From 989a41053f0e67569a66eb0f19f9fe380c2b2f4f Mon Sep 17 00:00:00 2001 From: Daniel Danis Date: Tue, 26 Sep 2023 14:38:57 -0400 Subject: [PATCH] Remove ellipsis. --- docs/tutorial.rst | 13 +++++++------ 1 file changed, 7 insertions(+), 6 deletions(-) diff --git a/docs/tutorial.rst b/docs/tutorial.rst index ae09781a..f07a513a 100644 --- a/docs/tutorial.rst +++ b/docs/tutorial.rst @@ -63,12 +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 - 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 + >>> 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 @@ -83,6 +83,7 @@ Or perform similar partitioning based on presence/absence of a *missense* varian 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