def load_family_details(self, pheno_covar):
"""Load family data updating the pheno_covar with family ids found.
:param pheno_covar: Phenotype/covariate object
:return: None
"""
file = open(self.fam_details)
header = file.readline()
format = file.readline()
self.file_index = 0
mask_components = [] # 1s indicate an individual is to be masked out
for line in file:
words = line.strip().split()
indid = ":".join(words[0:2])
if DataParser.valid_indid(indid):
mask_components.append(0)
sex = int(words[5])
pheno = float(words[6])
pheno_covar.add_subject(indid, sex, pheno)
else:
mask_components.append(1)
mask_components = numpy.array(mask_components)
self.ind_mask = numpy.zeros(len(mask_components) * 2,
dtype=numpy.int8).reshape(-1, 2)
self.ind_mask[0:, 0] = mask_components
self.ind_mask[0:, 1] = mask_components
self.ind_count = self.ind_mask.shape[0]
pheno_covar.freeze_subjects()
def load_fam(self, pheno_covar=None):
"""Load contents from the .fam file, updating the pheno_covar with \
family ids found.
:param pheno_covar: Phenotype/covariate object
:return: None
"""
logging.info("Loading file: %s" % (self.fam_file))
pheno_col = 5
if not DataParser.has_sex:
pheno_col -= 1
if not DataParser.has_parents:
pheno_col -= 2
if not DataParser.has_fid:
pheno_col -= 1
sex_col = pheno_col - 1
mask_components = []
for line in open(self.fam_file):
words = line.strip().split()
if len(words) > 1:
indid = ":".join(words[0:2])
if DataParser.valid_indid(indid):
mask_components.append(0)
sex = None
pheno = None
if DataParser.has_sex:
sex = int(words[sex_col])
if DataParser.has_pheno:
pheno = float(words[pheno_col])
if pheno_covar is not None:
pheno_covar.add_subject(indid, sex, pheno)
if len(words) > 0:
self.families.append(words)
else:
mask_components.append(1)
mask_components = numpy.array(mask_components)
self.ind_mask = numpy.zeros(len(mask_components), dtype=numpy.int8)
self.ind_mask = mask_components
self.ind_count = self.ind_mask.shape[0]
if pheno_covar is not None:
pheno_covar.freeze_subjects()
def load_tfam(self, pheno_covar):
"""Load the pedigree portion of the data and sort out exclusions"""
pheno_col = 5
if not DataParser.has_sex:
pheno_col -= 1
if not DataParser.has_parents:
pheno_col -= 2
if not DataParser.has_fid:
pheno_col -= 1
sex_col = pheno_col - 1
mask_components = []
for line in open(self.tfam_file):
words = line.strip().split()
if len(words) > 1:
indid = ":".join(words[0:2])
if DataParser.valid_indid(indid):
mask_components.append(0)
sex = None
pheno = None
if DataParser.has_sex:
sex = int(words[sex_col])
if DataParser.has_pheno:
pheno = float(words[pheno_col])
if pheno_covar is not None:
pheno_covar.add_subject(indid, sex, pheno)
if len(words) > 0:
self.families.append(words)
else:
mask_components.append(1)
mask_components = numpy.array(mask_components)
self.ind_mask = numpy.zeros(len(mask_components) * 2,
dtype=numpy.int8).reshape(-1, 2)
self.ind_mask[0:, 0] = mask_components
self.ind_mask[0:, 1] = mask_components
self.ind_count = self.ind_mask.shape[0]
if pheno_covar is not None:
pheno_covar.freeze_subjects()
self.load_genotypes()
def load_family_details(self, pheno_covar):
"""Load contents from the .fam file, updating the pheno_covar with \
family ids found.
:param pheno_covar: Phenotype/covariate object
:return: None
"""
self.file_index = 0
# 1s indicate an individual is to be masked out
mask_components = []
file = self.family_details
if DataParser.compressed_pedigree:
data, serr = sys_call('gunzip -c %s | wc -l' % (file))
self.line_count = int(data[0].strip().split(" ")[0])
iddata, serr = sys_call('gunzip -c %s | cut -f 1' % (file))
else:
data, serr = sys_call('wc -l %s' % (file))
self.line_count = int(data[0].strip().split(" ")[0])
iddata, serr = sys_call('cat %s | cut -f 1' % (file))
ids_observed = set()
for line in iddata:
indid = line.strip().split()[0]
indid = ":".join(indid.split("->"))
ExitIf("Duplicate ID found in dose file: %s" % (indid), indid
in ids_observed)
ids_observed.add(indid)
if DataParser.valid_indid(indid):
mask_components.append(0)
pheno_covar.add_subject(indid, PhenoCovar.missing_encoding,
PhenoCovar.missing_encoding)
else:
mask_components.append(1)
self.ind_mask = numpy.array(mask_components) == 1
self.ind_count = self.ind_mask.shape[0]
pheno_covar.freeze_subjects()
def load_genotypes(self, pheno_covar):
"""Load all data into memory and propagate valid individuals to \
pheno_covar.
:param pheno_covar: Phenotype/covariate object is updated with subject
information
:return: None
"""
first_genotype = 6
pheno_col = 5
if not DataParser.has_sex:
first_genotype -= 1
pheno_col -= 1
if not DataParser.has_parents:
first_genotype -= 2
pheno_col -= 2
if not DataParser.has_pheno:
first_genotype -= 1
if not DataParser.has_fid:
first_genotype -= 1
pheno_col -= 1
if DataParser.has_liability:
first_genotype += 1
sex_col = pheno_col - 1
individual_mask = []
self.individual_mask = []
dropped_individuals = []
# number of missing SNPs we can tolerate before dropping an individual
max_missing_for_individual = numpy.sum(
self.snp_mask[:, 0] == 0) * DataParser.ind_miss_tol
if DataParser.compressed_pedigree:
ind_count, err = sys_call("gzip -cd %s | wc -l" %
("%s.gz" % (self.datasource)))
else:
ind_count, err = sys_call("wc -l %s" % (self.datasource))
ind_count = int(ind_count[0].split()[0]) + 1
snp_count = numpy.sum(self.snp_mask[:, 0] == 0)
allelic_data = numpy.empty((ind_count, snp_count, 2), dtype='S1')
valid_allele_count = 0
if DataParser.compressed_pedigree:
input_file = gzip.open("%s.gz" % self.datasource, 'rb')
else:
input_file = open(self.datasource)
for line in input_file:
line = line.strip()
if len(line) > 0:
raw_data = line.strip().split()
alleles = numpy.ma.MaskedArray(
numpy.array(raw_data[first_genotype:]).reshape(-1, 2),
self.snp_mask).compressed().reshape(-1, 2)
# Convert the alleles into genotypes
indid = ":".join(raw_data[0:2])
if not DataParser.has_fid:
indid = raw_data[0]
# Ignore any subjects that are to be excluded and remove those
# that have too much missingness
if DataParser.valid_indid(indid):
missing = numpy.sum(
alleles[:, 0] == DataParser.missing_representation)
if missing > max_missing_for_individual:
individual_mask += [1, 1]
self.individual_mask.append(1)
dropped_individuals.append(indid)
else:
sex = None
phenotype = None
if DataParser.has_pheno:
phenotype = float(raw_data[pheno_col])
if DataParser.has_sex:
sex = int(raw_data[sex_col])
if pheno_covar is not None:
pheno_covar.add_subject(indid, sex, phenotype)
individual_mask += [0, 0]
self.individual_mask.append(0)
allelic_data[valid_allele_count] = alleles
valid_allele_count += 1
else:
individual_mask += [1, 1]
self.individual_mask.append(1)
self.ind_count = valid_allele_count
allelic_data = allelic_data[0:valid_allele_count]
self.genotypes = numpy.empty((snp_count, valid_allele_count))
max_missing_individuals = DataParser.snp_miss_tol * ind_count
dropped_loci = []
valid_snps = 0
valid_markers = []
valid_rsids = []
valid_maf = []
valid_allele_list = []
allele_count2s = []
for i in xrange(0, snp_count):
snp_geno = allelic_data[:, i]
alleles = list(
set(numpy.unique(snp_geno)) -
set([DataParser.missing_representation]))
if len(alleles) > 2:
raise TooManyAlleles(chr=self.markers[i][0],
rsid=self.rsids[i],
alleles=alleles)
allele_count1 = numpy.sum(snp_geno == alleles[0])
allele_count2 = 0
maf = 0
if len(alleles) > 1:
allele_count2 = numpy.sum(snp_geno == alleles[1])
real_allele_count2 = allele_count2
if allele_count2 > allele_count1:
sorted_alleles = [alleles[1], alleles[0]]
alleles = sorted_alleles
allele_count = allele_count1
allele_count1 = allele_count2
allele_count2 = allele_count
maf = allele_count2 / float(allele_count1 + allele_count2)
allele_count2s.append(allele_count2)
#genotypes = []
major_allele = alleles[0]
minor_allele = alleles[1]
genotype_data = numpy.sum(snp_geno == alleles[1], axis=1)
genotype_data[
snp_geno[:, 0]==DataParser.missing_representation] = \
DataParser.missing_storage
else:
major_allele = alleles[0]
minor_allele = '?'
missing = numpy.sum(genotype_data == DataParser.missing_storage)
if maf == 0 or maf < DataParser.min_maf or \
maf > DataParser.max_maf or \
max_missing_individuals < missing:
locus_details = self.markers[i]
DataParser.boundary.dropped_snps[locus_details[0]].add(
locus_details[1])
dropped_loci.append("%s:%s" %
(locus_details[0], locus_details[1]))
self.invalid_loci.append(i)
else:
self.genotypes[valid_snps, :] = genotype_data
valid_snps += 1
valid_markers.append(list(self.markers[i]))
valid_rsids.append(self.rsids[i])
valid_allele_list.append([major_allele, minor_allele])
valid_maf.append(maf)
self.markers = valid_markers
self.alleles = valid_allele_list
self.rsids = valid_rsids
self.locus_count = valid_snps
self.genotypes = self.genotypes[0:self.locus_count, :]
self.allele_count2s = allele_count2s
