def open_variant_file(var_type, var_file, burden_file, burden_regions,
uncompressed):
"""Open a variant file for use as an iterable
Args:
var_type (str)
Type of variants file (kmers, vcf, Rtab)
var_file (str)
Location of file
burden_file (str)
File containing regions to group burden tests
burden_regions (list)
List of burden regions to be filled in-place
uncompressed (bool)
True if kmer file is not gzipped
"""
sample_order = []
if var_type == "kmers":
if uncompressed:
infile = open(var_file)
else:
infile = gzip.open(var_file, 'r')
elif var_type == "vcf":
infile = VariantFile(var_file)
if burden_file:
load_burden(burden_file, burden_regions)
else:
# Rtab files have a header, rather than sample names accessible by row
infile = open(var_file)
header = infile.readline().rstrip()
sample_order = header.split()[1:]
return infile, sample_order
def main():
options = get_options()
# Create dummy pheno object from sample list
sample_list = []
with open(options.samples, 'r') as sample_file:
for sample in sample_file:
sample_list.append(sample.rstrip())
p = pd.Series(np.zeros(len(sample_list)), index=sample_list)
# Open variant file. Mostly copied from __main__
sample_order = []
all_strains = set(p.index)
if options.kmers:
var_type = "kmers"
if options.uncompressed:
infile = open(options.kmers)
else:
infile = gzip.open(options.kmers, 'r')
elif options.vcf:
var_type = "vcf"
infile = VariantFile(options.vcf)
else:
# Rtab files have a header, rather than sample names accessible by row
var_type = "Rtab"
infile = open(options.pres)
header = infile.readline().rstrip()
sample_order = header.split()[1:]
eof = 0
# no copy of first variant_mat made. Reserve memory
G = np.empty((len(p), block_size))
sys.stderr.write("Reading in variants\n")
v_iter = load_var_block(var_type, p, None, None, infile, all_strains,
sample_order, options.min_af, options.max_af,
options.max_missing, options.uncompressed,
block_size)
while not eof:
variants, variant_mat, eof = next(v_iter)
if G.shape[1] > block_size:
G = np.concatenate(G, variant_mat)
else:
G = variant_mat
sys.stderr.write("Calculating sample similarity\n")
K = np.matmul(G, np.transpose(G))
K_out = pd.DataFrame(K, index=p.index, columns=p.index)
K_out.to_csv(sys.stdout, sep='\t')
