def get_bin_nrd(filestats: str) -> pd.DataFrame:
"""
Calculates NRD for each bin of AF
:param filestats: path to file
# NRD and discordance is calculated as follows:
# m .. number of matches
# x .. number of mismatches
# NRD = (xRR + xRA + xAA) / (xRR + xRA + xAA + mRA + mAA)
# RR discordance = xRR / (xRR + mRR)
# RA discordance = xRA / (xRA + mRA)
# AA discordance = xAA / (xAA + mAA)
"""
genos = {0: 'RR Hom', 1: 'RA Het', 2: 'AA Hom'}
gcsaf = bcfstats.get_table_dataframe(filestats, "GCsAF")
gcsaf = gcsaf.astype(float)
gcsaf['tot matches'] = gcsaf['RR Hom matches'] + gcsaf[
'RA Het matches'] + gcsaf['AA Hom matches']
gcsaf['tot mismatches'] = gcsaf['RR Hom mismatches'] + gcsaf[
'RA Het mismatches'] + gcsaf['AA Hom mismatches']
gcsaf['NRD'] = (gcsaf['RR Hom mismatches'] + gcsaf['RA Het mismatches'] + gcsaf['AA Hom mismatches']) /\
(gcsaf['RR Hom mismatches'] + gcsaf['RA Het mismatches'] + gcsaf['AA Hom mismatches'] + gcsaf['RA Het matches'] + gcsaf['AA Hom matches'])
for geno in genos.values():
gcsaf['{} discordance'.format(geno)] = gcsaf['{} mismatches'.format(
geno)] / (gcsaf['{} mismatches'.format(geno)] +
gcsaf['{} matches'.format(geno)])
return gcsaf
def get_overall_nrd(filestats: str) -> pd.DataFrame:
"""
:param filestats: path to file
# NRD and discordance is calculated as follows:
# m .. number of matches
# x .. number of mismatches
# NRD = (xRR + xRA + xAA) / (xRR + xRA + xAA + mRA + mAA)
# RR discordance = xRR / (xRR + mRR)
# RA discordance = xRA / (xRA + mRA)
# AA discordance = xAA / (xAA + mAA)
"""
nrds = bcfstats.get_table_dataframe(filestats, "NRDs")
return nrds
def get_bin_accuracy(filestats: str) -> pd.DataFrame:
"""
:param filestats: path to file
ratio tp / (tp + fp + tn + fn) for each class
"""
genos = {0: 'RR Hom', 1: 'RA Het', 2: 'AA Hom'}
gcsaf = bcfstats.get_table_dataframe(filestats, "GCsAF")
gcsaf = gcsaf.astype(float)
for geno in genos.values():
gcsaf['{} accuracy'.format(geno)] = gcsaf['{} matches'.format(
geno)] / gcsaf['number of genotypes']
return gcsaf
def get_bin_recall(filestats: str) -> pd.DataFrame:
"""
:param filestats: path to file
ratio tp / (tp + fn) for each class
"""
# this is the per-class concordance
genos = {0: 'RR Hom', 1: 'RA Het', 2: 'AA Hom'}
gcsaf = bcfstats.get_table_dataframe(filestats, "GCsAF")
gcsaf = gcsaf.astype(float)
for geno in genos.values():
gcsaf['{} recall'.format(geno)] = gcsaf['{} matches'.format(geno)] / (
gcsaf['{} mismatches'.format(geno)] +
gcsaf['{} matches'.format(geno)])
return gcsaf
def get_bin_precision(filestats: str) -> pd.DataFrame:
"""
:param filestats: path to file
ratio tp / (tp + fp) where tp is the number of true positives and fp the number of false positives
"""
# this cannot be calculated: I do not know which one are the false positives?
genos = {0: 'RR Hom', 1: 'RA Het', 2: 'AA Hom'}
gcsaf = bcfstats.get_table_dataframe(filestats, "GCsAF")
gcsaf = gcsaf.astype(float)
for geno in genos.values():
pass
#gcsaf['{} accuracy'.format(geno)] = gcsaf['{} matches'.format(geno)] / gcsaf['number of genotypes']
# or?
# gcsaf['RR Hom precision'] = gcsaf['RR Hom matches'] /
# (gcsaf['RA Het mismatches'] + gcsaf['AA Hom mismatches'] - gcsaf['RR Hom mismatches'] + gcsaf['RR Hom matches'])
return None
def get_bin_maf_nrd(filestats: str) -> pd.DataFrame:
"""
Calculates NRD for each bin of AF
:param filestats: path to file
# NRD and discordance is calculated as follows:
# m .. number of matches
# x .. number of mismatches
# NRD = (xRR + xRA + xAA) / (xRR + xRA + xAA + mRA + mAA)
# RR discordance = xRR / (xRR + mRR)
# RA discordance = xRA / (xRA + mRA)
# AA discordance = xAA / (xAA + mAA)
"""
genos = {0: 'RR Hom', 1: 'RA Het', 2: 'AA Hom'}
gcsaf = bcfstats.get_table_dataframe(filestats, "GCsAF")
gcsaf = gcsaf.astype(float)
# Create MAF values from AF and aggregate the counts for MAF
mafconverter = lambda x: x if x <= 0.5 else 1 - x
gcsaf['minor allele frequency'] = gcsaf['allele frequency'].apply(
mafconverter).round(decimals=2)
gcsaf.drop(labels=['allele frequency', 'dosage r-squared'],
axis=1,
inplace=True)
gpby = gcsaf.groupby(['minor allele frequency'], axis=0)
gcsaf = gpby.sum()
# Compute number of (mis)matches
gcsaf['tot matches'] = gcsaf['RR Hom matches'] + gcsaf[
'RA Het matches'] + gcsaf['AA Hom matches']
gcsaf['tot mismatches'] = gcsaf['RR Hom mismatches'] + gcsaf[
'RA Het mismatches'] + gcsaf['AA Hom mismatches']
gcsaf['NRD'] = (gcsaf['RR Hom mismatches'] + gcsaf['RA Het mismatches'] + gcsaf['AA Hom mismatches']) / \
(gcsaf['RR Hom mismatches'] + gcsaf['RA Het mismatches'] + gcsaf['AA Hom mismatches'] + gcsaf[
'RA Het matches'] + gcsaf['AA Hom matches'])
for geno in genos.values():
gcsaf['{} discordance'.format(geno)] = gcsaf['{} mismatches'.format(
geno)] / (gcsaf['{} mismatches'.format(geno)] +
gcsaf['{} matches'.format(geno)])
return gcsaf