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helper.py
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helper.py
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import copy
import gzip
import os
import shutil
import statistics
import errno
PHENOTYPES = ['unfavorable_response', 'poor_function', 'poor_metabolizer', 'decreased_function', 'slow_metabolizer', 'intermediate_metabolizer', 'normal_function', 'normal_metabolizer', 'rapid_metabolizer', 'increased_function', 'ultrarapid_metabolizer', 'favorable_response', 'unknown', '.']
class VCF:
def __init__(self):
self.meta = []
self.header = []
self.data = []
class GDF:
def __init__(self):
self.header = []
self.data = []
class SNP:
def __init__(self):
self.pos = '' # reference genome position
self.wt = '' # wild type (*1) allele
self.var = '' # variant allele
self.rs = '' # rs ID
self.het = False # heterozygous
self.ad = 0 # allelic depth
self.td = 0 # total depth
self.n = '' # SNP table number
self.hg = '' # reference genome allele
self.so = '' # sequence ontology
self.effect = '' # coding effect
self.impact = '' # variant impact
self.rev = False # reverting variant
@property
def key(self):
return (self.pos, self.wt, self.var)
@property
def af(self):
return 0 if self.td == 0 else self.ad / self.td
def __eq__(self, other):
return self.key == other.key
def __hash__(self):
return hash(self.key)
def summary(self):
return '<{}:{}>{}:{}/{}:{:.2f}:{}:{}:{}>'.format(self.pos, self.wt, self.var, self.ad, self.td, self.af, self.so, self.impact, self.effect)
class Star:
def __init__(self):
self.name = ''
self.score = -1.0
self.core = []
self.tag = []
self.sv = ''
@property
def ranked_as(self):
if self.score == "unknown": # when sorted, unknown function alleles should be broken ties with normal function alleles using other attributes
return 1.0
elif self.score > 1: # when sorted, increased function alleles should come before normal function alleles
return 0.99
else:
return self.score
@property
def rank(self):
return (self.ranked_as, -1 * int(bool(self.sv)), -1 * len(self.core))
def __str__(self):
return self.name
def __repr__(self):
return str(self)
def __eq__(self, other):
return self.name == other.name
def __hash__(self):
return hash(self.name)
class Haplotype:
def __init__(self):
self.cand = []
self.obs = []
@property
def sv(self):
sv = 'no_sv'
sv_list = []
for star_allele in self.cand:
if star_allele.sv and star_allele.sv not in sv_list:
sv_list.append(star_allele.sv)
if len(sv_list) > 1:
raise ValueError('haplotype contains multiple structural variant calls')
if len(sv_list) == 1:
sv = sv_list[0]
return sv
@property
def af(self):
return [0] if not self.obs else [x.af for x in self.obs]
@property
def af_mean_main(self):
filtered = [x.af for x in self.obs if x.td > 10 and x.het and x in [y for y in self.cand[0].core]]
return -1 if not filtered else statistics.mean(filtered)
def af_mean_gene(self, start, end):
filtered = [x.af for x in self.obs if x.td > 10 and x.het and start <= int(x.pos) <= end]
return -1 if not filtered else statistics.mean(filtered)
def fit_data(self, total_cn, start, end):
"""Return the fit MAF and CN."""
maf_choices = []
for i in range(1, total_cn):
maf_choices.append(i / total_cn)
fit_maf = min(maf_choices, key = lambda x: abs(x - self.af_mean_gene(start, end)))
fit_cn = maf_choices.index(fit_maf) + 1
return fit_maf, fit_cn
def remove_star(self, sX):
"""Remove the given star allele from the candidates list."""
for i, star in enumerate(self.cand):
if star.name == sX.name:
del self.cand[i]
break
def add_dup(self, cn):
"""Duplicate the main star allele by the given CN."""
if cn == 1:
return
if cn > 10:
cn = 10
sX = self.cand[0]
if isinstance(sX.score, str):
score = 'unknown'
else:
score = sX.score * cn
name = sX.name + 'x' + str(cn)
sY = Star()
sY.name = name; sY.score = score; sY.core = copy.deepcopy(sX.core); sY.sv = 'cnv{}'.format(cn)
self.cand.insert(0, sY)
self.remove_star(sX)
class Sample:
def __init__(self):
self.name = '' # sample ID
self.gt = False # true if genotyped
self.sv = ['', ''] # SV calls
self.pt = '' # predicted phenotype
self.ssr = '' # sum of squared residuals
self.dip_cand = [] # candidate stars
self.hap = [Haplotype(), Haplotype()]
self.bad = False # true if QC failed
@property
def dip_score(self):
hap_scores = [self.hap[0].cand[0].score, self.hap[1].cand[0].score]
if 'unknown' in hap_scores:
return 'unknown'
else:
return sum(hap_scores)
def copy_vcf(original_vcf, items):
copied_vcf = VCF()
if 'meta' in items:
copied_vcf.meta = copy.deepcopy(original_vcf.meta)
if 'header' in items:
copied_vcf.header = copy.deepcopy(original_vcf.header)
if 'data' in items:
copied_vcf.data = copy.deepcopy(original_vcf.data)
return copied_vcf
def parse_region(region):
return {'chr': region.split(':')[0].replace('chr', ''), 'start': int(region.split(':')[1].split('-')[0]), 'end': int(region.split(':')[1].split('-')[1])}
def read_vcf_simple(file):
f = gzip.open(file, 'rt') if '.gz' in file else open(file)
vcf = VCF()
for line in f:
if '##' in line:
vcf.meta.append(line)
continue
fields = line.strip().split('\t')
if fields[0] == '#CHROM':
vcf.header = fields
continue
chr = fields[0].replace('chr', '')
vcf.data.append([chr] + fields[1:])
f.close()
return vcf
def read_vcf_region(file, region):
vcf = VCF()
region_dict = parse_region(region)
f = gzip.open(file, 'rt') if '.gz' in file else open(file)
for line in f:
if '##' in line:
vcf.meta.append(line)
continue
fields = line.strip().split('\t')
if fields[0] == '#CHROM':
vcf.header = fields
continue
chr, pos = fields[0].replace('chr', ''), int(fields[1])
if chr != region_dict['chr'] or pos < region_dict['start']:
continue
if pos > region_dict['end']:
break
vcf.data.append([chr] + fields[1:])
f.close()
return vcf
def read_vcf_minimum(file):
f = gzip.open(file, 'rt') if '.gz' in file else open(file)
vcf = VCF()
for line in f:
if '##' in line:
vcf.meta.append(line)
continue
fields = line.strip().split('\t')
if fields[0] == '#CHROM':
vcf.header = fields[:9]
continue
chr = fields[0].replace('chr', '')
vcf.data.append([chr] + fields[1:9])
f.close()
return vcf
def write_vcf(vcf, file):
with open(file, 'w') as f:
for line in vcf.meta:
f.write(line)
f.write('\t'.join(vcf.header) + '\n')
for fields in vcf.data:
f.write('\t'.join(fields) + '\n')
def write_gdf(gdf, file):
with open(file, 'w') as f:
f.write('\t'.join(gdf.header) + '\n')
for fields in gdf.data:
f.write('\t'.join([str(x) for x in fields]) + '\n')
def get_gene_dict():
gene_dict = {}
with open(os.path.dirname(os.path.realpath(__file__)) + '/gene_table.txt') as f:
header = next(f).strip().split('\t')
for line in f:
fields = line.strip().split('\t')
name = fields[header.index('name')]
chr = fields[header.index('chr')].replace('chr', '')
hg19_start = int(fields[header.index('hg19_start')])
hg19_end = int(fields[header.index('hg19_end')])
upstream = int(fields[header.index('upstream')])
downstream = int(fields[header.index('downstream')])
region = '{}:{}-{}'.format(chr, hg19_start - upstream, hg19_end + downstream)
gene_dict[name] = dict(zip(header + ['region'], fields + [region]))
return gene_dict
def get_snp_list(target_gene):
snp_list = []
with open(os.path.dirname(os.path.realpath(__file__)) + '/snp_table.txt') as f:
next(f)
for line in f:
fields = line.strip().split('\t')
gene = fields[0]
if gene != target_gene:
continue
snp = SNP()
snp.n, snp.effect, snp.pos, snp.id, snp.hg, snp.var, snp.wt, snp.so, snp.impact, snp.rev = fields[1], fields[4], fields[5], fields[6], fields[7], fields[8], fields[9], fields[10], fields[11], fields[12] == 'yes'
snp_list.append(snp)
return snp_list
def get_star_dict(target_gene, snp_list):
star_dict = {}
with open(os.path.dirname(os.path.realpath(__file__)) + '/star_table.txt') as f:
next(f)
for line in f:
fields = line.strip().split('\t')
gene = fields[0]
if gene != target_gene:
continue
star = Star()
star.name = fields[2]
star.score = float(fields[8]) if fields[8] != 'unknown' else fields[8]
star.core = [] if fields[4] == 'ref' or fields[4] == '.' else copy.deepcopy([x for x in snp_list if '{}:{}>{}'.format(x.pos, x.wt, x.var) in fields[4].split(',')])
star.tag = [] if fields[5] == '.' else copy.deepcopy([x for x in snp_list if '{}:{}>{}'.format(x.pos, x.wt, x.var) in fields[5].split(',')])
star.sv = '' if fields[6] == '.' else fields[6]
star_dict[star.name] = star
return star_dict
def vcf2samples(vcf):
samples = {}
for name in vcf.header[9:]:
sample = Sample()
sample.name = name
i = vcf.header.index(name)
for fields in vcf.data:
pos, rs, ref, alt, inf, fmt = fields[1], fields[2], fields[3], fields[4].split(','), fields[7].split(';'), fields[8]
if not any(['PS=D' in x for x in inf]):
continue
gt = [int(x) for x in fields[i].split(":")[0].split("|")]
al = [ref] + alt
vi_list = ['no_change'] + [x for x in inf if 'VI=' in x][0].replace('VI=', '').split(',')
so_list = ['no_change'] + [x for x in inf if 'SO=' in x][0].replace('SO=', '').split(',')
fe_list = ['no_change'] + [x for x in inf if 'FE=' in x][0].replace('FE=', '').split(',')
for j in [0, 1]:
snp = SNP()
snp.pos, snp.wt, snp.var, snp.rs, snp.het, snp.so, snp.impact, snp.effect = pos, ref, al[gt[j]], rs, gt[0] != gt[1], so_list[gt[j]], vi_list[gt[j]], fe_list[gt[j]]
if 'AD' in fmt:
ad_list = [int(x) for x in fields[i].split(':')[1].split(',')]
snp.ad = ad_list[gt[j]]; snp.td = sum(ad_list)
sample.hap[j].obs.append(snp)
samples[name] = sample
return samples
def delete_file(file):
try:
os.remove(file)
except OSError:
pass
def delete_dir(dir):
try:
shutil.rmtree(dir)
except OSError:
pass
def create_dir(dir):
delete_dir(dir)
os.mkdir(dir)
def sort_star_names(names):
def f(x):
cn = 1
if '*' not in x or x == '*DEL':
n = 999
else:
n = int(''.join([y for y in x.split('+')[0].split('x')[0] if y.isdigit()]))
if 'x' in x.split('+')[0]:
cn = int(x.split('+')[0].split('x')[1])
return (n, cn, len(x))
return sorted(names, key = f)
def check_file(file):
if os.path.isfile(file) or os.path.isdir(file):
return os.path.realpath(file)
raise FileNotFoundError(errno.ENOENT, os.strerror(errno.ENOENT), file)
def sample_regions(region):
size = 1000
region_dict = parse_region(region)
center = int((region_dict['start'] + region_dict['end']) / 2)
start = '{}:{}-{}'.format(region_dict['chr'], region_dict['start'], region_dict['start'] + size)
middle = '{}:{}-{}'.format(region_dict['chr'], round(center - size / 2), round(center + size / 2))
end = '{}:{}-{}'.format(region_dict['chr'], region_dict['end'] - size, region_dict['end'])
return [start, middle, end]
def sort_regions(regions):
def f(x):
region_dict = parse_region(x)
chr = 23 if region_dict['chr'] == 'X' else 24 if region_dict['chr'] == 'Y' else int(region_dict['chr'])
return (chr, region_dict['start'], region_dict['end'])
return sorted(regions, key = f)
def get_target_genes():
return [x for x in get_gene_dict() if get_gene_dict()[x]['type'] == 'target']
def get_control_genes():
return [x for x in get_gene_dict() if get_gene_dict()[x]['control'] == 'yes']
def get_cnsr(gene):
return get_gene_dict()[gene]['cnsr'].replace('chr', '')
def get_region(gene):
return get_gene_dict()[gene]['region']
def get_paralog(gene):
return get_gene_dict()[gene]['paralog']
def get_function(gene):
return get_gene_dict()[gene]['function'].capitalize()
def get_pv_member(gene):
return get_gene_dict()[gene]['pv_member'].capitalize()
def get_dpsv_member(gene):
return get_gene_dict()[gene]['dpsv_member'].capitalize()
def get_chr(gene):
return get_gene_dict()[gene]['chr'].replace('chr', '')
def get_masked_starts(gene):
return [int(x) for x in get_gene_dict()[gene]['masked_starts'].split(',')[:-1]]
def get_masked_ends(gene):
return [int(x) for x in get_gene_dict()[gene]['masked_ends'].split(',')[:-1]]
def get_hg19_start(gene):
return int(get_gene_dict()[gene]['hg19_start'])
def get_hg19_end(gene):
return int(get_gene_dict()[gene]['hg19_end'])
def check_args(arg_dict):
for arg in arg_dict:
if arg_dict[arg] == None:
raise ValueError(f"Required argument missing: --{arg}")