Fix some typos, use HPOPredicate.

README.md

@@ -3,7 +3,7 @@
 ![PyPi downloads](https://img.shields.io/pypi/dm/gpsea.svg?label=Pypi%20downloads)
 ![PyPI - Python Version](https://img.shields.io/pypi/pyversions/gpsea)
 
-GPSEA (Genotypes and Ghenotypes - Statistical Evaluation of Associations, pronounced "G"-"P"-"C") is a Python package designed to support genotype-phenotype correlation analysis.
+GPSEA (Genotypes and Phenotypes - Statistical Evaluation of Associations, pronounced "G"-"P"-"C") is a Python package designed to support genotype-phenotype correlation analysis.
 
 
 See the [Tutorial](https://monarch-initiative.github.io/gpsea/stable/tutorial.html) 

docs/index.rst

@@ -10,7 +10,7 @@ A key question in biology and human genetics concerns the relationships between
 genetics, the focus is generally placed on the study of whether specific disease-causing alleles are associated with specific phenotypic 
 manifestations of the disease. 
 
-`GPSEA`  (Genotypes and Ghenotypes - Statistical Evaluation of Associations, pronounced "G"-"P"-"C") is a Python package designed to support genotype-phenotype correlation analysis.
+`GPSEA`  (Genotypes and Phenotypes - Statistical Evaluation of Associations, pronounced "G"-"P"-"C") is a Python package designed to support genotype-phenotype correlation analysis.
 The input to `GPSEA` is a collection of `Global Alliance for Genomics and Health (GA4GH) Phenopackets <https://pubmed.ncbi.nlm.nih.gov/35705716/>`_.
 `gpsea` ingests data from these phenopackets and performs analysis of the correlation of specific variants,
 variant types (e.g., missense vs. premature termination codon), or variant location in protein motifs or other features.

docs/report/tbx5_frameshift_vs_missense.csv

@@ -1,4 +1,4 @@
-"Genotype group: Missense, Frameshift",Missense,Missense,Frameshift,Frameshift,,
+Genotype group,Missense,Missense,Frameshift,Frameshift,,
 ,Count,Percent,Count,Percent,Corrected p values,p values
 Ventricular septal defect [HP:0001629],31/60,52%,19/19,100%,0.0009552459156234353,5.6190936213143254e-05
 Abnormal atrioventricular conduction [HP:0005150],0/22,0%,3/3,100%,0.003695652173913043,0.00043478260869565214
@@ -13,10 +13,10 @@ Muscular ventricular septal defect [HP:0011623],6/59,10%,6/25,24%,0.286867598598
 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
+Short thumb [HP:0009778],11/41,27%,8/30,27%,1.0,1.0
 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%,,
+Absent radius [HP:0003974],7/32,22%,6/25,24%,1.0,1.0
+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%,,

docs/user-guide/predicates.rst

@@ -25,8 +25,6 @@ use the HPO terms to assign a group.
 All GPSEA analyses need at least one predicate (typically a *genotype* predicate) and many require both *genotype* and *phenotype* predicates.
 The following pages provide more information.
 
-.. _genotype-predicates:
-
 
 
 .. toctree::
@@ -37,35 +35,6 @@ The following pages provide more information.
   predicates/genotype_predicates
 
 
-
-
-.. _groups-predicate:
-
-
-
-.. _phenotype-predicates:
-
-
-
-
-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
 *******

docs/user-guide/predicates/diagnosis_predicate.rst

@@ -1,4 +1,4 @@
-.. _diagnosis_predicate:
+.. _diagnosis-predicate:
 
 ========================
 Partition by a diagnosis

docs/user-guide/predicates/filtering_predicate.rst

@@ -35,7 +35,7 @@ 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()
-'What is the genotype group?: HOM_REF, HET, BIALLELIC_ALT'
+'What is the genotype group: HOM_REF, HET, BIALLELIC_ALT'
 
 We see that the `gt_predicate` bins the patients into three groups:
 
@@ -53,4 +53,4 @@ that includes only the categories of interest:
 ...     targets=(cats[1], cats[2]),
 ... )
 >>> fgt_predicate.display_question()
-'What is the genotype group?: HET, BIALLELIC_ALT'
+'What is the genotype group: HET, BIALLELIC_ALT'

docs/user-guide/predicates/genotype_predicates.rst

@@ -1,4 +1,4 @@
-.. _genotype_predicates:
+.. _genotype-predicates:
 
 ===================
 Genotype Predicates

docs/user-guide/predicates/groups_predicate.rst

@@ -1,4 +1,4 @@
-.. _groups_predicate:
+.. _groups-predicate:
 
 ================
 Groups Predicate
@@ -7,8 +7,8 @@ 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.
+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`

docs/user-guide/predicates/hpo_predicate.rst

@@ -1,10 +1,10 @@
-.. _hpo_predicate:
+.. _hpo-predicate:
 
 
-Propagating phenotype predicate
-===============================
+HPO predicate
+=============
 
-When testing for presence or absence of an HPO term, the propagating phenotype predicate
+When testing for presence or absence of an HPO term, the :class:`~gpsea.analysis.predicate.phenotype.HpoPredicate`
 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 <https://hpo.jax.org/browse/term/HP:0001083>`_
@@ -15,14 +15,19 @@ is also annotated with `Abnormal lens morphology <https://hpo.jax.org/browse/ter
 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`
+Here we show how to set up :class:`~gpsea.analysis.predicate.phenotype.HpoPredicate`
 to test for a presence of `Abnormal lens morphology <https://hpo.jax.org/browse/term/HP:0000517>`_.
 
+We need to load :class:`~hpotk.MinimalOntology` with HPO data to access the HPO hierarchy:
+
+>>> import hpotk
+>>> store = hpotk.configure_ontology_store()
+>>> hpo = store.load_minimal_hpo(release='v2024-07-01')
+
+and now we can set up a predicate to test for presence of *Abnormal lens morphology*:
 
 >>> from gpsea.analysis.predicate.phenotype import HpoPredicate
 >>> query = hpotk.TermId.from_curie('HP:0000517')
@@ -41,4 +46,42 @@ missing_implies_phenotype_excluded
 In many cases, published reports of clinical data about individuals with rare diseases describes phenotypic features that were observed, but do not 
 provide a comprehensive list of features that were explicitly excluded. By default, GPSEA will only include features that are recorded as observed or excluded in a phenopacket.
 Setting this argument to True will cause "n/a" entries to be set to "excluded". We provide this option for exploration but do not recommend its use for the 
-final analysis unless the assumption behind it is known to be true.
+final analysis unless the assumption behind it is known to be true.
+
+
+
+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.
+
+For a given phenopacket collection (e.g. 156 patients with mutations in *WWOX* gene included in Phenopacket Store version `0.1.18`)
+
+>>> from ppktstore.registry import configure_phenopacket_registry
+>>> registry = configure_phenopacket_registry()
+>>> with registry.open_phenopacket_store(release='0.1.18') as ps:
+...     phenopackets = tuple(ps.iter_cohort_phenopackets('TBX5'))
+>>> len(phenopackets)
+156
+
+processed into a cohort
+
+>>> from gpsea.preprocessing import configure_caching_cohort_creator, load_phenopackets
+>>> cohort_creator = configure_caching_cohort_creator(hpo)
+>>> cohort, _ = load_phenopackets(phenopackets, cohort_creator)  # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE
+Patients Created: ...
+
+
+we can create HPO predicates for testing all 260 HPO terms used in the cohort
+
+>>> 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)
+260
+
+and subject the predicates into further analysis, such as :class:`~gpsea.analysis.pcats.HpoTermAnalysis`.

docs/user-guide/predicates/male_female_predicate.rst

@@ -1,4 +1,4 @@
-.. _male_female_predicate:
+.. _male-female-predicate:
 
 Partition by the sex of the individual
 ======================================

docs/user-guide/predicates/mode_of_inheritance_predicate.rst

@@ -1,4 +1,4 @@
-.. _mode_of_inheritance_predicate:
+.. _mode-of-inheritance-predicate:
 
 ==============================
 Mode of Inheritance Predicates
@@ -92,6 +92,6 @@ 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.display_question()
-'What is the genotype group?: HOM_REF, HET, BIALLELIC_ALT'
+'What is the genotype group: HOM_REF, HET, BIALLELIC_ALT'
 
-The `gt_predicate` can be used in downstream analysis, such as in :class:
+The `gt_predicate` can be used in downstream analysis, such as in :class:`~gpsea.analysis.pcats.HpoTermAnalysis`.

docs/user-guide/report/tbx5_frameshift.csv

@@ -1,4 +1,4 @@
-"What is the genotype group?: HOM_REF, HET",HOM_REF,HOM_REF,HET,HET,,
+What is the genotype group,HOM_REF,HOM_REF,HET,HET,,
 ,Count,Percent,Count,Percent,Corrected p values,p values
 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
@@ -13,10 +13,10 @@ Muscular ventricular septal defect [HP:0011623],8/84,10%,6/25,24%,0.144002047919
 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%,,
+Atrial septal defect [HP:0001631],63/65,97%,20/20,100%,1.0,1.0
+Aplasia/Hypoplasia of the thumb [HP:0009601],40/40,100%,19/19,100%,,
+Aplasia/Hypoplasia of fingers [HP:0006265],44/44,100%,19/19,100%,,
+Aplasia/hypoplasia involving bones of the hand [HP:0005927],44/44,100%,19/19,100%,,

docs/user-guide/report/tbx5_frameshift.mtc_report.html

@@ -103,14 +103,14 @@ <h1>Phenotype testing report</h1>
           <tr>
             <!-- TODO: plug the real reason code here -->
             <td>TODO</td>
-            <td>Skipping term with only 5 observations (not powered for 2x2)</td>
+            <td>Skipping term with maximum frequency that was less than threshold 0.2</td>
             <td>10</td>
           </tr>
 
           <tr>
             <!-- TODO: plug the real reason code here -->
             <td>TODO</td>
-            <td>Skipping term with maximum frequency that was less than threshold 0.2</td>
+            <td>Skipping term with only 5 observations (not powered for 2x2)</td>
             <td>10</td>
           </tr>
 

docs/user-guide/stats.rst

@@ -139,7 +139,7 @@ we expect the autosomal dominant mode of inheritance:
 >>> from gpsea.analysis.predicate.genotype import ModeOfInheritancePredicate
 >>> gt_predicate = ModeOfInheritancePredicate.autosomal_dominant(is_frameshift)
 >>> gt_predicate.display_question()
-'What is the genotype group?: HOM_REF, HET'
+'What is the genotype group: 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.

src/gpsea/analysis/predicate/genotype/_gt_predicates.py

@@ -1,4 +1,3 @@
-from cProfile import label
 import dataclasses
 import enum
 import typing
@@ -605,7 +604,7 @@ def __init__(
         )
         if issues:
             raise ValueError("Cannot create predicate: {}".format(", ".join(issues)))
-        self._question = "What is the genotype group?"
+        self._question = "What is the genotype group"
 
     def get_categorizations(self) -> typing.Sequence[Categorization]:
         return self._categorizations
@@ -727,7 +726,7 @@ 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.
+    See the :ref:`male-female-predicate` section for an example.
     """
     return INSTANCE
 

src/gpsea/analysis/predicate/phenotype/__init__.py

@@ -6,13 +6,13 @@
 or using the phenotype features encoded into HPO terms (:class:`PropagatingPhenotypePredicate`).
 """
 
-from ._pheno import PhenotypePolyPredicate, PropagatingPhenotypePredicate
+from ._pheno import PhenotypePolyPredicate, HpoPredicate
 from ._pheno import DiseasePresencePredicate
 from ._pheno import PhenotypeCategorization, P
 from ._util import prepare_predicates_for_terms_of_interest, prepare_hpo_terms_of_interest
 
 __all__ = [
-    'PhenotypePolyPredicate', 'PropagatingPhenotypePredicate',
+    'PhenotypePolyPredicate', 'HpoPredicate',
     'DiseasePresencePredicate',
     'PhenotypeCategorization', 'P',
     'prepare_predicates_for_terms_of_interest', 'prepare_hpo_terms_of_interest',

src/gpsea/analysis/predicate/phenotype/_pheno.py

@@ -92,13 +92,15 @@ def present_phenotype_category(self) -> PatientCategory:
         return self.present_phenotype_categorization.category
 
 
-class PropagatingPhenotypePredicate(PhenotypePolyPredicate[hpotk.TermId]):
+class HpoPredicate(PhenotypePolyPredicate[hpotk.TermId]):
     """
-    `PropagatingPhenotypePredicate` tests if a patient is annotated with an HPO term.
+    `HpoPredicate` tests if a patient is annotated with an HPO term.
 
     Note, `query` must be a term of the provided `hpo`!
 
-    :param hpo: HPO object
+    See :ref:`hpo-predicate` section for an example usage.
+
+    :param hpo: HPO ontology
     :param query: the HPO term to test
     :param missing_implies_phenotype_excluded: `True` if lack of an explicit annotation implies term's absence`.
     """

src/gpsea/analysis/predicate/phenotype/_util.py

@@ -4,7 +4,7 @@
 
 import hpotk
 
-from ._pheno import PhenotypePolyPredicate, PropagatingPhenotypePredicate
+from ._pheno import PhenotypePolyPredicate, HpoPredicate
 
 from gpsea.model import Patient
 
@@ -26,7 +26,7 @@ def prepare_predicates_for_terms_of_interest(
         (either directly or indirectly) for the term to be included in the analysis.
     """
     return tuple(
-        PropagatingPhenotypePredicate(
+        HpoPredicate(
             hpo=hpo,
             query=term,
             missing_implies_phenotype_excluded=missing_implies_excluded,

tests/analysis/test_mtc_filter.py

@@ -8,7 +8,7 @@
 
 from gpsea.analysis.mtc_filter import HpoMtcFilter, SpecifiedTermsMtcFilter
 from gpsea.analysis.predicate.genotype import GenotypePolyPredicate
-from gpsea.analysis.predicate.phenotype import PhenotypePolyPredicate, PropagatingPhenotypePredicate
+from gpsea.analysis.predicate.phenotype import PhenotypePolyPredicate, HpoPredicate
 from gpsea.analysis.pcats import apply_predicates_on_patients
 from gpsea.model import Cohort
 
@@ -55,7 +55,7 @@ def ph_predicate(
         For the purpose of testing counts, let's pretend the counts
         were created by this predicate.
         """
-        return PropagatingPhenotypePredicate(
+        return HpoPredicate(
             hpo=hpo,
             query=hpotk.TermId.from_curie("HP:0001250"),  # Seizure
             missing_implies_phenotype_excluded=False,