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For exact intersections (ie. [Query + A + B + nothing else]), set the --multiple-overlap-target-combi-size flag to the number of --more-bed plus one. You will know if the combinations are computed as inexact by the '...' in their name in the result file. Intersections not including the query file are discarded. +With inexact combinations, if A+B is very enriched and C is depleted, A+B+C will be enriched. It is more interesting to look at C's contribution to the enrichment. Relatedly, longer combinations are usually more enriched since they involve more theoretically independant sets. Combinations of similar orders should be compared. **Simple example:** @@ -226,8 +227,8 @@ As the computation of multiple overlaps can be RAM-intensive, if you have a very -Details ------------------ +Itemset mining details +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ In broad strokes, the custom itemset algorithm MODL (Multiple Overlap Dictionary Learning) will perform many matrix factorizations on the matrix of true overlaps to identify relevant correlation groups of genomic regions. Then a greedy algorithm based on how much these words improve the reconstruction will select the utmost best words. MODL is only used to filter the output of OLOGRAM : once it returns a list of interesting combination, OLOGRAM will compute their enrichment as usual, but for them only. Each combination is of the form [Query + A + B + C] where A, B and C are BED files given as --more-bed. You can also manually specify the combinations to be studied with the format defined in OLOGRAM notes (below). @@ -244,7 +245,7 @@ This itemset mining algorithm is a work-in-progress. Whether you use MODL will n This can work on any type of data, biological or not, that respects the conventional formatting for lists of transactions: the data needs to be a matrix with one line per transaction and one column per element. For example, if you have three possible elements A, B and C, a line of [1,0,1] means a transaction containing A and C. -For a factor allowance of k and n final queried words, the matrix will be rebuilt with k*n words in step 1. MODL will discard combinations rarer than 1/10000 occurences to reduce computing times. It will also reduce the abundance of all unique lines in the matrix to their square roots to reduce the emphasis on the most frequent elements. However, the latter can magnify the impact of the noise as well and can be disabled when using the manual API. To de-emphasize longer words, which can help in this case, we can also normalize words by their summed square in step 2. +For a factor allowance of k and n final queried words, the matrix will be rebuilt with k*n words in step 1. MODL will discard combinations rarer than 1/10000 occurences to reduce computing times. It will also reduce the abundance of all unique lines in the matrix to their square roots to reduce the emphasis on the most frequent elements. However, the latter can magnify the impact of the noise as well and can be disabled when using the manual API. To de-emphasize longer words, which can help in this case, we normalize words by their summed square in step 2. If you are passing a custom error function, it must have the signature error_function(X_true, X_rebuilt, code). X_true is the real data, X_rebuilt is the reconstruction to evaluate, and code is the encoded version which in our case is used to assess sparsity. All are NumPY matrices. @@ -267,7 +268,8 @@ Here is an example: step_1_factor_allowance = 2, # How many words to ask for in each step 1 rebuilding, as a multiplier of multiple_overlap_max_number_of_combinations error_function = None, # Custom error function in step 2 smother = True, # Should the smothering (quadratic reduction of abundance) be applied ? - normalize_words = False) # Normalize words by their summed squared in step 2 ? + normalize_words = True, # Normalize words by their summed squared in step 2 ? + step_2_alpha = None) # Override the alpha (sparsity control) used in step 2 interesting_combis = combi_miner.find_interesting_combinations() @@ -300,6 +302,7 @@ The resulting flags_matrix is a NumPy array that can be edited, and on which MOD Since the results of MODL only depend on the true intersections and not on the shuffles, you can run MODL with 1 shuffle or on a manually computed matrix as above to pre-select interesting combinations, and then run the full analysis on many shuffles. We then recommend selecting the combinations that interest you in the resulting tsv file, using MODL's selection as a starting point and adding or removing some combinations based on your own needs (eg. adding all the highest fold changes, or all particular combinations containing the Transcription Factor X that you are studying). +It is also possible to run any itemset miner you wish on this matrix. An implementation of apriori is provided in the `pygtftk.stats.intersect.modl.apriori.Apriori` class. ologram_merge_stats @@ -329,6 +332,10 @@ ologram_merge_stats This also works with OLOGRAM-MODL results, since they follow the same basic format of one element/combination per line. +Cases without a p-value diamond mean it was NaN. It usually means was too rare to be encountered in the shuffles. + +An example of use case for this tool would be to compare between different cell lines, or to slop (extend) your query regions by different lengths and compare the enrichment to find at which distance of each other several sets are on average. + **Arguments:** .. command-output:: gtftk ologram_merge_stats -h @@ -336,7 +343,6 @@ This also works with OLOGRAM-MODL results, since they follow the same basic form - ologram_modl_treeify ~~~~~~~~~~~~~~~~~~~~~~ @@ -344,9 +350,10 @@ ologram_modl_treeify This recommended representation is useful to find master regulators, by showing which additions to a combinations increase its enrichment, and allowing to see whether overlaps that contain the element X also contain the element Y (looking at how a child combination accounts for the S of its parent in an inexact counting). -The tsv result file can be edited before passing it to the command, for example by keeping only the combinations you are interested in, such as all combinations containing the Transcription Factor you are studying. We recommend running MODL to make a pre-selection. +P-values of NaN (-1 in the original tsv) are due to poor fitting. They are mostly present in high order combinations, that were so rare that they are not encountered in the shuffles even once. We also recommend discarding the rarest combinations found on such a very small number of basepairs that they are unlikely to be biologically significant. This is mostly relevant when you have many sets (k >= 5) since longer combinations will often be enriched through sheer unlikelihood. To that effect, there is a parameter to display only the combinations with the highest S. -We also recommend discarding the rarest combinations found on such a very small number of basepairs that they are unlikely tobe biologically significant. This is mostly relevant when you have many sets (k >= 5) since longer combinations will often be enriched through sheer unlikelihood. +The tsv result file can be edited before passing it to the command, for example by keeping only the combinations you are interested in. +You can either (1) run OLOGRAM-MODl with no filtering and get a tree of all combinations, (2) use MODL to get a pre-selection that can be tailored, or (3) take the run with all combinations from the possibility 1 and use the -t argument to take the most frequent combinations. .. command-output:: gtftk ologram_modl_treeify -i multiple_overlap_trivial_ologram_stats.tsv -o treeified.pdf -l ThisWasTheNameOfTheQuery :shell: @@ -369,8 +376,6 @@ We also recommend discarding the rarest combinations found on such a very small :shell: - - ologram_merge_runs ~~~~~~~~~~~~~~~~~~~~~~ diff --git a/docs/_static/documentation_options.js b/docs/_static/documentation_options.js index 87b5af96..ba70511b 100644 --- a/docs/_static/documentation_options.js +++ b/docs/_static/documentation_options.js @@ -1,6 +1,6 @@ var DOCUMENTATION_OPTIONS = { URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'), - VERSION: '1.2.6', + VERSION: '1.2.7', LANGUAGE: 'en', COLLAPSE_INDEX: false, BUILDER: 'html', diff --git a/docs/_static/example_01.png b/docs/_static/example_01.png index dd9d68e2..f50923b3 100644 Binary files a/docs/_static/example_01.png and b/docs/_static/example_01.png differ diff --git a/docs/_static/example_02.png b/docs/_static/example_02.png index 8439c50a..9a8e1feb 100644 Binary files a/docs/_static/example_02.png and b/docs/_static/example_02.png differ diff --git a/docs/_static/example_05.png b/docs/_static/example_05.png index 54a8799c..3f6c33d6 100644 Binary files a/docs/_static/example_05.png and b/docs/_static/example_05.png differ diff --git a/docs/_static/example_06.png b/docs/_static/example_06.png index c0a9d7ec..fdf5f647 100644 Binary files a/docs/_static/example_06.png and b/docs/_static/example_06.png differ diff --git a/docs/_static/example_06b.png b/docs/_static/example_06b.png index 0760d751..78e7933e 100644 Binary files a/docs/_static/example_06b.png and b/docs/_static/example_06b.png differ diff --git a/docs/_static/example_07.png b/docs/_static/example_07.png index 715ce153..22761fac 100644 Binary files a/docs/_static/example_07.png and b/docs/_static/example_07.png differ diff --git a/docs/_static/example_08.png b/docs/_static/example_08.png index 0ec24430..11b06dd0 100644 Binary files a/docs/_static/example_08.png and b/docs/_static/example_08.png differ diff --git a/docs/_static/example_13.png b/docs/_static/example_13.png index 3ff20046..4b05549d 100644 Binary files a/docs/_static/example_13.png and b/docs/_static/example_13.png differ diff --git a/docs/_static/example_pa_01.pdf b/docs/_static/example_pa_01.pdf index 8e192a34..f5b48620 100644 Binary files a/docs/_static/example_pa_01.pdf and b/docs/_static/example_pa_01.pdf differ diff --git a/docs/_static/example_pa_02.pdf b/docs/_static/example_pa_02.pdf index a2a43190..2fc29b88 100644 Binary files a/docs/_static/example_pa_02.pdf and b/docs/_static/example_pa_02.pdf differ diff --git a/docs/_static/example_pa_03.pdf b/docs/_static/example_pa_03.pdf index 2a2926b4..f4cf37aa 100644 Binary files a/docs/_static/example_pa_03.pdf and b/docs/_static/example_pa_03.pdf differ diff --git a/docs/_static/example_pa_04.pdf b/docs/_static/example_pa_04.pdf index 1e1c5b55..4b82a347 100644 Binary files a/docs/_static/example_pa_04.pdf and b/docs/_static/example_pa_04.pdf differ diff --git a/docs/_static/merge_ologram_stats_01.pdf b/docs/_static/merge_ologram_stats_01.pdf index 74a66845..a1147ec3 100644 Binary files a/docs/_static/merge_ologram_stats_01.pdf and b/docs/_static/merge_ologram_stats_01.pdf differ diff --git a/docs/_static/treeified.pdf b/docs/_static/treeified.pdf index 4923948f..d1e905b5 100644 Binary files a/docs/_static/treeified.pdf and b/docs/_static/treeified.pdf differ diff --git a/docs/about.html b/docs/about.html index 8f1f817b..e150bec0 100644 --- a/docs/about.html +++ b/docs/about.html @@ -16,7 +16,7 @@ var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); - Warning about supported GTF file formats — gtftk 1.2.6 documentation + Warning about supported GTF file formats — gtftk 1.2.7 documentation @@ -45,7 +45,7 @@

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    • diff --git a/docs/annotation.html b/docs/annotation.html index 24b60054..13403f9b 100644 --- a/docs/annotation.html +++ b/docs/annotation.html @@ -16,7 +16,7 @@ var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); - Commands from section ‘annotation’ — gtftk 1.2.6 documentation + Commands from section ‘annotation’ — gtftk 1.2.7 documentation @@ -45,7 +45,7 @@

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    • diff --git a/docs/api.html b/docs/api.html index 9a8a3a26..e5646a50 100644 --- a/docs/api.html +++ b/docs/api.html @@ -16,7 +16,7 @@ var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); - Help on Python package (pygtftk) — gtftk 1.2.6 documentation + Help on Python package (pygtftk) — gtftk 1.2.7 documentation @@ -45,7 +45,7 @@

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  • Help on Python package (pygtftk)
    • @@ -2619,7 +2619,7 @@

    Help on Python package (pygtftk)
    -class pygtftk.stats.intersect.modl.dict_learning.Modl(flags_matrix, multiple_overlap_target_combi_size=- 1, multiple_overlap_max_number_of_combinations=5, nb_threads=1, step_1_factor_allowance=2, error_function=None, smother=True, normalize_words=False)
    +class pygtftk.stats.intersect.modl.dict_learning.Modl(flags_matrix, multiple_overlap_target_combi_size=- 1, multiple_overlap_max_number_of_combinations=5, nb_threads=1, step_1_factor_allowance=2, error_function=None, smother=True, normalize_words=True, step_2_alpha=None)

    This class encapsulates the MODL approach :

    Takes as input a matrix of flags, with one flag per intersection, and returns the list of interesting combis.

    @@ -2634,7 +2634,8 @@

    Help on Python package (pygtftk) +
    +pygtftk.stats.intersect.modl.subroutines.normalize_and_jitter_matrix_rows(X, normalize=True, jitter=True)
    +

    Apply normalization so that sum of suquare of each row is 1, +Add a slight epsilon so two rows may not have exact same dot product with a given vector +Sort in lexicographic order

    +
    >>> import numpy as np
+>>> import numpy.testing as npt
+>>> from pygtftk.stats.intersect.modl.subroutines import normalize_and_jitter_matrix_rows
+>>> D = np.array([[1,1,0],[0,1,0],[0,1,1]])
+>>> Dc = normalize_and_jitter_matrix_rows(D)
+>>> Dc_theory = [[0, 1, 0], [7.0736E-5, 7.07107E-1, 7.07107E-1], [7.07107E-1, 7.07107E-1, 9.99859E-05]]
+>>> npt.assert_almost_equal(Dc, Dc_theory, decimal = 5)
+
    +
    +
    +

    The gtftk.utils module

    @@ -3511,13 +3529,13 @@

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    • diff --git a/docs/conversion.html b/docs/conversion.html index 482e1836..5ece26d7 100644 --- a/docs/conversion.html +++ b/docs/conversion.html @@ -16,7 +16,7 @@ var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); - Commands from section ‘conversion’ — gtftk 1.2.6 documentation + Commands from section ‘conversion’ — gtftk 1.2.7 documentation @@ -45,7 +45,7 @@

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  • Commands from section ‘coverage’
    • @@ -214,19 +214,19 @@

    profile
    $ gtftk profile -D -i mini_real_promoter.zip -o profile_prom -pf png -if  example_01.png
- |-- 19:50-WARNING-profile : --group-by not set. Choosing 'bwig'.
+ |-- 17:24-WARNING-profile : --group-by not set. Choosing 'bwig'.
    _images/example_01.png

    Changing colors and applying color order can be done using the following syntax:

    $ gtftk profile -D -i mini_real_promoter.zip -c 'red,blue,violet' -d H3K79me,H3K4me3,H3K36me3 -o profile_prom -pf png -if  example_01b.png
- |-- 19:50-WARNING-profile : --group-by not set. Choosing 'bwig'.
+ |-- 17:24-WARNING-profile : --group-by not set. Choosing 'bwig'.
    _images/example_01b.png

    Transcript coverage is obtained using the mini_real_tx.zip matrix. This provides a simple overlayed profile of all epigenetic marks along the transcript body extended in 5’ and 3’ regions:

    $ gtftk profile -D -i mini_real_tx.zip -o profile_tx -pf png -if  example_02.png
- |-- 19:50-WARNING-profile : --group-by not set. Choosing 'bwig'.
+ |-- 17:24-WARNING-profile : --group-by not set. Choosing 'bwig'.
    _images/example_02.png @@ -234,111 +234,111 @@

    profile
    $ gtftk profile -D -i mini_real_promoter.zip -f tx_classes -g bwig  -t tx_classes.txt -o profile_prom  -pf png -if  example_05.png -e -V 2 -fc 2
- |-- 19:50-DEBUG-profile : Using pandas version 1.1.3
- |-- 19:50-DEBUG-profile : Pandas location /Users/puthier/anaconda3/envs/pygtftk_37/lib/python3.7/site-packages/pandas/__init__.py
- |-- 19:50-DEBUG-profile : Using numpy version 1.19.1
- |-- 19:50-DEBUG-profile : Pandas numpy /Users/puthier/anaconda3/envs/pygtftk_37/lib/python3.7/site-packages/numpy/__init__.py
- |-- 19:50-DEBUG-profile : Using plotnine version 0.7.1
- |-- 19:50-DEBUG-profile : Pandas plotnine /Users/puthier/anaconda3/envs/pygtftk_37/lib/python3.7/site-packages/plotnine/__init__.py
- |-- 19:50-DEBUG-profile : Creating directory : /var/folders/zy/wl3dj2_n76zfc8sdvny1q06c0000gn/T/profile_matrix__pygtftk_qwap3eq7
- |-- 19:50-DEBUG-profile : Uncompressing : /var/folders/zy/wl3dj2_n76zfc8sdvny1q06c0000gn/T/profile_matrix__pygtftk_qwap3eq7
- |-- 19:50-DEBUG-profile : Reading : /var/folders/zy/wl3dj2_n76zfc8sdvny1q06c0000gn/T/profile_matrix__pygtftk_qwap3eq7/mini_real_promoter
- |-- 19:50-INFO-profile : Getting configuration info from input file.
- |-- 19:50-INFO-profile : Reading transcript file.
- |-- 19:50-INFO-profile : Deleting duplicates in transcript-file.
- |-- 19:50-INFO-profile : Checking how many transcripts where found in the transcript list.
- |-- 19:50-INFO-profile : Keeping 804 transcript out of 833 in input transcript list.
- |-- 19:50-DEBUG-profile : Color order : ['H3K4me3', 'H3K79me', 'H3K36me3']
- |-- 19:50-DEBUG-profile : Profile color : ['#000000', '#00bb00', '#cccccc']
- |-- 19:50-INFO-profile : Searching coverage columns.
- |-- 19:50-INFO-profile : Melting.
- |-- 19:50-INFO-profile : Ceiling
- |-- 19:50-INFO-profile : Computing column ordering.
- |-- 19:50-INFO-profile : Preparing diagram
- |-- 19:50-INFO-profile : Theming and ordering. Please be patient...
- |-- 19:50-INFO-profile : Preparing x axis
- |-- 19:50-INFO-profile : facet_col 2
- |-- 19:50-INFO-profile : Page width set to 6
- |-- 19:50-INFO-profile : Page height set to 5.0
- |-- 19:50-INFO-profile : Saving diagram to file : example_05.png
- |-- 19:50-INFO-profile : Be patient. This may be long for large datasets.
- |-- 19:50-DEBUG-profile : Deleting temp file : /var/folders/zy/wl3dj2_n76zfc8sdvny1q06c0000gn/T/profile_matrix__pygtftk_qwap3eq7
+ |-- 17:24-DEBUG-profile : Using pandas version 1.1.3
+ |-- 17:24-DEBUG-profile : Pandas location /Users/puthier/anaconda3/envs/pygtftk_37/lib/python3.7/site-packages/pandas/__init__.py
+ |-- 17:24-DEBUG-profile : Using numpy version 1.19.1
+ |-- 17:24-DEBUG-profile : Pandas numpy /Users/puthier/anaconda3/envs/pygtftk_37/lib/python3.7/site-packages/numpy/__init__.py
+ |-- 17:24-DEBUG-profile : Using plotnine version 0.7.1
+ |-- 17:24-DEBUG-profile : Pandas plotnine /Users/puthier/anaconda3/envs/pygtftk_37/lib/python3.7/site-packages/plotnine/__init__.py
+ |-- 17:24-DEBUG-profile : Creating directory : /var/folders/zy/wl3dj2_n76zfc8sdvny1q06c0000gn/T/profile_matrix__pygtftk_p4vznr5l
+ |-- 17:24-DEBUG-profile : Uncompressing : /var/folders/zy/wl3dj2_n76zfc8sdvny1q06c0000gn/T/profile_matrix__pygtftk_p4vznr5l
+ |-- 17:24-DEBUG-profile : Reading : /var/folders/zy/wl3dj2_n76zfc8sdvny1q06c0000gn/T/profile_matrix__pygtftk_p4vznr5l/mini_real_promoter
+ |-- 17:24-INFO-profile : Getting configuration info from input file.
+ |-- 17:24-INFO-profile : Reading transcript file.
+ |-- 17:24-INFO-profile : Deleting duplicates in transcript-file.
+ |-- 17:24-INFO-profile : Checking how many transcripts where found in the transcript list.
+ |-- 17:24-INFO-profile : Keeping 804 transcript out of 833 in input transcript list.
+ |-- 17:24-DEBUG-profile : Color order : ['H3K79me', 'H3K4me3', 'H3K36me3']
+ |-- 17:24-DEBUG-profile : Profile color : ['#000000', '#00bb00', '#cccccc']
+ |-- 17:24-INFO-profile : Searching coverage columns.
+ |-- 17:24-INFO-profile : Melting.
+ |-- 17:24-INFO-profile : Ceiling
+ |-- 17:24-INFO-profile : Computing column ordering.
+ |-- 17:24-INFO-profile : Preparing diagram
+ |-- 17:24-INFO-profile : Theming and ordering. Please be patient...
+ |-- 17:24-INFO-profile : Preparing x axis
+ |-- 17:24-INFO-profile : facet_col 2
+ |-- 17:24-INFO-profile : Page width set to 6
+ |-- 17:24-INFO-profile : Page height set to 5.0
+ |-- 17:24-INFO-profile : Saving diagram to file : example_05.png
+ |-- 17:24-INFO-profile : Be patient. This may be long for large datasets.
+ |-- 17:24-DEBUG-profile : Deleting temp file : /var/folders/zy/wl3dj2_n76zfc8sdvny1q06c0000gn/T/profile_matrix__pygtftk_p4vznr5l
    _images/example_05.png

    Alternatively, the groups can be set to chromosomes or transcript classes:

    $ gtftk profile -D -i mini_real_promoter.zip -g tx_classes -f bwig  -t tx_classes.txt -o profile_prom  -pf png -if  example_06.png -V 2 -nm ranging
- |-- 19:50-DEBUG-profile : Using pandas version 1.1.3
- |-- 19:50-DEBUG-profile : Pandas location /Users/puthier/anaconda3/envs/pygtftk_37/lib/python3.7/site-packages/pandas/__init__.py
- |-- 19:50-DEBUG-profile : Using numpy version 1.19.1
- |-- 19:50-DEBUG-profile : Pandas numpy /Users/puthier/anaconda3/envs/pygtftk_37/lib/python3.7/site-packages/numpy/__init__.py
- |-- 19:50-DEBUG-profile : Using plotnine version 0.7.1
- |-- 19:50-DEBUG-profile : Pandas plotnine /Users/puthier/anaconda3/envs/pygtftk_37/lib/python3.7/site-packages/plotnine/__init__.py
- |-- 19:50-DEBUG-profile : Creating directory : /var/folders/zy/wl3dj2_n76zfc8sdvny1q06c0000gn/T/profile_matrix__pygtftk_j3tecvv5
- |-- 19:50-DEBUG-profile : Uncompressing : /var/folders/zy/wl3dj2_n76zfc8sdvny1q06c0000gn/T/profile_matrix__pygtftk_j3tecvv5
- |-- 19:50-DEBUG-profile : Reading : /var/folders/zy/wl3dj2_n76zfc8sdvny1q06c0000gn/T/profile_matrix__pygtftk_j3tecvv5/mini_real_promoter
- |-- 19:50-INFO-profile : Getting configuration info from input file.
- |-- 19:50-INFO-profile : Reading transcript file.
- |-- 19:50-INFO-profile : Deleting duplicates in transcript-file.
- |-- 19:50-INFO-profile : Checking how many transcripts where found in the transcript list.
- |-- 19:50-INFO-profile : Keeping 804 transcript out of 833 in input transcript list.
- |-- 19:50-DEBUG-profile : Color order : ['lincRNA', 'protein_coding', 'antisense']
- |-- 19:50-DEBUG-profile : Profile color : ['#000000', '#00bb00', '#cccccc']
- |-- 19:50-INFO-profile : Searching coverage columns.
- |-- 19:50-INFO-profile : Melting.
- |-- 19:50-INFO-profile : Ceiling
- |-- 19:50-INFO-profile : Normalizing (ranging)
- |-- 19:50-INFO-profile : Computing column ordering.
- |-- 19:50-INFO-profile : Preparing diagram
- |-- 19:50-INFO-profile : Theming and ordering. Please be patient...
- |-- 19:50-INFO-profile : Preparing x axis
- |-- 19:50-INFO-profile : facet_col 3
- |-- 19:50-INFO-profile : Page width set to 9
- |-- 19:50-INFO-profile : Page height set to 2.0
- |-- 19:50-INFO-profile : Saving diagram to file : example_06.png
- |-- 19:50-INFO-profile : Be patient. This may be long for large datasets.
- |-- 19:50-DEBUG-profile : Deleting temp file : /var/folders/zy/wl3dj2_n76zfc8sdvny1q06c0000gn/T/profile_matrix__pygtftk_j3tecvv5
+ |-- 17:24-DEBUG-profile : Using pandas version 1.1.3
+ |-- 17:24-DEBUG-profile : Pandas location /Users/puthier/anaconda3/envs/pygtftk_37/lib/python3.7/site-packages/pandas/__init__.py
+ |-- 17:24-DEBUG-profile : Using numpy version 1.19.1
+ |-- 17:24-DEBUG-profile : Pandas numpy /Users/puthier/anaconda3/envs/pygtftk_37/lib/python3.7/site-packages/numpy/__init__.py
+ |-- 17:24-DEBUG-profile : Using plotnine version 0.7.1
+ |-- 17:24-DEBUG-profile : Pandas plotnine /Users/puthier/anaconda3/envs/pygtftk_37/lib/python3.7/site-packages/plotnine/__init__.py
+ |-- 17:24-DEBUG-profile : Creating directory : /var/folders/zy/wl3dj2_n76zfc8sdvny1q06c0000gn/T/profile_matrix__pygtftk_wuv6mlzp
+ |-- 17:24-DEBUG-profile : Uncompressing : /var/folders/zy/wl3dj2_n76zfc8sdvny1q06c0000gn/T/profile_matrix__pygtftk_wuv6mlzp
+ |-- 17:24-DEBUG-profile : Reading : /var/folders/zy/wl3dj2_n76zfc8sdvny1q06c0000gn/T/profile_matrix__pygtftk_wuv6mlzp/mini_real_promoter
+ |-- 17:24-INFO-profile : Getting configuration info from input file.
+ |-- 17:24-INFO-profile : Reading transcript file.
+ |-- 17:24-INFO-profile : Deleting duplicates in transcript-file.
+ |-- 17:24-INFO-profile : Checking how many transcripts where found in the transcript list.
+ |-- 17:24-INFO-profile : Keeping 804 transcript out of 833 in input transcript list.
+ |-- 17:24-DEBUG-profile : Color order : ['protein_coding', 'lincRNA', 'antisense']
+ |-- 17:24-DEBUG-profile : Profile color : ['#000000', '#00bb00', '#cccccc']
+ |-- 17:24-INFO-profile : Searching coverage columns.
+ |-- 17:24-INFO-profile : Melting.
+ |-- 17:24-INFO-profile : Ceiling
+ |-- 17:24-INFO-profile : Normalizing (ranging)
+ |-- 17:24-INFO-profile : Computing column ordering.
+ |-- 17:24-INFO-profile : Preparing diagram
+ |-- 17:24-INFO-profile : Theming and ordering. Please be patient...
+ |-- 17:24-INFO-profile : Preparing x axis
+ |-- 17:24-INFO-profile : facet_col 3
+ |-- 17:24-INFO-profile : Page width set to 9
+ |-- 17:24-INFO-profile : Page height set to 2.0
+ |-- 17:24-INFO-profile : Saving diagram to file : example_06.png
+ |-- 17:24-INFO-profile : Be patient. This may be long for large datasets.
+ |-- 17:24-DEBUG-profile : Deleting temp file : /var/folders/zy/wl3dj2_n76zfc8sdvny1q06c0000gn/T/profile_matrix__pygtftk_wuv6mlzp
    _images/example_06.png 
    $ gtftk profile -D -i mini_real_promoter.zip -g chrom -f bwig  -t tx_classes.txt -o profile_prom  -pf png -if  example_06b.png -V 2 -nm ranging
- |-- 19:50-DEBUG-profile : Using pandas version 1.1.3
- |-- 19:50-DEBUG-profile : Pandas location /Users/puthier/anaconda3/envs/pygtftk_37/lib/python3.7/site-packages/pandas/__init__.py
- |-- 19:50-DEBUG-profile : Using numpy version 1.19.1
- |-- 19:50-DEBUG-profile : Pandas numpy /Users/puthier/anaconda3/envs/pygtftk_37/lib/python3.7/site-packages/numpy/__init__.py
- |-- 19:50-DEBUG-profile : Using plotnine version 0.7.1
- |-- 19:50-DEBUG-profile : Pandas plotnine /Users/puthier/anaconda3/envs/pygtftk_37/lib/python3.7/site-packages/plotnine/__init__.py
- |-- 19:50-DEBUG-profile : Creating directory : /var/folders/zy/wl3dj2_n76zfc8sdvny1q06c0000gn/T/profile_matrix__pygtftk_7rz5i4rz
- |-- 19:50-DEBUG-profile : Uncompressing : /var/folders/zy/wl3dj2_n76zfc8sdvny1q06c0000gn/T/profile_matrix__pygtftk_7rz5i4rz
- |-- 19:50-DEBUG-profile : Reading : /var/folders/zy/wl3dj2_n76zfc8sdvny1q06c0000gn/T/profile_matrix__pygtftk_7rz5i4rz/mini_real_promoter
- |-- 19:50-INFO-profile : Getting configuration info from input file.
- |-- 19:50-DEBUG-profile : Color order : ['chr16', 'chr13', 'chr19', 'chr22', 'chr15', 'chr21', 'chr3', 'chr14', 'chrX', 'chr9', 'chr2', 'chr18', 'chr8', 'chr11', 'chr10', 'chr12', 'chr6', 'chr4', 'chr20', 'chr7', 'chr17', 'chr1', 'chr5']
- |-- 19:50-DEBUG-profile : Profile color : ['#000000', '#6c007c', '#850096', '#2500a5', '#0000ca', '#0041dd', '#0086dd', '#009fca', '#00aaa1', '#00a76f', '#009c00', '#00bb00', '#00da00', '#00f900', '#88ff00', '#dbf400', '#f7db00', '#ffb500', '#ff6100', '#f60000', '#da0000', '#cc1313', '#cccccc']
- |-- 19:50-INFO-profile : Searching coverage columns.
- |-- 19:50-INFO-profile : Melting.
- |-- 19:50-INFO-profile : Ceiling
- |-- 19:50-INFO-profile : Normalizing (ranging)
- |-- 19:51-INFO-profile : Computing column ordering.
- |-- 19:51-INFO-profile : Preparing diagram
- |-- 19:51-INFO-profile : Theming and ordering. Please be patient...
- |-- 19:51-INFO-profile : Preparing x axis
- |-- 19:51-INFO-profile : facet_col 3
- |-- 19:51-INFO-profile : Page width set to 9
- |-- 19:51-INFO-profile : Page height set to 2.0
- |-- 19:51-INFO-profile : Saving diagram to file : example_06b.png
- |-- 19:51-INFO-profile : Be patient. This may be long for large datasets.
- |-- 19:51-DEBUG-profile : Deleting temp file : /var/folders/zy/wl3dj2_n76zfc8sdvny1q06c0000gn/T/profile_matrix__pygtftk_7rz5i4rz
+ |-- 17:24-DEBUG-profile : Using pandas version 1.1.3
+ |-- 17:24-DEBUG-profile : Pandas location /Users/puthier/anaconda3/envs/pygtftk_37/lib/python3.7/site-packages/pandas/__init__.py
+ |-- 17:24-DEBUG-profile : Using numpy version 1.19.1
+ |-- 17:24-DEBUG-profile : Pandas numpy /Users/puthier/anaconda3/envs/pygtftk_37/lib/python3.7/site-packages/numpy/__init__.py
+ |-- 17:24-DEBUG-profile : Using plotnine version 0.7.1
+ |-- 17:24-DEBUG-profile : Pandas plotnine /Users/puthier/anaconda3/envs/pygtftk_37/lib/python3.7/site-packages/plotnine/__init__.py
+ |-- 17:24-DEBUG-profile : Creating directory : /var/folders/zy/wl3dj2_n76zfc8sdvny1q06c0000gn/T/profile_matrix__pygtftk_7trzmg6b
+ |-- 17:24-DEBUG-profile : Uncompressing : /var/folders/zy/wl3dj2_n76zfc8sdvny1q06c0000gn/T/profile_matrix__pygtftk_7trzmg6b
+ |-- 17:24-DEBUG-profile : Reading : /var/folders/zy/wl3dj2_n76zfc8sdvny1q06c0000gn/T/profile_matrix__pygtftk_7trzmg6b/mini_real_promoter
+ |-- 17:24-INFO-profile : Getting configuration info from input file.
+ |-- 17:24-DEBUG-profile : Color order : ['chr14', 'chr3', 'chr9', 'chr21', 'chr10', 'chr6', 'chr11', 'chr1', 'chr19', 'chrX', 'chr12', 'chr18', 'chr22', 'chr15', 'chr16', 'chr13', 'chr8', 'chr7', 'chr2', 'chr20', 'chr5', 'chr4', 'chr17']
+ |-- 17:24-DEBUG-profile : Profile color : ['#000000', '#6c007c', '#850096', '#2500a5', '#0000ca', '#0041dd', '#0086dd', '#009fca', '#00aaa1', '#00a76f', '#009c00', '#00bb00', '#00da00', '#00f900', '#88ff00', '#dbf400', '#f7db00', '#ffb500', '#ff6100', '#f60000', '#da0000', '#cc1313', '#cccccc']
+ |-- 17:24-INFO-profile : Searching coverage columns.
+ |-- 17:24-INFO-profile : Melting.
+ |-- 17:24-INFO-profile : Ceiling
+ |-- 17:24-INFO-profile : Normalizing (ranging)
+ |-- 17:24-INFO-profile : Computing column ordering.
+ |-- 17:24-INFO-profile : Preparing diagram
+ |-- 17:24-INFO-profile : Theming and ordering. Please be patient...
+ |-- 17:24-INFO-profile : Preparing x axis
+ |-- 17:24-INFO-profile : facet_col 3
+ |-- 17:24-INFO-profile : Page width set to 9
+ |-- 17:24-INFO-profile : Page height set to 2.0
+ |-- 17:24-INFO-profile : Saving diagram to file : example_06b.png
+ |-- 17:24-INFO-profile : Be patient. This may be long for large datasets.
+ |-- 17:24-DEBUG-profile : Deleting temp file : /var/folders/zy/wl3dj2_n76zfc8sdvny1q06c0000gn/T/profile_matrix__pygtftk_7trzmg6b
    _images/example_06b.png

    Note that facets may also be associated to epigenetic marks. In this case each the –group-by can be set to tx_classes or chrom.

    $ gtftk profile -D -i mini_real_tx.zip -g tx_classes -t tx_classes.txt -f bwig  -o profile_tx -pf png -if  example_07.png -w -nm ranging
- |-- 19:51-WARNING-profile : PlotnineError error, can not show group number: Aesthetics {'ha'} specified two times.
+ |-- 17:24-WARNING-profile : PlotnineError error, can not show group number: Aesthetics {'ha'} specified two times.
    _images/example_07.png 
    $ gtftk profile -D -i mini_real_tx.zip -g chrom -f bwig  -o profile_tx -pf png -if  example_08.png  -w -nm ranging
- |-- 19:51-WARNING-profile : PlotnineError error, can not show group number: Aesthetics {'ha'} specified two times.
+ |-- 17:25-WARNING-profile : PlotnineError error, can not show group number: Aesthetics {'ha'} specified two times.
    _images/example_08.png @@ -462,13 +462,13 @@

    Navigation

  • previous |
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  • gtftk 1.2.6 documentation »
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  • Commands from section ‘coverage’
    • diff --git a/docs/developers.html b/docs/developers.html index b1a0dd1a..911e8a5a 100644 --- a/docs/developers.html +++ b/docs/developers.html @@ -16,7 +16,7 @@ var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); - Writing your own commands — gtftk 1.2.6 documentation + Writing your own commands — gtftk 1.2.7 documentation @@ -41,7 +41,7 @@

    Navigation

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  • Writing your own commands
    • @@ -232,13 +232,13 @@

    Navigation

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  • Writing your own commands
    • diff --git a/docs/editing.html b/docs/editing.html index 3b839fd5..b0c00834 100644 --- a/docs/editing.html +++ b/docs/editing.html @@ -16,7 +16,7 @@ var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); - Commands from section ‘Editing’ — gtftk 1.2.6 documentation + Commands from section ‘Editing’ — gtftk 1.2.7 documentation @@ -45,7 +45,7 @@

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  • Commands from section ‘Editing’
    • @@ -436,7 +436,7 @@

    discretize_key
    $ gtftk join_attr -i simple.gtf -j simple.join_mat -k gene_id -m | gtftk discretize_key -k S1 -d S1_d -n 2 -l A,B  | gtftk select_by_key -k feature -v gene
- |-- 19:51-INFO-discretize_key : Categories: ['A', 'B']
+ |-- 17:25-INFO-discretize_key : Categories: ['A', 'B']
 chr1	gtftk	gene	125	138	.	+	.	gene_id "G0001";
 chr1	gtftk	gene	180	189	.	+	.	gene_id "G0002";
 chr1	gtftk	gene	50	61	.	-	.	gene_id "G0003"; S1 "0.2322"; S2 "0.4"; S1_d "A";
@@ -455,7 +455,7 @@ 
    discretize_key
    $ gtftk join_attr -i mini_real.gtf.gz -H -j mini_real_counts_ENCFF630HEX.tsv -k gene_name -n exprs -t gene | gtftk discretize_key -k exprs -p -d exprs_class -n 10 -l A,B,C,D,E,F,G,H,I,J  | gtftk tabulate -k exprs_class -Hn | sort | uniq -c
- |-- 19:52-INFO-discretize_key : Categories: ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J']
+ |-- 17:25-INFO-discretize_key : Categories: ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J']
   96 A
   83 B
   89 C
@@ -573,13 +573,13 @@ 
    Navigation
    
         
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    •  
       
     
    
     
   
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index 474d7dbc..c69d22bb 100644
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+++ b/docs/genindex.html
@@ -16,7 +16,7 @@
         var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s);
       })();
     
-    Index — gtftk 1.2.6 documentation
+    Index — gtftk 1.2.7 documentation
     
     
     
@@ -37,7 +37,7 @@ 
    Navigation
    
         
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+        
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    •
    @@ -441,11 +441,13 @@

    N

  • negbin_pval() (in module pygtftk.stats.negbin_fit)
    • - - + diff --git a/docs/gtftk_args.html b/docs/gtftk_args.html index 9bf09c56..0c416da9 100644 --- a/docs/gtftk_args.html +++ b/docs/gtftk_args.html @@ -16,7 +16,7 @@ var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); - Help on gtftk Unix commands — gtftk 1.2.6 documentation + Help on gtftk Unix commands — gtftk 1.2.7 documentation @@ -45,7 +45,7 @@

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    • @@ -187,7 +187,6 @@

    Getting help with -h @@ -298,13 +297,13 @@

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    • diff --git a/docs/index.html b/docs/index.html index 2c4f1cb5..74064382 100644 --- a/docs/index.html +++ b/docs/index.html @@ -18,7 +18,7 @@ var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); - Welcome to pygtftk documentation page — gtftk 1.2.6 documentation + Welcome to pygtftk documentation page — gtftk 1.2.7 documentation @@ -43,7 +43,7 @@

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    • @@ -136,9 +136,7 @@

    Table of contentCommands from section ‘ologram’ - -
  • Details diff --git a/docs/information.html b/docs/information.html index 34764803..0b7d7a2a 100644 --- a/docs/information.html +++ b/docs/information.html @@ -16,7 +16,7 @@ var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); - Commands from section ‘information’ — gtftk 1.2.6 documentation + Commands from section ‘information’ — gtftk 1.2.7 documentation @@ -45,7 +45,7 @@

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    • @@ -69,7 +69,7 @@

    apropos
    $ gtftk apropos -k promoter
- |-- 19:52-INFO-apropos : >> Keyword 'promoter' was found in the following command:
+ |-- 17:25-INFO-apropos : >> Keyword 'promoter' was found in the following command:
 	- coverage.
 	- ologram.
 	- divergent.
@@ -738,13 +738,13 @@ 
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    diff --git a/docs/installation.html b/docs/installation.html index e1787734..203f706c 100644 --- a/docs/installation.html +++ b/docs/installation.html @@ -16,7 +16,7 @@ var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); - Installing pygtftk/gtftk — gtftk 1.2.6 documentation + Installing pygtftk/gtftk — gtftk 1.2.7 documentation @@ -45,7 +45,7 @@

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    Navigation

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    • diff --git a/docs/merge_ologram_stats_01.pdf b/docs/merge_ologram_stats_01.pdf index 74a66845..a1147ec3 100644 Binary files a/docs/merge_ologram_stats_01.pdf and b/docs/merge_ologram_stats_01.pdf differ diff --git a/docs/miscellaneous.html b/docs/miscellaneous.html index 283aeb04..796e8861 100644 --- a/docs/miscellaneous.html +++ b/docs/miscellaneous.html @@ -16,7 +16,7 @@ var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); - Commands from section ‘miscellaneous’ — gtftk 1.2.6 documentation + Commands from section ‘miscellaneous’ — gtftk 1.2.7 documentation @@ -45,7 +45,7 @@

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  • Commands from section ‘miscellaneous’
    • @@ -69,13 +69,13 @@

    control_list
    $ gtftk control_list -i mini_real_counts_ENCFF630HEX.tsv -r mini_real_control_1.txt -D -V 2 -s -l -p 1 -ju -if example_13.png -pf png
- |-- 19:52-INFO-control_list : 0 duplicate lines have been deleted in reference file.
- |-- 19:52-INFO-control_list : Found 50 genes of the reference in the provided signal file
- |-- 19:52-INFO-control_list : All reference genes were found.
- |-- 19:52-INFO-control_list : Searching for genes with matched signal.
- |-- 19:52-INFO-control_list : Preparing a dataframe for plotting.
- |-- 19:52-INFO-control_list : Saving diagram to file : example_13.png
- |-- 19:52-INFO-control_list : Be patient. This may be long for large datasets.
+ |-- 17:26-INFO-control_list : 0 duplicate lines have been deleted in reference file.
+ |-- 17:26-INFO-control_list : Found 50 genes of the reference in the provided signal file
+ |-- 17:26-INFO-control_list : All reference genes were found.
+ |-- 17:26-INFO-control_list : Searching for genes with matched signal.
+ |-- 17:26-INFO-control_list : Preparing a dataframe for plotting.
+ |-- 17:26-INFO-control_list : Saving diagram to file : example_13.png
+ |-- 17:26-INFO-control_list : Be patient. This may be long for large datasets.
    _images/example_13.png @@ -237,13 +237,13 @@

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    • diff --git a/docs/objects.inv b/docs/objects.inv index e9950f3a..adf8cc5a 100644 Binary files a/docs/objects.inv and b/docs/objects.inv differ diff --git a/docs/ologram.html b/docs/ologram.html index 1bf2ff59..e0a01c82 100644 --- a/docs/ologram.html +++ b/docs/ologram.html @@ -16,7 +16,7 @@ var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); - Commands from section ‘ologram’ — gtftk 1.2.6 documentation + Commands from section ‘ologram’ — gtftk 1.2.7 documentation @@ -45,7 +45,7 @@

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  • Commands from section ‘ologram’
    • @@ -102,8 +102,8 @@

    ologram
    $ gtftk ologram -i hg38_chr1.gtf.gz -p ENCFF112BHN_H3K4me3_chr1.bed -c hg38_chr1.genome -u 1500 -d 1500 -D  -pf example_pa_01.pdf -k 8 -j summed_bp_overlaps_pvalue
- |-- 19:52-WARNING-ologram : Using only 8 threads, but 16 cores are available. Consider changing the --nb-threads parameter.
- |-- 19:53-WARNING-ologram : Computing log(p-val) for a Neg Binom with mean >= var ; var was set to mean+1 (start_codon)
+ |-- 17:26-WARNING-ologram : Using only 8 threads, but 16 cores are available. Consider changing the --nb-threads parameter.
+ |-- 17:26-WARNING-ologram : Computing log(p-val) for a Neg Binom with mean >= var ; var was set to mean+1 (start_codon)

    @@ -118,7 +118,7 @@

    ologram
    $ gtftk select_by_key -i mini_real.gtf.gz -k gene_biotype -v protein_coding,lincRNA,antisense,processed_transcript  |  gtftk ologram  -m gene_biotype -p ENCFF112BHN_H3K4me3_K562_sub.bed -c hg38 -D -n  -pf example_pa_02.pdf -k 8 -j summed_bp_overlaps_pvalue
- |-- 19:53-WARNING-ologram : Using only 8 threads, but 16 cores are available. Consider changing the --nb-threads parameter.
+ |-- 17:27-WARNING-ologram : Using only 8 threads, but 16 cores are available. Consider changing the --nb-threads parameter.

    @@ -137,8 +137,8 @@

    ologram
    $ gtftk join_attr -i mini_real.gtf.gz -H -j mini_real_counts_ENCFF630HEX.tsv -k gene_name -n exprs -t exon | gtftk discretize_key -k exprs -p -d exprs_class -n 6  -u | gtftk ologram -p ENCFF119BYM_H3K36me3_K562_sub.bed -c hg38 -D -n -m exprs_class -pf example_pa_03.pdf -k 8 -j summed_bp_overlaps_pvalue
- |-- 19:54-INFO-discretize_key : Categories: ['[0.0_183.0]', '(183.0_549.0]', '(549.0_1018.0]', '(1018.0_1631.0]', '(1631.0_3139.0]', '(3139.0_41703.0]']
- |-- 19:54-WARNING-ologram : Using only 8 threads, but 16 cores are available. Consider changing the --nb-threads parameter.
+ |-- 17:28-INFO-discretize_key : Categories: ['[0.0_183.0]', '(183.0_549.0]', '(549.0_1018.0]', '(1018.0_1631.0]', '(1631.0_3139.0]', '(3139.0_41703.0]']
+ |-- 17:28-WARNING-ologram : Using only 8 threads, but 16 cores are available. Consider changing the --nb-threads parameter.

    @@ -153,8 +153,8 @@

    ologram
    $ gtftk add_exon_nb -k exon_nbr -i mini_real.gtf.gz | gtftk discretize_key -p -d exon_nbr_cat -n 5  -k exon_nbr | gtftk ologram -p ENCFF112BHN_H3K4me3_K562_sub.bed -c hg38 -D -n -m exon_nbr_cat -pf example_pa_04.pdf -k 8 -j summed_bp_overlaps_pvalue
- |-- 19:55-INFO-discretize_key : Categories: ['[1.0_2.0]', '(2.0_4.0]', '(4.0_6.0]', '(6.0_12.0]', '(12.0_107.0]']
- |-- 19:55-WARNING-ologram : Using only 8 threads, but 16 cores are available. Consider changing the --nb-threads parameter.
+ |-- 17:29-INFO-discretize_key : Categories: ['[1.0_2.0]', '(2.0_4.0]', '(4.0_6.0]', '(6.0_12.0]', '(12.0_107.0]']
+ |-- 17:29-WARNING-ologram : Using only 8 threads, but 16 cores are available. Consider changing the --nb-threads parameter.

    @@ -178,17 +178,18 @@

    ologram (multiple overlaps)
    $ gtftk ologram -z -w -q -c simple_07.chromInfo -p simple_07_peaks.bed --more-bed simple_07_peaks.1.bed simple_07_peaks.2.bed --more-bed-multiple-overlap
- |-- 19:56-WARNING : Converting to bed6 format (simple_07_peaks.bed).
- |-- 19:56-WARNING : Converting to bed6 format (simple_07_peaks.1.bed).
- |-- 19:56-WARNING : Converting to bed6 format (simple_07_peaks.2.bed).
- |-- 19:56-WARNING-ologram : Using only 1 threads, but 16 cores are available. Consider changing the --nb-threads parameter.
- |-- 19:56-WARNING-ologram : --more-bed-labels was not set, automatically defaulting to --more-bed file names.
- |-- 19:56-WARNING-ologram : [Query + simple_07_peaks_1 + ... ]: there may be a poor fit for this feature. Check fit quality in the results. This is likely due to there being too few regions.
- |-- 19:56-WARNING-ologram : [Query + simple_07_peaks_1 + simple_07_peaks_2 + ... ]: there may be a poor fit for this feature. Check fit quality in the results. This is likely due to there being too few regions.
- |-- 19:56-WARNING-ologram : Computing log(p-val) for a Neg Binom with mean >= var ; var was set to mean+1 ([Query + simple_07_peaks_1 + simple_07_peaks_2 + ... ])
+ |-- 17:30-WARNING : Converting to bed6 format (simple_07_peaks.bed).
+ |-- 17:30-WARNING : Converting to bed6 format (simple_07_peaks.1.bed).
+ |-- 17:30-WARNING : Converting to bed6 format (simple_07_peaks.2.bed).
+ |-- 17:30-WARNING-ologram : Using only 1 threads, but 16 cores are available. Consider changing the --nb-threads parameter.
+ |-- 17:30-WARNING-ologram : --more-bed-labels was not set, automatically defaulting to --more-bed file names.
+ |-- 17:30-WARNING-ologram : [Query + simple_07_peaks_1 + ... ]: there may be a poor fit for this feature. Check fit quality in the results. This is likely due to there being too few regions.
+ |-- 17:30-WARNING-ologram : [Query + simple_07_peaks_1 + simple_07_peaks_2 + ... ]: there may be a poor fit for this feature. Check fit quality in the results. This is likely due to there being too few regions.
+ |-- 17:30-WARNING-ologram : Computing log(p-val) for a Neg Binom with mean >= var ; var was set to mean+1 ([Query + simple_07_peaks_1 + simple_07_peaks_2 + ... ])

    Detailed example:

    @@ -218,16 +219,15 @@

    ologram (multiple overlaps) -

    Details

    +
    +

    Itemset mining details

    In broad strokes, the custom itemset algorithm MODL (Multiple Overlap Dictionary Learning) will perform many matrix factorizations on the matrix of true overlaps to identify relevant correlation groups of genomic regions. Then a greedy algorithm based on how much these words improve the reconstruction will select the utmost best words. MODL is only used to filter the output of OLOGRAM : once it returns a list of interesting combination, OLOGRAM will compute their enrichment as usual, but for them only. Each combination is of the form [Query + A + B + C] where A, B and C are BED files given as –more-bed. You can also manually specify the combinations to be studied with the format defined in OLOGRAM notes (below).

    Unlike classical association rules mining algorithms, this focuses on mining relevant bio complexes/clusters and correlation groups (item sets), and you should not request more than 20-30 combinations. As a matrix factorization based algorithm, it is designed to be resistant to noise which is a known problem in biological data. Its goal is to extract meaningful frequent combinations from noisy data. As a result however, it is biased in favor of the most abundant combinations in the data, and may return correlation groups if you ask for too few words (ie. if AB, BC and AC are complexes, ABC might be returned).

    This itemset mining algorithm is a work-in-progress. Whether you use MODL will not change the results for each combination, it only changes which combinations are displayed. If you want the enrichment of all combinations, ignore it. To use MODL, use the –multiple-overlap-max-number-of-combinations argument.

    MODL algorithm API: MODL can also be used independantly as a combination mining algorithm.

    This can work on any type of data, biological or not, that respects the conventional formatting for lists of transactions: the data needs to be a matrix with one line per transaction and one column per element. For example, if you have three possible elements A, B and C, a line of [1,0,1] means a transaction containing A and C.

    -

    For a factor allowance of k and n final queried words, the matrix will be rebuilt with k*n words in step 1. MODL will discard combinations rarer than 1/10000 occurences to reduce computing times. It will also reduce the abundance of all unique lines in the matrix to their square roots to reduce the emphasis on the most frequent elements. However, the latter can magnify the impact of the noise as well and can be disabled when using the manual API. To de-emphasize longer words, which can help in this case, we can also normalize words by their summed square in step 2.

    +

    For a factor allowance of k and n final queried words, the matrix will be rebuilt with k*n words in step 1. MODL will discard combinations rarer than 1/10000 occurences to reduce computing times. It will also reduce the abundance of all unique lines in the matrix to their square roots to reduce the emphasis on the most frequent elements. However, the latter can magnify the impact of the noise as well and can be disabled when using the manual API. To de-emphasize longer words, which can help in this case, we normalize words by their summed square in step 2.

    If you are passing a custom error function, it must have the signature error_function(X_true, X_rebuilt, code). X_true is the real data, X_rebuilt is the reconstruction to evaluate, and code is the encoded version which in our case is used to assess sparsity. All are NumPY matrices.

    For more details, see code comments.

    Here is an example:

    @@ -244,7 +244,8 @@

    Detailsstep_1_factor_allowance = 2, # How many words to ask for in each step 1 rebuilding, as a multiplier of multiple_overlap_max_number_of_combinations error_function = None, # Custom error function in step 2 smother = True, # Should the smothering (quadratic reduction of abundance) be applied ? - normalize_words = False) # Normalize words by their summed squared in step 2 ? + normalize_words = True, # Normalize words by their summed squared in step 2 ? + step_2_alpha = None) # Override the alpha (sparsity control) used in step 2 interesting_combis = combi_miner.find_interesting_combinations()

    @@ -336,20 +337,22 @@

    Details

    ologram_merge_stats

    Description: Several tsv files resulting from OLOGRAM analyses can be merged into a single diagram report using the merge_ologram_stats.

    @@ -449,6 +454,8 @@

    ologram_merge_stats
    $ gtftk ologram_merge_stats -h
   Usage: gtftk ologram_merge_stats [-pw pdf_width] [-ph pdf_height] -o output [-l labels] [-h] [-V [verbosity]] [-D] [-C] [-K tmp_dir] [-A] [-L logger_file] [-W write_message_to_file] inputfiles [inputfiles ...]
@@ -462,6 +469,9 @@ 
    ologram_merge_stats
    
 
    Description: Visualize n-wise enrichment results (OLOGRAM-MODL) as a tree of combinations. Works on the result (tsv file) of an OLOGRAM analysis called with –more-bed-multiple-overlap. On the graph, S designated the total number of basepairs in which this combinations is encountered in the real data. Fold change gives the ratio with the number of basepairs in the shuffles, with the associated Negative Binomial p-value.
    
 
    This recommended representation is useful to find master regulators, by showing which additions to a combinations increase its enrichment, and allowing to see whether overlaps that contain the element X also contain the element Y (looking at how a child combination accounts for the S of its parent in an inexact counting).
    
-
    The tsv result file can be edited before passing it to the command, for example by keeping only the combinations you are interested in, such as all combinations containing the Transcription Factor you are studying. We recommend running MODL to make a pre-selection.
    
-
    We also recommend discarding the rarest combinations found on such a very small number of basepairs that they are unlikely tobe biologically significant. This is mostly relevant when you have many sets (k >= 5) since longer combinations will often be enriched through sheer unlikelihood.
    
+
    P-values of NaN (-1 in the original tsv) are due to poor fitting. They are mostly present in high order combinations, that were so rare that they are not encountered in the shuffles even once. We also recommend discarding the rarest combinations found on such a very small number of basepairs that they are unlikely to be biologically significant. This is mostly relevant when you have many sets (k >= 5) since longer combinations will often be enriched through sheer unlikelihood. To that effect, there is a parameter to display only the combinations with the highest S.
    
+
    The tsv result file can be edited before passing it to the command, for example by keeping only the combinations you are interested in.
+You can either (1) run OLOGRAM-MODl with no filtering and get a tree of all combinations, (2) use MODL to get a pre-selection that can be tailored, or (3) take the run with all combinations from the possibility 1 and use the -t argument to take the most frequent combinations.
    
 
    $ gtftk ologram_modl_treeify -i multiple_overlap_trivial_ologram_stats.tsv -o treeified.pdf -l ThisWasTheNameOfTheQuery
 
    
 
    
@@ -504,7 +515,7 @@ 
    ologram_modl_treeify
 
    
 
    $ gtftk ologram_modl_treeify -h
-  Usage: gtftk ologram_modl_treeify -i inputfile -o output [-l query_label] [-h] [-V [verbosity]] [-D] [-C] [-K tmp_dir] [-A] [-L logger_file] [-W write_message_to_file]
+  Usage: gtftk ologram_modl_treeify -i inputfile -o output [-l query_label] [-t top_s] [-h] [-V [verbosity]] [-D] [-C] [-K tmp_dir] [-A] [-L logger_file] [-W write_message_to_file]
 
   Description: 
      *  Turns a result of OLOGRAM-MODL multiple overlap (tsv file) in a tree for easier visualisation  See
@@ -519,6 +530,8 @@ 
    ologram_modl_treeifyologram_modl_treeifyTable of Contents
    
 
  • Commands from section ‘ologram’
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diff --git a/docs/py-modindex.html b/docs/py-modindex.html
index f81e8f49..226b2542 100644
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@@ -16,7 +16,7 @@
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    diff --git a/docs/search.html b/docs/search.html index 321d147d..55f3831e 100644 --- a/docs/search.html +++ b/docs/search.html @@ -16,7 +16,7 @@ var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); - Search — gtftk 1.2.6 documentation + Search — gtftk 1.2.7 documentation @@ -42,7 +42,7 @@

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about supported GTF file formats","Commands from section \u2018annotation\u2019","Help on Python package (pygtftk)","The pygtftk.bwig.bw_coverage module","Commands from section \u2018conversion\u2019","Commands from section \u2018coordinates\u2019","Commands from section \u2018coverage\u2019","Writing your own commands","Commands from section \u2018Editing\u2019","Help on gtftk Unix commands","Welcome to pygtftk documentation page","Commands from section \u2018information\u2019","Installing pygtftk/gtftk","Commands from section \u2018miscellaneous\u2019","Commands from section \u2018ologram\u2019","Commands from section \u2018selection\u2019","Commands from section \u2018sequence\u2019"],titleterms:{"class":2,The:[2,3],about:0,activ:9,add_exon_nb:11,add_prefix:8,annot:1,apropo:11,argument:9,bash:9,bed_to_gtf:4,beta:2,bw_coverag:3,bwig:3,closest_gen:1,col_from_tab:13,command:[1,4,5,6,7,8,9,11,13,14,15,16],complet:9,content:10,control_list:13,convent:0,converg:1,convers:4,convert:4,convert_ensembl:4,coordin:5,count:11,count_key_valu:11,coverag:6,del_attr:8,detail:14,dict_learn:2,discretize_kei:8,diverg:1,document:10,edit:8,exon_s:1,fasta:2,feature_s:11,file:0,format:0,from:[1,4,5,6,8,11,13,14,15,16],funtion:9,get:9,get_5p_3p_coord:5,get_attr_list:11,get_attr_value_list:11,get_exampl:11,get_feat_seq:16,get_feature_list:11,get_tx_seq:16,gtf:[0,2],gtftk:[2,9,12],help:[2,9],inform:11,instal:12,intergen:5,intersect:2,intron:5,intron_s:1,itemset:14,join_attr:8,join_multi_fil:8,line:2,list:9,main:9,merge_attr:8,midpoint:5,mine:14,miscellan:13,mk_matrix:6,modl:2,modul:[2,3],multipl:14,name:0,nb_exon:11,nb_transcript:11,negbin_fit:2,ologram:14,ologram_merge_run:14,ologram_merge_stat:14,ologram_modl_treeifi:14,overlap:[1,14],overlap_stats_comput:2,overlap_stats_shuffl:2,own:7,packag:2,page:10,parser:9,profil:6,pygtftk:[2,3,10,12],python:2,random_list:15,random_tx:15,retriev:11,rm_dup_tss:15,section:[1,4,5,6,8,11,13,14,15,16],select:15,select_by_go:15,select_by_intron_s:15,select_by_kei:15,select_by_loc:15,select_by_max_exon_nb:15,select_by_nb_exon:15,select_by_numeric_valu:15,select_by_regexp:15,select_by_tx_s:15,select_most_5p_tx:15,seqid_list:11,sequenc:16,shift:5,short_long:15,splicing_sit:5,stat:2,subroutin:2,support:0,tab:2,tabl:10,tabul:4,test:9,tree:2,tss_dist:11,unix:9,util:2,warn:0,welcom:10,wide:9,write:7,your:7}}) \ No newline at end of file diff --git a/docs/selection.html b/docs/selection.html index b6fed26f..7debe81f 100644 --- a/docs/selection.html +++ b/docs/selection.html @@ -16,7 +16,7 @@ var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); - Commands from section ‘selection’ — gtftk 1.2.6 documentation + Commands from section ‘selection’ — gtftk 1.2.7 documentation @@ -45,7 +45,7 @@

    Navigation

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  • gtftk 1.2.6 documentation »
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  • Commands from section ‘selection’
    • @@ -447,8 +447,8 @@

    random_list
    $ gtftk random_list -n 3 -i simple.gtf | gtftk count
 transcript	3
-exon	6
-CDS	6
+exon	7
+CDS	3

    Arguments:

    @@ -819,13 +819,13 @@

    Navigation

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    • diff --git a/docs/sequence.html b/docs/sequence.html index 64372530..3b3b8ef4 100644 --- a/docs/sequence.html +++ b/docs/sequence.html @@ -16,7 +16,7 @@ var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); - Commands from section ‘sequence’ — gtftk 1.2.6 documentation + Commands from section ‘sequence’ — gtftk 1.2.7 documentation @@ -45,7 +45,7 @@

    Navigation

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Then a greedy algorithm based on how much these words improve the reconstruction will select the utmost best words. MODL is only used to filter the output of OLOGRAM : once it returns a list of interesting combination, OLOGRAM will compute their enrichment as usual, but for them only. Each combination is of the form [Query + A + B + C] where A, B and C are BED files given as --more-bed. You can also manually specify the combinations to be studied with the format defined in OLOGRAM notes (below). diff --git a/docs/source/treeified.pdf b/docs/source/treeified.pdf index a4f02a54..3a6a0ba9 100644 Binary files a/docs/source/treeified.pdf and b/docs/source/treeified.pdf differ diff --git a/docs/treeified.pdf b/docs/treeified.pdf index 4923948f..d1e905b5 100644 Binary files a/docs/treeified.pdf and b/docs/treeified.pdf differ diff --git a/pygtftk/plugins/bigwig_lebesgue.py b/pygtftk/plugins/bigwig_lebesgue.py deleted file mode 100644 index e47377a0..00000000 --- a/pygtftk/plugins/bigwig_lebesgue.py +++ /dev/null @@ -1,176 +0,0 @@ -""" -For use with OLOGRAM in intra-set overlaps. - -This will produce a bed file by performing a Lebesgue integration (along the -y - or signal - axis) of a bigWig file. The intent is to emulate a bed file -containing one region per read, where instead of a single read each region here -represents a pack of reads. - -This "resampled" bed file has much less total regions/reads, meaning it can be -reasonably used in OLOGRAM to account for the signal via intra-set overlap. - - -Author : Quentin Ferré - -""" - -""" -This is a WORK-IN-PROGRESS. To be used once intra-set overlaps are implemented in OLOGRAM. - -TODO Here are the steps that would be required : - -1) Remove the merging of BEDs regions (calls to bedfile.merge()), and ensure negative inter-regions distances are kept. - -2) Remember that overlap_regions.find_intersection() can understand several region open at once and can for example return flags of [2,1] if A has 2 open regions. - -3) Dictionary learning is non binary so it can learn proportions. But remember that sum(V**2) = 1 always for the words, so for proportion have a set that contains always 1, so it that set has a value of 0.2 in the word you know you need to multiply by 5 for example. Another idea is to look closely at the coefficients in the encoding U - -4) In the display of the combination (combi_human_readable), add the factor, ie. "[2*A + B]" - -""" - - - -# # ------------------------------- PARAMETERS --------------------------------- # -# bwf = "./ENCFF431HAA_H3K36me3_K562_sub.bw" - -# # Lebesgue integration : how many bins to divide the range into ? -# BINS = 10 -# # Result intervals longer than this will be partitioned -# MAX_INTERVAL_LENGTH = 250 - -# result_filepath = bwf + ".lebesgue_"+str(BINS)+"_bins_"+str(MAX_INTERVAL_LENGTH)+"bp_max.bed" -# # ---------------------------------------------------------------------------- # - - -# import pyBigWig -# import numpy as np -# import time - - -# def one_runs(a): -# """ -# Find successive '1' on a numpy vector. -# """ -# # Create an array that is 1 where a is 1, and pad each end with an extra 0 -# # which is standing in for "false" -# isone = np.concatenate(([0], np.equal(a, 1).view(np.int8), [0])) -# absdiff = np.abs(np.diff(isone)) -# # Runs start and end where absdiff is 1. -# ranges = np.where(absdiff == 1)[0].reshape(-1, 2) -# return ranges - - -# def inclusive_slice(start, end, step): -# """ -# Like range, but keeps the end. -# """ -# points = list() -# pos = start -# while pos < end: # use strict inferior because we manually add the end -# points += [pos] -# pos += step -# points += [end] -# return points - - -# start = time.time() -# print("Processing...") - -# # Read the bigWig file -# bw = pyBigWig.open(bwf) - -# # Open result file -# result_file = open(result_filepath,'w+') - -# # Get all chromosomes -# all_chroms = list(bw.chroms().keys()) - -# # Retrieve min and max values to compute the bins -# h = bw.header() -# minval = h["minVal"] -# maxval = h["maxVal"] - -# ## Lebesgue integral -# # Divide the range (remove zero and add back 1 as a minimum) -# delta = int((maxval-minval)/BINS) -# bins = np.array([1]+ list(range(minval,maxval,delta))[1:]) - -# # TODO multiprocess - -# # Do the following for each chromosome... -# for current_chromosome in all_chroms : - -# # Get all intervals for this chromosome -# bw_intervals = bw.intervals(current_chromosome) - -# # Get all remarkable points (starts and stops) -# remarkable_points = [r[0] for r in bw_intervals] # Get all the starts... -# remarkable_points += [bw_intervals[-1][1] - 1] # ... and the last stop. -# # Substract 1 from the last stop to get the last known nucleotide - -# # Convert to numpy array -# remarkable_points = np.array(remarkable_points) - -# # Now use a sweep line -# flags = np.array([0] * len(bins)) - -# # Result numpy array -# result = np.zeros(shape=(len(remarkable_points),len(bins))) - -# for i in range(len(remarkable_points)): -# point = remarkable_points[i] - -# signal = bw.values(current_chromosome,point,point+1)[0] - -# # Get the indices of all bins smaller or equal to the signal -# signal_flags = np.nonzero(bins <= signal)[0].astype(int) -# unsignal_flags = np.nonzero(bins > signal)[0].astype(int) - -# flags[signal_flags] = 1 -# flags[unsignal_flags] = 0 - -# # Record this -# result[i,:] = flags - - -# # Finally do the lebesgue integration -# # For each column, get the ranges where there are consecutive nonzeros -# all_intervals = list() - -# for j in range(result.shape[1]): -# column_bin = result[:,j] -# intervals = one_runs(column_bin) - -# # Translate the intervals back into genomic coordinates -# intervals_genomic = remarkable_points[intervals] - -# # Record them -# all_intervals += [tuple(i) for i in intervals_genomic] - - -# # Option to truncate : intervals longer than MAX_INTERVAL_LENGTH bp should -# # be cut every X base pairs. -# sliced_intervals = [] -# for interval in all_intervals: -# start, end = interval -# sliced_points = r = inclusive_slice(start, end, MAX_INTERVAL_LENGTH) -# cut_intervals = [(r[i],r[i+1]) for i in range(len(r)-1)] -# sliced_intervals += cut_intervals - -# # Finally, write each (sliced) interval for all bins into a bed file -# for interval in sliced_intervals: -# start = interval[0] -# end = interval[1] -# to_write = str(current_chromosome)+'\t'+str(start)+'\t'+str(end)+'\n' -# result_file.write(to_write) - - -# # TODO : to help choose the bins, return the compression loss in decibels, like for MP3 compression - -# # Close result file -# result_file.close() - - -# end = time.time() -# print("Lebesgue integration performed in "+str(end-start)+" s.") diff --git a/pygtftk/version.py b/pygtftk/version.py index ebdaad8f..690dc387 100644 --- a/pygtftk/version.py +++ b/pygtftk/version.py @@ -1,2 +1,2 @@ -__base_version__='1.2.6' -__version__='1.2.6' +__base_version__='1.2.7' +__version__='1.2.7' diff --git a/setup.cfg b/setup.cfg index d6210bfd..b2894b7b 100644 --- a/setup.cfg +++ b/setup.cfg @@ -1,6 +1,6 @@ [metadata] name = pygtftk -version = 1.2.6 +version = 1.2.7 author = D. Puthier and F. Lopez author-email = denis.puthier@univ-amu.fr