def test_standardise_error(self):
""" test if a required column is missing, we raise an error
"""
self.initial = self.initial.drop("pp_dnm", axis=1)
with self.assertRaises(KeyError):
standardise_columns(self.initial)
def test_standardise_error(self):
''' test if a required column is missing, we raise an error
'''
self.initial = self.initial.drop('pp_dnm', axis=1)
with self.assertRaises(KeyError):
standardise_columns(self.initial)
def test_standardise_columns(self):
""" test that standardising the columns works
"""
self.initial["child_ref_F"] = [23, 35]
self.compare_tables(standardise_columns(self.initial), self.expected)
# if the dataframes are different, expect an error
self.expected["pp_dnm"] = [0.0, 0.0]
with self.assertRaises(AssertionError):
self.compare_tables(standardise_columns(self.initial), self.expected)
def test_standardise_columns(self):
''' test that standardising the columns works
'''
self.initial['child_ref_F'] = [23, 35]
self.compare_tables(standardise_columns(self.initial), self.expected)
# if the dataframes are different, expect an error
self.expected['pp_dnm'] = [0.0, 0.0]
with self.assertRaises(AssertionError):
self.compare_tables(standardise_columns(self.initial),
self.expected)
def test_standardise_columns_with_pass(self):
""" test that standardising the columns works when a 'pass' column exists
"""
self.initial["pass"] = [True, False]
self.initial["child_ref_F"] = [23, 35]
self.expected["pass"] = [True, False]
self.compare_tables(standardise_columns(self.initial), self.expected)
def test_standardise_columns_with_pass(self):
''' test that standardising the columns works when a 'pass' column exists
'''
self.initial['pass'] = [True, False]
self.initial['child_ref_F'] = [23, 35]
self.expected['pass'] = [True, False]
self.compare_tables(standardise_columns(self.initial), self.expected)
def screen_candidates(de_novos_path,
fails_path,
filter_function,
maf=0.01,
fix_symbols=True,
annotate_only=False,
build='grch37'):
""" load and optionally filter candidate de novo mutations.
Args:
de_novos_path: path to table of unfiltered canddiate DNMs
fails_path: path to file listing samples which failed QC, and therefore
all of their candidates need to be excluded.
filter_function: function for filtering the candidates, either
filter_denovogear_sites(), or filter_missing_indels().
maf: MAF threshold for filtering. This is 0.01 for denovogear sites,
and 0 for the missing indels.
fix_symbols: whether to annotate HGNC symbols for candidates
missing these.
annotate_only: whether to include a column indicating pass status, rather
than excluding all candidates which fail the filtering.
build: whether to use the 'grch37' or 'grch38' build to get
missing symbols.
Returns:
pandas DataFrame of candidate de novo mutations.
"""
if de_novos_path is None:
return None
# load the datasets
de_novos = load_candidates(de_novos_path)
sample_fails = []
if fails_path is not None:
sample_fails = [x.strip() for x in open(fails_path)]
# run some initial screening
status = preliminary_filtering(de_novos, sample_fails, maf_cutoff=maf)
segdup = check_segdups(de_novos)
if fix_symbols:
de_novos['symbol'] = fix_missing_gene_symbols(de_novos, build)
pass_status = filter_function(de_novos, status & segdup) & status & segdup
if annotate_only:
de_novos['pass'] = pass_status
else:
de_novos = de_novos[pass_status]
return standardise_columns(de_novos)
def screen_candidates(de_novos_path, fails_path, filter_function, maf=0.01,
fix_symbols=True, annotate_only=False):
""" load and optionally filter candidate de novo mutations.
Args:
de_novos_path: path to table of unfiltered canddiate DNMs
fails_path: path to file listing samples which failed QC, and therefore
all of their candidates need to be excluded.
filter_function: function for filtering the candidates, either
filter_denovogear_sites(), or filter_missing_indels().
maf: MAF threshold for filtering. This is 0.01 for denovogear sites,
and 0 for the missing indels.
fix_symbols: whether to annotate HGNC symbols for candidates
missing these.
annotate_only: whether to include a column indicating pass status, rather
than excluding all candidates which fail the filtering.
Returns:
pandas DataFrame of candidate de novo mutations.
"""
if de_novos_path is None:
return None
# load the datasets
de_novos = load_candidates(de_novos_path)
sample_fails = []
if fails_path is not None:
sample_fails = [ x.strip() for x in open(fails_path) ]
# run some initial screening
status = preliminary_filtering(de_novos, sample_fails, maf_cutoff=maf)
segdup = check_segdups(de_novos)
if fix_symbols:
de_novos['symbol'] = fix_missing_gene_symbols(de_novos)
pass_status = filter_function(de_novos, status & segdup) & status & segdup
if annotate_only:
de_novos['pass'] = pass_status
else:
de_novos = de_novos[pass_status]
return standardise_columns(de_novos)
