class PlinkReader(GenotypesReader):
def __init__(self, prefix):
"""Binary plink file reader.
Args:
prefix (str): the prefix of the Plink binary files.
"""
self.bed = PyPlink(prefix)
self.bim = self.bed.get_bim()
self.fam = self.bed.get_fam()
# Identify all multi-allelics.
self.bim["multiallelic"] = False
self.bim.loc[self.bim.duplicated(["chrom", "pos"], keep=False),
"multiallelic"] = True
# We want to set the index for the FAM file
try:
self.fam = self.fam.set_index("iid", verify_integrity=True)
except ValueError:
logger.info(
"Setting the index as 'fid_iid' because the individual IDs "
"are not unique.")
self.fam["fid_iid"] = [
"{fid}_{iid}".format(fid=fid, iid=iid)
for fid, iid in zip(self.fam.fid, self.fam.iid)
]
self.fam = self.fam.set_index("fid_iid", verify_integrity=True)
def close(self):
self.bed.close()
def get_variant_genotypes(self, variant):
"""Get the genotypes from a well formed variant instance.
Args:
marker (Variant): A Variant instance.
Returns:
A list of Genotypes instance containing a pointer to the variant as
well as a vector of encoded genotypes.
Note
====
If the sample IDs are not unique, the index is changed to be the
sample family ID and individual ID (i.e. fid_iid).
"""
# Find the variant in the bim.
plink_chrom = CHROM_STR_TO_INT[variant.chrom]
info = self.bim.loc[(self.bim.chrom == plink_chrom) &
(self.bim.pos == variant.pos), :]
if info.shape[0] == 0:
return []
elif info.shape[0] == 1:
return self._get_biallelic_variant(variant, info)
else:
return self._get_multialleic_variant(variant, info)
def _get_biallelic_variant(self, variant, info, _check_alleles=True):
# From 1.3.2 onwards, PyPlink sets unique names.
info = info.iloc[0, :]
variant_alleles = variant._encode_alleles([info.a2, info.a1])
if (_check_alleles and variant_alleles != variant.alleles):
# Variant with requested alleles is unavailable.
return []
geno = self._normalize_missing(self.bed.get_geno_marker(info.name))
return [Genotypes(variant, geno, info.a2, info.a1, False)]
def _get_multialleic_variant(self, variant, info):
# Check if alleles are specified.
out = []
if variant.alleles is None:
# If no alleles are specified, we return all the possible
# bi-allelic variats.
for name, row in info.iterrows():
geno = self.bed.get_geno_marker(name)
geno = self._normalize_missing(geno)
out.append(Genotypes(variant, geno, row.a2, row.a1, True))
else:
# Find the requested alleles.
for name, row in info.iterrows():
row_alleles = set(Variant._encode_alleles((row.a1, row.a2)))
if row_alleles.issubset(variant.alleles_set):
out.extend(
self._get_biallelic_variant(variant,
info.loc[[name], :],
_check_alleles=False))
return out
def iter_genotypes(self):
"""Iterates on available markers.
Returns:
Genotypes instances.
Note
====
If the sample IDs are not unique, the index is changed to be the
sample family ID and individual ID (i.e. fid_iid).
"""
# Iterating over all markers
for i, (_, genotypes) in enumerate(self.bed.iter_geno()):
info = self.bim.iloc[i, :]
yield Genotypes(Variant(info.name, CHROM_INT_TO_STR[info.chrom],
info.pos, [info.a1, info.a2]),
self._normalize_missing(genotypes),
reference=info.a2,
coded=info.a1,
multiallelic=info.multiallelic)
def iter_variants(self):
"""Iterate over marker information."""
for idx, row in self.bim.iterrows():
yield Variant(row.name, CHROM_INT_TO_STR[row.chrom], row.pos,
[row.a1, row.a2])
def get_variants_in_region(self, chrom, start, end):
"""Iterate over variants in a region."""
bim = self.bim.loc[(self.bim["chrom"] == CHROM_STR_TO_INT[chrom])
& (start <= self.bim["pos"]) &
(self.bim["pos"] <= end)]
for i, g in enumerate(self.bed.iter_geno_marker(bim.index)):
info = bim.iloc[i, :]
name, geno = g
yield Genotypes(Variant(info.name, CHROM_INT_TO_STR[info.chrom],
info.pos, [info.a1, info.a2]),
self._normalize_missing(geno),
reference=info.a2,
coded=info.a1,
multiallelic=info.multiallelic)
def get_variant_by_name(self, name):
"""Get the genotype of a marker using it's name.
Args:
name (str): The name of the marker.
Returns:
list: A list of Genotypes (only one for PyPlink, see note below).
Note
====
From PyPlink version 1.3.2 and onwards, each name is unique in the
dataset. Hence, we can use the 'get_geno_marker' function and be
sure only one variant is returned.
"""
# From 1.3.2 onwards, PyPlink sets unique names.
# Getting the genotypes
try:
geno, i = self.bed.get_geno_marker(name, return_index=True)
except ValueError:
if name in self.bed.get_duplicated_markers():
# The variant is a duplicated one, so we go through all the
# variants with the same name and the :dupx suffix
return [
self.get_variant_by_name(dup_name).pop()
for dup_name in self.bed.get_duplicated_markers()[name]
]
else:
# The variant is not in the BIM file, so we return an empty
# list
logger.warning("Variant {} was not found".format(name))
return []
else:
info = self.bim.iloc[i, :]
return [
Genotypes(
Variant(info.name, CHROM_INT_TO_STR[info.chrom], info.pos,
[info.a1, info.a2]),
self._normalize_missing(geno),
reference=info.a2,
coded=info.a1,
multiallelic=info.multiallelic,
)
]
def get_number_samples(self):
"""Returns the number of samples.
Returns:
int: The number of samples.
"""
return self.bed.get_nb_samples()
def get_number_variants(self):
"""Returns the number of markers.
Returns:
int: The number of markers.
"""
return self.bed.get_nb_markers()
def get_samples(self):
return list(self.fam.index)
@staticmethod
def _normalize_missing(g):
"""Normalize a plink genotype vector."""
g = g.astype(float)
g[g == -1.0] = np.nan
return g
if marker_name[:3] == 'AA_':
conditional_list.append('HLA_' + marker_name.split('_')[1])
elif marker_name[:5] == 'SNPS_':
conditional_list.append('HLA_' + marker_name.split('_')[1])
elif marker_name[:4] == 'HLA_':
conditional_list.append(
'HLA_' + marker_name.split('_')[1].split('*')[0])
elif marker_name[:9] == 'INS_SNPS_':
conditional_list.append('HLA_' + marker_name.split('_')[2])
else:
#conditional_list.append(marker_name)
r2_list = []
for idx_bim, (SNP, row) in enumerate(
plink_KCHIP_HLA_AA_SNP_1000G_bim.iterrows()):
r2 = pearsonr(
plink_KCHIP_HLA_AA_SNP_1000G.get_geno_marker(
marker_name),
plink_KCHIP_HLA_AA_SNP_1000G.get_geno_marker(
SNP))[0]**2
r2_list.append(r2)
r2_df = pd.DataFrame(
r2_list, index=plink_KCHIP_HLA_AA_SNP_1000G_bim.index)
if gene_assign[r2_df[0] > 0.95][[
'HLA_A', 'HLA_B', 'HLA_C', 'HLA_DPA1', 'HLA_DPB1',
'HLA_DQA1', 'HLA_DQB1', 'HLA_DRB1'
]].sum().sum() == 0:
conditional_list.append(marker_name)
log.info("{} not in HLA polymorphism-> added".format(
marker_name))
HLA_count = gene_assign[r2_df[0] > 0.7][[