From f82825a524af6bb7f1341414c423f34f0d685a72 Mon Sep 17 00:00:00 2001 From: Daniel Danis Date: Tue, 26 Sep 2023 14:25:54 -0400 Subject: [PATCH] Set width. --- docs/tutorial.rst | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/docs/tutorial.rst b/docs/tutorial.rst index 67a6d26d..4fe9bce5 100644 --- a/docs/tutorial.rst +++ b/docs/tutorial.rst @@ -63,13 +63,12 @@ For instance, we can partition the patients into two groups based on presence/ab >>> from genophenocorr.constants import VariantEffect >>> cohort_analysis = CohortAnalysis(cohort, 'NM_1234.5', hpo, include_unmeasured=False) >>> frameshift = cohort_analysis.compare_by_variant_type(VariantEffect.FRAMESHIFT_VARIANT) - >>> pprint(frameshift) # doctest: +NORMALIZE_WHITESPACE, +ELLIPSIS + >>> pprint(frameshift, width=120) # doctest: +NORMALIZE_WHITESPACE With frameshift_variant Without frameshift_variant Count Percent Count Percent p-value HP:0001166 (Arachnodactyly) 4 30.77% 10 76.92% 0.04718 HP:0001250 (Seizure) 11 84.62% 9 69.23% 0.64472 HP:0001257 (Spasticity) 8 61.54% 9 69.23% 1.00000 - Or perform similar partitioning based on presence/absence of a *missense* variant: @@ -77,13 +76,12 @@ Or perform similar partitioning based on presence/absence of a *missense* varian .. doctest:: tutorial >>> missense = cohort_analysis.compare_by_variant_type(VariantEffect.MISSENSE_VARIANT) - >>> pprint(missense) # doctest: +NORMALIZE_WHITESPACE, +ELLIPSIS + >>> pprint(missense, width=120) # doctest: +NORMALIZE_WHITESPACE With missense_variant Without missense_variant Count Percent Count Percent p-value HP:0001166 (Arachnodactyly) 13 81.25% 1 10.00% 0.000781 HP:0001257 (Spasticity) 11 68.75% 6 60.00% 0.692449 HP:0001250 (Seizure) 12 75.00% 8 80.00% 1.000000 - The tables present the HPO terms that annotate the cohort members and report their counts and p values