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Merge pull request #7 from gagneurlab/absplice_rna_for_drop
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include outliers in absplice_rna
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@WagnerNils
WagnerNils authored Feb 9, 2023
2 parents 568dd54 + bfd61f8 commit 8e83023
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Showing 2 changed files with 39 additions and 8 deletions.
1 change: 1 addition & 0 deletions absplice/cat_dataloader.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2,6 +2,7 @@
from absplice.dataloader import SpliceMapMixin
from absplice.utils import delta_logit_PSI_to_delta_PSI, logit
import pandas as pd
pd.options.mode.chained_assignment = None
import re
from typing import List
from splicemap.splice_map import SpliceMap
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46 changes: 38 additions & 8 deletions absplice/result.py
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Original file line number Diff line number Diff line change
Expand Up @@ -68,6 +68,7 @@ def __init__(self,
df_spliceai=None,
df_cadd_splice=None,
df_mmsplice_cat=None,
df_outliers_cat=None,
gene_map=None,
gene_tpm=None,
df_var_samples=None,
Expand All @@ -79,6 +80,7 @@ def __init__(self,
self.df_var_samples = self.validate_df_var_samples(df_var_samples)
self.df_mmsplice = self.validate_df_mmsplice(df_mmsplice)
self.df_mmsplice_cat = self.validate_df_mmsplice_cat(df_mmsplice_cat)
self.df_outliers_cat = self.validate_df_outliers_cat(df_outliers_cat)
self.gene_map = self.validate_df_gene_map(gene_map)
self.gene_tpm = self.validate_df_gene_tpm(gene_tpm)
self.df_spliceai = self.validate_df_spliceai(df_spliceai)
Expand All @@ -101,6 +103,7 @@ def __init__(self,
self._gene_absplice_rna = None
self._variant_mmsplice = None
self._variant_mmsplice_cat = None
self._variant_outliers_cat = None
self._variant_spliceai = None
self._variant_absplice_dna = None
self._variant_absplice_rna = None
Expand Down Expand Up @@ -143,6 +146,18 @@ def validate_df_mmsplice_cat(self, df_mmsplice_cat):
~df_mmsplice_cat['delta_psi_cat'].isna()
]
return df_mmsplice_cat

def validate_df_outliers_cat(self, df_outliers_cat):
if df_outliers_cat is not None:
df_outliers_cat = self._validate_df(
df_outliers_cat,
columns=['variant', 'gene_id', 'tissue', 'sample'
'pValueGene_g_minus_log10'])
df_outliers_cat = self._validate_dtype(df_outliers_cat)
df_outliers_cat = df_outliers_cat[
~df_outliers_cat['pValueGene_g_minus_log10'].isna()
]
return df_outliers_cat

def validate_df_spliceai(self, df_spliceai):
if df_spliceai is not None:
Expand Down Expand Up @@ -239,7 +254,7 @@ def validate_absplice_rna_input(self, df_absplice_rna_input):
'junction',
'delta_score', 'delta_logit_psi', 'delta_psi', 'ref_psi', 'median_n',
'tissue_cat', 'k_cat', 'n_cat', 'median_n_cat', 'psi_cat', 'ref_psi_cat',
'delta_logit_psi_cat', 'delta_psi_cat'
'delta_logit_psi_cat', 'delta_psi_cat', 'pValueGene_g_minus_log10'
])
df_absplice_rna_input = self._validate_dtype(df_absplice_rna_input)
groupby = ['variant', 'gene_id', 'tissue', 'sample']
Expand Down Expand Up @@ -316,8 +331,9 @@ def _add_tissue_info_to_cadd_splice(self):
return self._df_cadd_splice_tissue

def _add_samples(self, df):
assert 'chr' not in df.iloc[0]['variant']
assert 'chr' not in self.df_var_samples.iloc[0]['variant']
chr_df = 'chr' in df.iloc[0]['variant']
chr_samples = 'chr' in self.df_var_samples.iloc[0]['variant']
assert (chr_df == True and chr_samples == True) or (chr_df == False and chr_samples == False)
df = df.set_index('variant') \
.join(self.df_var_samples.set_index('variant'),
how='inner') \
Expand Down Expand Up @@ -471,6 +487,14 @@ def variant_mmsplice_cat(self):
self._variant_mmsplice_cat = self._get_maximum_effect(
self.df_mmsplice_cat, groupby, score='delta_psi_cat')
return self._variant_mmsplice_cat

@property
def variant_outliers_cat(self):
groupby = ['variant', 'gene_id', 'tissue', 'sample']
if self._variant_outliers_cat is None:
self._variant_outliers_cat = self._get_maximum_effect(
self.df_outliers_cat, groupby, score='pValueGene_g_minus_log10')
return self._variant_outliers_cat

@property
def variant_spliceai(self): # NOTE: max aggregate for variant on each gene
Expand All @@ -491,9 +515,11 @@ def absplice_dna_input(self):

# MMSplice (SpliceMap)
cols_mmsplice = [
'Chromosome', 'Start', 'End', 'Strand', 'junction', 'event_type', 'splice_site', 'gene_name',
'delta_logit_psi', 'delta_psi', 'ref_psi', 'k', 'n', 'median_n',
'novel_junction', 'weak_site_donor', 'weak_site_acceptor']
'junction', 'event_type',
'splice_site', 'ref_psi', 'median_n',
'gene_name',
'delta_logit_psi', 'delta_psi',
]
if self.df_mmsplice is not None:
df_mmsplice = self._get_maximum_effect(
self.df_mmsplice, groupby, score='delta_psi')
Expand Down Expand Up @@ -532,7 +558,7 @@ def absplice_dna_input(self):
return self._absplice_dna_input

@property
def absplice_rna_input(self):
def absplice_rna_input(self): #TODO: check if tissue_cat should be included in groubpy
if self._absplice_rna_input is None:
groupby = ['variant', 'gene_id', 'tissue', 'sample']
if not pd.Series(groupby).isin(self.absplice_dna_input.index.names).all():
Expand All @@ -542,8 +568,12 @@ def absplice_rna_input(self):
cols_mmsplice_cat = [
'junction', 'delta_psi', 'ref_psi', 'median_n',
*[col for col in df_mmsplice_cat.columns if 'cat' in col]]
df_outliers_cat = self._get_maximum_effect(
self.df_outliers_cat, groupby, score='pValueGene_g_minus_log10')
cols_outliers_cat = ['pValueGene_g_minus_log10']
self._absplice_rna_input = self.absplice_dna_input.join(
df_mmsplice_cat[cols_mmsplice_cat], how='outer', rsuffix='_from_cat_infer')
df_mmsplice_cat[cols_mmsplice_cat], how='outer', rsuffix='_from_cat_infer').join(
df_outliers_cat[cols_outliers_cat], how='outer', rsuffix='_outlier_cat')
return self._absplice_rna_input

def _predict_absplice(self, df, absplice_score, pickle_file, features, abs_features, median_n_cutoff, tpm_cutoff):
Expand Down

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