def test_get_recurrent_genes(self):
''' check that we identify the recurrently mutated genes correctly
'''
variants = DataFrame({'symbol': ['TEST1', 'TEST1', 'TEST2', 'TEST3']})
self.assertEqual(get_recurrent_genes(variants), Series(['TEST1']))
def test_get_recurrent_genes(self):
''' check that we identify the recurrently mutated genes correctly
'''
variants = DataFrame({'symbol': ['TEST1', 'TEST1', 'TEST2', 'TEST3']})
self.assertEqual(get_recurrent_genes(variants), Series(['TEST1']))
def filter_denovogear_sites(de_novos, status):
""" set flags for filtering, fail samples with strand bias < threshold, or any 2 of
(i) both parents have ALTs
(ii) site-specific parental alts < threshold,
(iii) gene-specific parental alts < threshold, if > 1 sites called in gene
Args:
de_novos: dataframe of de novo variants
status: list (or pandas Series) or boolea
Returns:
vector of true/false for whether each variant passes the filters
"""
counts = extract_alt_and_ref_counts(de_novos)
recurrent = get_recurrent_genes(de_novos)
# check if sites deviate from expected strand bias and parental alt depths
strand_bias, parental_site_bias = test_sites(counts, status)
parental_gene_bias = test_genes(counts, strand_bias, status)
# fail SNVs with excessive strand bias. Don't check strand bias in indels.
overall_pass = (strand_bias >= P_CUTOFF) | (de_novos["ref"].str.len() != 1) | \
(de_novos["alt"].str.len() != 1)
# find if each de novo has passed each of three different filtering strategies
# fail sites with gene-specific parental alts, only if >1 sites called per gene
gene_fail = (parental_gene_bias < P_CUTOFF) & de_novos["symbol"].isin(recurrent)
site_fail = (parental_site_bias < P_CUTOFF)
counts['dad_depth'] = counts[['father_ref_F', 'father_ref_R', 'father_alt_F', 'father_alt_R']].sum(axis=1)
counts['mom_depth'] = counts[['mother_ref_F', 'mother_ref_R', 'mother_alt_F', 'mother_alt_R']].sum(axis=1)
counts['parental_depth_threshold'] = counts[['dad_depth', 'mom_depth']].apply(min_depth, error=ERROR_RATE, axis=1)
excess_alts = counts["min_parent_alt"] > counts['parental_depth_threshold']
# exclude sites that fail two of three classes
sites = pandas.DataFrame({"gene": gene_fail, "site": site_fail, "alts": excess_alts})
overall_pass[sites.sum(axis=1) >= 2] = False
return overall_pass
def filter_denovogear_sites(de_novos, status):
""" set flags for filtering, fail samples with strand bias < threshold, or any 2 of
(i) both parents have ALTs
(ii) site-specific parental alts < threshold,
(iii) gene-specific parental alts < threshold, if > 1 sites called in gene
Args:
de_novos: dataframe of de novo variants
status: list (or pandas Series) or boolea
Returns:
vector of true/false for whether each variant passes the filters
"""
counts = extract_alt_and_ref_counts(de_novos)
recurrent = get_recurrent_genes(de_novos)
counts['child_alts'] = counts[['child_alt_F', 'child_alt_R']].sum(axis=1)
counts['child_depth'] = counts[[
'child_ref_F', 'child_ref_R', 'child_alt_F', 'child_alt_R'
]].sum(axis=1)
counts['dad_depth'] = counts[[
'father_ref_F', 'father_ref_R', 'father_alt_F', 'father_alt_R'
]].sum(axis=1)
counts['mom_depth'] = counts[[
'mother_ref_F', 'mother_ref_R', 'mother_alt_F', 'mother_alt_R'
]].sum(axis=1)
# only include sites with good sample depths (different threshold for child
# and parents) and sufficient alts in the child
good_depth = (counts['child_alts'] > 1) & (counts['child_depth'] > 7) & \
(counts['dad_depth'] > 5) &(counts['mom_depth'] > 5)
status &= good_depth
# check if sites deviate from expected strand bias and parental alt depths
strand_bias, parental_site_bias = test_sites(counts, status)
parental_gene_bias = test_genes(counts, strand_bias, status)
# fail SNVs with excessive strand bias. Don't check strand bias in indels.
overall_pass = (strand_bias >= P_CUTOFF) | (de_novos["ref"].str.len() != 1) | \
(de_novos["alt"].str.len() != 1)
# find if each de novo has passed each of three different filtering strategies
# fail sites with gene-specific parental alts, only if >1 sites called per gene
gene_fail = (parental_gene_bias <
P_CUTOFF) & de_novos["symbol"].isin(recurrent)
site_fail = (parental_site_bias < P_CUTOFF)
counts['parental_depth_threshold'] = counts[['dad_depth', 'mom_depth'
]].apply(min_depth,
error=ERROR_RATE,
axis=1)
excess_alts = counts["min_parent_alt"] > counts['parental_depth_threshold']
# exclude sites that fail two of three classes
sites = pandas.DataFrame({
"gene": gene_fail,
"site": site_fail,
"alts": excess_alts
})
overall_pass[sites.sum(axis=1) >= 2] = False
# drop out sites with poor depths
overall_pass &= good_depth
return overall_pass
