gt_predicates.py b/src/gpsea/analysis/predicate/genotype/_gt_predicates.py index a032aae9..23fc4a5d 100644 --- a/src/gpsea/analysis/predicate/genotype/_gt_predicates.py +++ b/src/gpsea/analysis/predicate/genotype/_gt_predicates.py @@ -458,52 +458,89 @@ def __repr__(self) -> str: class ModeOfInheritancePredicate(GenotypePolyPredicate): + """ + `ModeOfInheritancePredicate` assigns an individual into a group based on compatibility with + the selected mode of inheritance. + """ @staticmethod def autosomal_dominant( variant_predicate: VariantPredicate, ) -> "ModeOfInheritancePredicate": + """ + Create a predicate that assigns the patient either + into homozygous reference or heterozygous + group in line with the autosomal dominant mode of inheritance. + + :param variant_predicate: a predicate for choosing the variants for testing. + """ return ModeOfInheritancePredicate.from_moi_info( variant_predicate=variant_predicate, - mode_of_inheritance_info=ModeOfInheritanceInfo.autosomal_dominant(), + mode_of_inheritance_data=ModeOfInheritanceInfo.autosomal_dominant(), ) @staticmethod def autosomal_recessive( variant_predicate: VariantPredicate, ) -> "ModeOfInheritancePredicate": + """ + Create a predicate that assigns the patient either into + homozygous reference, heterozygous, or biallelic alternative allele + (homozygous alternative or compound heterozygous) + group in line with the autosomal recessive mode of inheritance. + + :param variant_predicate: a predicate for choosing the variants for testing. + """ return ModeOfInheritancePredicate.from_moi_info( variant_predicate=variant_predicate, - mode_of_inheritance_info=ModeOfInheritanceInfo.autosomal_recessive(), + mode_of_inheritance_data=ModeOfInheritanceInfo.autosomal_recessive(), ) @staticmethod def x_dominant( variant_predicate: VariantPredicate, ) -> "ModeOfInheritancePredicate": + """ + Create a predicate that assigns the patient either into + homozygous reference or heterozygous + group in line with the X-linked dominant mode of inheritance. + + :param variant_predicate: a predicate for choosing the variants for testing. + """ return ModeOfInheritancePredicate.from_moi_info( variant_predicate=variant_predicate, - mode_of_inheritance_info=ModeOfInheritanceInfo.x_dominant(), + mode_of_inheritance_data=ModeOfInheritanceInfo.x_dominant(), ) @staticmethod def x_recessive( variant_predicate: VariantPredicate, ) -> "ModeOfInheritancePredicate": + """ + Create a predicate that assigns the patient either into + homozygous reference, heterozygous, biallelic alternative allele + (homozygous alternative or compound heterozygous), or hemizygous + group in line with the X-linked recessive mode of inheritance. + + :param variant_predicate: a predicate for choosing the variants for testing. + """ return ModeOfInheritancePredicate.from_moi_info( variant_predicate=variant_predicate, - mode_of_inheritance_info=ModeOfInheritanceInfo.x_recessive(), + mode_of_inheritance_data=ModeOfInheritanceInfo.x_recessive(), ) @staticmethod def from_moi_info( variant_predicate: VariantPredicate, - mode_of_inheritance_info: ModeOfInheritanceInfo, + mode_of_inheritance_data: ModeOfInheritanceInfo, ) -> "ModeOfInheritancePredicate": + """ + Create a predicate for specified mode of inheritance data. + """ allele_counter = AlleleCounter(predicate=variant_predicate) return ModeOfInheritancePredicate( allele_counter=allele_counter, - mode_of_inheritance_info=mode_of_inheritance_info, + mode_of_inheritance_info=mode_of_inheritance_data, ) def __init__( diff --git a/src/gpsea/analysis/predicate/genotype/_variant.py b/src/gpsea/analysis/predicate/genotype/_variant.py index aeaf6e1d..54d39735 100644 --- a/src/gpsea/analysis/predicate/genotype/_variant.py +++ b/src/gpsea/analysis/predicate/genotype/_variant.py @@ -313,7 +313,7 @@ def is_structural_deletion( to determine if the length of the variant is above or below `threshold`. **IMPORTANT**: the change lengths of deletions are *negative*, since the alternate allele - is shorter than the reference allele. See the method's documentation for more info. + is shorter than the reference allele. See :ref:`change-length-of-an-allele` for more info. **Example** From 304df63f25dd4aac4d66c87d63913fe7371cae97 Mon Sep 17 00:00:00 2001 From: Daniel Danis Date: Mon, 2 Sep 2024 10:13:44 +0200 Subject: [PATCH 30/47] Fix compatibility checking bug. --- src/gpsea/analysis/pcats/_impl.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/gpsea/analysis/pcats/_impl.py b/src/gpsea/analysis/pcats/_impl.py index 6600dab4..3448ba93 100644 --- a/src/gpsea/analysis/pcats/_impl.py +++ b/src/gpsea/analysis/pcats/_impl.py @@ -331,7 +331,7 @@ def _check_compatibility( pheno_failed = [] for i, ph_predicate in enumerate(pheno_predicates): - if ph_predicate.n_categorizations not in pheno_accepted: + if ph_predicate.n_categorizations() not in pheno_accepted: pheno_failed.append(i) if len(pheno_failed) != 0: issues.append( @@ -350,7 +350,7 @@ def _check_compatibility( else: raise ValueError(f'Cannot use a count statistic that supports shape {pheno, geno}') - if gt_predicate.n_categorizations not in geno_accepted: + if gt_predicate.n_categorizations() not in geno_accepted: issues.append('Genotype predicate is incompatible with the count statistic') return issues From ade5d5c71913d83fb9721c65cec7511ef60bf21a Mon Sep 17 00:00:00 2001 From: Daniel Danis Date: Tue, 3 Sep 2024 11:29:08 +0200 Subject: [PATCH 31/47] Remove the obsolete `PhenotypeMtcFilter.filter_terms_to_test` method. --- src/gpsea/analysis/mtc_filter/_impl.py | 127 ----------------- src/gpsea/analysis/pcats/__init__.py | 2 + src/gpsea/analysis/pcats/_impl.py | 2 + .../analysis/pcats/test_hpo_term_analysis.py | 56 +++++++- tests/analysis/test_analysis.py | 26 +--- tests/analysis/test_mtc_filter.py | 130 ++++++++++-------- 6 files changed, 124 insertions(+), 219 deletions(-) diff --git a/src/gpsea/analysis/mtc_filter/_impl.py b/src/gpsea/analysis/mtc_filter/_impl.py index 0aca8cc7..82a044c1 100644 --- a/src/gpsea/analysis/mtc_filter/_impl.py +++ b/src/gpsea/analysis/mtc_filter/_impl.py @@ -90,36 +90,6 @@ def filter( """ pass - @abc.abstractmethod - def filter_terms_to_test( - self, - gt_predicate: GenotypePolyPredicate, - n_usable: typing.Mapping[hpotk.TermId, int], - all_counts: typing.Mapping[hpotk.TermId, pd.DataFrame], - ) -> typing.Tuple[ - typing.Mapping[hpotk.TermId, int], - typing.Mapping[hpotk.TermId, pd.DataFrame], - typing.Mapping[str, int], - ]: - """ - Decide which terms to pass through for statistical testing. - The intention of this class is to reduce multiple testing burden by removing terms that are unlikely - to lead to interesting statistical/analytical results. - - Args: - gt_predicate: the predicate used to bin patients into groups along the genotype axis - n_usable: a mapping from the :class:`hpotk.TermId` to an `int` with the count of patients - that could be binned according to the used genotype/phenotype predicate. - all_counts: a mapping from the :class:`hpotk.TermId` to counts of patients - in the i-th phenotype (rows) and j-th genotype (column) group - Returns: - a tuple with three items: - - a mapping from :class:`hpotk.TermId` -> - - a mapping from :class:`hpotk.TermId` -> - - a mapping from a `str` with reason why a term was filtered out (e.g. *Skipping top level term*) - """ - pass - @abc.abstractmethod def filter_method_name(self) -> str: """ @@ -453,103 +423,6 @@ def filter( return tuple(results) - def filter_terms_to_test( - self, - gt_predicate: GenotypePolyPredicate, - n_usable: typing.Mapping[hpotk.TermId, int], - all_counts: typing.Mapping[hpotk.TermId, pd.DataFrame], - ) -> typing.Tuple[ - typing.Mapping[hpotk.TermId, int], - typing.Mapping[hpotk.TermId, pd.DataFrame], - typing.Mapping[str, int], - ]: - """ - Args: - gt_predicate: the predicate used to bin patients into groups along the genotype axis - n_usable: a mapping from the :class:`hpotk.TermId` to an `int` with the count of patients - that could be binned according to the used genotype/phenotype predicate. - all_counts: a mapping from the :class:`hpotk.TermId` to counts of patients - in the i-th phenotype (rows) and j-th genotype (column) group - """ - filtered_n_usable = {} - filtered_all_counts = {} - reason_for_filtering_out = defaultdict(int) - tested_counts_pf = defaultdict( - pd.DataFrame - ) # key is an HP id, value is a tuple with counts, i.e., - - # iterate through the terms in the sorted order, starting from the leaves of the induced graph. - categories = tuple(gt_predicate.get_categories()) - for term_id in self._get_ordered_terms(all_counts.keys()): - if term_id in self._general_hpo_terms: - reason_for_filtering_out["Skipping general term"] += 1 - continue - - if not self._hpo.graph.is_ancestor_of( - hpotk.constants.hpo.base.PHENOTYPIC_ABNORMALITY, term_id - ): - reason_for_filtering_out["Skipping non phenotype term"] += 1 - continue - - # get total number of observations - if term_id not in all_counts: - reason_for_filtering_out["Skipping non-target term"] += 1 - continue - - counts_frame = all_counts[term_id] - total = counts_frame.sum().sum() - max_freq = HpoMtcFilter.get_maximum_group_observed_HPO_frequency( - counts_frame - ) - if max_freq < self._hpo_term_frequency_filter: - reason = ( - "Skipping term with maximum frequency " - f"that was less than threshold {self._hpo_term_frequency_filter}" - ) - reason_for_filtering_out[reason] += 1 - continue - - if counts_frame.shape == (2, 2) and total < 7: - reason = f"Skipping term with only {total} observations (not powered for 2x2)" - reason_for_filtering_out[reason] += 1 - continue - - # todo -- similar for (3,2) - if not HpoMtcFilter.some_cell_has_greater_than_one_count(counts_frame): - reason = "Skipping term because no genotype has more than one observed HPO count" - reason_for_filtering_out[reason] += 1 - continue - - elif HpoMtcFilter.genotypes_have_same_hpo_proportions( - counts_frame, - categories, - ): - reason = "Skipping term because all genotypes have same HPO observed proportions" - reason_for_filtering_out[reason] += 1 - continue - - elif HpoMtcFilter.one_genotype_has_zero_hpo_observations( - counts_frame, - categories, - ): - reason = "Skipping term because one genotype had zero observations" - reason_for_filtering_out[reason] += 1 - continue - - for child_term_id in self._hpo.graph.get_children(term_id): - if child_term_id in tested_counts_pf: - if tested_counts_pf[child_term_id].equals(counts_frame): - # TODO: should we make the match fuzzier by adding a tolerance instead of the exact matches? - reason = "Child term with same counts previously tested" - reason_for_filtering_out[reason] += 1 - continue - - # if we get here, then we include the test for `term_id` - filtered_n_usable[term_id] = n_usable[term_id] - filtered_all_counts[term_id] = all_counts[term_id] - - return filtered_n_usable, filtered_all_counts, reason_for_filtering_out - def filter_method_name(self) -> str: return "HPO MTC filter" diff --git a/src/gpsea/analysis/pcats/__init__.py b/src/gpsea/analysis/pcats/__init__.py index 6a82d92f..59eceb97 100644 --- a/src/gpsea/analysis/pcats/__init__.py +++ b/src/gpsea/analysis/pcats/__init__.py @@ -21,9 +21,11 @@ from ._impl import MultiPhenotypeAnalysis, MultiPhenotypeAnalysisResult from ._impl import DiseaseAnalysis from ._impl import HpoTermAnalysis, HpoTermAnalysisResult +from ._impl import apply_predicates_on_patients __all__ = [ 'MultiPhenotypeAnalysis', 'MultiPhenotypeAnalysisResult', 'DiseaseAnalysis', 'HpoTermAnalysis', 'HpoTermAnalysisResult', + 'apply_predicates_on_patients', ] diff --git a/src/gpsea/analysis/pcats/_impl.py b/src/gpsea/analysis/pcats/_impl.py index 3448ba93..43d8c707 100644 --- a/src/gpsea/analysis/pcats/_impl.py +++ b/src/gpsea/analysis/pcats/_impl.py @@ -606,6 +606,8 @@ def _compute_result( counts=all_counts, ) mtc_mask = np.array([r.is_passed() for r in mtc_filter_results]) + if not mtc_mask.any(): + raise ValueError("No phenotypes are left for the analysis after MTC filtering step") # 3 - Compute nominal p values pvals = np.full(shape=(len(n_usable),), fill_value=np.nan) diff --git a/tests/analysis/pcats/test_hpo_term_analysis.py b/tests/analysis/pcats/test_hpo_term_analysis.py index 690e9dd1..dbd8d508 100644 --- a/tests/analysis/pcats/test_hpo_term_analysis.py +++ b/tests/analysis/pcats/test_hpo_term_analysis.py @@ -14,18 +14,18 @@ class TestHpoTermAnalysis: - @pytest.fixture(scope='class') + @pytest.fixture(scope="class") def count_statistic(self) -> CountStatistic: return FisherExactTest() - @pytest.fixture(scope='class') + @pytest.fixture(scope="class") def phenotype_mtc_filter( self, hpo: hpotk.MinimalOntology, ) -> PhenotypeMtcFilter: return HpoMtcFilter.default_filter( hpo=hpo, - term_frequency_threshold=.2, + term_frequency_threshold=0.2, ) @pytest.fixture @@ -37,8 +37,8 @@ def analysis( return HpoTermAnalysis( count_statistic=count_statistic, mtc_filter=phenotype_mtc_filter, - mtc_correction='fdr_bh', - mtc_alpha=.05, + mtc_correction="fdr_bh", + mtc_alpha=0.05, ) def test_compare_genotype_vs_phenotypes( @@ -46,7 +46,7 @@ def test_compare_genotype_vs_phenotypes( analysis: HpoTermAnalysis, suox_cohort: Cohort, suox_gt_predicate: GenotypePolyPredicate, - suox_pheno_predicates: typing.Sequence[PhenotypePolyPredicate[hpotk.TermId]] + suox_pheno_predicates: typing.Sequence[PhenotypePolyPredicate[hpotk.TermId]], ): results = analysis.compare_genotype_vs_phenotypes( cohort=suox_cohort.all_patients, @@ -55,4 +55,46 @@ def test_compare_genotype_vs_phenotypes( ) assert results is not None - # TODO: improve testing + + assert results.total_tests == 4 + assert results.n_usable == (35, 18, 13, 25, 23) + assert results.pvals == pytest.approx( + [ + 0.0721291631224236, + 1.0, + float("nan"), + 0.35921595820909313, + 0.6668461434917216, + ], + nan_ok=True, + ) + assert results.corrected_pvals is not None + assert results.corrected_pvals == pytest.approx( + [ + 0.2885166524896944, + 1.0, + float("nan"), + 0.7184319164181863, + 0.8891281913222954, + ], + nan_ok=True, + ) + + def test_compare_genotype_vs_phenotypes_explodes_if_no_phenotypes_are_left_after_mtc_filter( + self, + analysis: HpoTermAnalysis, + degenerated_cohort: Cohort, + suox_gt_predicate: GenotypePolyPredicate, + suox_pheno_predicates: typing.Sequence[PhenotypePolyPredicate[hpotk.TermId]], + ): + with pytest.raises(ValueError) as e: + analysis.compare_genotype_vs_phenotypes( + cohort=degenerated_cohort, + gt_predicate=suox_gt_predicate, + pheno_predicates=suox_pheno_predicates, + ) + + assert ( + e.value.args[0] + == "No phenotypes are left for the analysis after MTC filtering step" + ) diff --git a/tests/analysis/test_analysis.py b/tests/analysis/test_analysis.py index 5db24621..ea5408e2 100644 --- a/tests/analysis/test_analysis.py +++ b/tests/analysis/test_analysis.py @@ -8,10 +8,11 @@ from gpsea.analysis import ( apply_predicates_on_patients, - CohortAnalysis, CohortAnalysisConfiguration, + CohortAnalysis, configure_cohort_analysis, ) +from gpsea.analysis.mtc_filter._impl import HpoMtcFilter from gpsea.model import * from gpsea.model.genome import * from gpsea.analysis.predicate import PatientCategories @@ -69,26 +70,3 @@ def test_analysis_passes_if_variant_predicate_always_returns_false( predicate=always_false_variant_predicate ) assert results is not None - - def test_analysis_explodes_if_no_phenotypes_are_left_for_analysis( - self, - hpo: hpotk.MinimalOntology, - degenerated_cohort: Cohort, - tmp_path: pathlib.Path, - always_false_variant_predicate: VariantPredicate, - ): - config = CohortAnalysisConfiguration() - config.hpo_mtc_strategy() - cohort_analysis = configure_cohort_analysis( - hpo=hpo, - cohort=degenerated_cohort, - cache_dir=str(tmp_path), - config=config, - ) - - with pytest.raises(ValueError) as e: - _ = cohort_analysis.compare_hpo_vs_genotype( - predicate=always_false_variant_predicate, - ) - - assert e.value.args[0] == 'No phenotypes are left for the analysis after MTC filtering step' diff --git a/tests/analysis/test_mtc_filter.py b/tests/analysis/test_mtc_filter.py index c07600ef..af6f1439 100644 --- a/tests/analysis/test_mtc_filter.py +++ b/tests/analysis/test_mtc_filter.py @@ -1,3 +1,4 @@ +from itertools import count import typing import hpotk @@ -5,11 +6,11 @@ import pandas as pd import pytest -from gpsea.analysis import apply_predicates_on_patients from gpsea.analysis.mtc_filter import HpoMtcFilter, SpecifiedTermsMtcFilter from gpsea.analysis.predicate import PatientCategories from gpsea.analysis.predicate.genotype import GenotypePolyPredicate from gpsea.analysis.predicate.phenotype import PhenotypePolyPredicate +from gpsea.analysis.pcats import apply_predicates_on_patients from gpsea.model import Cohort @@ -31,16 +32,13 @@ def patient_counts( suox_cohort: Cohort, suox_gt_predicate: GenotypePolyPredicate, suox_pheno_predicates: typing.Sequence[PhenotypePolyPredicate[hpotk.TermId]], - ) -> typing.Tuple[ - typing.Mapping[hpotk.TermId, int], - typing.Mapping[hpotk.TermId, pd.DataFrame], - ]: - categories, n_usable, all_counts = apply_predicates_on_patients( + ) -> typing.Sequence[pd.DataFrame]: + _, counts = apply_predicates_on_patients( patients=suox_cohort.all_patients, gt_predicate=suox_gt_predicate, pheno_predicates=suox_pheno_predicates, ) - return n_usable, all_counts + return counts @pytest.fixture(scope='class') def gt_categories(self) -> pd.Index: @@ -135,74 +133,80 @@ def test_genotypes_have_same_hpo_proportions( assert actual == expected def test_filter_terms_to_test( - self, - mtc_filter: HpoMtcFilter, - suox_gt_predicate: GenotypePolyPredicate, - patient_counts: typing.Tuple[ - typing.Mapping[hpotk.TermId, int], - typing.Mapping[hpotk.TermId, pd.DataFrame], - ], + self, + mtc_filter: HpoMtcFilter, + suox_gt_predicate: GenotypePolyPredicate, + suox_pheno_predicates: typing.Sequence[PhenotypePolyPredicate[hpotk.TermId]], + patient_counts: typing.Sequence[pd.DataFrame], ): - n_usable, all_counts = patient_counts - mtc_report = mtc_filter.filter_terms_to_test( - suox_gt_predicate, - n_usable, - all_counts, + phenotypes = [p.phenotype for p in suox_pheno_predicates] + + mtc_report = mtc_filter.filter( + gt_predicate=suox_gt_predicate, + phenotypes=phenotypes, + counts=patient_counts, ) - assert isinstance(mtc_report, tuple) - assert len(mtc_report) == 3 - - filtered_n_usable, filtered_all_counts, reason_for_filtering_out = mtc_report + assert isinstance(mtc_report, typing.Sequence) + assert len(mtc_report) == 5 - assert reason_for_filtering_out['Skipping term because all genotypes have same HPO observed proportions'] == 1 - assert reason_for_filtering_out['Skipping general term'] == 16 - assert reason_for_filtering_out['Skipping non-target term'] == 5 - assert reason_for_filtering_out['Skipping top level term'] == 0 + is_ok = [r.is_passed() for r in mtc_report] + assert is_ok == [True, True, False, True, True] - assert len(filtered_n_usable) == 4 - assert len(filtered_all_counts) == 4 + reasons = [r.reason for r in mtc_report] + assert reasons == [ + None, None, + 'Skipping term because all genotypes have same HPO observed proportions', + None, None, + ] def test_specified_term_mtc_filter( - self, - suox_gt_predicate: GenotypePolyPredicate, - patient_counts: typing.Tuple[ - typing.Mapping[hpotk.TermId, int], - typing.Mapping[hpotk.TermId, pd.DataFrame], - ], + self, + suox_gt_predicate: GenotypePolyPredicate, + suox_pheno_predicates: typing.Sequence[PhenotypePolyPredicate[hpotk.TermId]], + patient_counts: typing.Sequence[pd.DataFrame], ): """ The point of this test is to check that if we filter to test only one term ("HP:0032350"), then this is the only term that should survive the filter. We start with a total of five terms (n_usable==5), - but after our filter, only one survives (filtered_n_usable == 1), and we have four cases in which the - reason for filtering out is 'Skipping non-specified term' + but after our filter, only one survives, and we have four cases in which the + reason for filtering out is 'Non-specified term' """ - specified_filter = SpecifiedTermsMtcFilter(terms_to_test={hpotk.TermId.from_curie("HP:0032350")}) - n_usable, all_counts = patient_counts - mtc_report = specified_filter.filter_terms_to_test( - suox_gt_predicate, - n_usable, - all_counts, + specified_filter = SpecifiedTermsMtcFilter( + terms_to_test=(hpotk.TermId.from_curie("HP:0032350"),), + ) + phenotypes = [p.phenotype for p in suox_pheno_predicates] + + mtc_report = specified_filter.filter( + gt_predicate=suox_gt_predicate, + phenotypes=phenotypes, + counts=patient_counts, ) - assert isinstance(mtc_report, tuple) - assert len(mtc_report) == 3 # # Skipping non-specified term (n=5) + assert isinstance(mtc_report, typing.Sequence) + assert len(mtc_report) == 5 + + is_passed = [r.is_passed() for r in mtc_report] + assert is_passed == [ + False, False, True, False, False, + ] - filtered_n_usable, filtered_all_counts, reason_for_filtering_out = mtc_report - assert len(n_usable) == 5 - assert len(filtered_n_usable) == 1 - assert reason_for_filtering_out['Skipping non-specified term'] == 4 + reasons = [r.reason for r in mtc_report] + assert reasons == [ + 'Non-specified term', + 'Non-specified term', + None, + 'Non-specified term', + 'Non-specified term', + ] def test_min_observed_HPO_threshold( self, - patient_counts: typing.Tuple[ - typing.Mapping[hpotk.TermId, int], - typing.Mapping[hpotk.TermId, pd.DataFrame], - ], + suox_pheno_predicates: typing.Sequence[PhenotypePolyPredicate[hpotk.TermId]], + patient_counts: typing.Sequence[pd.DataFrame], ): """ In our heuristic filter, we only test terms that have at least a threshold - frequency in at least one of the groups. We use the "all counts" datastructure, that - is a dictionary whose keys are hpotoolkit TermIds and whose values are pandas DataFrames + frequency in at least one of the groups. We use the `patient_counts` - a sequence of DataFrames with 2x2 contingenicy tables of counts. For instance, each column will have one row for PatientCategories.YES and one for PatientCategories.NO, indicating counts of measured observed/excluded HPO phenotypes. Each column is a certain genotype, e.g., MISSENSE or NON-MISSENSE. We want the @@ -211,28 +215,32 @@ def test_min_observed_HPO_threshold( for all of the HPO terms in the dictionary. """ EPSILON = 0.001 - _, all_counts = patient_counts + curie2idx = { + p.phenotype.value: i + for i, p + in enumerate(suox_pheno_predicates) + } # Ectopia lentis HP:0001083 (6 9 1 2), freqs are 6/15=0.4 and 1/3=0.33 - ectopia = all_counts[hpotk.TermId.from_curie("HP:0001083")] + ectopia = patient_counts[curie2idx["HP:0001083"]] max_f = HpoMtcFilter.get_maximum_group_observed_HPO_frequency(ectopia) assert max_f == pytest.approx(0.4, abs=EPSILON) # Seizure HP:0001250 (17 7 11 0), freqs are 17/24=0.7083 and 11/11=1 - seizure = all_counts[hpotk.TermId.from_curie("HP:0001250")] + seizure = patient_counts[curie2idx["HP:0001250"]] max_f = HpoMtcFilter.get_maximum_group_observed_HPO_frequency(seizure) assert max_f == pytest.approx(1.0, abs=EPSILON) # Sulfocysteinuria HP:0032350 (11 0 2 0), freqs are both 1 - sulfocysteinuria = all_counts[hpotk.TermId.from_curie("HP:0032350")] + sulfocysteinuria = patient_counts[curie2idx["HP:0032350"]] max_f = HpoMtcFilter.get_maximum_group_observed_HPO_frequency(sulfocysteinuria) assert max_f == pytest.approx(1.0, abs=EPSILON) # Neurodevelopmental delay HP:0012758 (4 13 4 4), freqs are 4/17 = 0.235 and 4/8=0.5 - ndelay = all_counts[hpotk.TermId.from_curie("HP:0012758")] + ndelay = patient_counts[curie2idx["HP:0012758"]] max_f = HpoMtcFilter.get_maximum_group_observed_HPO_frequency(ndelay) assert max_f == pytest.approx(0.5, abs=EPSILON) # Hypertonia HP:0001276 (7 9 4 3) fresa are 7/16=0.4375 and 4/7=0.5714 - hypertonia = all_counts[hpotk.TermId.from_curie("HP:0001276")] + hypertonia = patient_counts[curie2idx["HP:0001276"]] max_f = HpoMtcFilter.get_maximum_group_observed_HPO_frequency(hypertonia) assert max_f == pytest.approx(0.5714, abs=EPSILON) From ab0a116e13baa937ec3a6f57a928cdfd3fa632f3 Mon Sep 17 00:00:00 2001 From: Daniel Danis Date: Tue, 3 Sep 2024 11:56:20 +0200 Subject: [PATCH 32/47] Add `PhenotypePolyPredicate.present_phenotype_categorization` property. --- .../analysis/predicate/phenotype/_pheno.py | 30 +++++++++++++++---- 1 file changed, 24 insertions(+), 6 deletions(-) diff --git a/src/gpsea/analysis/predicate/phenotype/_pheno.py b/src/gpsea/analysis/predicate/phenotype/_pheno.py index c2646f72..9a9c496c 100644 --- a/src/gpsea/analysis/predicate/phenotype/_pheno.py +++ b/src/gpsea/analysis/predicate/phenotype/_pheno.py @@ -60,11 +60,12 @@ class PhenotypePolyPredicate( Each phenotype category `P` can be a :class:`~hpotk.model.TermId` representing an HPO term or an OMIM/MONDO term. - Most of the time, only one category is investigated, and :attr:`phenotype` will return a sequence with - one element only (e.g. *Arachnodactyly* `HP:0001166`). However, multiple categories can be sought as well, - such as when the predicate bins the patient into one of discrete diseases - (e.g. Geleophysic dysplasia, Marfan syndrome, ...). Then the predicate should return a sequence of disease - identifiers. + Only one category can be investigated, and :attr:`phenotype` returns the investigated phenotype + (e.g. *Arachnodactyly* `HP:0001166`). + + As another hallmark of this predicate, one of the categorizations must correspond to the group of patients + who exibit the investigated phenotype. The categorization is provided + via :attr:`present_phenotype_categorization` property. """ @property @@ -75,6 +76,14 @@ def phenotype(self) -> P: """ pass + @property + @abc.abstractmethod + def present_phenotype_categorization(self) -> PhenotypeCategorization[P]: + """ + Get the categorization which represents the group of the patients who exibit the investigated phenotype. + """ + pass + class PropagatingPhenotypePredicate(PhenotypePolyPredicate[hpotk.TermId]): """ @@ -112,6 +121,7 @@ def __init__( category=PatientCategories.NO, phenotype=self._query, ) + self._categorizations = (self._phenotype_observed, self._phenotype_excluded) def get_question(self) -> str: return f"Is {self._query_label} present in the patient?" @@ -120,10 +130,14 @@ def get_question(self) -> str: def phenotype(self) -> hpotk.TermId: return self._query + @property + def present_phenotype_categorization(self) -> PhenotypeCategorization[hpotk.TermId]: + return self._phenotype_observed + def get_categorizations( self, ) -> typing.Sequence[PhenotypeCategorization[hpotk.TermId]]: - return self._phenotype_observed, self._phenotype_excluded + return self._categorizations def test( self, patient: Patient @@ -207,6 +221,10 @@ def get_question(self) -> str: def phenotype(self) -> hpotk.TermId: return self._query + @property + def present_phenotype_categorization(self) -> PhenotypeCategorization[hpotk.TermId]: + return self._diagnosis_present + def get_categorizations( self, ) -> typing.Sequence[PhenotypeCategorization[hpotk.TermId]]: From b35790b8a6794428b2eef0d4fe3d830e6e6b8650 Mon Sep 17 00:00:00 2001 From: Daniel Danis Date: Tue, 3 Sep 2024 16:07:11 +0200 Subject: [PATCH 33/47] Improve `PhenotypeMtcFilter` tests. --- docs/report/tbx5_frameshift_vs_missense.csv | 36 ++-- ...bx5_frameshift_vs_missense.mtc_report.html | 21 +- docs/tutorial.rst | 8 +- src/gpsea/analysis/mtc_filter/_impl.py | 202 ++++++------------ src/gpsea/analysis/pcats/_impl.py | 2 +- .../analysis/predicate/phenotype/_pheno.py | 8 + tests/analysis/test_analysis.py | 72 ------- tests/analysis/test_examples.py | 2 + tests/analysis/test_mtc_filter.py | 151 +++++++++---- 9 files changed, 219 insertions(+), 283 deletions(-) delete mode 100644 tests/analysis/test_analysis.py diff --git a/docs/report/tbx5_frameshift_vs_missense.csv b/docs/report/tbx5_frameshift_vs_missense.csv index 07245a44..0bc1e5c6 100644 --- a/docs/report/tbx5_frameshift_vs_missense.csv +++ b/docs/report/tbx5_frameshift_vs_missense.csv @@ -1,22 +1,22 @@ "Genotype group: Missense, Frameshift",Missense,Missense,Frameshift,Frameshift,, ,Count,Percent,Count,Percent,Corrected p values,p values -Ventricular septal defect [HP:0001629],31/60,52%,19/19,100%,0.0008990549794102921,5.6190936213143254e-05 -Abnormal atrioventricular conduction [HP:0005150],0/22,0%,3/3,100%,0.003478260869565217,0.00043478260869565214 -Atrioventricular block [HP:0001678],0/22,0%,2/2,100%,0.014492753623188406,0.0036231884057971015 -Heart block [HP:0012722],0/22,0%,2/2,100%,0.014492753623188406,0.0036231884057971015 -Absent thumb [HP:0009777],12/71,17%,14/31,45%,0.018011232501600187,0.005628510156750059 -Patent ductus arteriosus [HP:0001643],3/37,8%,2/2,100%,0.03598740440845704,0.01349527665317139 -Triphalangeal thumb [HP:0001199],13/72,18%,13/32,41%,0.058517997576307476,0.02560162393963452 -Cardiac conduction abnormality [HP:0031546],14/36,39%,3/3,100%,0.14881278039172777,0.07440639019586388 -Secundum atrial septal defect [HP:0001684],14/35,40%,4/22,18%,0.2532484592139712,0.1424522583078588 -Muscular ventricular septal defect [HP:0011623],6/59,10%,6/25,24%,0.2699930339748754,0.1687456462342971 -Pulmonary arterial hypertension [HP:0002092],4/6,67%,0/2,0%,0.6233766233766234,0.42857142857142855 -Hypoplasia of the ulna [HP:0003022],1/12,8%,2/10,20%,0.7619047619047618,0.5714285714285713 -Hypoplasia of the radius [HP:0002984],30/62,48%,6/14,43%,0.9520604334894502,0.7735491022101784 -Atrial septal defect [HP:0001631],42/44,95%,20/20,100%,1.0,1.0 +Ventricular septal defect [HP:0001629],31/60,52%,19/19,100%,0.0009552459156234353,5.6190936213143254e-05 +Abnormal atrioventricular conduction [HP:0005150],0/22,0%,3/3,100%,0.003695652173913043,0.00043478260869565214 +Atrioventricular block [HP:0001678],0/22,0%,2/2,100%,0.015398550724637682,0.0036231884057971015 +Heart block [HP:0012722],0/22,0%,2/2,100%,0.015398550724637682,0.0036231884057971015 +Absent thumb [HP:0009777],12/71,17%,14/31,45%,0.0191369345329502,0.005628510156750059 +Patent ductus arteriosus [HP:0001643],3/37,8%,2/2,100%,0.038236617183985605,0.01349527665317139 +Triphalangeal thumb [HP:0001199],13/72,18%,13/32,41%,0.062175372424826694,0.02560162393963452 +Cardiac conduction abnormality [HP:0031546],14/36,39%,3/3,100%,0.15811357916621074,0.07440639019586388 +Secundum atrial septal defect [HP:0001684],14/35,40%,4/22,18%,0.2690764879148444,0.1424522583078588 +Muscular ventricular septal defect [HP:0011623],6/59,10%,6/25,24%,0.2868675985983051,0.1687456462342971 +Pulmonary arterial hypertension [HP:0002092],4/6,67%,0/2,0%,0.6623376623376622,0.42857142857142855 +Hypoplasia of the ulna [HP:0003022],1/12,8%,2/10,20%,0.8095238095238093,0.5714285714285713 +Hypoplasia of the radius [HP:0002984],30/62,48%,6/14,43%,1.0,0.7735491022101784 +Short thumb [HP:0009778],11/41,27%,8/30,27%,1.0,1.0 Absent radius [HP:0003974],7/32,22%,6/25,24%,1.0,1.0 Short humerus [HP:0005792],7/17,41%,4/9,44%,1.0,1.0 -Forearm undergrowth [HP:0009821],30/30,100%,7/7,100%,, -Abnormal upper limb bone morphology [HP:0040070],40/40,100%,14/14,100%,, -Abnormality of the upper limb [HP:0002817],73/73,100%,34/34,100%,, -Abnormality of limbs [HP:0040064],73/73,100%,34/34,100%,, +Atrial septal defect [HP:0001631],42/44,95%,20/20,100%,1.0,1.0 +Abnormal ventricular septum morphology [HP:0010438],31/31,100%,19/19,100%,, +Abnormal cardiac ventricle morphology [HP:0001713],31/31,100%,19/19,100%,, +Abnormal heart morphology [HP:0001627],62/62,100%,30/30,100%,, diff --git a/docs/report/tbx5_frameshift_vs_missense.mtc_report.html b/docs/report/tbx5_frameshift_vs_missense.mtc_report.html index 5b149af7..b6ac1747 100644 --- a/docs/report/tbx5_frameshift_vs_missense.mtc_report.html +++ b/docs/report/tbx5_frameshift_vs_missense.mtc_report.html @@ -48,9 +48,9 @@

Phenotype testing report

Phenotype MTC filter: HPO MTC filter

Multiple testing correction: fdr_bh

-

Performed statistical tests for 16 out of the total of 260 HPO terms.

+

Performed statistical tests for 17 out of the total of 260 HPO terms.

- + @@ -61,21 +61,21 @@

Phenotype testing report

- - + + - - + + - + @@ -114,13 +114,6 @@

Phenotype testing report

- - - - - - - diff --git a/docs/tutorial.rst b/docs/tutorial.rst index e047943c..29d8f2d9 100644 --- a/docs/tutorial.rst +++ b/docs/tutorial.rst @@ -246,15 +246,15 @@ Now we can perform the analysis and investigate the results. ... pheno_predicates=pheno_predicates, ... ) >>> result.total_tests -16 +17 -We only tested 16 HPO terms. This is despite the individuals being collectively annotated with +We only tested 1y HPO terms. This is despite the individuals being collectively annotated with 260 direct and indirect HPO terms >>> len(result.phenotypes) 260 -We can show the reasoning behind *not* testing 244 (`260 - 16`) HPO terms +We can show the reasoning behind *not* testing 243 (`260 - 17`) HPO terms by exploring the phenotype MTC filtering report. >>> from gpsea.view import MtcStatsViewer @@ -283,4 +283,4 @@ was observed in 31/60 (52%) patients with a missense variant but it was observed in 19/19 (100%) patients with a frameshift variant. Fisher exact test computed a p value of `~0.0000562` and the p value corrected by Benjamini-Hochberg procedure -is `~0.000899`. +is `~0.000955`. diff --git a/src/gpsea/analysis/mtc_filter/_impl.py b/src/gpsea/analysis/mtc_filter/_impl.py index 82a044c1..c52d10b9 100644 --- a/src/gpsea/analysis/mtc_filter/_impl.py +++ b/src/gpsea/analysis/mtc_filter/_impl.py @@ -1,14 +1,15 @@ import abc import typing -from collections import defaultdict, deque +from collections import deque import hpotk +from matplotlib import axis +import numpy as np import pandas as pd -from ..predicate import PatientCategories, PatientCategory from ..predicate.genotype import GenotypePolyPredicate -from ..predicate.phenotype import P +from ..predicate.phenotype import PhenotypePolyPredicate, P class PhenotypeMtcResult: @@ -76,14 +77,14 @@ class PhenotypeMtcFilter(typing.Generic[P], metaclass=abc.ABCMeta): def filter( self, gt_predicate: GenotypePolyPredicate, - phenotypes: typing.Sequence[P], + ph_predicates: typing.Sequence[PhenotypePolyPredicate[P]], counts: typing.Sequence[pd.DataFrame], ) -> typing.Sequence[PhenotypeMtcResult]: """ Test if the phenotype with given counts should be included in the downstream analysis. :param gt_predicate: the predicate that produced the columns of the `count` data frame. - :param phenotypes: the tested phenotypes. + :param ph_predicates: the phenotype predicates that produced the rows of the `counts` data frames. :param counts: a sequence of 2D data frames for the tested phenotypes. Each data frame corrresponds to a genotype/phenotype contingency matrix. :returns: a sequence of filter results for the input `phenotypes`. @@ -111,27 +112,11 @@ def __init__(self): def filter( self, gt_predicate: GenotypePolyPredicate, - phenotypes: typing.Sequence[P], + ph_predicates: typing.Sequence[PhenotypePolyPredicate[P]], counts: typing.Sequence[pd.DataFrame], ) -> typing.Sequence[PhenotypeMtcResult]: # Always OK! 😏 - return tuple(self._ok for _ in phenotypes) - - def filter_terms_to_test( - self, - gt_predicate: GenotypePolyPredicate, - n_usable: typing.Mapping[hpotk.TermId, int], - all_counts: typing.Mapping[hpotk.TermId, pd.DataFrame], - ) -> typing.Tuple[ - typing.Mapping[hpotk.TermId, int], - typing.Mapping[hpotk.TermId, pd.DataFrame], - typing.Mapping[str, int], - ]: - """ - Use this implementation to test all available HPO terms. - No HPO terms will be filtered out! - """ - return n_usable, all_counts, {} + return tuple(self._ok for _ in ph_predicates) def filter_method_name(self) -> str: return "All HPO terms" @@ -164,55 +149,17 @@ def __init__( def filter( self, gt_predicate: GenotypePolyPredicate, - phenotypes: typing.Sequence[P], + ph_predicates: typing.Sequence[PhenotypePolyPredicate[P]], counts: typing.Sequence[pd.DataFrame], ) -> typing.Sequence[PhenotypeMtcResult]: results = [] - for phenotype in phenotypes: - if phenotype in self._terms_to_test_set: + for predicate in ph_predicates: + if predicate.phenotype in self._terms_to_test_set: results.append(self._ok) else: results.append(self._fail) return tuple(results) - def filter_terms_to_test( - self, - gt_predicate: GenotypePolyPredicate, - n_usable: typing.Mapping[hpotk.TermId, int], - all_counts: typing.Mapping[hpotk.TermId, pd.DataFrame], - ) -> typing.Tuple[ - typing.Mapping[hpotk.TermId, int], - typing.Mapping[hpotk.TermId, pd.DataFrame], - typing.Mapping[str, int], - ]: - """ - Remove terms that are not members of the specific set of HPO terms to be tested. - - Args: - gt_predicate: the predicate used to bin patients into groups along the genotype axis - n_usable: a mapping from the :class:`hpotk.TermId` to an `int` with the count of patients - that could be binned according to the used genotype/phenotype predicate. - all_counts: a mapping from the :class:`hpotk.TermId` to counts of patients - in the i-th phenotype (rows) and j-th genotype (column) group - - Returns: - filtered versions of the two dictionaries above and dataframe with reasons for skipping - """ - filtered_n_usable = {} - filtered_all_counts = {} - reason_for_filtering_out: typing.DefaultDict[str, int] = defaultdict(int) - - for term_id in all_counts.keys(): - if term_id not in self._terms_to_test_set: - reason_for_filtering_out["Skipping non-specified term"] += 1 - continue - - # if we get here, then the term is a member of our list of terms to be tested. - filtered_n_usable[term_id] = n_usable[term_id] - filtered_all_counts[term_id] = all_counts[term_id] - - return filtered_n_usable, filtered_all_counts, reason_for_filtering_out - def filter_method_name(self) -> str: return "Specified terms MTC filter" @@ -326,13 +273,13 @@ def __init__( def filter( self, gt_predicate: GenotypePolyPredicate, - phenotypes: typing.Sequence[hpotk.TermId], + ph_predicates: typing.Sequence[PhenotypePolyPredicate[P]], counts: typing.Sequence[pd.DataFrame], ) -> typing.Sequence[PhenotypeMtcResult]: + phenotypes = [p.phenotype for p in ph_predicates] p_to_idx = {p: i for i, p in enumerate(phenotypes)} - results = [None for _ in range(len(phenotypes))] - categories = tuple(gt_predicate.get_categories()) + results: typing.MutableSequence[typing.Optional[PhenotypeMtcResult]] = [None for _ in range(len(phenotypes))] for term_id in self._get_ordered_terms(phenotypes): try: idx = p_to_idx[term_id] @@ -357,10 +304,12 @@ def filter( # reason_for_filtering_out["Skipping non-target term"] += 1 # continue + ph_predicate = ph_predicates[idx] contingency_matrix = counts[idx] total = contingency_matrix.sum().sum() max_freq = HpoMtcFilter.get_maximum_group_observed_HPO_frequency( - contingency_matrix + contingency_matrix, + ph_predicate=ph_predicate, ) if max_freq < self._hpo_term_frequency_filter: reason = ( @@ -375,23 +324,26 @@ def filter( results[idx] = PhenotypeMtcResult.fail(reason) continue - # todo -- similar for (3,2) - if not HpoMtcFilter.some_cell_has_greater_than_one_count(contingency_matrix): + if not HpoMtcFilter.some_cell_has_greater_than_one_count( + counts=contingency_matrix, + ph_predicate=ph_predicate, + ): reason = "Skipping term because no genotype has more than one observed HPO count" results[idx] = PhenotypeMtcResult.fail(reason) continue - + elif HpoMtcFilter.genotypes_have_same_hpo_proportions( contingency_matrix, - categories, + gt_predicate=gt_predicate, + ph_predicate=ph_predicate, ): reason = "Skipping term because all genotypes have same HPO observed proportions" results[idx] = PhenotypeMtcResult.fail(reason) continue elif HpoMtcFilter.one_genotype_has_zero_hpo_observations( - contingency_matrix, - categories, + counts=contingency_matrix, + gt_predicate=gt_predicate, ): reason = "Skipping term because one genotype had zero observations" results[idx] = PhenotypeMtcResult.fail(reason) @@ -417,78 +369,70 @@ def filter( term_name = self._hpo.get_term_name(phenotypes[i]) missing.append(term_name) - print('BLAAAAAA') msg = 'Missing results for {}'.format(', '.join(missing)) raise ValueError(msg) - return tuple(results) + # Ignoring the type hint, because we checked the type match above. + return tuple(results) # type: ignore def filter_method_name(self) -> str: return "HPO MTC filter" @staticmethod - def get_number_of_observed_hpo_observations(counts_frame: pd.DataFrame) -> int: - return counts_frame.loc[PatientCategories.YES].sum() + def get_number_of_observed_hpo_observations( + counts_frame: pd.DataFrame, + ph_predicate: PhenotypePolyPredicate[hpotk.TermId], + ) -> int: + return counts_frame.loc[ph_predicate.present_phenotype_category].sum() @staticmethod - def get_maximum_group_observed_HPO_frequency(counts_frame: pd.DataFrame) -> float: + def get_maximum_group_observed_HPO_frequency( + counts_frame: pd.DataFrame, + ph_predicate: PhenotypePolyPredicate[hpotk.TermId], + ) -> float: """ Returns: The maximum frequency of observed HPO annotations across all genotypes. """ - df = counts_frame.loc[PatientCategories.YES] / ( - counts_frame.loc[PatientCategories.YES] - + counts_frame.loc[PatientCategories.NO] - ) - return df.max() + all_hpo_count_per_gt = counts_frame.sum() + if (all_hpo_count_per_gt == 0).all(): + # Prevent division by zeros + return 0. + + present_hpo_count_per_gt = counts_frame.loc[ph_predicate.present_phenotype_category] + return (present_hpo_count_per_gt / all_hpo_count_per_gt).max() @staticmethod def one_genotype_has_zero_hpo_observations( counts: pd.DataFrame, - gt_categories: typing.Sequence[PatientCategory], + gt_predicate: GenotypePolyPredicate, ): - if not isinstance(counts, pd.DataFrame): - raise ValueError( - f"argument 'counts' must be pandas DataFrame but was {type(counts)}" - ) - - if counts.shape == (2, 2): - assert len(gt_categories) == 2, \ - f"The counts frame is 2x2 but we found {len(gt_categories)} patient categories!" - a, b = gt_categories - return ( - counts.loc[:, a].sum() == 0 or counts.loc[:, b].sum() == 0 - ) - elif counts.shape == (2, 3): - raise ValueError("(2,3) not yet implemented") - else: - raise ValueError( - f"Did not recognize shape of counts matrix: {counts.shape}" - ) + return any(counts.loc[:, c].sum() == 0 for c in gt_predicate.get_categories()) @staticmethod - def some_cell_has_greater_than_one_count(counts: pd.DataFrame) -> bool: + def some_cell_has_greater_than_one_count( + counts: pd.DataFrame, + ph_predicate: PhenotypePolyPredicate[hpotk.TermId], + ) -> bool: """ If no genotype has more than one HPO count, we do not want to do a test. For instance, if MISSENSE has one observed HPO and N excluded, and NOT MISSENSE has zero or one observed HPO, then we will skip the test + Args: counts: pandas DataFrame with counts + ph_predicate: the phenotype predicate that produced the counts Returns: true if at least one of the genotypes has more than one observed HPO count """ - if not isinstance(counts, pd.DataFrame): - raise ValueError( - f"argument 'counts' must be pandas DataFrame but was {type(counts)}" - ) - - return (counts.loc[PatientCategories.YES] > 1).any() + return (counts.loc[ph_predicate.present_phenotype_category] > 1).any() @staticmethod def genotypes_have_same_hpo_proportions( counts: pd.DataFrame, - gt_categories: typing.Sequence[PatientCategory], - delta: float = 0.01, + gt_predicate: GenotypePolyPredicate, + ph_predicate: PhenotypePolyPredicate[hpotk.TermId], + delta: float = 5e-4, ) -> bool: """ If each genotype has the same proportion of observed HPOs, then we do not want to do a test. @@ -500,28 +444,20 @@ def genotypes_have_same_hpo_proportions( Returns: true if the genotypes differ by more than `delta` """ - if not isinstance(counts, pd.DataFrame): - raise ValueError( - f"argument 'counts' must be pandas DataFrame but was {type(counts)}" - ) + numerators = np.array([ + counts.loc[ph_predicate.present_phenotype_category, c] for c in gt_predicate.get_categories() + ]) + denominators = np.array([ + counts.loc[:, c].sum() for c in gt_predicate.get_categories() + ]) + + if np.any(denominators == 0): + return False - if counts.shape == (2, 2): - assert len(gt_categories) == 2, \ - f"The counts frame is 2x2 but we found {len(gt_categories)} patient categories!" - a, b = gt_categories - num1 = counts.loc[PatientCategories.YES, a] - denom1 = counts.loc[:, a].sum() - num2 = counts.loc[PatientCategories.YES, b] - denom2 = counts.loc[:, b].sum() - if denom1 == 0 or denom2 == 0: - return False - return abs(num1 / denom1 - num2 / denom2) < delta - elif counts.shape == (2, 3): - raise ValueError("(2,3) not implemented yet") - else: - raise ValueError( - f"Did not recognize shape of counts matrix: {counts.shape}" - ) + ratios = numerators / denominators + mean_ratio = np.mean(ratios) + abs_diff = np.abs(ratios - mean_ratio) + return bool(np.all(abs_diff < delta)) def _get_ordered_terms( self, diff --git a/src/gpsea/analysis/pcats/_impl.py b/src/gpsea/analysis/pcats/_impl.py index 43d8c707..408a9fd9 100644 --- a/src/gpsea/analysis/pcats/_impl.py +++ b/src/gpsea/analysis/pcats/_impl.py @@ -602,7 +602,7 @@ def _compute_result( # 2 - Apply MTC filter and select p values to MTC mtc_filter_results = self._mtc_filter.filter( gt_predicate=gt_predicate, - phenotypes=phenotypes, + ph_predicates=pheno_predicates, counts=all_counts, ) mtc_mask = np.array([r.is_passed() for r in mtc_filter_results]) diff --git a/src/gpsea/analysis/predicate/phenotype/_pheno.py b/src/gpsea/analysis/predicate/phenotype/_pheno.py index 9a9c496c..ecfb1401 100644 --- a/src/gpsea/analysis/predicate/phenotype/_pheno.py +++ b/src/gpsea/analysis/predicate/phenotype/_pheno.py @@ -84,6 +84,13 @@ def present_phenotype_categorization(self) -> PhenotypeCategorization[P]: """ pass + @property + def present_phenotype_category(self) -> PatientCategory: + """ + Get the patient category that correspond to the group of the patients who exibit the investigated phenotype. + """ + return self.present_phenotype_categorization.category + class PropagatingPhenotypePredicate(PhenotypePolyPredicate[hpotk.TermId]): """ @@ -121,6 +128,7 @@ def __init__( category=PatientCategories.NO, phenotype=self._query, ) + # Some tests depend on the order of `self._categorizations`. self._categorizations = (self._phenotype_observed, self._phenotype_excluded) def get_question(self) -> str: diff --git a/tests/analysis/test_analysis.py b/tests/analysis/test_analysis.py deleted file mode 100644 index ea5408e2..00000000 --- a/tests/analysis/test_analysis.py +++ /dev/null @@ -1,72 +0,0 @@ -import pathlib -import typing - -import hpotk -import numpy as np -import pandas as pd -import pytest - -from gpsea.analysis import ( - apply_predicates_on_patients, - CohortAnalysis, - configure_cohort_analysis, -) - -from gpsea.analysis.mtc_filter._impl import HpoMtcFilter -from gpsea.model import * -from gpsea.model.genome import * -from gpsea.analysis.predicate import PatientCategories -from gpsea.analysis.predicate.genotype import GenotypePolyPredicate, VariantPredicate -from gpsea.analysis.predicate.phenotype import PhenotypePolyPredicate - - -def test_apply_predicates_on_patients( - suox_cohort: Cohort, - suox_pheno_predicates: typing.Sequence[PhenotypePolyPredicate[hpotk.TermId]], - suox_gt_predicate: GenotypePolyPredicate, -): - categories, n_usable, counts = apply_predicates_on_patients( - patients=suox_cohort.all_patients, - pheno_predicates=suox_pheno_predicates, - gt_predicate=suox_gt_predicate, - ) - - assert isinstance(categories, typing.Collection) - assert PatientCategories.YES in categories - assert PatientCategories.NO in categories - assert len(categories) == 2 - - assert isinstance(n_usable, pd.Series) - assert len(n_usable) == 5 - - assert isinstance(counts, typing.Mapping) - assert len(counts) == 5 - - seizure_counts = counts[hpotk.TermId.from_curie('HP:0001250')] - assert np.array_equal(seizure_counts.to_numpy(), np.array([[17, 11], [7, 0]])) - - -class TestCohortAnalysis: - - @pytest.fixture - def cohort_analysis( - self, - suox_cohort: Cohort, - hpo: hpotk.MinimalOntology, - tmp_path: pathlib.Path, - ) -> CohortAnalysis: - return configure_cohort_analysis( - cohort=suox_cohort, - hpo=hpo, - cache_dir=str(tmp_path), - ) - - def test_analysis_passes_if_variant_predicate_always_returns_false( - self, - cohort_analysis: CohortAnalysis, - always_false_variant_predicate: VariantPredicate, - ): - results = cohort_analysis.compare_hpo_vs_genotype( - predicate=always_false_variant_predicate - ) - assert results is not None diff --git a/tests/analysis/test_examples.py b/tests/analysis/test_examples.py index 536f9907..8f17161d 100644 --- a/tests/analysis/test_examples.py +++ b/tests/analysis/test_examples.py @@ -11,6 +11,8 @@ from gpsea.model import Cohort, VariantEffect +# TODO: remove at some point! +@pytest.mark.skip('Obsolete tests') class TestCohortAnalysis: def test_compare_by_variant_effect( diff --git a/tests/analysis/test_mtc_filter.py b/tests/analysis/test_mtc_filter.py index af6f1439..d904cfe4 100644 --- a/tests/analysis/test_mtc_filter.py +++ b/tests/analysis/test_mtc_filter.py @@ -7,9 +7,8 @@ import pytest from gpsea.analysis.mtc_filter import HpoMtcFilter, SpecifiedTermsMtcFilter -from gpsea.analysis.predicate import PatientCategories from gpsea.analysis.predicate.genotype import GenotypePolyPredicate -from gpsea.analysis.predicate.phenotype import PhenotypePolyPredicate +from gpsea.analysis.predicate.phenotype import PhenotypePolyPredicate, PropagatingPhenotypePredicate from gpsea.analysis.pcats import apply_predicates_on_patients from gpsea.model import Cohort @@ -41,20 +40,36 @@ def patient_counts( return counts @pytest.fixture(scope='class') - def gt_categories(self) -> pd.Index: - return pd.Index([PatientCategories.YES, PatientCategories.NO]) + def gt_predicate( + self, + suox_gt_predicate: GenotypePolyPredicate, + ) -> GenotypePolyPredicate: + return suox_gt_predicate @pytest.fixture(scope='class') - def pheno_categories(self) -> pd.Index: - return pd.Index([PatientCategories.YES, PatientCategories.NO]) + def ph_predicate( + self, + hpo: hpotk.MinimalOntology, + ) -> PhenotypePolyPredicate[hpotk.TermId]: + """ + For the purpose of testing counts, let's pretend the counts + were created by this predicate. + """ + return PropagatingPhenotypePredicate( + hpo=hpo, + query=hpotk.TermId.from_curie("HP:0001250"), # Seizure + missing_implies_phenotype_excluded=False, + ) @staticmethod def prepare_counts_df( counts, - index: pd.Index, - columns: pd.Index, + gt_predicate: GenotypePolyPredicate, + ph_predicate: PhenotypePolyPredicate[hpotk.TermId], ): values = np.array(counts).reshape((2, 2)) + index = pd.Index(gt_predicate.get_categories()) + columns = pd.Index(ph_predicate.get_categories()) return pd.DataFrame(data=values, index=index, columns=columns) @pytest.mark.parametrize( @@ -67,17 +82,17 @@ def prepare_counts_df( ] ) def test_one_genotype_has_zero_hpo_observations( - self, - counts: typing.Tuple[int], - expected: bool, - gt_categories: pd.Index, - pheno_categories: pd.Index, + self, + counts: typing.Tuple[int], + expected: bool, + gt_predicate: GenotypePolyPredicate, + ph_predicate: PhenotypePolyPredicate[hpotk.TermId], ): - counts_df = TestHpoMtcFilter.prepare_counts_df(counts, gt_categories, pheno_categories) + counts_df = TestHpoMtcFilter.prepare_counts_df(counts, gt_predicate, ph_predicate) actual = HpoMtcFilter.one_genotype_has_zero_hpo_observations( counts=counts_df, - gt_categories=gt_categories, + gt_predicate=gt_predicate, ) assert actual == expected @@ -99,12 +114,15 @@ def test_some_cell_has_greater_than_one_count( self, counts: typing.Tuple[int], expected: bool, - gt_categories: pd.Index, - pheno_categories: pd.Index, + gt_predicate: GenotypePolyPredicate, + ph_predicate: PhenotypePolyPredicate[hpotk.TermId], ): - counts_df = TestHpoMtcFilter.prepare_counts_df(counts, gt_categories, pheno_categories) + counts_df = TestHpoMtcFilter.prepare_counts_df(counts, gt_predicate, ph_predicate) - actual = HpoMtcFilter.some_cell_has_greater_than_one_count(counts=counts_df) + actual = HpoMtcFilter.some_cell_has_greater_than_one_count( + counts=counts_df, + ph_predicate=ph_predicate, + ) assert actual == expected @@ -112,26 +130,55 @@ def test_some_cell_has_greater_than_one_count( "counts, expected", [ ((10, 100, 50, 500), True), + ((0, 0, 100, 100), True), + ((10, 100, 50, 500), True), + ((10, 100, 10, 105), False), ((95, 60, 144 - 95, 71 - 60), False), ((40, 15, 18, 15), False), ] ) def test_genotypes_have_same_hpo_proportions( - self, - counts: typing.Tuple[int], - expected: bool, - gt_categories: pd.Index, - pheno_categories: pd.Index, + self, + counts: typing.Tuple[int], + expected: bool, + gt_predicate: GenotypePolyPredicate, + ph_predicate: PhenotypePolyPredicate[hpotk.TermId], ): - counts_df = TestHpoMtcFilter.prepare_counts_df(counts, gt_categories, pheno_categories) + counts_df = TestHpoMtcFilter.prepare_counts_df(counts, gt_predicate, ph_predicate) actual = HpoMtcFilter.genotypes_have_same_hpo_proportions( counts=counts_df, - gt_categories=gt_categories, + gt_predicate=gt_predicate, + ph_predicate=ph_predicate, ) assert actual == expected + @pytest.mark.parametrize( + "counts, expected", + [ + ((1, 2, 99, 198), .01), + ((1, 3, 99, 197), .015), + ((0, 0, 100, 200), 0.), + ((0, 0, 0, 0), 0.), + ] + ) + def test_get_maximum_group_observed_HPO_frequency( + self, + counts: typing.Tuple[int], + expected: float, + gt_predicate: GenotypePolyPredicate, + ph_predicate: PhenotypePolyPredicate[hpotk.TermId], + ): + counts_df = TestHpoMtcFilter.prepare_counts_df(counts, gt_predicate, ph_predicate) + + actual = HpoMtcFilter.get_maximum_group_observed_HPO_frequency( + counts_frame=counts_df, + ph_predicate=ph_predicate, + ) + + assert actual == pytest.approx(expected) + def test_filter_terms_to_test( self, mtc_filter: HpoMtcFilter, @@ -139,11 +186,9 @@ def test_filter_terms_to_test( suox_pheno_predicates: typing.Sequence[PhenotypePolyPredicate[hpotk.TermId]], patient_counts: typing.Sequence[pd.DataFrame], ): - phenotypes = [p.phenotype for p in suox_pheno_predicates] - mtc_report = mtc_filter.filter( gt_predicate=suox_gt_predicate, - phenotypes=phenotypes, + ph_predicates=suox_pheno_predicates, counts=patient_counts, ) @@ -175,11 +220,10 @@ def test_specified_term_mtc_filter( specified_filter = SpecifiedTermsMtcFilter( terms_to_test=(hpotk.TermId.from_curie("HP:0032350"),), ) - phenotypes = [p.phenotype for p in suox_pheno_predicates] mtc_report = specified_filter.filter( gt_predicate=suox_gt_predicate, - phenotypes=phenotypes, + ph_predicates=suox_pheno_predicates, counts=patient_counts, ) assert isinstance(mtc_report, typing.Sequence) @@ -221,26 +265,51 @@ def test_min_observed_HPO_threshold( in enumerate(suox_pheno_predicates) } # Ectopia lentis HP:0001083 (6 9 1 2), freqs are 6/15=0.4 and 1/3=0.33 - ectopia = patient_counts[curie2idx["HP:0001083"]] - max_f = HpoMtcFilter.get_maximum_group_observed_HPO_frequency(ectopia) + idx = curie2idx["HP:0001083"] + ectopia = patient_counts[idx] + ectopia_predicate = suox_pheno_predicates[idx] + max_f = HpoMtcFilter.get_maximum_group_observed_HPO_frequency( + ectopia, + ph_predicate=ectopia_predicate, + ) assert max_f == pytest.approx(0.4, abs=EPSILON) # Seizure HP:0001250 (17 7 11 0), freqs are 17/24=0.7083 and 11/11=1 - seizure = patient_counts[curie2idx["HP:0001250"]] - max_f = HpoMtcFilter.get_maximum_group_observed_HPO_frequency(seizure) + idx = curie2idx["HP:0001250"] + seizure = patient_counts[idx] + seizure_predicate = suox_pheno_predicates[idx] + max_f = HpoMtcFilter.get_maximum_group_observed_HPO_frequency( + seizure, + ph_predicate=seizure_predicate + ) assert max_f == pytest.approx(1.0, abs=EPSILON) # Sulfocysteinuria HP:0032350 (11 0 2 0), freqs are both 1 - sulfocysteinuria = patient_counts[curie2idx["HP:0032350"]] - max_f = HpoMtcFilter.get_maximum_group_observed_HPO_frequency(sulfocysteinuria) + idx = curie2idx["HP:0032350"] + sulfocysteinuria = patient_counts[idx] + sulfocysteinuria_predicate = suox_pheno_predicates[idx] + max_f = HpoMtcFilter.get_maximum_group_observed_HPO_frequency( + sulfocysteinuria, + ph_predicate=sulfocysteinuria_predicate, + ) assert max_f == pytest.approx(1.0, abs=EPSILON) # Neurodevelopmental delay HP:0012758 (4 13 4 4), freqs are 4/17 = 0.235 and 4/8=0.5 - ndelay = patient_counts[curie2idx["HP:0012758"]] - max_f = HpoMtcFilter.get_maximum_group_observed_HPO_frequency(ndelay) + idx = curie2idx["HP:0012758"] + ndelay = patient_counts[idx] + ndelay_predicate = suox_pheno_predicates[idx] + max_f = HpoMtcFilter.get_maximum_group_observed_HPO_frequency( + ndelay, + ph_predicate=ndelay_predicate, + ) assert max_f == pytest.approx(0.5, abs=EPSILON) # Hypertonia HP:0001276 (7 9 4 3) fresa are 7/16=0.4375 and 4/7=0.5714 - hypertonia = patient_counts[curie2idx["HP:0001276"]] - max_f = HpoMtcFilter.get_maximum_group_observed_HPO_frequency(hypertonia) + idx = curie2idx["HP:0001276"] + hypertonia = patient_counts[idx] + hypertonia_predicate = suox_pheno_predicates[idx] + max_f = HpoMtcFilter.get_maximum_group_observed_HPO_frequency( + hypertonia, + ph_predicate=hypertonia_predicate, + ) assert max_f == pytest.approx(0.5714, abs=EPSILON) From af302c0a61fe5c38c88c893eb834075c1ca16917 Mon Sep 17 00:00:00 2001 From: Daniel Danis Date: Tue, 3 Sep 2024 17:03:59 +0200 Subject: [PATCH 34/47] Implement filtering genotype predicate for choosing the genotype categories of interest. --- src/gpsea/analysis/predicate/genotype/_api.py | 82 ++++++++++++++++++- .../predicate/genotype/test_gt_predicates.py | 82 +++++++++++++++++++ 2 files changed, 162 insertions(+), 2 deletions(-) diff --git a/src/gpsea/analysis/predicate/genotype/_api.py b/src/gpsea/analysis/predicate/genotype/_api.py index 39cf020b..a90bb5c8 100644 --- a/src/gpsea/analysis/predicate/genotype/_api.py +++ b/src/gpsea/analysis/predicate/genotype/_api.py @@ -1,7 +1,7 @@ import abc import typing -from gpsea.model import Variant +from gpsea.model import Patient, Variant from .._api import PolyPredicate, Categorization, PatientCategory @@ -10,7 +10,85 @@ class GenotypePolyPredicate(PolyPredicate[Categorization], metaclass=abc.ABCMeta `GenotypePolyPredicate` is a base class for all :class:`PolyPredicate` that test the genotype axis. """ - pass + + @staticmethod + def filtering_predicate( + predicate: "GenotypePolyPredicate", + targets: typing.Collection[Categorization], + ) -> "GenotypePolyPredicate": + """ + """ + return FilteringGenotypePolyPredicate.create( + predicate=predicate, + targets=targets, + ) + + +class FilteringGenotypePolyPredicate(GenotypePolyPredicate): + # NOT PART OF THE PUBLIC API + + @staticmethod + def create( + predicate: "GenotypePolyPredicate", + targets: typing.Collection[Categorization], + ) -> "FilteringGenotypePolyPredicate": + # At least 2 target categorizations must be provided + if len(targets) <= 1: + raise ValueError(f'At least 2 target categorizations must be provided but got {len(targets)}') + + good_boys = tuple(isinstance(cat, Categorization) for cat in targets) + if not all(good_boys): + offenders = ', '.join( + str(i) + for i, is_instance + in enumerate(good_boys) if not is_instance + ) + raise ValueError(f'The targets at following indices are not categorizations: [{offenders}]') + + # All `allowed` categorizations must in fact be present in the `base` predicate. + cats_are_in_fact_present = tuple(cat in predicate.get_categorizations() for cat in targets) + if not all(cats_are_in_fact_present): + missing = ', '.join( + c.category.name + for c, is_present + in zip(targets, cats_are_in_fact_present) if not is_present + ) + raise ValueError(f'Some from the categories are not present: {missing}') + + if len(targets) == predicate.n_categorizations(): + raise ValueError( + f'It makes no sense to subset the a predicate with {predicate.n_categorizations()} categorizations ' + f'with the same number ({len(targets)}) of targets' + ) + + return FilteringGenotypePolyPredicate( + predicate=predicate, + allowed=targets, + ) + + def __init__( + self, + predicate: "GenotypePolyPredicate", + allowed: typing.Iterable[Categorization], + ): + self._predicate = predicate + self._allowed = tuple(allowed) + + def get_categorizations(self) -> typing.Sequence[Categorization]: + return self._allowed + + def get_question(self) -> str: + return self._predicate.get_question() + + def test(self, patient: Patient) -> typing.Optional[Categorization]: + cat = self._predicate.test(patient) + if cat in self._allowed: + return cat + else: + return None + + def __repr__(self): + return f"FilteringGenotypePolyPredicate(predicate={self._predicate}, allowed={self._allowed})" class RecessiveGroupingPredicate(GenotypePolyPredicate, metaclass=abc.ABCMeta): diff --git a/tests/analysis/predicate/genotype/test_gt_predicates.py b/tests/analysis/predicate/genotype/test_gt_predicates.py index 36904159..adbcf532 100644 --- a/tests/analysis/predicate/genotype/test_gt_predicates.py +++ b/tests/analysis/predicate/genotype/test_gt_predicates.py @@ -1,3 +1,5 @@ +import typing + import pytest from gpsea.model import * @@ -180,3 +182,83 @@ def test_x_recessive( assert categorization is not None assert categorization.category.name == name + + +class TestPolyPredicate: + + @pytest.fixture(scope="class") + def x_recessive_gt_predicate(self) -> GenotypePolyPredicate: + affects_suox = VariantPredicates.gene("SUOX") + return ModeOfInheritancePredicate.x_recessive( + variant_predicate=affects_suox, + ) + + @pytest.mark.parametrize( + "indices, expected", + [ + ((0, 1), 2), + ((1, 0), 2), + ((1, 2), 2), + ], + ) + def test_filtering_predicate( + self, + indices: typing.Sequence[int], + expected: int, + x_recessive_gt_predicate: GenotypePolyPredicate, + ): + cats = x_recessive_gt_predicate.get_categorizations() + targets = [cats[i] for i in indices] + predicate = GenotypePolyPredicate.filtering_predicate( + predicate=x_recessive_gt_predicate, + targets=targets, + ) + + actual = len(predicate.get_categorizations()) + + assert actual == expected + + def test_filtering_predicate__explodes_when_not_subsetting( + self, + x_recessive_gt_predicate: GenotypePolyPredicate, + ): + with pytest.raises(ValueError) as ve: + GenotypePolyPredicate.filtering_predicate( + predicate=x_recessive_gt_predicate, + targets=x_recessive_gt_predicate.get_categorizations(), + ) + + assert ( + ve.value.args[0] + == "It makes no sense to subset the a predicate with 4 categorizations with the same number (4) of targets" + ) + + def test_filtering_predicate__explodes_when_using_random_junk( + self, + x_recessive_gt_predicate: GenotypePolyPredicate, + ): + with pytest.raises(ValueError) as ve: + GenotypePolyPredicate.filtering_predicate( + predicate=x_recessive_gt_predicate, + targets=(0, 1), + ) + + assert ( + ve.value.args[0] + == "The targets at following indices are not categorizations: [0, 1]" + ) + + def test_filtering_predicate__explodes_when_using_one_category( + self, + x_recessive_gt_predicate: GenotypePolyPredicate, + ): + with pytest.raises(ValueError) as ve: + GenotypePolyPredicate.filtering_predicate( + predicate=x_recessive_gt_predicate, + targets=(x_recessive_gt_predicate.get_categorizations()[0],), + ) + + assert ( + ve.value.args[0] + == "At least 2 target categorizations must be provided but got 1" + ) From 21caddc68d76261ca9f494e62d8de818cfdb73b8 Mon Sep 17 00:00:00 2001 From: Daniel Danis Date: Tue, 3 Sep 2024 17:22:05 +0200 Subject: [PATCH 35/47] Improve generating the polypredicate question. --- docs/tutorial.rst | 2 +- docs/user-guide/predicates.rst | 8 ++++---- docs/user-guide/stats.rst | 4 ++-- src/gpsea/analysis/_api.py | 2 +- src/gpsea/analysis/_gp_analysis.py | 4 ++-- src/gpsea/analysis/pcats/_impl.py | 6 +++--- src/gpsea/analysis/predicate/_api.py | 17 ++++++++++++++++- src/gpsea/analysis/predicate/genotype/_api.py | 4 ++-- .../predicate/genotype/_gt_predicates.py | 17 +++++++---------- .../analysis/predicate/phenotype/_pheno.py | 6 +++--- .../predicate/genotype/test_gt_predicates.py | 4 ++-- 11 files changed, 43 insertions(+), 31 deletions(-) diff --git a/docs/tutorial.rst b/docs/tutorial.rst index 29d8f2d9..0a583b39 100644 --- a/docs/tutorial.rst +++ b/docs/tutorial.rst @@ -171,7 +171,7 @@ in the individuals of the *TBX5* cohort. ... ), ... group_names=('Missense', 'Frameshift'), ... ) ->>> gt_predicate.get_question() +>>> gt_predicate.display_question() 'Genotype group: Missense, Frameshift' .. note:: diff --git a/docs/user-guide/predicates.rst b/docs/user-guide/predicates.rst index 908afa14..6d95dfc5 100644 --- a/docs/user-guide/predicates.rst +++ b/docs/user-guide/predicates.rst @@ -300,7 +300,7 @@ for assigning a patient into a genotype group: >>> from gpsea.analysis.predicate.genotype import ModeOfInheritancePredicate >>> gt_predicate = ModeOfInheritancePredicate.autosomal_recessive(is_frameshift_or_stop_gain) ->>> gt_predicate.get_question() +>>> gt_predicate.display_question() 'Which genotype group does the patient fit in: HOM_REF, HET, BIALLELIC_ALT' The `gt_predicate` can be used in downstream analysis, such as in :class: @@ -337,7 +337,7 @@ for testing if the individual has at least one missense vs. frameshift vs. synon ... ), ... group_names=('Missense', 'Frameshift', 'Synonymous'), ... ) ->>> gt_predicate.get_question() +>>> gt_predicate.display_question() 'Genotype group: Missense, Frameshift, Synonymous' @@ -380,8 +380,8 @@ to test for a presence of `Abnormal lens morphology >> pheno_predicate.get_question() -'Is Abnormal lens morphology present in the patient?' +>>> pheno_predicate.display_question() +'Is Abnormal lens morphology present in the patient: Yes, No' TODO: explain ``missing_implies_phenotype_excluded`` diff --git a/docs/user-guide/stats.rst b/docs/user-guide/stats.rst index 16f0060b..5a8717f3 100644 --- a/docs/user-guide/stats.rst +++ b/docs/user-guide/stats.rst @@ -138,7 +138,7 @@ we expect the autosomal dominant mode of inheritance: >>> from gpsea.analysis.predicate.genotype import ModeOfInheritancePredicate >>> gt_predicate = ModeOfInheritancePredicate.autosomal_dominant(is_frameshift) ->>> gt_predicate.get_question() +>>> gt_predicate.display_question() 'Which genotype group does the patient fit in: HOM_REF, HET' `gt_predicate` will assign the patients with no frameshift variant allele into `HOM_REF` group @@ -409,7 +409,7 @@ The genotype predicate will bin the patient into two groups: a point mutation gr ... predicates=(point_mutation, lof_mutation), ... group_names=('Point', 'LoF'), ... ) ->>> gt_predicate.get_question() +>>> gt_predicate.display_question() 'Genotype group: Point, LoF' diff --git a/src/gpsea/analysis/_api.py b/src/gpsea/analysis/_api.py index ca7bd8e4..b2b7d41d 100644 --- a/src/gpsea/analysis/_api.py +++ b/src/gpsea/analysis/_api.py @@ -189,7 +189,7 @@ def summarize( # Column index: multiindex of counts and percentages for all genotype predicate groups geno_idx = pd.MultiIndex.from_product( iterables=(self._geno_predicate.get_categories(), ('Count', 'Percent')), - names=(self._geno_predicate.get_question(), None), + names=(self._geno_predicate.get_question_base(), None), ) # We'll fill this frame with data diff --git a/src/gpsea/analysis/_gp_analysis.py b/src/gpsea/analysis/_gp_analysis.py index 35d14305..f786049c 100644 --- a/src/gpsea/analysis/_gp_analysis.py +++ b/src/gpsea/analysis/_gp_analysis.py @@ -63,11 +63,11 @@ def apply_predicates_on_patients( data=0, index=pd.Index( data=ph_predicate.get_categories(), - name=ph_predicate.get_question(), + name=ph_predicate.get_question_base(), ), columns=pd.Index( data=gt_predicate.get_categories(), - name=gt_predicate.get_question(), + name=gt_predicate.get_question_base(), ), ) diff --git a/src/gpsea/analysis/pcats/_impl.py b/src/gpsea/analysis/pcats/_impl.py index 408a9fd9..e8045d2d 100644 --- a/src/gpsea/analysis/pcats/_impl.py +++ b/src/gpsea/analysis/pcats/_impl.py @@ -64,11 +64,11 @@ def apply_predicates_on_patients( data=0, index=pd.Index( data=ph_predicate.get_categories(), - name=ph_predicate.get_question(), + name=ph_predicate.get_question_base(), ), columns=pd.Index( data=gt_predicate.get_categories(), - name=gt_predicate.get_question(), + name=gt_predicate.get_question_base(), ), ) @@ -198,7 +198,7 @@ def summarize( gt_idx = pd.MultiIndex.from_product( # TODO: fix the below iterables=(self._gt_predicate.get_categories(), ("Count", "Percent")), - names=(self._gt_predicate.get_question(), None), + names=(self._gt_predicate.get_question_base(), None), ) # We'll fill this frame with data diff --git a/src/gpsea/analysis/predicate/_api.py b/src/gpsea/analysis/predicate/_api.py index da446a54..534eca18 100644 --- a/src/gpsea/analysis/predicate/_api.py +++ b/src/gpsea/analysis/predicate/_api.py @@ -154,6 +154,12 @@ def get_categories(self) -> typing.Iterator[PatientCategory]: """ return (c.category for c in self.get_categorizations()) + def get_category_names(self) -> typing.Iterator[str]: + """ + Get an iterator with names of the :class:`PatientCategory` items that the predicate can produce. + """ + return (cat.name for cat in self.get_categories()) + def get_category( self, cat_id: int, @@ -182,12 +188,21 @@ def get_category_name( return self.get_category(cat_id).name @abc.abstractmethod - def get_question(self) -> str: + def get_question_base(self) -> str: """ Prepare a `str` with the question the predicate can answer. """ pass + def display_question(self) -> str: + """ + Prepare the question which the predicate can answer. + + The question includes the question base and the category names + """ + cat_names = ', '.join(self.get_category_names()) + return f'{self.get_question_base()}: {cat_names}' + @abc.abstractmethod def test(self, patient: Patient) -> typing.Optional[C]: """ diff --git a/src/gpsea/analysis/predicate/genotype/_api.py b/src/gpsea/analysis/predicate/genotype/_api.py index a90bb5c8..db29c573 100644 --- a/src/gpsea/analysis/predicate/genotype/_api.py +++ b/src/gpsea/analysis/predicate/genotype/_api.py @@ -77,8 +77,8 @@ def __init__( def get_categorizations(self) -> typing.Sequence[Categorization]: return self._allowed - def get_question(self) -> str: - return self._predicate.get_question() + def get_question_base(self) -> str: + return self._predicate.get_question_base() def test(self, patient: Patient) -> typing.Optional[Categorization]: cat = self._predicate.test(patient) diff --git a/src/gpsea/analysis/predicate/genotype/_gt_predicates.py b/src/gpsea/analysis/predicate/genotype/_gt_predicates.py index 23fc4a5d..4007e8d1 100644 --- a/src/gpsea/analysis/predicate/genotype/_gt_predicates.py +++ b/src/gpsea/analysis/predicate/genotype/_gt_predicates.py @@ -39,7 +39,7 @@ def get_categorizations(self) -> typing.Sequence[Categorization]: AlleleCountingGenotypeBooleanPredicate.NO, ) - def get_question(self) -> str: + def get_question_base(self) -> str: return self._allele_counter.get_question() def test(self, patient: Patient) -> typing.Optional[Categorization]: @@ -92,13 +92,12 @@ def __init__( ): self._counters = tuple(counters) self._categorizations = tuple(categorizations) - group_names = ", ".join(c.category.name for c in self._categorizations) - self._question = f"Genotype group: {group_names}" + self._question = "Genotype group" def get_categorizations(self) -> typing.Sequence[Categorization]: return self._categorizations - def get_question(self) -> str: + def get_question_base(self) -> str: return self._question def test(self, patient: Patient) -> typing.Optional[Categorization]: @@ -132,7 +131,7 @@ def __hash__(self) -> int: ) def __str__(self) -> str: - return self.get_question() + return self.get_question_base() def __repr__(self) -> str: return ( @@ -194,7 +193,7 @@ def __init__( ): self._allele_counter = allele_counter - def get_question(self) -> str: + def get_question_base(self) -> str: return self._allele_counter.get_question() def test(self, patient: Patient) -> typing.Optional[Categorization]: @@ -558,14 +557,12 @@ def __init__( issues = ModeOfInheritancePredicate._check_categorizations(self._categorizations) if issues: raise ValueError('Cannot create predicate: {}'.format(', '.join(issues))) - self._question = 'Which genotype group does the patient fit in: {}'.format( - ', '.join(cat.category.name for cat in self._categorizations), - ) + self._question = 'Which genotype group does the patient fit in' def get_categorizations(self) -> typing.Sequence[Categorization]: return self._categorizations - def get_question(self) -> str: + def get_question_base(self) -> str: return self._question def test( diff --git a/src/gpsea/analysis/predicate/phenotype/_pheno.py b/src/gpsea/analysis/predicate/phenotype/_pheno.py index ecfb1401..3b2d9a7b 100644 --- a/src/gpsea/analysis/predicate/phenotype/_pheno.py +++ b/src/gpsea/analysis/predicate/phenotype/_pheno.py @@ -131,8 +131,8 @@ def __init__( # Some tests depend on the order of `self._categorizations`. self._categorizations = (self._phenotype_observed, self._phenotype_excluded) - def get_question(self) -> str: - return f"Is {self._query_label} present in the patient?" + def get_question_base(self) -> str: + return f"Is {self._query_label} present in the patient" @property def phenotype(self) -> hpotk.TermId: @@ -222,7 +222,7 @@ def __init__(self, disease_id_query: hpotk.TermId): phenotype=disease_id_query, ) - def get_question(self) -> str: + def get_question_base(self) -> str: return f"Was {self._query} diagnosed in the patient" @property diff --git a/tests/analysis/predicate/genotype/test_gt_predicates.py b/tests/analysis/predicate/genotype/test_gt_predicates.py index adbcf532..97d4fee0 100644 --- a/tests/analysis/predicate/genotype/test_gt_predicates.py +++ b/tests/analysis/predicate/genotype/test_gt_predicates.py @@ -36,8 +36,8 @@ def test_get_question( self, predicate: GenotypePolyPredicate, ): - question = predicate.get_question() - assert question == "Genotype group: Point, LoF" + question = predicate.get_question_base() + assert question == "Genotype group" def test_get_categorizations( self, From 8ba618e3f71f599fd1629d19b104c307504c1fcd Mon Sep 17 00:00:00 2001 From: Daniel Danis Date: Tue, 3 Sep 2024 20:46:24 +0200 Subject: [PATCH 36/47] Improve docs for `filtering_predicate`. --- docs/user-guide/predicates.rst | 58 +++++++++++ .../analysis/predicate/genotype/__init__.py | 4 +- src/gpsea/analysis/predicate/genotype/_api.py | 82 +--------------- .../predicate/genotype/_gt_predicates.py | 95 +++++++++++++++++++ .../predicate/genotype/test_gt_predicates.py | 9 +- .../predicate/genotype/test_predicates.py | 1 + 6 files changed, 163 insertions(+), 86 deletions(-) diff --git a/docs/user-guide/predicates.rst b/docs/user-guide/predicates.rst index 6d95dfc5..b224516f 100644 --- a/docs/user-guide/predicates.rst +++ b/docs/user-guide/predicates.rst @@ -306,6 +306,64 @@ for assigning a patient into a genotype group: The `gt_predicate` can be used in downstream analysis, such as in :class: +.. _filtering-predicate: + +Filtering predicate +=================== + +Sometimes a predicate can bin individuals into more genotype groups than necessary and there may be need +to consider only a subset of the groups. A `GenotypePolyPredicate` +created by :class:`~gpsea.analysis.predicate.genotype.filtering_predicate` can retain only a subset +of the target categorizations of interest. + +Example +------- + +Let's suppose we want test the genotype-phenotype association between variants +that lead to frameshift or a stop gain in a fictional transcript `NM_1234.5`, +and we are specifically interested in comparing the heterozygous variants +in a biallelic alternative allele genotypes (homozygous alternate and compound heterozygous). + +First, we set up a :class:`~gpsea.analysis.predicate.genotype.VariantPredicate` +for testing if a variant introduces a premature stop codon or leads to the shift of the reading frame: + +>>> from gpsea.model import VariantEffect +>>> from gpsea.analysis.predicate.genotype import VariantPredicates +>>> tx_id = 'NM_1234.5' +>>> is_frameshift_or_stop_gain = VariantPredicates.variant_effect(VariantEffect.FRAMESHIFT_VARIANT, tx_id) \ +... | VariantPredicates.variant_effect(VariantEffect.STOP_GAINED, tx_id) +>>> is_frameshift_or_stop_gain.get_question() +'(FRAMESHIFT_VARIANT on NM_1234.5 OR STOP_GAINED on NM_1234.5)' + +Then, we create :class:`~gpsea.analysis.predicate.genotype.ModeOfInheritancePredicate.autosomal_recessive` +to bin according to a genotype group: + +>>> from gpsea.analysis.predicate.genotype import ModeOfInheritancePredicate +>>> gt_predicate = ModeOfInheritancePredicate.autosomal_recessive(is_frameshift_or_stop_gain) +>>> gt_predicate.display_question() +'Which genotype group does the patient fit in: HOM_REF, HET, BIALLELIC_ALT' + +We see that the `gt_predicate` bins the patients into three groups: + +>>> cats = gt_predicate.get_categorizations() +>>> cats +(Categorization(category=HOM_REF), Categorization(category=HET), Categorization(category=BIALLELIC_ALT)) + +We wrap the categorizations of interest along with the `gt_predicate` by the `filtering_predicate` function, +and we will get a :class:`~gpsea.analysis.predicate.genotype.GenotypePolyPredicate` +that includes only the categories of interest: + +>>> from gpsea.analysis.predicate.genotype import filtering_predicate +>>> fgt_predicate = filtering_predicate( +... predicate=gt_predicate, +... targets=(cats[1], cats[2]), +... ) +>>> fgt_predicate.display_question() +'Which genotype group does the patient fit in: HET, BIALLELIC_ALT' + + +.. _groups-predicate: + Groups predicate ================ diff --git a/src/gpsea/analysis/predicate/genotype/__init__.py b/src/gpsea/analysis/predicate/genotype/__init__.py index ff8d16e1..86dd261a 100644 --- a/src/gpsea/analysis/predicate/genotype/__init__.py +++ b/src/gpsea/analysis/predicate/genotype/__init__.py @@ -1,13 +1,13 @@ from ._api import GenotypePolyPredicate from ._api import VariantPredicate from ._counter import AlleleCounter -from ._gt_predicates import boolean_predicate, groups_predicate, recessive_predicate +from ._gt_predicates import boolean_predicate, groups_predicate, filtering_predicate, recessive_predicate from ._gt_predicates import ModeOfInheritancePredicate from ._variant import VariantPredicates, ProteinPredicates __all__ = [ 'GenotypePolyPredicate', - 'boolean_predicate', 'groups_predicate', 'recessive_predicate', + 'boolean_predicate', 'groups_predicate', 'filtering_predicate', 'recessive_predicate', 'ModeOfInheritancePredicate', 'AlleleCounter', 'VariantPredicate', 'VariantPredicates', 'ProteinPredicates', diff --git a/src/gpsea/analysis/predicate/genotype/_api.py b/src/gpsea/analysis/predicate/genotype/_api.py index db29c573..39cf020b 100644 --- a/src/gpsea/analysis/predicate/genotype/_api.py +++ b/src/gpsea/analysis/predicate/genotype/_api.py @@ -1,7 +1,7 @@ import abc import typing -from gpsea.model import Patient, Variant +from gpsea.model import Variant from .._api import PolyPredicate, Categorization, PatientCategory @@ -10,85 +10,7 @@ class GenotypePolyPredicate(PolyPredicate[Categorization], metaclass=abc.ABCMeta `GenotypePolyPredicate` is a base class for all :class:`PolyPredicate` that test the genotype axis. """ - - @staticmethod - def filtering_predicate( - predicate: "GenotypePolyPredicate", - targets: typing.Collection[Categorization], - ) -> "GenotypePolyPredicate": - """ - """ - return FilteringGenotypePolyPredicate.create( - predicate=predicate, - targets=targets, - ) - - -class FilteringGenotypePolyPredicate(GenotypePolyPredicate): - # NOT PART OF THE PUBLIC API - - @staticmethod - def create( - predicate: "GenotypePolyPredicate", - targets: typing.Collection[Categorization], - ) -> "FilteringGenotypePolyPredicate": - # At least 2 target categorizations must be provided - if len(targets) <= 1: - raise ValueError(f'At least 2 target categorizations must be provided but got {len(targets)}') - - good_boys = tuple(isinstance(cat, Categorization) for cat in targets) - if not all(good_boys): - offenders = ', '.join( - str(i) - for i, is_instance - in enumerate(good_boys) if not is_instance - ) - raise ValueError(f'The targets at following indices are not categorizations: [{offenders}]') - - # All `allowed` categorizations must in fact be present in the `base` predicate. - cats_are_in_fact_present = tuple(cat in predicate.get_categorizations() for cat in targets) - if not all(cats_are_in_fact_present): - missing = ', '.join( - c.category.name - for c, is_present - in zip(targets, cats_are_in_fact_present) if not is_present - ) - raise ValueError(f'Some from the categories are not present: {missing}') - - if len(targets) == predicate.n_categorizations(): - raise ValueError( - f'It makes no sense to subset the a predicate with {predicate.n_categorizations()} categorizations ' - f'with the same number ({len(targets)}) of targets' - ) - - return FilteringGenotypePolyPredicate( - predicate=predicate, - allowed=targets, - ) - - def __init__( - self, - predicate: "GenotypePolyPredicate", - allowed: typing.Iterable[Categorization], - ): - self._predicate = predicate - self._allowed = tuple(allowed) - - def get_categorizations(self) -> typing.Sequence[Categorization]: - return self._allowed - - def get_question_base(self) -> str: - return self._predicate.get_question_base() - - def test(self, patient: Patient) -> typing.Optional[Categorization]: - cat = self._predicate.test(patient) - if cat in self._allowed: - return cat - else: - return None - - def __repr__(self): - return f"FilteringGenotypePolyPredicate(predicate={self._predicate}, allowed={self._allowed})" + pass class RecessiveGroupingPredicate(GenotypePolyPredicate, metaclass=abc.ABCMeta): diff --git a/src/gpsea/analysis/predicate/genotype/_gt_predicates.py b/src/gpsea/analysis/predicate/genotype/_gt_predicates.py index 4007e8d1..43a39594 100644 --- a/src/gpsea/analysis/predicate/genotype/_gt_predicates.py +++ b/src/gpsea/analysis/predicate/genotype/_gt_predicates.py @@ -151,6 +151,8 @@ def groups_predicate( The genotype groups *should* not overlap. In case of an overlap, the patient will be assigned into no group (`None`). + See the :ref:`groups-predicate` section for an example. + :param predicates: an iterable with at least 2 variant predicates to determine a genotype group. :param group_names: an iterable with group names. The number of group names must match the number of predicates. """ @@ -182,6 +184,99 @@ def groups_predicate( ) +class FilteringGenotypePolyPredicate(GenotypePolyPredicate): + # NOT PART OF THE PUBLIC API + + @staticmethod + def create( + predicate: "GenotypePolyPredicate", + targets: typing.Collection[Categorization], + ) -> "FilteringGenotypePolyPredicate": + # At least 2 target categorizations must be provided + if len(targets) <= 1: + raise ValueError(f'At least 2 target categorizations must be provided but got {len(targets)}') + + good_boys = tuple(isinstance(cat, Categorization) for cat in targets) + if not all(good_boys): + offenders = ', '.join( + str(i) + for i, is_instance + in enumerate(good_boys) if not is_instance + ) + raise ValueError(f'The targets at following indices are not categorizations: [{offenders}]') + + # All `allowed` categorizations must in fact be present in the `base` predicate. + cats_are_in_fact_present = tuple(cat in predicate.get_categorizations() for cat in targets) + if not all(cats_are_in_fact_present): + missing = ', '.join( + c.category.name + for c, is_present + in zip(targets, cats_are_in_fact_present) if not is_present + ) + raise ValueError(f'Some from the categories are not present: {missing}') + + if len(targets) == predicate.n_categorizations(): + raise ValueError( + f'It makes no sense to subset the a predicate with {predicate.n_categorizations()} categorizations ' + f'with the same number ({len(targets)}) of targets' + ) + + return FilteringGenotypePolyPredicate( + predicate=predicate, + allowed=targets, + ) + + def __init__( + self, + predicate: "GenotypePolyPredicate", + allowed: typing.Iterable[Categorization], + ): + self._predicate = predicate + self._allowed = tuple(allowed) + + def get_categorizations(self) -> typing.Sequence[Categorization]: + return self._allowed + + def get_question_base(self) -> str: + return self._predicate.get_question_base() + + def test(self, patient: Patient) -> typing.Optional[Categorization]: + cat = self._predicate.test(patient) + if cat in self._allowed: + return cat + else: + return None + + def __repr__(self): + return f"FilteringGenotypePolyPredicate(predicate={self._predicate}, allowed={self._allowed})" + + +def filtering_predicate( + predicate: GenotypePolyPredicate, + targets: typing.Collection[Categorization], +) -> GenotypePolyPredicate: + """ + Filtering predicate applies the base `predicate` but only returns the categorizations + from the provided `targets` collection. + + This can be useful if only some of the categorizations are interesting. + For instance, if we only seek to compare the differences between heterozygous and hemizygous variants, + but the predicate also bins the patients into homozygous reference, and biallelic alt genotype groups. + + See the :ref:`filtering-predicate` section for an example. + + The `predicate` is checked for being able to produce the all items in `targets` + and the `targets` must include at least 2 categorizations. + + :param predicate: the base predicate whose categorizations are subject to filteration. + :param targets: the categorizations to retain + """ + return FilteringGenotypePolyPredicate.create( + predicate=predicate, + targets=targets, + ) + + class AlleleCountingRecessivePredicate(RecessiveGroupingPredicate): # NOT PART OF THE PUBLIC API # TODO: this predicate is a bit weird and I think it should eventually go away. diff --git a/tests/analysis/predicate/genotype/test_gt_predicates.py b/tests/analysis/predicate/genotype/test_gt_predicates.py index 97d4fee0..67bc2d11 100644 --- a/tests/analysis/predicate/genotype/test_gt_predicates.py +++ b/tests/analysis/predicate/genotype/test_gt_predicates.py @@ -6,6 +6,7 @@ from gpsea.analysis.predicate.genotype import ( GenotypePolyPredicate, groups_predicate, + filtering_predicate, VariantPredicates, VariantPredicate, ModeOfInheritancePredicate, @@ -209,7 +210,7 @@ def test_filtering_predicate( ): cats = x_recessive_gt_predicate.get_categorizations() targets = [cats[i] for i in indices] - predicate = GenotypePolyPredicate.filtering_predicate( + predicate = filtering_predicate( predicate=x_recessive_gt_predicate, targets=targets, ) @@ -223,7 +224,7 @@ def test_filtering_predicate__explodes_when_not_subsetting( x_recessive_gt_predicate: GenotypePolyPredicate, ): with pytest.raises(ValueError) as ve: - GenotypePolyPredicate.filtering_predicate( + filtering_predicate( predicate=x_recessive_gt_predicate, targets=x_recessive_gt_predicate.get_categorizations(), ) @@ -238,7 +239,7 @@ def test_filtering_predicate__explodes_when_using_random_junk( x_recessive_gt_predicate: GenotypePolyPredicate, ): with pytest.raises(ValueError) as ve: - GenotypePolyPredicate.filtering_predicate( + filtering_predicate( predicate=x_recessive_gt_predicate, targets=(0, 1), ) @@ -253,7 +254,7 @@ def test_filtering_predicate__explodes_when_using_one_category( x_recessive_gt_predicate: GenotypePolyPredicate, ): with pytest.raises(ValueError) as ve: - GenotypePolyPredicate.filtering_predicate( + filtering_predicate( predicate=x_recessive_gt_predicate, targets=(x_recessive_gt_predicate.get_categorizations()[0],), ) diff --git a/tests/analysis/predicate/genotype/test_predicates.py b/tests/analysis/predicate/genotype/test_predicates.py index 2f78d718..a076adac 100644 --- a/tests/analysis/predicate/genotype/test_predicates.py +++ b/tests/analysis/predicate/genotype/test_predicates.py @@ -202,6 +202,7 @@ def test_protein_feature_id( assert predicate.test(missense_variant) == expected + class TestLogicalVariantPredicate: """ Test that the AND and OR variant predicate combinators work as expected. From b65a69d42bc695820fb69cf31eb418bb95ac2535 Mon Sep 17 00:00:00 2001 From: Daniel Danis Date: Wed, 4 Sep 2024 12:05:15 +0200 Subject: [PATCH 37/47] Allow loading of a bunch of file paths. --- src/gpsea/preprocessing/_config.py | 48 +++++++++++++++++++++++------- 1 file changed, 38 insertions(+), 10 deletions(-) diff --git a/src/gpsea/preprocessing/_config.py b/src/gpsea/preprocessing/_config.py index 348bd5b1..32d8b255 100644 --- a/src/gpsea/preprocessing/_config.py +++ b/src/gpsea/preprocessing/_config.py @@ -383,20 +383,47 @@ def load_phenopacket_folder( """ Load phenopacket JSON files from a directory, validate the patient data, and assemble the patients into a cohort. + A file with `.json` suffix is considered to be a JSON file and all JSON files are assumed to be phenopackets. + Non-JSON files are ignored. + :param pp_directory: path to a folder with phenopacket JSON files. An error is raised if the path does not point to a directory with at least one phenopacket. :param cohort_creator: cohort creator for turning a sequence of phenopacket into a :class:`~gpsea.model.Cohort`. :param validation_policy: a `str` with the validation policy. The value must be one of `{'none', 'lenient', 'strict'}` - :return: a tuple with the cohort and the preprocessing validation result. + :return: a tuple with the cohort and the validation result. """ # Load phenopackets - phenopackets = _load_phenopacket_dir(pp_directory) + pp_files = _find_phenopacket_files(pp_directory) # Map to patients + return load_phenopacket_files( + pp_files=pp_files, + cohort_creator=cohort_creator, + validation_policy=validation_policy, + ) + + +def load_phenopacket_files( + pp_files: typing.Iterator[str], + cohort_creator: CohortCreator[Phenopacket], + validation_policy: typing.Literal["none", "lenient", "strict"] = "none", +) -> typing.Tuple[Cohort, PreprocessingValidationResult]: + """ + Load phenopacket JSON files, validate the data, and assemble into a :class:`~gpsea.model.Cohort`. + + Phenopackets are validated, assembled into a cohort, and the validation results are reported back. + + :param pp_files: an iterator with paths to phenopacket JSON files. + :param cohort_creator: cohort creator for turning a phenopacket collection + into a :class:`~gpsea.model.Cohort`. + :param validation_policy: a `str` with the validation policy. + The value must be one of `{'none', 'lenient', 'strict'}` + :return: a tuple with the cohort and the validation result. + """ return load_phenopackets( - phenopackets=phenopackets, + phenopackets=(_load_phenopacket(pp_file) for pp_file in pp_files), cohort_creator=cohort_creator, validation_policy=validation_policy, ) @@ -408,7 +435,9 @@ def load_phenopackets( validation_policy: typing.Literal["none", "lenient", "strict"] = "none", ) -> typing.Tuple[Cohort, PreprocessingValidationResult]: """ - Map phenopacket JSON file into patient, validate the patient data, and assemble the patients into a cohort. + Validate the phenopackets and assemble into a :class:`~gpsea.model.Cohort`. + + The results of the validation are reported back. :param phenopackets: path to a folder with phenopacket JSON files. An error is raised if the path does not point to a directory with at least one phenopacket. @@ -416,7 +445,7 @@ def load_phenopackets( into a :class:`~gpsea.model.Cohort`. :param validation_policy: a `str` with the validation policy. The value must be one of `{'none', 'lenient', 'strict'}` - :return: a tuple with the cohort and the preprocessing validation result. + :return: a tuple with the cohort and the validation result. """ # Check inputs before doing anything hpotk.util.validate_instance(cohort_creator, CohortCreator, "cohort_creator") @@ -438,17 +467,16 @@ def load_phenopackets( return cohort, validation_result -def _load_phenopacket_dir( +def _find_phenopacket_files( pp_dir: str, -) -> typing.Iterator[Phenopacket]: +) -> typing.Iterator[str]: fpath_pp_abs = os.path.abspath(pp_dir) if not os.path.isdir(fpath_pp_abs): raise ValueError(f"`{fpath_pp_abs}` does not point to a directory") - + for patient_file in os.listdir(pp_dir): if patient_file.endswith(".json"): - phenopacket_path = os.path.join(pp_dir, patient_file) - yield _load_phenopacket(phenopacket_path) + yield os.path.join(pp_dir, patient_file) def _load_phenopacket(phenopacket_path: str) -> Phenopacket: From 7136a1c766a3c8d0958b7341f43bc9fe2aa7009b Mon Sep 17 00:00:00 2001 From: Daniel Danis Date: Wed, 4 Sep 2024 12:16:40 +0200 Subject: [PATCH 38/47] Describe new loading in the docs. --- docs/user-guide/input-data.rst | 44 +++++++++++++++++++++++++--------- 1 file changed, 33 insertions(+), 11 deletions(-) diff --git a/docs/user-guide/input-data.rst b/docs/user-guide/input-data.rst index f0413295..b94379b3 100644 --- a/docs/user-guide/input-data.rst +++ b/docs/user-guide/input-data.rst @@ -5,9 +5,8 @@ Input data ========== The `gpsea` analysis needs to be provided with a standardized form of genotype and phenotype data. -The analyses require an instance of :class:`gpsea.model.Cohort` that consists -of :class:`gpsea.model.Patient`\ s - the cohort members. The cohort and the members -hold the standardized data and provide convenience functions for dataset exploration. +The analyses require an instance of :class:`~gpsea.model.Cohort` that consists +of individuals in form of a :class:`~gpsea.model.Patient` class. .. seealso:: @@ -18,14 +17,21 @@ and performing functional annotation of the variants. Here we describe how to pr for the exploratory and downstream analysis. +*************************************** Create a cohort from GA4GH phenopackets ---------------------------------------- +*************************************** The easiest way to input data into `gpsea` is to use the `GA4GH Phenopacket Schema `_ phenopackets. `gpsea` provides an out-of-the-box solution for loading a cohort from a folder of phenopacket JSON files. +Create cohort creator +===================== + +Next, let's prepare a :class:`~gpsea.preprocessing.CohortCreator` that will turn a phenopacket collection +into a :class:`~gpsea.model.Cohort`. The cohort creator also performs an input validation. +The validation needs Human Phenotype Ontology data. Let's start with loading Human Phenotype Ontology, a requisite for the input Q/C steps. We'll use the amazing `hpo-toolkit `_ library which is installed along with the standard `gpsea` installation: @@ -34,9 +40,6 @@ the standard `gpsea` installation: >>> store = hpotk.configure_ontology_store() >>> hpo = store.load_minimal_hpo(release='v2024-07-01') -Next, let's prepare a :class:`~gpsea.preprocessing.CohortCreator` that will turn a collection of phenopacket -into a :class:`~gpsea.model.Cohort`, required in the downstream steps. - The easiest way to get the `CohortCreator` is to use the :func:`~gpsea.preprocessing.configure_caching_cohort_creator` convenience method: @@ -53,7 +56,12 @@ The easiest way to get the `CohortCreator` is to use the and the responses will be cached in the current working directory to reduce the network bandwidth. See the :func:`~gpsea.preprocessing.configure_caching_cohort_creator` pydoc for more options. -We can create a cohort starting from a `Phenopacket` collection. + +Load phenopackets +================= + +We can create a cohort starting from a collection of `Phenopacket` objects +provided by Python `Phenopackets `_ library. For the purpose of this example, we will load a cohort of patients with pathogenic mutations in *RERE* gene included in the release `0.1.18` of `Phenopacket Store `_. We use `Phenopacket Store Toolkit `_ @@ -86,8 +94,22 @@ Validated under none policy No errors or warnings were found -Create a cohort from other data -------------------------------- +Alternative phenopacket sources +=============================== + +In case you do not already have a `Phenopacket` collection at your fingertips, +GPSEA provides a few other convenience functions for loading phenopackets from JSON files. + +The :func:`~gpsea.preprocessing.load_phenopacket_files` function can be used to load +a bunch of phenopacket JSON files: + +>>> from gpsea.preprocessing import load_phenopacket_files +>>> pp_files = ('path/to/phenopacket1.json', 'path/to/phenopacket2.json') +>>> cohort, qc_results = load_phenopacket_files(pp_files, cohort_creator) # doctest: +SKIP + +or you can load an entire directory of JSON files with :func:`~gpsea.preprocessing.load_phenopacket_folder`: -TODO - describe how to construct a Patient from raw HPO terms and variant coordinates. +>>> from gpsea.preprocessing import load_phenopacket_folder +>>> pp_dir = 'path/to/folder/with/many/phenopacket/json/files' +>>> cohort, qc_results = load_phenopacket_folder(pp_dir, cohort_creator) # doctest: +SKIP From e4533eb950cc93a3809d47342ffa5f8942d51d34 Mon Sep 17 00:00:00 2001 From: Daniel Danis Date: Wed, 4 Sep 2024 12:19:46 +0200 Subject: [PATCH 39/47] Add missing exports. --- src/gpsea/preprocessing/__init__.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/src/gpsea/preprocessing/__init__.py b/src/gpsea/preprocessing/__init__.py index 1551b4d6..6c9872b0 100644 --- a/src/gpsea/preprocessing/__init__.py +++ b/src/gpsea/preprocessing/__init__.py @@ -2,7 +2,7 @@ from ._api import VariantCoordinateFinder, FunctionalAnnotator, ImpreciseSvFunctionalAnnotator, ProteinMetadataService from ._audit import Auditor, DataSanityIssue, Level, Notepad, NotepadTree from ._config import configure_caching_patient_creator, configure_patient_creator -from ._config import load_phenopacket_folder, load_phenopackets +from ._config import load_phenopacket_folder, load_phenopacket_files, load_phenopackets from ._config import configure_caching_cohort_creator, configure_cohort_creator from ._config import configure_default_protein_metadata_service, configure_protein_metadata_service from ._generic import DefaultImpreciseSvFunctionalAnnotator @@ -21,7 +21,8 @@ 'configure_default_protein_metadata_service', 'configure_protein_metadata_service', 'VariantCoordinateFinder', 'FunctionalAnnotator', 'ImpreciseSvFunctionalAnnotator', 'ProteinMetadataService', 'PatientCreator', 'CohortCreator', - 'PhenopacketVariantCoordinateFinder', 'PhenopacketPatientCreator', 'load_phenopacket_folder', 'load_phenopackets', + 'PhenopacketVariantCoordinateFinder', 'PhenopacketPatientCreator', + 'load_phenopacket_folder', 'load_phenopacket_files', 'load_phenopackets', 'TranscriptCoordinateService', 'GeneCoordinateService', 'PhenotypeCreator', 'ProteinAnnotationCache', 'ProtCachingMetadataService', From a9f66b2925f7d9aee4e38e877367ed156b5ae3e3 Mon Sep 17 00:00:00 2001 From: Daniel Danis Date: Wed, 4 Sep 2024 13:02:41 +0200 Subject: [PATCH 40/47] Add `TTestStatistic`. --- src/gpsea/analysis/pscore/stats/__init__.py | 4 +- src/gpsea/analysis/pscore/stats/_stats.py | 26 +++++++++- tests/analysis/pscore/test_stats.py | 54 +++++++++++++++++++++ 3 files changed, 81 insertions(+), 3 deletions(-) create mode 100644 tests/analysis/pscore/test_stats.py diff --git a/src/gpsea/analysis/pscore/stats/__init__.py b/src/gpsea/analysis/pscore/stats/__init__.py index e46bfb5e..0d3956fa 100644 --- a/src/gpsea/analysis/pscore/stats/__init__.py +++ b/src/gpsea/analysis/pscore/stats/__init__.py @@ -1,7 +1,7 @@ from ._stats import PhenotypeScoreStatistic -from ._stats import MannWhitneyStatistic +from ._stats import MannWhitneyStatistic, TTestStatistic __all__ = [ 'PhenotypeScoreStatistic', - 'MannWhitneyStatistic', + 'MannWhitneyStatistic', 'TTestStatistic', ] diff --git a/src/gpsea/analysis/pscore/stats/_stats.py b/src/gpsea/analysis/pscore/stats/_stats.py index 01cfa4d9..80f09fa4 100644 --- a/src/gpsea/analysis/pscore/stats/_stats.py +++ b/src/gpsea/analysis/pscore/stats/_stats.py @@ -1,7 +1,7 @@ import abc import typing -from scipy.stats import mannwhitneyu +from scipy.stats import mannwhitneyu, ttest_ind class PhenotypeScoreStatistic(metaclass=abc.ABCMeta): @@ -42,3 +42,27 @@ def compute_pval( ) return pval + + +class TTestStatistic(PhenotypeScoreStatistic): + """ + `TTestStatistic` is a wrapper around SciPy's + :func:`~scipy.stats.ttest_ind` function to apply + T test on 2 phenotype scores. + + See :ref:`phenotype-score-stats` for an example usage. + """ + + def compute_pval( + self, + scores: typing.Collection[typing.Sequence[float]], + ) -> float: + assert len(scores) == 2, 'T test only supports 2 categories at this time' + + x, y = scores + res = ttest_ind( + a=x, b=y, + alternative='two-sided', + ) + + return res.pvalue diff --git a/tests/analysis/pscore/test_stats.py b/tests/analysis/pscore/test_stats.py new file mode 100644 index 00000000..42fdd2ba --- /dev/null +++ b/tests/analysis/pscore/test_stats.py @@ -0,0 +1,54 @@ +import typing +import pytest + +from gpsea.analysis.pscore.stats import MannWhitneyStatistic, TTestStatistic + + +class TestMannWhitneyStatistic: + + @pytest.fixture(scope='class') + def statistic(self) -> MannWhitneyStatistic: + return MannWhitneyStatistic() + + @pytest.mark.parametrize( + 'x, y, expected', + [ + ((1., 2., 3., ), (1., 2., 3., ), 1.), + ((11., 15, 8., 12.,), (4., 2., 3., 3.5, 4.,), 0.01945103333136247), + ] + ) + def test_compute_pval( + self, + statistic: MannWhitneyStatistic, + x: typing.Sequence[float], + y: typing.Sequence[float], + expected: float, + ): + actual = statistic.compute_pval((x, y)) + + assert actual == pytest.approx(expected) + + +class TestTTestStatistic: + + @pytest.fixture(scope='class') + def statistic(self) -> TTestStatistic: + return TTestStatistic() + + @pytest.mark.parametrize( + 'x, y, expected', + [ + ((1., 2., 3., ), (1., 2., 3., ), 1.), + ((11., 15, 8., 12.,), (4., 2., 3., 3.5, 4.,), 0.0004749950471148506), + ] + ) + def test_compute_pval( + self, + statistic: TTestStatistic, + x: typing.Sequence[float], + y: typing.Sequence[float], + expected: float, + ): + actual = statistic.compute_pval((x, y)) + + assert actual == pytest.approx(expected) From cda263f6a31443aa4c187a279c6a71dbfee41ceb Mon Sep 17 00:00:00 2001 From: Daniel Danis Date: Wed, 4 Sep 2024 13:08:42 +0200 Subject: [PATCH 41/47] Mention the `pscore.stats` module in the docs. --- docs/user-guide/stats.rst | 6 ++++++ src/gpsea/analysis/pscore/stats/_stats.py | 2 -- 2 files changed, 6 insertions(+), 2 deletions(-) diff --git a/docs/user-guide/stats.rst b/docs/user-guide/stats.rst index 5a8717f3..55757ef3 100644 --- a/docs/user-guide/stats.rst +++ b/docs/user-guide/stats.rst @@ -463,6 +463,12 @@ We will use :ref:`mann-whitney-u-test` as described above. >>> score_statistic = MannWhitneyStatistic() +.. tip:: + + See :mod:`gpsea.analysis.pscore.stats` module for more statistical tests available + for using with phenotype scores. + + **Final analysis** We will put the final analysis together into :class:`~gpsea.analysis.pscore.PhenotypeScoreAnalysis`. diff --git a/src/gpsea/analysis/pscore/stats/_stats.py b/src/gpsea/analysis/pscore/stats/_stats.py index 80f09fa4..7a520e84 100644 --- a/src/gpsea/analysis/pscore/stats/_stats.py +++ b/src/gpsea/analysis/pscore/stats/_stats.py @@ -49,8 +49,6 @@ class TTestStatistic(PhenotypeScoreStatistic): `TTestStatistic` is a wrapper around SciPy's :func:`~scipy.stats.ttest_ind` function to apply T test on 2 phenotype scores. - - See :ref:`phenotype-score-stats` for an example usage. """ def compute_pval( From 7241b08bbfffa4502c1ddaa4fcb96bd034e1e64c Mon Sep 17 00:00:00 2001 From: Daniel Danis Date: Wed, 4 Sep 2024 13:26:44 +0200 Subject: [PATCH 42/47] Remove obsolete analysis code. --- src/gpsea/analysis/__init__.py | 15 - src/gpsea/analysis/_api.py | 472 ------------------ src/gpsea/analysis/_config.py | 352 ------------- src/gpsea/analysis/_gp_analysis.py | 202 -------- src/gpsea/analysis/_gp_impl.py | 195 -------- src/gpsea/analysis/_stats.py | 154 ------ src/gpsea/analysis/_test_fisherExact.py | 28 -- src/gpsea/analysis/_util.py | 61 --- .../analysis/predicate/genotype/__init__.py | 4 +- src/gpsea/analysis/predicate/genotype/_api.py | 11 +- .../predicate/genotype/_gt_predicates.py | 66 +-- .../test_counting_scorer.py} | 0 tests/analysis/test_config.py | 52 -- tests/analysis/test_examples.py | 137 ----- 14 files changed, 4 insertions(+), 1745 deletions(-) delete mode 100644 src/gpsea/analysis/_api.py delete mode 100644 src/gpsea/analysis/_config.py delete mode 100644 src/gpsea/analysis/_gp_analysis.py delete mode 100644 src/gpsea/analysis/_gp_impl.py delete mode 100644 src/gpsea/analysis/_stats.py delete mode 100644 src/gpsea/analysis/_test_fisherExact.py delete mode 100644 src/gpsea/analysis/_util.py rename tests/analysis/{test_pscore.py => pscore/test_counting_scorer.py} (100%) delete mode 100644 tests/analysis/test_config.py delete mode 100644 tests/analysis/test_examples.py diff --git a/src/gpsea/analysis/__init__.py b/src/gpsea/analysis/__init__.py index 1adf3c2e..e69de29b 100644 --- a/src/gpsea/analysis/__init__.py +++ b/src/gpsea/analysis/__init__.py @@ -1,15 +0,0 @@ -from ._api import CohortAnalysis, GenotypePhenotypeAnalysisResult, HpoMtcReport -# TODO This should go away -from ._config import CohortAnalysisConfiguration, configure_cohort_analysis, configure_default_protein_metadata_service, MtcStrategy -from ._gp_analysis import apply_predicates_on_patients -from ._util import prepare_hpo_terms_of_interest - -__all__ = [ - 'configure_cohort_analysis', - 'CohortAnalysis', 'GenotypePhenotypeAnalysisResult', - 'CohortAnalysisConfiguration', 'MtcStrategy', - 'HpoMtcReport', - 'apply_predicates_on_patients', - 'configure_default_protein_metadata_service', - 'prepare_hpo_terms_of_interest', -] diff --git a/src/gpsea/analysis/_api.py b/src/gpsea/analysis/_api.py deleted file mode 100644 index b2b7d41d..00000000 --- a/src/gpsea/analysis/_api.py +++ /dev/null @@ -1,472 +0,0 @@ -import abc -import typing -from collections import namedtuple, defaultdict - -import hpotk -import pandas as pd - -from gpsea.model import Patient -from gpsea.preprocessing import ProteinMetadataService -from .predicate import PolyPredicate, PatientCategory -from .predicate.genotype import GenotypePolyPredicate, VariantPredicate, ProteinPredicates -from .predicate.phenotype import P, PhenotypePolyPredicate -from .pscore import PhenotypeScorer, CountingPhenotypeScorer - -PatientsByHPO = namedtuple('PatientsByHPO', field_names=['all_with_hpo', 'all_without_hpo']) - - -class HpoMtcReport: - """ - Class to simplify reporting results of multiple testing filtering by HpoMtcFilter subclasses. - """ - # TODO: delete with no replacement. - - def __init__( - self, - filter_name: str, - mtc_name: str, - filter_results_map: typing.Mapping[str, int], - n_terms_before_filtering: int, - ): - """ - Args: - filter_name: name of the MTC filter strategy (e.g. `heuristic sampler`) - mtc_name: name of the MTC function (e.g. `bonferroni`) - filter_results_map: mapping with reasons for filtering out a term as keys, and counts of filtered terms as values - n_terms_before_filtering: the number of HPO terms before filtering - """ - self._filter_name = filter_name - self._mtc_name = mtc_name - self._results_map = filter_results_map - self._n_terms_before_filtering = n_terms_before_filtering - - @property - def filter_method(self) -> str: - """ - Returns: - the name of the HpoMtcFilter method used. - """ - return self._filter_name - - @property - def skipped_terms_dict(self) -> typing.Mapping[str, int]: - """ - Returns: - a mapping with reasons why an HPO term was skipped as keys and counts of the skipped terms as values. - """ - return self._results_map - - @property - def mtc_method(self) -> str: - """ - Returns: - the name of the multiple testing correction method used (e.g. `bonferroni`). - """ - return self._mtc_name - - @property - def n_terms_before_filtering(self) -> int: - """ - Get the number of terms before filtering. - """ - return self._n_terms_before_filtering - - -class GenotypePhenotypeAnalysisResult: - """ - `GenotypePhenotypeAnalysisResult` summarizes results of genotype-phenotype correlation analysis of a cohort. - """ - # TODO: delete and use `gpsea.analysis.pcats.MultiPhenotypeAnalysisResult`. - - def __init__( - self, - n_usable: typing.Mapping[P, int], - all_counts: typing.Mapping[P, pd.DataFrame], - pvals: pd.Series, - corrected_pvals: typing.Optional[pd.Series], - phenotype_categories: typing.Iterable[PatientCategory], - geno_predicate: PolyPredicate, - mtc_filter_report: typing.Optional[HpoMtcReport] = None - ): - self._n_usable = n_usable - self._all_counts = all_counts - self._pvals = pvals - self._corrected_pvals = corrected_pvals - self._phenotype_categories = tuple(phenotype_categories) - self._geno_predicate = geno_predicate - self._mtc_filter_report = mtc_filter_report - - @property - def n_usable(self) -> typing.Mapping[P, int]: - """ - Get a mapping from a phenotype `P` (either an HPO term or a disease ID) - to an `int` with the number of patients where the phenotype was assessable, - and are, thus, usable for genotype-phenotype correlation analysis. - """ - return self._n_usable - - @property - def all_counts(self) -> typing.Mapping[P, pd.DataFrame]: - """ - Get a mapping from the phenotype item to :class:`pandas.DataFrame` with counts of patients - in genotype and phenotype groups. - - An example for a genotype predicate that bins into two categories (`Yes` and `No`) based on presence - of a missense variant in transcript `NM_123456.7`, and phenotype predicate that checks - presence/absence of `HP:0001166` (a phenotype term):: - - Has MISSENSE_VARIANT in NM_123456.7 - No Yes - Present - Yes 1 13 - No 7 5 - - The rows correspond to the phenotype categories, and the columns represent the genotype categories. - """ - return self._all_counts - - @property - def pvals(self) -> pd.Series: - """ - Get a :class:`pandas.Series` with p values for each tested HPO term. - """ - return self._pvals - - @property - def corrected_pvals(self) -> typing.Optional[pd.Series]: - """ - Get an optional :class:`pandas.Series` with p values for each tested HPO term after multiple testing correction. - """ - return self._corrected_pvals - - @property - def phenotype_categories(self) -> typing.Sequence[PatientCategory]: - """ - Get a sequence of phenotype patient categories that can be investigated. - """ - return self._phenotype_categories - - @property - def total_tests(self) -> int: - """ - Get total count of tests that were run for this analysis. - """ - return len(self._all_counts) - - @property - def mtc_filter_report(self) -> typing.Optional[HpoMtcReport]: - return self._mtc_filter_report - - def summarize( - self, hpo: hpotk.MinimalOntology, - category: PatientCategory, - ) -> pd.DataFrame: - """ - Create a data frame with summary of the genotype phenotype analysis. - - The *rows* of the frame correspond to the analyzed HPO terms. - - The columns of the data frame have `Count` and `Percentage` per used genotype predicate. - - **Example** - - If we use :class:`~gpsea.analysis.predicate.genotype.VariantEffectPredicate` - which can compare phenotype with and without a missense variant, we will have a data frame - that looks like this:: - - MISSENSE_VARIANT on `NM_1234.5` No Yes - Count Percent Count Percent p value Corrected p value - Arachnodactyly [HP:0001166] 1/10 10% 13/16 81% 0.000781 0.020299 - Abnormality of the musculature [HP:0003011] 6/6 100% 11/11 100% 1.000000 1.000000 - Abnormal nervous system physiology [HP:0012638] 9/9 100% 15/15 100% 1.000000 1.000000 - ... ... ... ... ... ... ... - """ - if category not in self._phenotype_categories: - raise ValueError(f'Unknown phenotype category: {category}. Use one of {self._phenotype_categories}') - - # Row index: a list of tested HPO terms - pheno_idx = pd.Index(self._n_usable.keys()) - # Column index: multiindex of counts and percentages for all genotype predicate groups - geno_idx = pd.MultiIndex.from_product( - iterables=(self._geno_predicate.get_categories(), ('Count', 'Percent')), - names=(self._geno_predicate.get_question_base(), None), - ) - - # We'll fill this frame with data - df = pd.DataFrame(index=pheno_idx, columns=geno_idx) - - for pf, count in self._all_counts.items(): - gt_totals = count.sum() # Sum across the phenotype categories (collapse the rows). - for gt_cat in count.columns: - cnt = count.loc[category, gt_cat] - total = gt_totals[gt_cat] - df.loc[pf, (gt_cat, 'Count')] = f'{cnt}/{total}' - pct = 0 if total == 0 else round(cnt * 100 / total) - df.loc[pf, (gt_cat, 'Percent')] = f'{pct}%' - - # Add columns with p values and corrected p values (if present) - df.insert(df.shape[1], ('', self._pvals.name), self._pvals) - if self._corrected_pvals is not None: - df.insert(df.shape[1], ('', self._corrected_pvals.name), self._corrected_pvals) - - # Format the index values: `HP:0001250` -> `Seizure [HP:0001250]` if the index members are HPO terms - # or just use the term ID CURIE otherwise (e.g. `OMIM:123000`). - labeled_idx = df.index.map(lambda term_id: GenotypePhenotypeAnalysisResult._format_term_id(hpo, term_id)) - - # Last, sort by corrected p value or just p value - df = df.set_index(labeled_idx) - if self._corrected_pvals is not None: - return df.sort_values(by=[('', self._corrected_pvals.name), ('', self._pvals.name)]) - else: - return df.sort_values(by=('', self._pvals.name)) - - @staticmethod - def _format_term_id( - hpo: hpotk.MinimalOntology, - term_id: hpotk.TermId, - ) -> str: - """ - Format a `term_id` as a `str`. HPO term ID is formatted as ` []` whereas other term IDs - are formatted as CURIEs (e.g. `OMIM:123000`). - """ - if term_id.prefix == 'HP': - min_onto = hpo.get_term(term_id) - return f'{min_onto.name} [{term_id.value}]' - else: - return term_id.value - - -class PhenotypeScoreAnalysisResult: - """ - `PhenotypeScoreAnalysisResult` includes results of testing genotypes vs. phenotype scores. - - See :ref:`Mann Whitney U Test for phenotype score ` for more background. - """ - # TODO: delete and use `gpsea.analysis.pscore.PhenotypeScoreAnalysisResult` - - def __init__( - self, - genotype_phenotype_scores: pd.DataFrame, - p_value: float, - ): - self._genotype_phenotype_scores = genotype_phenotype_scores - self._p_value = p_value - - @property - def genotype_phenotype_scores( - self, - ) -> pd.DataFrame: - """ - Get the DataFrame with the genotype group and the phenotype score for each patient. - - The DataFrame has the following structure: - - ========== ======== ========= - patient_id genotype phenotype - ========== ======== ========= - patient_1 0 1 - patient_2 0 3 - patient_3 1 2 - ... ... ... - ========== ======== ========= - - The DataFrame index includes the patient IDs, and then there are 2 columns - with the `genotype` group id (:attr:`~gpsea.analysis.predicate.PatientCategory.cat_id`) - and the `phenotype` score. - """ - return self._genotype_phenotype_scores - - @property - def p_value(self) -> float: - return self._p_value - - def __str__(self) -> str: - return 'PhenotypeGroupAnalysisResult(' \ - f'genotype_phenotype_scores={self._genotype_phenotype_scores}, ' \ - f'p_value={self._p_value})' - - def __repr__(self) -> str: - return str(self) - - -class CohortAnalysis(metaclass=abc.ABCMeta): - """ - `CohortAnalysis` is a driver class for running genotype-phenotype correlation analyses. - - The class provides various methods to test genotype-phenotype correlations. All methods wrap results - into :class:`GenotypePhenotypeAnalysisResult`. - """ - # TODO: remove and use the analyses described in `User Guide > Statistical tests`. - - def __init__( - self, - hpo: hpotk.MinimalOntology, - protein_service: ProteinMetadataService, - ): - self._hpo = hpotk.util.validate_instance(hpo, hpotk.MinimalOntology, 'hpo') - self._protein_service = protein_service - self._protein_predicates = ProteinPredicates(self._protein_service) - - @abc.abstractmethod - def compare_hpo_vs_genotype( - self, - predicate: VariantPredicate, - ) -> GenotypePhenotypeAnalysisResult: - """ - Bin patients according to a presence of at least one allele that matches `predicate` - and test for genotype-phenotype correlations. - """ - pass - - @abc.abstractmethod - def compare_hpo_vs_recessive_genotype( - self, - predicate: VariantPredicate, - ) -> GenotypePhenotypeAnalysisResult: - """ - Bin patients according to a presence of zero, one, or two alleles that matche the `predicate` - and test for genotype-phenotype correlations. - """ - pass - - @abc.abstractmethod - def compare_hpo_vs_genotype_groups( - self, - predicates: typing.Iterable[VariantPredicate], - group_names: typing.Iterable[str], - ) -> GenotypePhenotypeAnalysisResult: - """ - Bin patients according to a presence of at least one allele that matches - any of the provided `predicates` and test for genotype-phenotype correlations - between the groups. - - Note, the patients that pass testing by >1 genotype predicate are *OMITTED* from the analysis! - """ - pass - - @abc.abstractmethod - def compare_disease_vs_genotype( - self, - predicate: VariantPredicate, - disease_ids: typing.Optional[typing.Sequence[typing.Union[str, hpotk.TermId]]] = None, - ) -> GenotypePhenotypeAnalysisResult: - pass - - def compare_genotype_vs_phenotype_group_count( - self, - gt_predicate: GenotypePolyPredicate, - phenotype_group_terms: typing.Iterable[typing.Union[str, hpotk.TermId]], - ) -> PhenotypeScoreAnalysisResult: - # TODO: separate into pscore module - assert isinstance(gt_predicate, GenotypePolyPredicate) - assert gt_predicate.n_categorizations() == 2 - - counting_scorer = CountingPhenotypeScorer.from_query_curies( - hpo=self._hpo, - query=phenotype_group_terms, - ) - - return self.compare_genotype_vs_phenotype_score( - gt_predicate=gt_predicate, - phenotype_scorer=counting_scorer, - ) - - @abc.abstractmethod - def compare_genotype_vs_phenotype_score( - self, - gt_predicate: GenotypePolyPredicate, - phenotype_scorer: PhenotypeScorer, - ) -> PhenotypeScoreAnalysisResult: - """ - Score the patients with a phenotype scoring method and test for correlation between the genotype group - and the phenotype score. - - Args: - gt_predicate: a genotype predicate for binning the patients along the genotype axis. - phenotype_scorer: a callable that computes a phenotype score for a given `Patient`. - """ - pass - - @abc.abstractmethod - def compare_genotype_vs_cohort_phenotypes( - self, - gt_predicate: GenotypePolyPredicate, - ) -> GenotypePhenotypeAnalysisResult: - pass - - @abc.abstractmethod - def compare_genotype_vs_phenotypes( - self, - gt_predicate: GenotypePolyPredicate, - pheno_predicates: typing.Iterable[PhenotypePolyPredicate[P]], - ): - """ - All analysis functions go through this function. - - The genotype predicate will partition the individuals into non-overlapping groups - along the genotype axis. - The phenotype predicates represent the phenotypes we want to test. - Less phenotypes may actually be tested thanks to :class:`~gpsea.analysis.PhenotypeMtcFilter`. - - Args: - gt_predicate: a predicate for binning the individuals along the genotype axis - pheno_predicates: phenotype predicates for test the individuals along the phenotype axis - """ - pass - - @staticmethod - def _check_min_perc_patients_w_hpo(min_perc_patients_w_hpo: typing.Union[int, float], - cohort_size: int) -> float: - """ - Check if the input meets the requirements. - """ - if isinstance(min_perc_patients_w_hpo, int): - if min_perc_patients_w_hpo > 0: - return min_perc_patients_w_hpo / cohort_size - else: - raise ValueError(f'`min_perc_patients_w_hpo` must be a positive `int` ' - f'but got {min_perc_patients_w_hpo}') - elif isinstance(min_perc_patients_w_hpo, float): - if 0 < min_perc_patients_w_hpo <= 1: - return min_perc_patients_w_hpo - else: - raise ValueError(f'`min_perc_patients_w_hpo` must be a `float` in range (0, 1] ' - f'but got {min_perc_patients_w_hpo}') - else: - raise ValueError(f'`min_perc_patients_w_hpo` must be a positive `int` or a `float` in range (0, 1] ' - f'but got {type(min_perc_patients_w_hpo)}') - - @staticmethod - def _group_patients_by_hpo(phenotypic_features: typing.Iterable[hpotk.TermId], - patients: typing.Iterable[Patient], - hpo: hpotk.GraphAware, - missing_implies_excluded: bool) -> PatientsByHPO: - all_with_hpo = defaultdict(list) - all_without_hpo = defaultdict(list) - for hpo_term in phenotypic_features: - for patient in patients: - found = False - for pf in patient.present_phenotypes(): - if hpo_term == pf.identifier or hpo.graph.is_ancestor_of(hpo_term, pf): - # Patient is annotated with `hpo_term` because `pf` is equal to `hpo_term` - # or it is a descendant of `hpo_term`. - all_with_hpo[hpo_term].append(patient) - - # If one `pf` of the patient is found to be a descendant of `hpo`, we must break to prevent - # adding the patient to `present_hpo` more than once due to another descendant! - found = True - break - if not found: - # The patient is not annotated by the `hpo_term`. - - if missing_implies_excluded: - # The `hpo_term` annotation is missing, hence implicitly excluded. - all_without_hpo[hpo_term].append(patient) - else: - # The `hpo_term` must be explicitly excluded patient to be accounted for. - for ef in patient.excluded_phenotypes(): - if hpo_term == ef.identifier or hpo.graph.is_descendant_of(hpo_term, ef): - all_with_hpo[hpo_term].append(patient) - break - - return PatientsByHPO(all_with_hpo, all_without_hpo) diff --git a/src/gpsea/analysis/_config.py b/src/gpsea/analysis/_config.py deleted file mode 100644 index 2c6d68c6..00000000 --- a/src/gpsea/analysis/_config.py +++ /dev/null @@ -1,352 +0,0 @@ -import enum -import logging -import os -import typing -import warnings - -import hpotk - -from gpsea.config import get_cache_dir_path -from gpsea.model import Cohort -from gpsea.preprocessing import ProteinMetadataService -from gpsea.preprocessing import configure_default_protein_metadata_service as backup_pms -from .mtc_filter import PhenotypeMtcFilter, UseAllTermsMtcFilter, SpecifiedTermsMtcFilter, HpoMtcFilter -from ._api import CohortAnalysis -from ._gp_analysis import FisherExactAnalyzer -from ._gp_impl import GpCohortAnalysis - -P_VAL_OPTIONS = ( - 'bonferroni', 'b', - 'sidak', 's', - 'holm-sidak', 'hs', - 'holm', 'h', - 'simes-hochberg', 'sh', - 'hommel', 'ho', - 'fdr_bh', 'fdr_by', - 'fdr_tsbh', 'fdr_tsbky', - 'fdr_gbs', - None, -) - - -class MtcStrategy(enum.Enum): - """ - A strategy for mitigating the multiple testing correction (MTC) burden. - """ - - ALL_PHENOTYPE_TERMS = 0 - """ - All phenotype terms (HPO or disease IDs) will be tested. - """ - - SPECIFY_TERMS = 1 - """ - Only the manually provided HPO terms will be tested. - """ - - HPO_MTC = 2 - """ - Only HPO terms present in at least a certain fraction of patients will be tested. - """ - - -class CohortAnalysisConfiguration: - """ - `CohortAnalysisConfiguration` is a value class for storing :class:`~gpsea.analysis.CohortAnalysis` - configuration options. - - The class contains the default values upon creation and the configuration option values can be set as properties. - - If an invalid value option is passed to the property setter, a warning is logged and the previous value is retained. - Therefore, it is *impossible* to mis-configure the analysis. - - Default values - ^^^^^^^^^^^^^^ - - ============================== ======================= ========================================= - Option Type Default value - ============================== ======================= ========================================= - ``pval_correction`` `str` `bonferroni` - ``min_n_patients_with_term`` `int` `2` - ``mtc_alpha`` `float` `0.05` - ``include_sv`` `bool` `False` - ``mtc_strategy`` :class:`MtcStrategy` :class:`MtcStrategy.ALL_PHENOTYPE_TERMS` - ============================== ======================= ========================================= - - """ - - def __init__(self): - self._logger = logging.getLogger(__name__) - self._pval_correction = 'bonferroni' - self._mtc_alpha = .05 - self._min_n_patients_with_term = 2 - self._include_sv = False - self._mtc_strategy = MtcStrategy.ALL_PHENOTYPE_TERMS - self._terms_to_test = None # # only relevant for SPECIFIED_TERMS strategy - self._min_patients_w_hpo = None # # only relevant for HPO_MTC strategy - - @property - def pval_correction(self) -> typing.Optional[str]: - """ - Get method for multiple testing p value correction. Default: `bonferroni`. - """ - return self._pval_correction - - @pval_correction.setter - def pval_correction(self, pval_correction: typing.Optional[str]): - """ - Set the method for p value correction. - - See Statsmodels' - `documentation `_ - for the acceptable values. - - :param pval_correction: a `str` with the name of the desired multiple testing correction method or `None` - if no MTC should be applied. - """ - if pval_correction in P_VAL_OPTIONS: - self._pval_correction = pval_correction - else: - self._logger.warning('Ignoring invalid `pval_correction` value %s. Using %s correction.', pval_correction, - self._pval_correction) - - @property - def min_n_patients_with_term(self) -> int: - """ - Get the minimum number of patients that must be annotated with an HPO term - for including the term in the analysis. - """ - return self._min_n_patients_with_term - - @min_n_patients_with_term.setter - def min_n_patients_with_term(self, value: int): - if isinstance(value, int) and value >= 0: - self._min_n_patients_with_term = value - else: - self._logger.warning( - 'Ignoring invalid `min_n_patients_with_term` value %s. Using %s', - value, self._min_n_patients_with_term, - ) - - @property - def mtc_alpha(self) -> float: - """ - The alpha value for multiple testing correction. - """ - return self._mtc_alpha - - @mtc_alpha.setter - def mtc_alpha(self, mtc_alpha: float): - """ - Set new multiple testing correction alpha value. - - :param mtc_alpha: a `float` in range :math:`(0,1]`. - """ - if isinstance(mtc_alpha, float) and 0. < mtc_alpha <= 1.: - self._mtc_alpha = mtc_alpha - else: - self._logger.warning( - '`mtc_alpha` should be a `float` in range `(0, 1]` but was %s. Keeping the previous value %s', - mtc_alpha, self._mtc_alpha, - ) - - @property - def include_sv(self) -> bool: - """ - `True` if we want to include structural variants in the analysis - (i.e. the variants that use symbolic VCF notation). - """ - return self._include_sv - - @include_sv.setter - def include_sv(self, include_sv: bool): - """ - Set `include_sv` option. - - :param include_sv: a `bool` with the value. - """ - if isinstance(include_sv, bool): - self._include_sv = include_sv - else: - self._logger.warning('Ignoring invalid `include_sv` value %s. Using %s', include_sv, self._include_sv) - - @property - def mtc_strategy(self) -> MtcStrategy: - """ - Get the MTC filtering strategy to be used. - """ - return self._mtc_strategy - - def all_terms_strategy(self): - """ - Test all phenotype terms. - - See :ref:`use-all-terms-strategy` for more info. - """ - self._mtc_strategy = MtcStrategy.ALL_PHENOTYPE_TERMS - self._min_patients_w_hpo = None - self._terms_to_test = None - - def hpo_mtc_strategy( - self, - min_patients_w_hpo: float = 0.2, - ): - """ - Only test the HPO terms that pass all rules of the HPO filter strategy. - - See :ref:`hpo-mtc-filter-strategy` section for more info on the rules. - - :param threshold_HPO_observed_frequency: a float in range :math:`(0, 1]` to represent - the minimum fraction of patients for an HPO term to be included. - """ - if not isinstance(min_patients_w_hpo, float): - raise ValueError(f'`min_patients_w_hpo` is not a `float`: {min_patients_w_hpo}') - if not 0 < min_patients_w_hpo <= 1: - raise ValueError(f'`min_patients_w_hpo` must be in range (0, 1] but was {min_patients_w_hpo}') - - self._mtc_strategy = MtcStrategy.HPO_MTC - self._min_patients_w_hpo = min_patients_w_hpo - self._terms_to_test = None - - def specify_terms_strategy( - self, - terms_to_test: typing.Iterable[typing.Union[str, hpotk.TermId]], - ): - """ - Mitigate the MTC burden by only testing the specified HPO terms. - - The HPO terms are validated before running the analysis, - to point out invalid CURIE (e.g. `WHATEVER`) values, - or HPO term IDs that are not in the currently used HPO. - - Calling this method will clear any previously specified terms. - - See :ref:`specify-terms-strategy` for more info. - - :param terms_to_test: an iterable with CURIEs (e.g. `HP:0001250`) - or :class:`hpotk.TermId` instances representing the terms to test. - """ - self._mtc_strategy = MtcStrategy.SPECIFY_TERMS - self._min_patients_w_hpo = None - self._terms_to_test = tuple(terms_to_test) - - @property - def terms_to_test(self) -> typing.Optional[typing.Iterable[typing.Union[str, hpotk.TermId]]]: - """ - Get the ids of the terms to be tested in `specify_terms_strategy` - or `None` if :class:`MtcStrategy.SPECIFY_TERMS` will *not* be used. - """ - return self._terms_to_test - - @property - def min_patients_w_hpo(self) -> typing.Optional[float]: - """ - Get the minimum fraction of patients needed to be annotated with an HPO term - to justify being tested or `None` if :class:`MtcStrategy.HPO_MTC` will *not* be used. - """ - return self._min_patients_w_hpo - - -def configure_cohort_analysis( - cohort: Cohort, - hpo: hpotk.MinimalOntology, - protein_source: str = 'UNIPROT', - cache_dir: typing.Optional[str] = None, - config: typing.Optional[CohortAnalysisConfiguration] = None, -) -> CohortAnalysis: - """ - Configure :class:`~gpsea.analysis.CohortAnalysis` for given `cohort`. - - :param cohort: a :class:`~gpsea.model.Cohort` to analyze - :param hpo: a :class:`~hpotk.MinimalOntology` with HPO to use in the analysis - :param protein_source: the resource to retrieve protein annotations from if we cannot find the annotations locally. - Choose from ``{'UNIPROT'}`` (just one fallback implementation is available at the moment). - :param config: an optional :class:`CohortAnalysisConfiguration` to parameterize the analysis. - The default parameters will be used if `None`. - """ - if config is None: - config = CohortAnalysisConfiguration() # Use the default config - - cache_path = get_cache_dir_path(cache_dir) - os.makedirs(cache_path, exist_ok=True) - - cache_dir = str(cache_path) - protein_metadata_service = backup_pms(protein_source, cache_dir) - - mtc_filter: PhenotypeMtcFilter - if config.mtc_strategy == MtcStrategy.HPO_MTC: - assert config.min_patients_w_hpo is not None, '`min_patients_w_hpo` must be set if using `HPO_MTC` strategy' - mtc_filter = HpoMtcFilter.default_filter( - hpo=hpo, - term_frequency_threshold=config.min_patients_w_hpo, - ) - elif config.mtc_strategy == MtcStrategy.SPECIFY_TERMS: - assert config.terms_to_test is not None, '`terms_to_test` must be set if using `SPECIFY_TERMS` strategy' - validated_terms_to_test = _validate_terms_to_test(hpo, config.terms_to_test) - mtc_filter = SpecifiedTermsMtcFilter(hpo=hpo, terms_to_test=validated_terms_to_test) - elif config.mtc_strategy == MtcStrategy.ALL_PHENOTYPE_TERMS: - mtc_filter = UseAllTermsMtcFilter() - else: - raise ValueError(f"Did not recognize MtcStrategy {config.mtc_strategy}") - - # Choosing a simple Fisher's exact test for now. - gp_analyzer = FisherExactAnalyzer( - mtc_filter=mtc_filter, - p_val_correction=config.pval_correction, - mtc_alpha=config.mtc_alpha, - ) - - return GpCohortAnalysis( - cohort=cohort, - hpo=hpo, - protein_service=protein_metadata_service, - gp_analyzer=gp_analyzer, - min_n_of_patients_with_term=config.min_n_patients_with_term, - include_sv=config.include_sv, - ) - - -def _validate_terms_to_test( - hpo: hpotk.MinimalOntology, - terms_to_test: typing.Iterable[typing.Union[hpotk.TermId, str]], -) -> typing.Iterable[hpotk.TermId]: - """ - Check that: - * all terms to test are valid TermIds/CURIES, - * the term IDs are in used HPO, and - * there is at least one term to test - """ - validated_terms_to_test = set() - - for term in terms_to_test: - if isinstance(term, hpotk.TermId): - pass - elif isinstance(term, str): - term = hpotk.TermId.from_curie(term) - else: - raise ValueError(f'{term} is neither a TermId nor a CURIE `str`!') - - if term not in hpo: - raise ValueError(f"HPO ID {term} not in HPO ontology {hpo.version}") - validated_terms_to_test.add(term) - if len(validated_terms_to_test) == 0: - raise ValueError('Cannot run use {MTC_Strategy.SPECIFY_TERMS} with no HPO terms!') - - return validated_terms_to_test - - -def configure_default_protein_metadata_service( - protein_source: str = 'UNIPROT', - cache_dir: typing.Optional[str] = None, -) -> ProteinMetadataService: - """ - Create default protein metadata service that will cache the protein metadata - in current working directory under `.gpsea_cache/protein_cache` - and reach out to UNIPROT REST API if a cache entry is missing. - """ - # TODO: remove at some point. - warnings.warn( - 'Use gpsea.preprocessing.configure_default_protein_metadata_service` instead', - DeprecationWarning, stacklevel=2, - ) - return backup_pms(protein_source=protein_source, cache_dir=cache_dir) diff --git a/src/gpsea/analysis/_gp_analysis.py b/src/gpsea/analysis/_gp_analysis.py deleted file mode 100644 index f786049c..00000000 --- a/src/gpsea/analysis/_gp_analysis.py +++ /dev/null @@ -1,202 +0,0 @@ -import abc -import typing - -import pandas as pd - -from statsmodels.stats import multitest - -from gpsea.model import Patient -from .mtc_filter import PhenotypeMtcFilter -from .predicate import PatientCategory -from .predicate.genotype import GenotypePolyPredicate -from .predicate.phenotype import PhenotypePolyPredicate, P -from ._api import GenotypePhenotypeAnalysisResult, HpoMtcReport -from ._stats import run_fisher_exact, run_recessive_fisher_exact - - -def apply_predicates_on_patients( - patients: typing.Iterable[Patient], - pheno_predicates: typing.Iterable[PhenotypePolyPredicate[P]], - gt_predicate: GenotypePolyPredicate, -) -> typing.Tuple[ - typing.Collection[PatientCategory], - typing.Mapping[P, int], - typing.Mapping[P, pd.DataFrame], -]: - """ - Apply the phenotype predicates `pheno_predicates` and the genotype predicate `gt_predicate` - to bin the `patients` into categories. - - Note, it may not be possible to bin *all* patients with a genotype/phenotype pair, - since a predicate is allowed to return `None` (e.g. if it bins the patient into MISSENSE or NONSENSE groups - but the patient has no MISSENSE or NONSENSE variants). If this happens, the patient will not be "usable" - for the phenotype `P`. - - Args: - patients: an iterable with the patients to bin into categories - pheno_predicates: an iterable with the phenotype predicates to apply - gt_predicate: a genotype predicate to apply - - Returns: - a tuple with 3 items: - - a collection of unique :class:`PatientCategory` items that the patients were binned into - - a mapping from a phenotype :class:`P` (e.g. an HPO term or a disease) - to an `int` with count of patients that could be binned according to the phenotype `P` - - a mapping from phenotype :class:`P` to a data frame with counts of patients - in i-th phenotype category and j-th genotype category where i and j are rows and columns of the data frame - """ - # TODO: delete with no replacement. - phenotypes = set() - categories = set() - for predicate in pheno_predicates: - categories.update(predicate.get_categories()) - phenotypes.add(predicate.phenotype) - - n_usable_patients = pd.Series(data=0, index=pd.Index(phenotypes)) - - # Apply genotype and phenotype predicates - counts = {} - for ph_predicate in pheno_predicates: - if ph_predicate.phenotype not in counts: - # Make an empty frame for keeping track of the counts. - counts[ph_predicate.phenotype] = pd.DataFrame( - data=0, - index=pd.Index( - data=ph_predicate.get_categories(), - name=ph_predicate.get_question_base(), - ), - columns=pd.Index( - data=gt_predicate.get_categories(), - name=gt_predicate.get_question_base(), - ), - ) - - for patient in patients: - pheno_cat = ph_predicate.test(patient) - geno_cat = gt_predicate.test(patient) - - if pheno_cat is not None and geno_cat is not None: - counts[pheno_cat.phenotype].loc[pheno_cat.category, geno_cat.category] += 1 - n_usable_patients[pheno_cat.phenotype] += 1 - - return categories, n_usable_patients, counts - - -class GPAnalyzer(typing.Generic[P], metaclass=abc.ABCMeta): - """ - `GPAnalyzer` calculates p values for genotype-phenotype correlation of phenotypic features of interest. - """ - # TODO: delete with no replacement. - - @abc.abstractmethod - def analyze( - self, - patients: typing.Iterable[Patient], - pheno_predicates: typing.Iterable[PhenotypePolyPredicate[P]], - gt_predicate: GenotypePolyPredicate, - ) -> GenotypePhenotypeAnalysisResult: - """ - Test for association between `phenotypic_features` of interest groups or `patients` determined - by the `predicate`. - - :return: :class:`GenotypePhenotypeAnalysisResult` object with the results. - """ - pass - - -class FisherExactAnalyzer(typing.Generic[P], GPAnalyzer[P]): - """ - `FisherExactAnalyzer` uses Fisher's exact test to calculate p value for phenotypic features of interest. - - Following the test, the code applies one of the multiple testing corrections provided by - :func:`statsmodels.stats.multitest.multipletests` function at given `mtc_alpha`. - - :param p_val_correction: a `str` with name of the multiple testing correction method or `None` if no correction - should be applied. - :param mtc_alpha: a `float` in range :math:`(0, 1]` with the multiple testing correction alpha value. - """ - # TODO: delete and use `gpsea.analysis.pcats.MultiPhenotypeAnalysis`. - - def __init__( - self, - mtc_filter: PhenotypeMtcFilter, - p_val_correction: typing.Optional[str] = None, - mtc_alpha: float = .05, - ): - self._correction = p_val_correction - self._mtc_alpha = mtc_alpha - self._mtc_filter = mtc_filter - - def analyze( - self, - patients: typing.Iterable[Patient], - pheno_predicates: typing.Iterable[PhenotypePolyPredicate[P]], - gt_predicate: GenotypePolyPredicate, - ) -> GenotypePhenotypeAnalysisResult: - pheno_predicates = tuple(pheno_predicates) - if len(pheno_predicates) == 0: - raise ValueError('No phenotype predicates were provided') - - # 1) Count the patients - categories, n_usable, all_counts = apply_predicates_on_patients( - patients, pheno_predicates, gt_predicate, - ) - n_terms_before_filtering = len(n_usable) - - # 1.5) Filter terms for MTC - n_usable_filtered, all_counts_filtered, reason2count = self._mtc_filter.filter_terms_to_test( - gt_predicate=gt_predicate, - n_usable=n_usable, - all_counts=all_counts, - ) - if len(n_usable_filtered) == 0: - raise ValueError("No phenotypes are left for the analysis after MTC filtering step") - - assert len(n_usable_filtered) == len(all_counts_filtered) - - # 2) Statistical tests - pheno_idx = pd.Index(all_counts_filtered.keys(), name='p_val') - pvals = pd.Series(float('nan'), index=pheno_idx, name='p value') - for phenotype in pheno_idx: - counts = all_counts_filtered[phenotype] - # TODO - this is where we must fail unless we have the contingency table of the right size! - if counts.shape == (2, 2): - pvals[phenotype] = run_fisher_exact(counts) - elif counts.shape == (2, 3): - pvals[phenotype] = run_recessive_fisher_exact(counts) - else: - raise ValueError( - "Invalid number of categories. " - f"A {counts.shape} table was created. Only (2, 2) and (2, 3) are valid sizes." - ) - - # 3) Multiple correction - if self._correction is not None: - _, pvals_corrected, _, _ = multitest.multipletests(pvals, alpha=self._mtc_alpha, method=self._correction) - corrected_idx = pd.Index(all_counts_filtered.keys(), name='p_val_corrected') - corrected_pvals_series = pd.Series( - data=pvals_corrected, index=corrected_idx, name='Corrected p value', - ) - else: - corrected_pvals_series = None - - mtc_method = "none" - if self._correction is not None: - mtc_method = self._correction - mtc_filter_report = HpoMtcReport( - filter_name=self._mtc_filter.filter_method_name(), - mtc_name=mtc_method, - filter_results_map=reason2count, - n_terms_before_filtering=n_terms_before_filtering, - ) - - # 4) Wrap up - return GenotypePhenotypeAnalysisResult( - n_usable=n_usable_filtered, - all_counts=all_counts_filtered, - pvals=pvals, - corrected_pvals=corrected_pvals_series, - phenotype_categories=categories, - geno_predicate=gt_predicate, - mtc_filter_report=mtc_filter_report - ) diff --git a/src/gpsea/analysis/_gp_impl.py b/src/gpsea/analysis/_gp_impl.py deleted file mode 100644 index 1d01045c..00000000 --- a/src/gpsea/analysis/_gp_impl.py +++ /dev/null @@ -1,195 +0,0 @@ -import logging -import typing - -import hpotk -import pandas as pd - -from scipy.stats import mannwhitneyu - -from gpsea.model import Cohort -from gpsea.preprocessing import ProteinMetadataService -from .pscore import PhenotypeScorer -from .predicate.genotype import GenotypePolyPredicate, VariantPredicate -from .predicate.genotype import boolean_predicate as wrap_as_boolean_predicate -from .predicate.genotype import groups_predicate as wrap_as_groups_predicate -from .predicate.genotype import recessive_predicate as wrap_as_recessive_predicate -from .predicate.phenotype import PhenotypePolyPredicate, P, PropagatingPhenotypePredicate, DiseasePresencePredicate - -from ._api import CohortAnalysis, GenotypePhenotypeAnalysisResult, PhenotypeScoreAnalysisResult -from ._gp_analysis import GPAnalyzer -from ._util import prepare_hpo_terms_of_interest - - -class GpCohortAnalysis(CohortAnalysis): - - def __init__( - self, cohort: Cohort, - hpo: hpotk.MinimalOntology, - protein_service: ProteinMetadataService, - gp_analyzer: GPAnalyzer, - min_n_of_patients_with_term: int, - include_sv: bool = False, - ): - super().__init__( - hpo, - protein_service, - ) - if not isinstance(cohort, Cohort): - raise ValueError(f"cohort must be type Cohort but was type {type(cohort)}") - - self._logger = logging.getLogger(__name__) - self._cohort = cohort - # self._phenotype_filter = hpotk.util.validate_instance(phenotype_filter, PhenotypeFilter, 'phenotype_filter') - self._gp_analyzer = hpotk.util.validate_instance(gp_analyzer, GPAnalyzer, 'gp_analyzer') - - self._patient_list = list(cohort.all_patients) \ - if include_sv \ - else [pat for pat in cohort.all_patients if not all(var.variant_info.is_structural() for var in pat.variants)] - if len(self._patient_list) == 0: - raise ValueError('No patients left for analysis!') - - self._hpo_terms_of_interest = prepare_hpo_terms_of_interest( - hpo=self._hpo, - patients=self._patient_list, - min_n_of_patients_with_term=min_n_of_patients_with_term, - ) - - def compare_hpo_vs_genotype( - self, - predicate: VariantPredicate, - ) -> GenotypePhenotypeAnalysisResult: - """ - Bin patients according to a presence of at least one allele that matches `predicate` - and test for genotype-phenotype correlations. - """ - assert isinstance(predicate, VariantPredicate), \ - f'{type(predicate)} is not an instance of `VariantPredicate`' - bool_predicate = wrap_as_boolean_predicate(predicate) - return self.compare_genotype_vs_cohort_phenotypes(bool_predicate) - - def compare_hpo_vs_recessive_genotype( - self, - predicate: VariantPredicate, - ) -> GenotypePhenotypeAnalysisResult: - """ - Bin patients according to a presence of zero, one, or two alleles that matche the `predicate` - and test for genotype-phenotype correlations. - """ - rec_predicate = wrap_as_recessive_predicate(predicate) - return self.compare_genotype_vs_cohort_phenotypes(rec_predicate) - - def compare_hpo_vs_genotype_groups( - self, - predicates: typing.Iterable[VariantPredicate], - group_names: typing.Iterable[str], - ) -> GenotypePhenotypeAnalysisResult: - """ - Bin patients according to a presence of at least one allele that matches - any of the provided `predicates` and test for genotype-phenotype correlations - between the groups. - - Note, the patients that pass testing by >1 genotype predicate are *OMITTED* from the analysis! - """ - predicate = wrap_as_groups_predicate( - predicates=predicates, - group_names=group_names, - ) - return self.compare_genotype_vs_cohort_phenotypes(predicate) - - def compare_disease_vs_genotype( - self, - predicate: VariantPredicate, - disease_ids: typing.Optional[typing.Sequence[typing.Union[str, hpotk.TermId]]] = None, - ) -> GenotypePhenotypeAnalysisResult: - pheno_predicates = self._prepare_disease_predicates(disease_ids) - - # This can be updated to any genotype poly predicate in future, if necessary. - genotype_predicate = wrap_as_boolean_predicate(predicate) - return self.compare_genotype_vs_phenotypes(genotype_predicate, pheno_predicates) - - def _prepare_disease_predicates( - self, - disease_ids: typing.Optional[typing.Sequence[typing.Union[str, hpotk.TermId]]], - ) -> typing.Sequence[DiseasePresencePredicate]: - testing_diseases = [] - if disease_ids is None: - disease_ids = [dis.identifier for dis in self._cohort.all_diseases()] - if len(disease_ids) < 1: - raise ValueError("No diseases available for testing.") - for disease_id in disease_ids: - if isinstance(disease_id, str): - testing_diseases.append(hpotk.TermId.from_curie(disease_id)) - elif isinstance(disease_id, hpotk.TermId): - testing_diseases.append(disease_id) - else: - raise ValueError(f'{disease_id} must be a `str` or a `hpotk.TermId`') - pheno_predicates = [] - for disease in testing_diseases: - pheno_predicates.append(DiseasePresencePredicate(disease)) - return pheno_predicates - - def compare_genotype_vs_phenotype_score( - self, - gt_predicate: GenotypePolyPredicate, - phenotype_scorer: PhenotypeScorer, - ) -> PhenotypeScoreAnalysisResult: - idx = pd.Index(tuple(p.patient_id for p in self._patient_list), name='patient_id') - data = pd.DataFrame( - None, - index=idx, - columns=['genotype', 'phenotype'], - ) - - # Apply the predicates on the patients - for patient in self._patient_list: - gt_cat = gt_predicate.test(patient) - data.loc[patient.patient_id, 'genotype'] = None if gt_cat is None else gt_cat.category.cat_id - data.loc[patient.patient_id, 'phenotype'] = phenotype_scorer.score(patient) - - # To improve the determinism - data.sort_index(inplace=True) - - # Sort by PatientCategory.cat_id and unpack. - # For now, we only allow to have up to 2 groups. - x_key, y_key = sorted(data['genotype'].dropna().unique()) - x = data.loc[data['genotype'] == x_key, 'phenotype'].to_numpy(dtype=float) - y = data.loc[data['genotype'] == y_key, 'phenotype'].to_numpy(dtype=float) - result = mannwhitneyu( - x=x, - y=y, - alternative='two-sided', - ) - - return PhenotypeScoreAnalysisResult( - genotype_phenotype_scores=data, - p_value=float(result.pvalue), - ) - - def compare_genotype_vs_cohort_phenotypes( - self, - gt_predicate: GenotypePolyPredicate, - ) -> GenotypePhenotypeAnalysisResult: - assert isinstance(gt_predicate, GenotypePolyPredicate), \ - f'{type(gt_predicate)} is not an instance of `GenotypePolyPredicate`' - - pheno_predicates = self._prepare_phenotype_predicates() - return self.compare_genotype_vs_phenotypes(gt_predicate, pheno_predicates) - - def _prepare_phenotype_predicates(self) -> typing.Sequence[PhenotypePolyPredicate[P]]: - return tuple( - PropagatingPhenotypePredicate( - hpo=self._hpo, - query=query, - ) - for query in self._hpo_terms_of_interest) - - def compare_genotype_vs_phenotypes( - self, - gt_predicate: GenotypePolyPredicate, - pheno_predicates: typing.Iterable[PhenotypePolyPredicate[P]], - ) -> GenotypePhenotypeAnalysisResult: - return self._gp_analyzer.analyze( - patients=self._patient_list, - pheno_predicates=pheno_predicates, - gt_predicate=gt_predicate, - ) diff --git a/src/gpsea/analysis/_stats.py b/src/gpsea/analysis/_stats.py deleted file mode 100644 index 1364e7d5..00000000 --- a/src/gpsea/analysis/_stats.py +++ /dev/null @@ -1,154 +0,0 @@ -import abc -import math -import typing -from decimal import Decimal - -import numpy as np -import scipy - -# TODO: delete the module - - -class MultiFisherExact(metaclass=abc.ABCMeta): - - @abc.abstractmethod - def calculate(self, a: np.ndarray) -> float: - """ - :param a: a 2x3 int array with counts - :returns: a p value calculated with Fisher's exact test - """ - pass - - @staticmethod - def _check_input(a: np.ndarray): - if not isinstance(a, np.ndarray): - raise ValueError(f'Expected a numpy array but got {type(a)}') - if not a.shape == (2, 3): - raise ValueError(f'Shape of the array must be (2, 3) but got {a.shape}') - if np.array_equal(a, np.zeros_like(a)): - raise ValueError(f'Array is all zeros, cannot run analysis') - - -class PythonMultiFisherExact(MultiFisherExact): - - def calculate(self, a: np.ndarray) -> float: - MultiFisherExact._check_input(a) - return self._fisher_exact(a) - - def _fisher_exact(self, table): - row_sum = [] - col_sum = [] - - for i in range(len(table)): - temp = 0 - for j in range(len(table[0])): - temp += table[i][j] - row_sum.append(temp) - - for j in range(len(table[0])): - temp = 0 - for i in range(len(table)): - temp += table[i][j] - col_sum.append(temp) - - mat = [[0] * len(col_sum)] * len(row_sum) - pos = (0, 0) - - p_0 = 1 - - for x in row_sum: - p_0 *= math.factorial(x) - for y in col_sum: - p_0 *= math.factorial(y) - - n = 0 - for x in row_sum: - n += x - p_0 /= Decimal(math.factorial(n)) - - for i in range(len(table)): - for j in range(len(table[0])): - p_0 /= Decimal(math.factorial(table[i][j])) - - p = [0] - self._dfs(mat, pos, row_sum, col_sum, p_0, p) - - return float(p[0]) - - def _dfs(self, mat, pos, r_sum, c_sum, p_0, p): - - (xx, yy) = pos - (r, c) = (len(r_sum), len(c_sum)) - - mat_new = [] - - for i in range(len(mat)): - temp = [] - for j in range(len(mat[0])): - temp.append(mat[i][j]) - mat_new.append(temp) - - if xx == -1 and yy == -1: - for i in range(r - 1): - temp = r_sum[i] - for j in range(c - 1): - temp -= mat_new[i][j] - mat_new[i][c - 1] = temp - for j in range(c - 1): - temp = c_sum[j] - for i in range(r - 1): - temp -= mat_new[i][j] - mat_new[r - 1][j] = temp - temp = r_sum[r - 1] - for j in range(c - 1): - temp -= mat_new[r - 1][j] - if temp < 0: - return - mat_new[r - 1][c - 1] = temp - - p_1 = 1 - for x in r_sum: - p_1 *= math.factorial(x) - for y in c_sum: - p_1 *= math.factorial(y) - - n = 0 - for x in r_sum: - n += x - p_1 /= Decimal(math.factorial(n)) - - for i in range(len(mat_new)): - for j in range(len(mat_new[0])): - p_1 /= Decimal(math.factorial(mat_new[i][j])) - if p_1 <= p_0 + Decimal(0.00000001): - # print(mat_new) - # print(p_1) - p[0] += p_1 - else: - max_1 = r_sum[xx] - max_2 = c_sum[yy] - for j in range(c): - max_1 -= mat_new[xx][j] - for i in range(r): - max_2 -= mat_new[i][yy] - for k in range(min(max_1, max_2) + 1): - mat_new[xx][yy] = k - if xx == r - 2 and yy == c - 2: - pos_new = (-1, -1) - elif xx == r - 2: - pos_new = (0, yy + 1) - else: - pos_new = (xx + 1, yy) - self._dfs(mat_new, pos_new, r_sum, c_sum, p_0, p) - - -def run_recessive_fisher_exact(two_by_three_table: typing.Sequence[typing.Sequence[int]]): - a = np.array(two_by_three_table, dtype=np.int64) - test_class = PythonMultiFisherExact() - val = test_class.calculate(a) - return val - - -def run_fisher_exact(two_by_two_table: typing.Sequence[typing.Sequence[int]]): - oddsr, p = scipy.stats.fisher_exact(two_by_two_table, alternative='two-sided') - return p diff --git a/src/gpsea/analysis/_test_fisherExact.py b/src/gpsea/analysis/_test_fisherExact.py deleted file mode 100644 index fd530f5c..00000000 --- a/src/gpsea/analysis/_test_fisherExact.py +++ /dev/null @@ -1,28 +0,0 @@ -import pytest -import numpy as np -from contextlib import nullcontext as does_not_raise -from ._stats import PythonMultiFisherExact - - -@pytest.fixture -def MultiExact() -> PythonMultiFisherExact: - return PythonMultiFisherExact() - -# TODO: remove - -@pytest.mark.parametrize('table, raise_error, pVal', - ([[[0,0,0],[0,0,0]], pytest.raises(ValueError), None], - [[[2, 1, 0],[3, 0, 2]], does_not_raise(), 0.6429], - #[[[100, 150], [500, 460], [420, 400]], pytest.raises(OverflowError), None], - [[[5,5,5],[5,5,5]], does_not_raise(), 1], - [[[10, 5, 20], [15, 5]], pytest.raises(ValueError), None], - [[[10, 2, 3],[1, 3, 4]], does_not_raise(), 0.0395], - [[[6, 0, 0],[6, 5, 8]], does_not_raise(), 0.0233], - [[[],[]], pytest.raises(ValueError), None] -)) -def test_multiFisherExact(table, raise_error, pVal, MultiExact): - with raise_error: - np_table = np.array(table, dtype=np.int64) - final_pval = MultiExact.calculate(np_table) - assert round(final_pval, 4) == pVal - \ No newline at end of file diff --git a/src/gpsea/analysis/_util.py b/src/gpsea/analysis/_util.py deleted file mode 100644 index 2c3445b5..00000000 --- a/src/gpsea/analysis/_util.py +++ /dev/null @@ -1,61 +0,0 @@ -import typing - -from collections import Counter - -import hpotk - - -from gpsea.model import Patient - - -def prepare_hpo_terms_of_interest( - hpo: hpotk.graph.GraphAware, - patients: typing.Iterable[Patient], - min_n_of_patients_with_term: int, -) -> typing.Collection[hpotk.TermId]: - """ - Prepare a collection of HPO terms to test. - - This includes the direct HPO patient annotations - as well as the ancestors of the present terms and the descendants of the excluded terms. - - :param hpo: an entity with an HPO graph (e.g. :class:`hpotk.MinimalOntology`). - :param patients: an iterable with patients. - :param min_n_of_patients_with_term: the minimum number of patients that must feature an HPO term - (either directly or indirectly) for the term to be included in the analysis. - """ - # TODO remove in favor of `gpsea.analysis.predicate.phenotype` - present_count = Counter() - excluded_count = Counter() - - for patient in patients: - for pf in patient.phenotypes: - if pf.is_present: - # A present phenotypic feature must be counted in. - present_count[pf.identifier] += 1 - # and it also implies presence of its ancestors. - for anc in hpo.graph.get_ancestors(pf): - present_count[anc] += 1 - else: - # An excluded phenotypic feature - excluded_count[pf.identifier] += 1 - for desc in hpo.graph.get_descendants(pf): - # implies exclusion of its descendants. - excluded_count[desc] += 1 - - total_count = Counter() - for term_id, count in present_count.items(): - total_count[term_id] += count - - for term_id, count in excluded_count.items(): - total_count[term_id] += count - - final_hpo = [] - for term_id in present_count: - # Keep the term if it is mentioned at least *n* times (incl. being excluded) - # in the cohort - n_all = total_count[term_id] - if n_all >= min_n_of_patients_with_term: - final_hpo.append(term_id) - - return tuple(final_hpo) diff --git a/src/gpsea/analysis/predicate/genotype/__init__.py b/src/gpsea/analysis/predicate/genotype/__init__.py index 86dd261a..82d2819d 100644 --- a/src/gpsea/analysis/predicate/genotype/__init__.py +++ b/src/gpsea/analysis/predicate/genotype/__init__.py @@ -1,13 +1,13 @@ from ._api import GenotypePolyPredicate from ._api import VariantPredicate from ._counter import AlleleCounter -from ._gt_predicates import boolean_predicate, groups_predicate, filtering_predicate, recessive_predicate +from ._gt_predicates import boolean_predicate, groups_predicate, filtering_predicate from ._gt_predicates import ModeOfInheritancePredicate from ._variant import VariantPredicates, ProteinPredicates __all__ = [ 'GenotypePolyPredicate', - 'boolean_predicate', 'groups_predicate', 'filtering_predicate', 'recessive_predicate', + 'boolean_predicate', 'groups_predicate', 'filtering_predicate', 'ModeOfInheritancePredicate', 'AlleleCounter', 'VariantPredicate', 'VariantPredicates', 'ProteinPredicates', diff --git a/src/gpsea/analysis/predicate/genotype/_api.py b/src/gpsea/analysis/predicate/genotype/_api.py index 39cf020b..07d6fa07 100644 --- a/src/gpsea/analysis/predicate/genotype/_api.py +++ b/src/gpsea/analysis/predicate/genotype/_api.py @@ -2,7 +2,7 @@ import typing from gpsea.model import Variant -from .._api import PolyPredicate, Categorization, PatientCategory +from .._api import PolyPredicate, Categorization class GenotypePolyPredicate(PolyPredicate[Categorization], metaclass=abc.ABCMeta): @@ -13,15 +13,6 @@ class GenotypePolyPredicate(PolyPredicate[Categorization], metaclass=abc.ABCMeta pass -class RecessiveGroupingPredicate(GenotypePolyPredicate, metaclass=abc.ABCMeta): - BOTH = Categorization(PatientCategory(0, 'Both', 'The patient belongs in both groups.')) - ONE = Categorization(PatientCategory(1, 'One', 'The patient belongs in one of the two groups.')) - NEITHER = Categorization(PatientCategory(2, 'Neither', 'The patient does not belong in either group.')) - - def get_categorizations(self) -> typing.Sequence[Categorization]: - return RecessiveGroupingPredicate.BOTH, RecessiveGroupingPredicate.ONE, RecessiveGroupingPredicate.NEITHER - - class VariantPredicate(metaclass=abc.ABCMeta): """ `VariantPredicate` tests if a variant meets a certain criterion. diff --git a/src/gpsea/analysis/predicate/genotype/_gt_predicates.py b/src/gpsea/analysis/predicate/genotype/_gt_predicates.py index 43a39594..8693b67b 100644 --- a/src/gpsea/analysis/predicate/genotype/_gt_predicates.py +++ b/src/gpsea/analysis/predicate/genotype/_gt_predicates.py @@ -7,7 +7,7 @@ from gpsea.model import Patient, Sex from .._api import Categorization, PatientCategory, PatientCategories -from ._api import GenotypePolyPredicate, RecessiveGroupingPredicate +from ._api import GenotypePolyPredicate from ._api import VariantPredicate from ._counter import AlleleCounter @@ -277,70 +277,6 @@ def filtering_predicate( ) -class AlleleCountingRecessivePredicate(RecessiveGroupingPredicate): - # NOT PART OF THE PUBLIC API - # TODO: this predicate is a bit weird and I think it should eventually go away. - # Therefore, I do not write any tests at this point. - - def __init__( - self, - allele_counter: AlleleCounter, - ): - self._allele_counter = allele_counter - - def get_question_base(self) -> str: - return self._allele_counter.get_question() - - def test(self, patient: Patient) -> typing.Optional[Categorization]: - self._check_patient(patient) - - allele_count = self._allele_counter.count(patient) - if allele_count == 0: - return RecessiveGroupingPredicate.NEITHER - elif allele_count == 1: - return RecessiveGroupingPredicate.ONE - elif allele_count == 2: - return RecessiveGroupingPredicate.BOTH - else: - return None - - def __eq__(self, value: object) -> bool: - return ( - isinstance(value, AlleleCountingRecessivePredicate) - and self._allele_counter == value._allele_counter - ) - - def __hash__(self) -> int: - return hash((self._allele_counter,)) - - def __str__(self) -> str: - return ( - f"AlleleCountingRecessivePredicate(allele_counter={self._allele_counter})" - ) - - def __repr__(self) -> str: - return str(self) - - -def recessive_predicate( - variant_predicate: VariantPredicate, -) -> GenotypePolyPredicate: - """ - Create a recessive grouping predicate from given `variant_predicate` - to bin the patient into :class:`RecessiveGroupingPredicate.NEITHER`, - :class:`RecessiveGroupingPredicate.ONE`, or :class:`RecessiveGroupingPredicate.BOTH`, - depending on the number of variant alleles matching the variant predicate. - - The patient is assigned into a group in the following manner: - * 0 alleles: :class:`RecessiveGroupingPredicate.NEITHER` - * 1 alleles: :class:`RecessiveGroupingPredicate.ONE` - * 2 alleles: :class:`RecessiveGroupingPredicate.BOTH` - * other: `None` - """ - allele_counter = AlleleCounter(predicate=variant_predicate) - return AlleleCountingRecessivePredicate(allele_counter=allele_counter) - - @dataclasses.dataclass(eq=True, frozen=True) class GenotypeGroup: allele_count: int diff --git a/tests/analysis/test_pscore.py b/tests/analysis/pscore/test_counting_scorer.py similarity index 100% rename from tests/analysis/test_pscore.py rename to tests/analysis/pscore/test_counting_scorer.py diff --git a/tests/analysis/test_config.py b/tests/analysis/test_config.py deleted file mode 100644 index afff55e1..00000000 --- a/tests/analysis/test_config.py +++ /dev/null @@ -1,52 +0,0 @@ -import pytest - -from gpsea.analysis import CohortAnalysisConfiguration, MtcStrategy - - -class TestCohortAnalysisConfiguration: - - def test_default_values(self): - config = CohortAnalysisConfiguration() - - assert config.pval_correction == 'bonferroni' - assert config.min_patients_w_hpo is None - assert config.include_sv is False - assert config.mtc_alpha == pytest.approx(.05) - assert config.mtc_strategy == MtcStrategy.ALL_PHENOTYPE_TERMS - assert config.terms_to_test is None - - def test_set_all_terms_strategy(self): - config = CohortAnalysisConfiguration() - assert config.mtc_strategy == MtcStrategy.ALL_PHENOTYPE_TERMS - - config.specify_terms_strategy(('HP:0001250', 'HP:0001166')) - - assert config.mtc_strategy == MtcStrategy.SPECIFY_TERMS - assert config.terms_to_test == ('HP:0001250', 'HP:0001166') - - def test_set_hpo_mtc_strategy(self): - config = CohortAnalysisConfiguration() - assert config.mtc_strategy == MtcStrategy.ALL_PHENOTYPE_TERMS - - config.hpo_mtc_strategy() - - assert config.mtc_strategy == MtcStrategy.HPO_MTC - assert config.min_patients_w_hpo == pytest.approx(0.2) - - @pytest.mark.parametrize( - 'value', - [ - -.0, - 1.01, - ] - ) - def test_cannot_set_invalid_threshold_in_hpo_mtc_strategy( - self, - value: float, - ): - config = CohortAnalysisConfiguration() - - with pytest.raises(ValueError) as e: - config.hpo_mtc_strategy(value) - - assert e.value.args[0] == f'`min_patients_w_hpo` must be in range (0, 1] but was {value}' diff --git a/tests/analysis/test_examples.py b/tests/analysis/test_examples.py deleted file mode 100644 index 8f17161d..00000000 --- a/tests/analysis/test_examples.py +++ /dev/null @@ -1,137 +0,0 @@ -import typing - -import hpotk -import pytest - -import pandas as pd - -from gpsea.analysis import configure_cohort_analysis, GenotypePhenotypeAnalysisResult -from gpsea.analysis.predicate import PatientCategories -from gpsea.analysis.predicate.genotype import VariantPredicates, boolean_predicate -from gpsea.model import Cohort, VariantEffect - - -# TODO: remove at some point! -@pytest.mark.skip('Obsolete tests') -class TestCohortAnalysis: - - def test_compare_by_variant_effect( - self, - suox_cohort: Cohort, - hpo: hpotk.MinimalOntology, - ): - pd.set_option('expand_frame_repr', False) - cohort_analysis = configure_cohort_analysis(suox_cohort, hpo) - is_missense = VariantPredicates.variant_effect(VariantEffect.MISSENSE_VARIANT, tx_id='NM_001032386.2') - results = cohort_analysis.compare_hpo_vs_genotype(is_missense) - print(results) - summary = results.summarize(hpo, PatientCategories.YES) - print(summary) - - def test_get_count( - self, - suox_cohort: Cohort, - hpo: hpotk.MinimalOntology, - ): - """ - This test shows how to manipulate the results object to get the counts we need - Let's use Arachnodactyly [HP:0001166] Yes (Genotype NO 1/10); No (Genotype YES 13/16) as an example - that is - Arachnodactyly (+) MISSENSE (-) = 1 - Arachnodactyly (-) MISSENSE (-) = 9 - Arachnodactyly (+) MISSENSE (+) = 13 - Arachnodactyly (-) MISSENSE (+) = 3 - In the Toy file, we have - Arachnodactyly TRUE, MISSENSE (snv) TRUE: A,B,D,E;G;J;M;P;Q;R;T;V;Y = 13 - Arachnodactyly FALSE, MISSENSE (snv) TRUE: C,K,N=3 - Arachnodactyly TRUE, MISSENSE (snv) FALSE: H = 1 - Arachnodactyly FALSE, MISSENSE (snv) FALSE: F,I,L,O,S;U,W,X,Z=9 - This is the output - MISSENSE_VARIANT on NM_1234.5 No Yes - Count Percent Count Percent p value Corrected p value - Arachnodactyly [HP:0001166] 1/10 10.0% 13/16 81.25% 0.000781 0.020299 - """ - pd.set_option('expand_frame_repr', False) - cohort_analysis = configure_cohort_analysis(suox_cohort, hpo) - is_missense = VariantPredicates.variant_effect(VariantEffect.MISSENSE_VARIANT, tx_id='NM_001032386.2') - results = cohort_analysis.compare_hpo_vs_genotype(is_missense) - - # Let's make sure we know what class we have - assert isinstance(results, GenotypePhenotypeAnalysisResult) - - # We expect that we have YES and NO as phenotype categories - phenotype_categories_tuple = results.phenotype_categories - assert len(phenotype_categories_tuple) == 2 - assert PatientCategories.YES in phenotype_categories_tuple - assert PatientCategories.NO in phenotype_categories_tuple - - # The all_counts field has the counts of the phenotypes - all_counts = results.all_counts - assert isinstance(all_counts, typing.Mapping) - - # We tested 66 HPO terms - assert len(all_counts) == 66 - - # The index of all_counts is a Tuple with (HPO TermId, BooleanPredicate - # Let's test Seizure - we should have one row for each Patient Predicate - counts = all_counts[hpotk.TermId.from_curie("HP:0001250")] - - # The YES row is Seizure YES -- according to the above, we have 11 (MISSENSE NO) and 17 (MISSENSE YES) - assert counts.loc[PatientCategories.YES, PatientCategories.NO] == 11 - assert counts.loc[PatientCategories.YES, PatientCategories.YES] == 17 - - # The NO row is Seizure NO -- according to the above, we have 0 (MISSENSE NO) and 7 (MISSENSE YES) - assert counts.loc[PatientCategories.NO, PatientCategories.NO] == 0 - assert counts.loc[PatientCategories.NO, PatientCategories.YES] == 7 - - # In total, 35 patients were categorized - assert counts.sum().sum() == 35 - - def test_get_positive_count( - self, - suox_cohort: Cohort, - hpo: hpotk.MinimalOntology, - ): - """ - This test shows how to get the counts for positive HPO terms - Let's use Seizure [HP:0001250] Yes (Genotype NO 11/11); No (Genotype YES 17/24) as an example - that is - Seizure (+) MISSENSE (-) = 11 - Seizure (-) MISSENSE (-) = 0 - Seizure (+) MISSENSE (+) = 17 - Seizure (-) MISSENSE (+) = 7 - This means we expect 11+17=28 - See the previous test for further information - """ - pd.set_option('expand_frame_repr', False) - cohort_analysis = configure_cohort_analysis(suox_cohort, hpo) - is_missense = VariantPredicates.variant_effect(VariantEffect.MISSENSE_VARIANT, tx_id='NM_001032386.2') - results = cohort_analysis.compare_hpo_vs_genotype(is_missense) - all_counts = results.all_counts - # The index of all_counts is a Tuple with (HPO TermId, BooleanPredicate - # Let's test Arachnodactyly - we should have one row for each Patient Predicate - counts = all_counts[hpotk.TermId.from_curie("HP:0001250")] - # total_observed_HPO = HeuristicSamplerMtcFilter.get_number_of_positive_observations(arachnodactyly_counts) - total_observed_HPO = counts.loc[PatientCategories.YES, PatientCategories.NO] + counts.loc[PatientCategories.YES, PatientCategories.YES] - assert total_observed_HPO == 28 - - def test_compare_symptom_count_vs_genotype( - self, - suox_cohort: Cohort, - hpo: hpotk.MinimalOntology, - ): - cohort_analysis = configure_cohort_analysis(suox_cohort, hpo) - - phenotype_group_terms = ( - 'HP:0012638', # Abnormal nervous system physiology - 'HP:0001939', # Abnormality of metabolism/homeostasis - ) - variant_predicate = VariantPredicates.variant_effect(VariantEffect.MISSENSE_VARIANT, 'NM_001032386.2') - gt_predicate = boolean_predicate(variant_predicate) - - phenotype_group_results = cohort_analysis.compare_genotype_vs_phenotype_group_count( - gt_predicate=gt_predicate, - phenotype_group_terms=phenotype_group_terms, - ) - - assert phenotype_group_results.p_value == pytest.approx(0.9345982107594922) From e9ff7d45689895719b821abcddb1eddd8f8a352c Mon Sep 17 00:00:00 2001 From: Daniel Danis Date: Wed, 4 Sep 2024 13:46:18 +0200 Subject: [PATCH 43/47] Update `README.md`. --- README.md | 43 ++++--------------------------------------- 1 file changed, 4 insertions(+), 39 deletions(-) diff --git a/README.md b/README.md index 9ceab180..2a498eb4 100644 --- a/README.md +++ b/README.md @@ -5,46 +5,11 @@ GPSEA is a Python library for discovery of genotype-phenotype associations. -An example of simple genotype-phenotype association analysis +See the [Tutorial](https://monarch-initiative.github.io/gpsea/stable/tutorial.html) +and a comprehensive [User guide](https://monarch-initiative.github.io/gpsea/stable/user-guide/index.html) +for more information. -```python -# Load HPO -import hpotk - -store = hpotk.configure_ontology_store() -hpo = store.load_minimal_hpo() - -# Load a cohort of phenopackets -from gpsea.data import get_toy_cohort - -cohort = get_toy_cohort() - -# Analyze genotype-phenotype associations -from gpsea.analysis import configure_cohort_analysis -from gpsea.analysis.predicate import PatientCategories - -from gpsea.model import VariantEffect - -cohort_analysis = configure_cohort_analysis(cohort, hpo) -frameshift = cohort_analysis.compare_by_variant_effect(VariantEffect.FRAMESHIFT_VARIANT, tx_id='NM_1234.5') - -frameshift.summarize(hpo, category=PatientCategories.YES) -``` - -provides a pandas data frame with genotype-phenotype correlations: - -```text -FRAMESHIFT_VARIANT on NM_1234.5 No Yes - Count Percent Count Percent p value Corrected p value - Arachnodactyly [HP:0001166] 1/10 10% 13/16 81% 0.000781 0.020299 - Abnormality of the musculature [HP:0003011] 6/6 100% 11/11 100% 1.000000 1.000000 - Abnormal nervous system physiology [HP:0012638] 9/9 100% 15/15 100% 1.000000 1.000000 - ... ... ... ... ... ... ... -``` - -## Documentation - -Check out the User guide and the API reference for more info: +The documentation comes in two flavors: - [Stable documentation](https://monarch-initiative.github.io/gpsea/stable/) (last release on `main` branch) - [Latest documentation](https://monarch-initiative.github.io/gpsea/latest) (bleeding edge, latest commit on `develop` branch) From 53653e5d813197b19ecd0c2b2a8bf9f5a925daab Mon Sep 17 00:00:00 2001 From: Daniel Danis Date: Wed, 4 Sep 2024 14:13:19 +0200 Subject: [PATCH 44/47] Allow using `Sex` as a genotype predicate. --- .../analysis/predicate/genotype/__init__.py | 4 +- .../predicate/genotype/_gt_predicates.py | 46 ++++++++++++++++++- src/gpsea/model/_base.py | 2 +- .../predicate/genotype/test_gt_predicates.py | 34 +++++++++++++- 4 files changed, 81 insertions(+), 5 deletions(-) diff --git a/src/gpsea/analysis/predicate/genotype/__init__.py b/src/gpsea/analysis/predicate/genotype/__init__.py index 82d2819d..069cb9f6 100644 --- a/src/gpsea/analysis/predicate/genotype/__init__.py +++ b/src/gpsea/analysis/predicate/genotype/__init__.py @@ -1,13 +1,13 @@ from ._api import GenotypePolyPredicate from ._api import VariantPredicate from ._counter import AlleleCounter -from ._gt_predicates import boolean_predicate, groups_predicate, filtering_predicate +from ._gt_predicates import boolean_predicate, groups_predicate, filtering_predicate, sex_predicate from ._gt_predicates import ModeOfInheritancePredicate from ._variant import VariantPredicates, ProteinPredicates __all__ = [ 'GenotypePolyPredicate', - 'boolean_predicate', 'groups_predicate', 'filtering_predicate', + 'boolean_predicate', 'groups_predicate', 'filtering_predicate', 'sex_predicate', 'ModeOfInheritancePredicate', 'AlleleCounter', 'VariantPredicate', 'VariantPredicates', 'ProteinPredicates', diff --git a/src/gpsea/analysis/predicate/genotype/_gt_predicates.py b/src/gpsea/analysis/predicate/genotype/_gt_predicates.py index 8693b67b..66a839ea 100644 --- a/src/gpsea/analysis/predicate/genotype/_gt_predicates.py +++ b/src/gpsea/analysis/predicate/genotype/_gt_predicates.py @@ -71,7 +71,6 @@ def __repr__(self) -> str: return str(self) -# TODO: write AD, AR, XLR, XLD def boolean_predicate(variant_predicate: VariantPredicate) -> GenotypePolyPredicate: """ Create a genotype boolean predicate from given `variant_predicate` @@ -657,3 +656,48 @@ def __str__(self) -> str: def __repr__(self) -> str: return str(self) + + +class SexGenotypePredicate(GenotypePolyPredicate): + # NOT PART OF THE PUBLIC API + + def __init__(self): + self._categorizations = ( + Categorization( + PatientCategory( + cat_id=0, name="FEMALE", description="Female", + ), + ), + Categorization( + PatientCategory( + cat_id=1, name="MALE", description="Male", + ), + ) + ) + + def get_categorizations(self) -> typing.Sequence[Categorization]: + return self._categorizations + + def get_question_base(self) -> str: + return "Sex of the individual" + + def test(self, patient: Patient) -> typing.Optional[Categorization]: + if patient.sex.is_provided(): + if patient.sex.is_female(): + return self._categorizations[0] + elif patient.sex.is_male(): + return self._categorizations[1] + else: + raise ValueError(f'Unsupported sex {patient.sex}') + else: + return None + + +INSTANCE = SexGenotypePredicate() + + +def sex_predicate() -> GenotypePolyPredicate: + """ + Get a genotype predicate for categorizing patients by their :class:`~gpsea.model.Sex`. + """ + return INSTANCE diff --git a/src/gpsea/model/_base.py b/src/gpsea/model/_base.py index 09a1c913..53b51802 100644 --- a/src/gpsea/model/_base.py +++ b/src/gpsea/model/_base.py @@ -42,7 +42,7 @@ def is_female(self) -> bool: """ Return `True` if the sex represents a `FEMALE`. """ - return self == Sex.MALE + return self == Sex.FEMALE def is_male(self) -> bool: """ diff --git a/tests/analysis/predicate/genotype/test_gt_predicates.py b/tests/analysis/predicate/genotype/test_gt_predicates.py index 67bc2d11..61ff29c3 100644 --- a/tests/analysis/predicate/genotype/test_gt_predicates.py +++ b/tests/analysis/predicate/genotype/test_gt_predicates.py @@ -7,6 +7,7 @@ GenotypePolyPredicate, groups_predicate, filtering_predicate, + sex_predicate, VariantPredicates, VariantPredicate, ModeOfInheritancePredicate, @@ -185,7 +186,7 @@ def test_x_recessive( assert categorization.category.name == name -class TestPolyPredicate: +class TestFilteringPredicate: @pytest.fixture(scope="class") def x_recessive_gt_predicate(self) -> GenotypePolyPredicate: @@ -263,3 +264,34 @@ def test_filtering_predicate__explodes_when_using_one_category( ve.value.args[0] == "At least 2 target categorizations must be provided but got 1" ) + + +class TestSexPredicate: + + def test_sex_predicate( + self, + ): + joe = TestSexPredicate.make_patient('Joe', Sex.MALE) + jane = TestSexPredicate.make_patient('Jane', Sex.FEMALE) + miffy = TestSexPredicate.make_patient('Miffy', Sex.UNKNOWN_SEX) + + gt_predicate = sex_predicate() + female, male = gt_predicate.get_categorizations() + + assert gt_predicate.test(joe) == male + assert gt_predicate.test(jane) == female + assert gt_predicate.test(miffy) is None + + def test_get_question(self): + gt_predicate = sex_predicate() + assert gt_predicate.display_question() == 'Sex of the individual: FEMALE, MALE' + + @staticmethod + def make_patient(label: str, sex: Sex) -> Patient: + return Patient( + SampleLabels(label), + sex, + phenotypes=(), + diseases=(), + variants=(), + ) From c61b087254e1e4721507ad9f3c8f28206c6171d2 Mon Sep 17 00:00:00 2001 From: Daniel Danis Date: Wed, 4 Sep 2024 14:53:14 +0200 Subject: [PATCH 45/47] Allow using disease diagnosis as a genotype predicate. --- docs/user-guide/predicates.rst | 39 ++++ .../analysis/predicate/genotype/__init__.py | 4 +- .../predicate/genotype/_gt_predicates.py | 199 +++++++++++++++--- 3 files changed, 205 insertions(+), 37 deletions(-) diff --git a/docs/user-guide/predicates.rst b/docs/user-guide/predicates.rst index b224516f..785c3ef0 100644 --- a/docs/user-guide/predicates.rst +++ b/docs/user-guide/predicates.rst @@ -399,6 +399,45 @@ for testing if the individual has at least one missense vs. frameshift vs. synon 'Genotype group: Missense, Frameshift, Synonymous' +.. _sex-predicate: + +Partition by the sex of the individual +====================================== + +It is easy to investigate the phenotypic differences between females and males. +The :func:`~gpsea.analysis.predicate.genotype.sex_predicate` provides a predicate +for partitioning based on the sex of the individual: + +>>> from gpsea.analysis.predicate.genotype import sex_predicate +>>> gt_predicate = sex_predicate() +>>> gt_predicate.display_question() +'Sex of the individual: FEMALE, MALE' + +The individuals with :class:`~gpsea.model.Sex.UNKNOWN_SEX` will be omitted from the analysis. + + +.. _diagnosis-predicate: + +Partition by a diagnosis +======================== + +It is also possible to bin the individuals based on a diagnosis. +The :func:`~gpsea.analysis.predicate.genotype.diagnosis_predicate` +prepares a genotype predicate for assigning an individual into a diagnosis group: + +>>> from gpsea.analysis.predicate.genotype import diagnosis_predicate +>>> gt_predicate = diagnosis_predicate( +... diagnoses=('OMIM:154700', 'OMIM:129600'), +... labels=('Marfan syndrome', 'Ectopia lentis, familial'), +... ) +>>> gt_predicate.display_question() +'What disease was diagnosed: OMIM:154700, OMIM:129600' + +Note, an individual must match only one diagnosis group. Any individuals labeled with two or more diagnoses +(e.g. an individual with both *Marfan syndrome* and *Ectopia lentis, familial*) +will be automatically omitted from the analysis. + + .. _phenotype-predicates: ******************** diff --git a/src/gpsea/analysis/predicate/genotype/__init__.py b/src/gpsea/analysis/predicate/genotype/__init__.py index 069cb9f6..23ad8cfb 100644 --- a/src/gpsea/analysis/predicate/genotype/__init__.py +++ b/src/gpsea/analysis/predicate/genotype/__init__.py @@ -1,13 +1,13 @@ from ._api import GenotypePolyPredicate from ._api import VariantPredicate from ._counter import AlleleCounter -from ._gt_predicates import boolean_predicate, groups_predicate, filtering_predicate, sex_predicate +from ._gt_predicates import boolean_predicate, groups_predicate, filtering_predicate, sex_predicate, diagnosis_predicate from ._gt_predicates import ModeOfInheritancePredicate from ._variant import VariantPredicates, ProteinPredicates __all__ = [ 'GenotypePolyPredicate', - 'boolean_predicate', 'groups_predicate', 'filtering_predicate', 'sex_predicate', + 'boolean_predicate', 'groups_predicate', 'filtering_predicate', 'sex_predicate', 'diagnosis_predicate', 'ModeOfInheritancePredicate', 'AlleleCounter', 'VariantPredicate', 'VariantPredicates', 'ProteinPredicates', diff --git a/src/gpsea/analysis/predicate/genotype/_gt_predicates.py b/src/gpsea/analysis/predicate/genotype/_gt_predicates.py index 66a839ea..3a930127 100644 --- a/src/gpsea/analysis/predicate/genotype/_gt_predicates.py +++ b/src/gpsea/analysis/predicate/genotype/_gt_predicates.py @@ -1,9 +1,12 @@ +from cProfile import label import dataclasses import enum import typing from collections import defaultdict +import hpotk + from gpsea.model import Patient, Sex from .._api import Categorization, PatientCategory, PatientCategories @@ -193,31 +196,35 @@ def create( ) -> "FilteringGenotypePolyPredicate": # At least 2 target categorizations must be provided if len(targets) <= 1: - raise ValueError(f'At least 2 target categorizations must be provided but got {len(targets)}') + raise ValueError( + f"At least 2 target categorizations must be provided but got {len(targets)}" + ) good_boys = tuple(isinstance(cat, Categorization) for cat in targets) if not all(good_boys): - offenders = ', '.join( - str(i) - for i, is_instance - in enumerate(good_boys) if not is_instance + offenders = ", ".join( + str(i) for i, is_instance in enumerate(good_boys) if not is_instance + ) + raise ValueError( + f"The targets at following indices are not categorizations: [{offenders}]" ) - raise ValueError(f'The targets at following indices are not categorizations: [{offenders}]') # All `allowed` categorizations must in fact be present in the `base` predicate. - cats_are_in_fact_present = tuple(cat in predicate.get_categorizations() for cat in targets) + cats_are_in_fact_present = tuple( + cat in predicate.get_categorizations() for cat in targets + ) if not all(cats_are_in_fact_present): - missing = ', '.join( + missing = ", ".join( c.category.name - for c, is_present - in zip(targets, cats_are_in_fact_present) if not is_present + for c, is_present in zip(targets, cats_are_in_fact_present) + if not is_present ) - raise ValueError(f'Some from the categories are not present: {missing}') - + raise ValueError(f"Some from the categories are not present: {missing}") + if len(targets) == predicate.n_categorizations(): raise ValueError( - f'It makes no sense to subset the a predicate with {predicate.n_categorizations()} categorizations ' - f'with the same number ({len(targets)}) of targets' + f"It makes no sense to subset the a predicate with {predicate.n_categorizations()} categorizations " + f"with the same number ({len(targets)}) of targets" ) return FilteringGenotypePolyPredicate( @@ -232,7 +239,7 @@ def __init__( ): self._predicate = predicate self._allowed = tuple(allowed) - + def get_categorizations(self) -> typing.Sequence[Categorization]: return self._allowed @@ -249,7 +256,7 @@ def test(self, patient: Patient) -> typing.Optional[Categorization]: def __repr__(self): return f"FilteringGenotypePolyPredicate(predicate={self._predicate}, allowed={self._allowed})" - + def filtering_predicate( predicate: GenotypePolyPredicate, targets: typing.Collection[Categorization], @@ -288,12 +295,12 @@ class MendelianInheritanceAspect(enum.Enum): """ Related to chromosomes that do *not* determine the sex of an individual. """ - + GONOSOMAL = 1 """ Related to chromosomes that determine the sex of an individual. """ - + MITOCHONDRIAL = 2 """ Related to mitochondrial DNA. @@ -306,23 +313,30 @@ class ModeOfInheritanceInfo: HOM_REF = Categorization( PatientCategory( - cat_id=0, name="HOM_REF", description="Homozygous reference", + cat_id=0, + name="HOM_REF", + description="Homozygous reference", ), ) HET = Categorization( PatientCategory( - cat_id=1, name="HET", description="Heterozygous", + cat_id=1, + name="HET", + description="Heterozygous", ), ) BIALLELIC_ALT = Categorization( PatientCategory( - cat_id=2, name="BIALLELIC_ALT", + cat_id=2, + name="BIALLELIC_ALT", description="Homozygous alternate or compound heterozygous", ), ) HEMI = Categorization( PatientCategory( - cat_id=3, name="HEMI", description="Hemizygous", + cat_id=3, + name="HEMI", + description="Hemizygous", ), ) @@ -438,7 +452,7 @@ def __init__( assert isinstance(group, GenotypeGroup) self._groups[group.allele_count].append(group) hash_value += 13 * hash(group) - + self._hash = hash_value @property @@ -507,7 +521,7 @@ def autosomal_dominant( variant_predicate=variant_predicate, mode_of_inheritance_data=ModeOfInheritanceInfo.autosomal_dominant(), ) - + @staticmethod def autosomal_recessive( variant_predicate: VariantPredicate, @@ -524,7 +538,7 @@ def autosomal_recessive( variant_predicate=variant_predicate, mode_of_inheritance_data=ModeOfInheritanceInfo.autosomal_recessive(), ) - + @staticmethod def x_dominant( variant_predicate: VariantPredicate, @@ -533,7 +547,7 @@ def x_dominant( Create a predicate that assigns the patient either into homozygous reference or heterozygous group in line with the X-linked dominant mode of inheritance. - + :param variant_predicate: a predicate for choosing the variants for testing. """ return ModeOfInheritancePredicate.from_moi_info( @@ -583,11 +597,15 @@ def __init__( assert isinstance(mode_of_inheritance_info, ModeOfInheritanceInfo) self._moi_info = mode_of_inheritance_info - self._categorizations = tuple(group.categorization for group in mode_of_inheritance_info.groups) - issues = ModeOfInheritancePredicate._check_categorizations(self._categorizations) + self._categorizations = tuple( + group.categorization for group in mode_of_inheritance_info.groups + ) + issues = ModeOfInheritancePredicate._check_categorizations( + self._categorizations + ) if issues: - raise ValueError('Cannot create predicate: {}'.format(', '.join(issues))) - self._question = 'Which genotype group does the patient fit in' + raise ValueError("Cannot create predicate: {}".format(", ".join(issues))) + self._question = "Which genotype group does the patient fit in" def get_categorizations(self) -> typing.Sequence[Categorization]: return self._categorizations @@ -645,7 +663,12 @@ def __eq__(self, value: object) -> bool: ) def __hash__(self) -> int: - return hash((self._allele_counter, self._moi_info,)) + return hash( + ( + self._allele_counter, + self._moi_info, + ) + ) def __str__(self) -> str: return ( @@ -665,14 +688,18 @@ def __init__(self): self._categorizations = ( Categorization( PatientCategory( - cat_id=0, name="FEMALE", description="Female", + cat_id=0, + name="FEMALE", + description="Female", ), ), Categorization( PatientCategory( - cat_id=1, name="MALE", description="Male", + cat_id=1, + name="MALE", + description="Male", ), - ) + ), ) def get_categorizations(self) -> typing.Sequence[Categorization]: @@ -688,7 +715,7 @@ def test(self, patient: Patient) -> typing.Optional[Categorization]: elif patient.sex.is_male(): return self._categorizations[1] else: - raise ValueError(f'Unsupported sex {patient.sex}') + raise ValueError(f"Unsupported sex {patient.sex}") else: return None @@ -699,5 +726,107 @@ def test(self, patient: Patient) -> typing.Optional[Categorization]: def sex_predicate() -> GenotypePolyPredicate: """ Get a genotype predicate for categorizing patients by their :class:`~gpsea.model.Sex`. + + See the :ref:`sex-predicate` section for an example. """ return INSTANCE + + +class DiagnosisPredicate(GenotypePolyPredicate): + + @staticmethod + def create( + diagnoses: typing.Iterable[typing.Union[str, hpotk.TermId]], + labels: typing.Optional[typing.Iterable[str]] = None, + ) -> "DiagnosisPredicate": + # First, collect the iterables and check sanity. + diagnosis_ids = [] + for d in diagnoses: + if isinstance(d, str): + d = hpotk.TermId.from_curie(d) + elif isinstance(d, hpotk.TermId): + pass + else: + raise ValueError(f"{d} is neither `str` nor `hpotk.TermId`") + + diagnosis_ids.append(d) + + if labels is None: + labels = tuple(d.value for d in diagnosis_ids) + else: + labels = tuple(labels) + + assert (len(diagnosis_ids) >= 2), \ + f"We need at least 2 diagnoses: {len(diagnosis_ids)}" + assert len(diagnosis_ids) == len(labels), \ + f"The number of labels must match the number of diagnose IDs: {len(diagnosis_ids)}!={len(labels)}" + + # Then, prepare the categorizations. + categorizations = { + diagnosis_id: Categorization.from_raw_parts( + cat_id=i, + name=diagnosis_id.value, + description=label, + ) + for i, (diagnosis_id, label) in enumerate(zip(diagnosis_ids, labels)) + } + + # Last, put the predicate together. + return DiagnosisPredicate(categorizations) + + def __init__( + self, + categorizations: typing.Mapping[hpotk.TermId, Categorization], + ): + self._id2cat = dict(categorizations) + self._categorizations = tuple( + sorted(categorizations.values(), key=lambda c: c.category.cat_id) + ) + + def get_categorizations(self) -> typing.Sequence[Categorization]: + return self._categorizations + + def get_question_base(self) -> str: + return 'What disease was diagnosed' + + def test(self, patient: Patient) -> typing.Optional[Categorization]: + categorization = None + for disease in patient.diseases: + try: + candidate = self._id2cat[disease.identifier] + except KeyError: + # No match for this disease, no problem. + continue + + if categorization is None: + # First time we found a candidate disease + categorization = candidate + else: + # Ambiguous match. We found several matching diagnoses! + return None + + return categorization + + +def diagnosis_predicate( + diagnoses: typing.Iterable[typing.Union[str, hpotk.TermId]], + labels: typing.Optional[typing.Iterable[str]] = None, +) -> GenotypePolyPredicate: + """ + Create a genotype predicate that bins the patient based on presence of a disease diagnosis, + as listed in :attr:`~gpsea.model.Patient.diseases` attribute. + + If an individual is diagnosed with more than one disease from the provided `diagnoses`, + the individual will be assigned into no group (`None`). + + See the :ref:`diagnosis-predicate` section for an example. + + :param diagnoses: an iterable with at least 2 diagnose IDs, either as a `str` or a :class:`~hpotk.TermId` + to determine the genotype group. + :param labels: an iterable with diagnose names or `None` if CURIEs should be used instead. + The number of labels must match the number of predicates. + """ + return DiagnosisPredicate.create( + diagnoses=diagnoses, + labels=labels, + ) From 620bd861035608e758d4ce1d81ba27c23c1192d6 Mon Sep 17 00:00:00 2001 From: Daniel Danis Date: Wed, 4 Sep 2024 14:58:02 +0200 Subject: [PATCH 46/47] Update `SUOX` notebook. --- notebooks/SUOX/SUOX.ipynb | 798 +++++++++++++++++++------------------- 1 file changed, 391 insertions(+), 407 deletions(-) diff --git a/notebooks/SUOX/SUOX.ipynb b/notebooks/SUOX/SUOX.ipynb index 29f87378..cc712385 100644 --- a/notebooks/SUOX/SUOX.ipynb +++ b/notebooks/SUOX/SUOX.ipynb @@ -77,7 +77,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Patients Created: 100%|██████████| 35/35 [01:26<00:00, 2.46s/it]\n", + "Patients Created: 100%|██████████| 35/35 [00:00<00:00, 691.09it/s]\n", "Validated under none policy\n", "No errors or warnings were found\n" ] @@ -214,26 +214,26 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -282,6 +282,14 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -297,14 +305,6 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -314,14 +314,6 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", @@ -337,19 +329,27 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -390,13 +390,13 @@ " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -428,7 +428,16 @@ "execution_count": 5, "id": "615010fa", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_36711/3306755006.py:7: DeprecationWarning: Use `configure_default_protein_metadata_service` instead\n", + " pms = configure_protein_metadata_service()\n" + ] + } + ], "source": [ "from gpsea.model.genome import GRCh38\n", "from gpsea.preprocessing import configure_protein_metadata_service, VVMultiCoordinateService\n", @@ -458,7 +467,7 @@ }, { "data": { - "image/png": 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" ] @@ -611,15 +620,223 @@ "id": "8357f23c", "metadata": {}, "source": [ - "## Genotype Phenotype Correlation Analysis analysis" + "## Genotype phenotype correlation analysis" + ] + }, + { + "cell_type": "markdown", + "id": "8684a75c", + "metadata": {}, + "source": [ + "### Genotype predicate" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "9e1f53e4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'Which genotype group does the patient fit in: HOM_REF, HET, BIALLELIC_ALT'" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from gpsea.model import VariantEffect\n", + "from gpsea.analysis.predicate.genotype import VariantPredicates, ModeOfInheritancePredicate\n", + "\n", + "is_missense = VariantPredicates.variant_effect(VariantEffect.MISSENSE_VARIANT, SUOX_transcript_id)\n", + "moi_predicate = ModeOfInheritancePredicate.autosomal_recessive(\n", + " variant_predicate=is_missense,\n", + ")\n", + "moi_predicate.display_question()" + ] + }, + { + "cell_type": "markdown", + "id": "83fb2a82", + "metadata": {}, + "source": [ + "These are the categorizations that can be produced:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "3564694a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(Categorization(category=HOM_REF),\n", + " Categorization(category=HET),\n", + " Categorization(category=BIALLELIC_ALT))" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cats = moi_predicate.get_categorizations()\n", + "cats" + ] + }, + { + "cell_type": "markdown", + "id": "fc32bb65", + "metadata": {}, + "source": [ + "However, we are only interested in comparing `HET` vs `BIALLELIC_ALT`, so we will filter the other category away:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "50f42273", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'Which genotype group does the patient fit in: HET, BIALLELIC_ALT'" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from gpsea.analysis.predicate.genotype import filtering_predicate\n", + "\n", + "cats_of_interest = (cats[1], cats[2])\n", + "gt_predicate = filtering_predicate(\n", + " predicate=moi_predicate,\n", + " targets=cats_of_interest,\n", + ")\n", + "gt_predicate.display_question()" + ] + }, + { + "cell_type": "markdown", + "id": "6ec1d80e", + "metadata": {}, + "source": [ + "### Phenotype predicates" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "21d4e4cd", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "64" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from gpsea.analysis.predicate.phenotype import prepare_predicates_for_terms_of_interest\n", + "\n", + "pheno_predicates = prepare_predicates_for_terms_of_interest(\n", + " cohort=cohort,\n", + " hpo=hpo,\n", + " missing_implies_excluded=False,\n", + " min_n_of_patients_with_term=2,\n", + ")\n", + "len(pheno_predicates)" + ] + }, + { + "cell_type": "markdown", + "id": "0e9a9dd3", + "metadata": {}, + "source": [ + "### MTC phenotype filter" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "ade36715", + "metadata": {}, + "outputs": [], + "source": [ + "from gpsea.analysis.mtc_filter import HpoMtcFilter\n", + "mtc_filter = HpoMtcFilter.default_filter(\n", + " hpo=hpo,\n", + " term_frequency_threshold=0.2,\n", + ")\n", + "mtc_correction = 'fdr_bh'\n", + "mtc_alpha = 0.05" + ] + }, + { + "cell_type": "markdown", + "id": "efe92d03", + "metadata": {}, + "source": [ + "### Count statistic" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "b4c8a4d1", + "metadata": {}, + "outputs": [], + "source": [ + "from gpsea.analysis.pcats.stats import FisherExactTest\n", + "\n", + "count_statistic = FisherExactTest()" + ] + }, + { + "cell_type": "markdown", + "id": "12af4002", + "metadata": {}, + "source": [ + "### Finalize the analysis" ] }, { "cell_type": "code", "execution_count": 14, - "id": "531e077d", + "id": "7a9ad29e", "metadata": {}, "outputs": [], + "source": [ + "from gpsea.analysis.pcats import HpoTermAnalysis\n", + "\n", + "analysis = HpoTermAnalysis(\n", + " count_statistic=count_statistic,\n", + " mtc_filter=mtc_filter,\n", + " mtc_correction=mtc_correction,\n", + " mtc_alpha=mtc_alpha,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "531e077d", + "metadata": {}, "source": [ "from gpsea.analysis import configure_cohort_analysis, CohortAnalysisConfiguration\n", "\n", @@ -649,247 +866,42 @@ { "cell_type": "code", "execution_count": 15, - "id": "f4fb138e", + "id": "4847121e", + "metadata": {}, + "outputs": [], + "source": [ + "result = analysis.compare_genotype_vs_phenotypes(\n", + " cohort=cohort,\n", + " gt_predicate=gt_predicate,\n", + " pheno_predicates=pheno_predicates,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "217afa4e", "metadata": {}, "outputs": [ { "data": { - "text/html": [ - "
\n", - "\n", - "
Using HPO MTC filter, 244 term(s) were omitted from statistical analysis.Using HPO MTC filter, 243 term(s) were omitted from statistical analysis.
Code
TODOSkipping general term44Skipping term with maximum frequency that was less than threshold 0.251
TODOSkipping term because all genotypes have same HPO observed proportions42Skipping general term44
TODOSkipping term with only 0 observations (not powered for 2x2)Skipping term because all genotypes have same HPO observed proportions 41
12
TODOSkipping term with maximum frequency that was less than threshold 0.210
TODO
Abnormality of extrapyramidal motor functionHP:0002071HypertoniaHP:000127611
HypertoniaHP:0001276Abnormality of extrapyramidal motor functionHP:000207111
HypohomocysteinemiaHP:0020222MicrocephalyHP:000025210
MicrocephalyHP:0000252HypohomocysteinemiaHP:002022210
312_56004039_56004039_G_Ac.650G>Ap.Arg217GlnMISSENSE_VARIANT
312_56004485_56004485_C_Tc.1096C>Tp.Arg366Cys
312_56004039_56004039_G_Ac.650G>Ap.Arg217GlnMISSENSE_VARIANT
212_56004933_56004933_A_ACAATGTGCAGCCAGACACCGTGGCCCc.1549_1574dup
212_56004771_56004771_A_Tc.1382A>Tp.Asp461ValMISSENSE_VARIANT
212_56004905_56004909_ATTGT_Ac.1521_1524delp.Cys508ArgfsTer109
212_56004771_56004771_A_Tc.1382A>Tp.Asp461ValMISSENSE_VARIANT
112_56004183_56004183_C_Ac.794C>Ap.Ala265Asp12_56004525_56004525_A_Gc.1136A>Gp.Lys379ArgMISSENSE_VARIANT
112_56004669_56004669_C_Ac.1280C>Ap.Ser427TerSTOP_GAINED12_56004120_56004124_CTCTT_Cc.734_737delp.Leu245ProfsTer27FRAMESHIFT_VARIANT
STOP_GAINED10FRAMESHIFT_VARIANT9
FRAMESHIFT_VARIANT9STOP_GAINED10
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MISSENSE_VARIANT on NM_001032386.2BothOneNeither
CountPercentCountPercentCountPercentp valueCorrected p value
Cognitive regression [HP:0034332]6/1250%0/50%0/80%0.0233431.0
Hypotonia [HP:0001252]10/1191%2/540%3/743%0.0395301.0
Seizure [HP:0001250]12/1867%5/683%11/11100%0.0826401.0
Abnormality of extrapyramidal motor function [HP:0002071]8/1267%1/520%2/825%0.1393771.0
Neurodevelopmental delay [HP:0012758]4/1233%0/50%4/850%0.1518741.0
...........................
Decreased head circumference [HP:0040195]4/4100%2/2100%4/4100%1.0000001.0
Abnormality of the head [HP:0000234]4/4100%2/2100%4/4100%1.0000001.0
Abnormal circulating homocysteine concentration [HP:0010919]7/7100%2/2100%1/1100%1.0000001.0
Abnormal urine metabolite level [HP:0033354]9/9100%3/3100%2/2100%1.0000001.0
Abnormal circulating amino acid concentration [HP:0003112]8/8100%2/2100%5/5100%1.0000001.0
\n", - "

64 rows × 8 columns

\n", - "" - ], "text/plain": [ - "MISSENSE_VARIANT on NM_001032386.2 Both One \\\n", - " Count Percent Count \n", - "Cognitive regression [HP:0034332] 6/12 50% 0/5 \n", - "Hypotonia [HP:0001252] 10/11 91% 2/5 \n", - "Seizure [HP:0001250] 12/18 67% 5/6 \n", - "Abnormality of extrapyramidal motor function [H... 8/12 67% 1/5 \n", - "Neurodevelopmental delay [HP:0012758] 4/12 33% 0/5 \n", - "... ... ... ... \n", - "Decreased head circumference [HP:0040195] 4/4 100% 2/2 \n", - "Abnormality of the head [HP:0000234] 4/4 100% 2/2 \n", - "Abnormal circulating homocysteine concentration... 7/7 100% 2/2 \n", - "Abnormal urine metabolite level [HP:0033354] 9/9 100% 3/3 \n", - "Abnormal circulating amino acid concentration [... 8/8 100% 2/2 \n", - "\n", - "MISSENSE_VARIANT on NM_001032386.2 Neither \\\n", - " Percent Count Percent \n", - "Cognitive regression [HP:0034332] 0% 0/8 0% \n", - "Hypotonia [HP:0001252] 40% 3/7 43% \n", - "Seizure [HP:0001250] 83% 11/11 100% \n", - "Abnormality of extrapyramidal motor function [H... 20% 2/8 25% \n", - "Neurodevelopmental delay [HP:0012758] 0% 4/8 50% \n", - "... ... ... ... \n", - "Decreased head circumference [HP:0040195] 100% 4/4 100% \n", - "Abnormality of the head [HP:0000234] 100% 4/4 100% \n", - "Abnormal circulating homocysteine concentration... 100% 1/1 100% \n", - "Abnormal urine metabolite level [HP:0033354] 100% 2/2 100% \n", - "Abnormal circulating amino acid concentration [... 100% 5/5 100% \n", - "\n", - "MISSENSE_VARIANT on NM_001032386.2 \n", - " p value Corrected p value \n", - "Cognitive regression [HP:0034332] 0.023343 1.0 \n", - "Hypotonia [HP:0001252] 0.039530 1.0 \n", - "Seizure [HP:0001250] 0.082640 1.0 \n", - "Abnormality of extrapyramidal motor function [H... 0.139377 1.0 \n", - "Neurodevelopmental delay [HP:0012758] 0.151874 1.0 \n", - "... ... ... \n", - "Decreased head circumference [HP:0040195] 1.000000 1.0 \n", - "Abnormality of the head [HP:0000234] 1.000000 1.0 \n", - "Abnormal circulating homocysteine concentration... 1.000000 1.0 \n", - "Abnormal urine metabolite level [HP:0033354] 1.000000 1.0 \n", - "Abnormal circulating amino acid concentration [... 1.000000 1.0 \n", - "\n", - "[64 rows x 8 columns]" + "9" ] }, - "execution_count": 15, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "from gpsea.model import VariantEffect\n", - "from gpsea.analysis.predicate import PatientCategories\n", - "from gpsea.analysis.predicate.genotype import VariantPredicates\n", - "\n", - "is_missense = VariantPredicates.variant_effect(VariantEffect.MISSENSE_VARIANT, SUOX_transcript_id)\n", - "missense = analysis.compare_hpo_vs_recessive_genotype(is_missense)\n", - "missense.summarize(hpo, PatientCategories.YES)" - ] - }, - { - "cell_type": "markdown", - "id": "31eea804", - "metadata": {}, - "source": [ - "Test for presence of genotype-phenotype correlations between subjects with >=1 allele of a variant vs. the others." + "result.total_tests" ] }, { "cell_type": "code", - "execution_count": 16, - "id": "743954bd", + "execution_count": 17, + "id": "0327c94f", "metadata": {}, "outputs": [ { @@ -912,10 +924,9 @@ "\n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -924,195 +935,168 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", - " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", - " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "
variant has ID of 12_56004589_56004589_C_GBothOneNeitherWhich genotype group does the patient fit inHETBIALLELIC_ALT
PercentCountPercentCountPercentp valueCorrected p valueCorrected p valuesp values
Hypotonia [HP:0001252]0/10%1/425%14/1878%0.0328691.0
Ectopia lentis [HP:0001083]0/10%2/2100%5/1533%0.1372551.02/540%10/1191%0.3936650.063187
Neurodevelopmental delay [HP:0012758]0/10%Cognitive regression [HP:0034332]0/50%8/1942%0.1757581.06/1250%0.3936650.102295
Abnormality of extrapyramidal motor function [HP:0002071]0/10%1/520%10/1953%0.3405801.08/1267%0.3936650.131222
Cognitive regression [HP:0034332]0/10%Ectopia lentis [HP:0001083]3/475%3/1127%0.4689080.235165
Neurodevelopmental delay [HP:0012758]0/50%6/1932%0.4528511.04/1233%0.4689080.260504
...........................Seizure [HP:0001250]5/683%12/1867%0.9430000.628667
Abnormality of the head [HP:0000234]1/1100%1/1100%8/8100%Hypertonia [HP:0001276]2/540%5/1145%1.0000001.0
Seizure [HP:0001250]2/2100%4/580%22/2879%1.0000001.0
Abnormal circulating homocysteine concentration [HP:0010919]0/00%2/2100%8/8100%Microcephaly [HP:0000252]2/450%4/1040%1.0000001.0000001.0
Abnormal urine metabolite level [HP:0033354]0/0Xanthinuria [HP:0010934]0/20%2/2100%12/12100%2/729%1.0000001.0000001.0
Abnormal circulating amino acid concentration [HP:0003112]1/1100%2/2Abnormal nervous system physiology [HP:0012638]5/5100%12/1217/17100%1.0000001.0NaNNaN
\n", - "

64 rows × 8 columns

\n", "" ], "text/plain": [ - "variant has ID of 12_56004589_56004589_C_G Both One \\\n", - " Count Percent Count \n", - "Hypotonia [HP:0001252] 0/1 0% 1/4 \n", - "Ectopia lentis [HP:0001083] 0/1 0% 2/2 \n", - "Neurodevelopmental delay [HP:0012758] 0/1 0% 0/5 \n", - "Abnormality of extrapyramidal motor function [H... 0/1 0% 1/5 \n", - "Cognitive regression [HP:0034332] 0/1 0% 0/5 \n", - "... ... ... ... \n", - "Abnormality of the head [HP:0000234] 1/1 100% 1/1 \n", - "Seizure [HP:0001250] 2/2 100% 4/5 \n", - "Abnormal circulating homocysteine concentration... 0/0 0% 2/2 \n", - "Abnormal urine metabolite level [HP:0033354] 0/0 0% 2/2 \n", - "Abnormal circulating amino acid concentration [... 1/1 100% 2/2 \n", + "Which genotype group does the patient fit in HET \\\n", + " Count Percent \n", + "Hypotonia [HP:0001252] 2/5 40% \n", + "Cognitive regression [HP:0034332] 0/5 0% \n", + "Abnormality of extrapyramidal motor function [H... 1/5 20% \n", + "Ectopia lentis [HP:0001083] 3/4 75% \n", + "Neurodevelopmental delay [HP:0012758] 0/5 0% \n", + "Seizure [HP:0001250] 5/6 83% \n", + "Hypertonia [HP:0001276] 2/5 40% \n", + "Microcephaly [HP:0000252] 2/4 50% \n", + "Xanthinuria [HP:0010934] 0/2 0% \n", + "Abnormal nervous system physiology [HP:0012638] 5/5 100% \n", "\n", - "variant has ID of 12_56004589_56004589_C_G Neither \\\n", - " Percent Count Percent \n", - "Hypotonia [HP:0001252] 25% 14/18 78% \n", - "Ectopia lentis [HP:0001083] 100% 5/15 33% \n", - "Neurodevelopmental delay [HP:0012758] 0% 8/19 42% \n", - "Abnormality of extrapyramidal motor function [H... 20% 10/19 53% \n", - "Cognitive regression [HP:0034332] 0% 6/19 32% \n", - "... ... ... ... \n", - "Abnormality of the head [HP:0000234] 100% 8/8 100% \n", - "Seizure [HP:0001250] 80% 22/28 79% \n", - "Abnormal circulating homocysteine concentration... 100% 8/8 100% \n", - "Abnormal urine metabolite level [HP:0033354] 100% 12/12 100% \n", - "Abnormal circulating amino acid concentration [... 100% 12/12 100% \n", + "Which genotype group does the patient fit in BIALLELIC_ALT \\\n", + " Count Percent \n", + "Hypotonia [HP:0001252] 10/11 91% \n", + "Cognitive regression [HP:0034332] 6/12 50% \n", + "Abnormality of extrapyramidal motor function [H... 8/12 67% \n", + "Ectopia lentis [HP:0001083] 3/11 27% \n", + "Neurodevelopmental delay [HP:0012758] 4/12 33% \n", + "Seizure [HP:0001250] 12/18 67% \n", + "Hypertonia [HP:0001276] 5/11 45% \n", + "Microcephaly [HP:0000252] 4/10 40% \n", + "Xanthinuria [HP:0010934] 2/7 29% \n", + "Abnormal nervous system physiology [HP:0012638] 17/17 100% \n", "\n", - "variant has ID of 12_56004589_56004589_C_G \n", - " p value Corrected p value \n", - "Hypotonia [HP:0001252] 0.032869 1.0 \n", - "Ectopia lentis [HP:0001083] 0.137255 1.0 \n", - "Neurodevelopmental delay [HP:0012758] 0.175758 1.0 \n", - "Abnormality of extrapyramidal motor function [H... 0.340580 1.0 \n", - "Cognitive regression [HP:0034332] 0.452851 1.0 \n", - "... ... ... \n", - "Abnormality of the head [HP:0000234] 1.000000 1.0 \n", - "Seizure [HP:0001250] 1.000000 1.0 \n", - "Abnormal circulating homocysteine concentration... 1.000000 1.0 \n", - "Abnormal urine metabolite level [HP:0033354] 1.000000 1.0 \n", - "Abnormal circulating amino acid concentration [... 1.000000 1.0 \n", + "Which genotype group does the patient fit in \\\n", + " Corrected p values \n", + "Hypotonia [HP:0001252] 0.393665 \n", + "Cognitive regression [HP:0034332] 0.393665 \n", + "Abnormality of extrapyramidal motor function [H... 0.393665 \n", + "Ectopia lentis [HP:0001083] 0.468908 \n", + "Neurodevelopmental delay [HP:0012758] 0.468908 \n", + "Seizure [HP:0001250] 0.943000 \n", + "Hypertonia [HP:0001276] 1.000000 \n", + "Microcephaly [HP:0000252] 1.000000 \n", + "Xanthinuria [HP:0010934] 1.000000 \n", + "Abnormal nervous system physiology [HP:0012638] NaN \n", "\n", - "[64 rows x 8 columns]" + "Which genotype group does the patient fit in \n", + " p values \n", + "Hypotonia [HP:0001252] 0.063187 \n", + "Cognitive regression [HP:0034332] 0.102295 \n", + "Abnormality of extrapyramidal motor function [H... 0.131222 \n", + "Ectopia lentis [HP:0001083] 0.235165 \n", + "Neurodevelopmental delay [HP:0012758] 0.260504 \n", + "Seizure [HP:0001250] 0.628667 \n", + "Hypertonia [HP:0001276] 1.000000 \n", + "Microcephaly [HP:0000252] 1.000000 \n", + "Xanthinuria [HP:0010934] 1.000000 \n", + "Abnormal nervous system physiology [HP:0012638] NaN " ] }, - "execution_count": 16, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "is_variant = VariantPredicates.variant_key('12_56004589_56004589_C_G')\n", + "from gpsea.analysis.predicate import PatientCategories\n", "\n", - "by_variant = analysis.compare_hpo_vs_recessive_genotype(is_variant)\n", - "by_variant.summarize(hpo, PatientCategories.YES)" + "result.summarize(hpo, PatientCategories.YES).head(10)" ] }, { @@ -1140,7 +1124,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.10" + "version": "3.12.3" } }, "nbformat": 4, From 140a8f07e0e36fc99f73ebd8fd6d826fdc7177d9 Mon Sep 17 00:00:00 2001 From: Daniel Danis Date: Wed, 4 Sep 2024 14:59:19 +0200 Subject: [PATCH 47/47] Make release `0.3.0`. --- docs/conf.py | 4 ++-- src/gpsea/__init__.py | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/docs/conf.py b/docs/conf.py index 7d7c2356..6233764c 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -60,9 +60,9 @@ # built documents. # # The short X.Y version. -version = u'0.1' +version = u'0.3' # The full version, including alpha/beta/rc tags. -release = u'0.1.1dev' +release = u'0.3.0' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. diff --git a/src/gpsea/__init__.py b/src/gpsea/__init__.py index c52b5596..402fc6cc 100644 --- a/src/gpsea/__init__.py +++ b/src/gpsea/__init__.py @@ -2,4 +2,4 @@ GPSEA is a library for analyzing genotype-phenotype correlations in cohorts of rare disease patients. """ -__version__ = "0.2.1.dev0" +__version__ = "0.3.0"