def main():
#parse command line arguments
parser = argparse.ArgumentParser(description='Performs fetal CNV analysis from maternal plasma and phased parental data.')
parser.add_argument('input', type=str, nargs=1, help='path to input file with allele counts in plasma and parental haplotypes')
parser.add_argument('target', type=str, nargs=1, help='path to file with background truth - "target"')
parser.add_argument('plasma', type=str, nargs=1, help='path to file with plasma sequencing DOC for all chromosomal positions')
parser.add_argument('ref', type=str, nargs=1, help='path to file with reference plasma sequencing DOC for all chromosomal positions')
parser.add_argument('seq', type=str, nargs=1, help='path to ref. genomic sequence in fasta format')
parser.add_argument('param', type=str, nargs=1, help='path to file with method parameters')
parser.add_argument('--ff', type=float, help='fetal mixture ratio', default=-1.)
parser.add_argument('--useCvrg', help='use coverage flag', action="store_true")
parser.add_argument('--trainGrad', type=str, help='train by maxll gradient and output new params to given file', default="")
parser.add_argument('--trainMargin', type=str, help='train by max margin and output new params to given file', default="")
parser.add_argument('--getObsCounts', help='get observed allele counts', action="store_true")
args = parser.parse_args()
in_file_name = args.input[0]
target_file_name = args.target[0]
plasma_doc_file = open(args.plasma[0], "r")
ref_doc_file = open(args.ref[0], "r")
seq_file = open(args.seq[0], "r")
param_file = open(args.param[0], "r")
if args.ff > 0: mix = args.ff
runGradTraining = False
if args.trainGrad != "":
runGradTraining = True
res_param_file_name = args.trainGrad
runMarginTraining = False
if args.trainMargin != "":
runMarginTraining = True
res_param_file_name = args.trainMargin
#print input info
print "------------------------------------------"
print "Running fCNV, input parameters:"
print "input:", in_file_name
print "target:", target_file_name
print "plasma:", plasma_doc_file
print "refDOC:", ref_doc_file
print "seq:", seq_file
print "param:", param_file
print "--ff:", args.ff
print "--useCvrg:", args.useCvrg
print "--trainGrad:", args.trainGrad
print "------------------------------------------"
os.system("hostname")
#read the pre-processed input
snp_positions, samples, M, P, MSC, PSC = readInput(in_file_name)
#fetch the method parameters from file
crfParams = readParams(param_file)
print "============ CRF PARAMETERS =============="
for p in sorted(crfParams):
print p, "=", crfParams[p]
print "=========================================="
#get genomic positions on the last lines of the pileup files to estimate the length of the chromosome
if args.useCvrg:
with open(args.plasma[0], 'rb') as fh:
fh.seek(-256, 2)
last_pos_plasma = int(fh.readlines()[-1].decode().split(' ')[0])
fh.close()
# with open(args.ref[0], 'rb') as fh:
# fh.seek(-256, 2)
# last_pos_ref = int(fh.readlines()[-1].decode().split(' ')[0])
# fh.close()
chr_length = last_pos_plasma + 4742
gc_sum = [0] * chr_length
prefix_sum_plasma = [0] * chr_length
prefix_count_plasma = [0] * chr_length
prefix_sum_ref = [0] * chr_length
prefix_count_ref = [0] * chr_length
#get GC content prefix sums from the reference
gen_pos = 0
keep_reading = True
while keep_reading:
line = seq_file.readline().strip().upper()
if len(line) == 0: break
if line[0] == '>': continue
for i in range(len(line)):
gc_sum[gen_pos] = gc_sum[max(gen_pos - 1, 0)]
if line[i] in 'GC': gc_sum[gen_pos] += 1
gen_pos += 1
if gen_pos >= chr_length:
keep_reading = False
break
seq_file.close()
last = 0
while True:
line = plasma_doc_file.readline()
if not line: break
row = map(int, line.split(' '))
if row[0] >= chr_length: break
prefix_sum_plasma[row[0]] = prefix_sum_plasma[last] + row[1]
prefix_count_plasma[row[0]] = prefix_count_plasma[last] + 1
last = row[0]
plasma_doc_file.close()
last = 0
while True:
line = ref_doc_file.readline()
if not line: break
row = map(int, line.split(' '))
if row[0] >= chr_length: break
prefix_sum_ref[row[0]] = prefix_sum_ref[last] + row[1]
prefix_count_ref[row[0]] = prefix_count_ref[last] + 1
last = row[0]
ref_doc_file.close()
#ENDIF
#snp_positions = []
ground_truth = []
target_file = open(target_file_name, 'r')
for line in target_file.readlines():
line = line.rstrip("\n").split("\t")
#snp_positions.append(int(line[0]))
ground_truth.append(int(line[-1]))
target_file.close()
#fcnv = fcnvCRF.FCNV(crfParams, None, None, False)
#mix, mix_median, ct = fcnv.estimateMixture(samples, M, P)
#del fcnv
#print "Est. Mixture: ", mix, mix_median, '(', ct ,')',
mix = 0.13 #proportion of fetal genome in plasma
if args.ff > 0: mix = args.ff
print "used:", mix
cnv_prior = None
if args.useCvrg:
cvrg = cvrgHMM.coverageFCNV(snp_positions, prefix_sum_plasma, prefix_count_plasma, prefix_sum_ref, prefix_count_ref, gc_sum)
cvrg_posterior = cvrg.posteriorDecoding(mix)
del prefix_sum_plasma, prefix_count_plasma, prefix_sum_ref, prefix_count_ref, gc_sum
cnv_prior = [ [0., 0., 0.] for x in range(len(snp_positions)) ]
for pos in range(len(cvrg_posterior)):
for cp_num_posterior in cvrg_posterior[pos]:
cnv_prior[pos][cp_num_posterior[1]] = cp_num_posterior[0]
del cvrg, cvrg_posterior
fcnv = fcnvCRF.FCNV(crfParams, snp_positions, cnv_prior, args.useCvrg)
parameterStats = dict()
#get get observed allele counts
if args.getObsCounts:
parameterStats = fcnv.computeCountsTable(ground_truth, samples, M, P, MSC, PSC, mix, parameterStats)
varFileName = target_file_name.split('/')[-1].split('.')[0].replace(':', '-')+'.observedCounts.txt'
varFile = open(varFileName, 'w')
print >>varFile, len(parameterStats)
for expP in parameterStats.keys():
print >>varFile, expP
print >>varFile, " ".join(map( lambda x: '/'.join(map(str, x)), parameterStats[expP]) )
varFile.close()
return 0
#run gradient training
if runGradTraining:
#run the training iterations
for iterNum in range(1):
#print "iterNum: ", iterNum
ll, params = fcnv.computeLLandGradient(ground_truth, samples, M, P, MSC, PSC, mix)
print ll, params
print "------------------------------------------------------------------"
#save the trained parameters to the file
res_param_file = open(res_param_file_name, "w")
for p in sorted(params):
if isinstance(params[p], list):
print >>res_param_file, p, "=", " ".join(map(str, params[p]))
else:
print >>res_param_file, p, "=", params[p]
res_param_file.close()
return 0
#run max margin training
if runMarginTraining:
#run the training iterations
for iterNum in range(5): #change
print "iterNum: ", iterNum
compute_postloss = True #change + the const C
pregts, preps, preloss, params, postgts, postps, postloss = fcnv.computeLLandMaxMarginUpdate(ground_truth, samples, M, P, MSC, PSC, mix, 0.0001, compute_postloss)
print preloss, params
print "{0} !>= {1}".format(pregts - preps, preloss)
if compute_postloss: print "{0} >= {1}".format(postgts - postps, postloss)
print "------------------------------------------------------------------\n\n\n"
#save the trained parameters to the file
res_param_file = open(res_param_file_name, "w")
for p in sorted(params):
if isinstance(params[p], list):
print >>res_param_file, p, "=", " ".join(map(str, params[p]))
else:
print >>res_param_file, p, "=", params[p]
res_param_file.close()
return 0
#res_file = open(out_file_name, 'w')
file_name_prefix = target_file_name.split('/')[-1].split('.')[0].replace(':', '-')
print "------------------ w/o TRAINING -------------------"
test(fcnv, snp_positions, samples, M, P, MSC, PSC, mix, ground_truth, file_name_prefix)
def main():
#parse command line arguments
parser = argparse.ArgumentParser(description='Performs fetal CNV analysis from maternal plasma and phased parental data.')
parser.add_argument('target', type=str, nargs=1, help='path to file with background truth - "target"')
parser.add_argument('plasma', type=str, nargs=1, help='path to file with plasma sequencing DOC for all chromosomal positions')
parser.add_argument('ref', type=str, nargs=1, help='path to file with reference plasma sequencing DOC for all chromosomal positions')
parser.add_argument('seq', type=str, nargs=1, help='path to ref. genomic sequence in fasta format')
args = parser.parse_args()
target_file_name = args.target[0]
plasma_doc_file = open(args.plasma[0], "r")
ref_doc_file = open(args.ref[0], "r")
seq_file = open(args.seq[0], "r")
#get genomic positions on the last lines of the pileup files to estimate the length of the chromosome
with open(args.plasma[0], 'rb') as fh:
fh.seek(-256, 2)
last_pos_plasma = int(fh.readlines()[-1].decode().split(' ')[0])
fh.close()
with open(args.ref[0], 'rb') as fh:
fh.seek(-256, 2)
last_pos_ref = int(fh.readlines()[-1].decode().split(' ')[0])
fh.close()
chr_length = max(last_pos_plasma, last_pos_ref) + 4742
gc_sum = [0] * chr_length
prefix_sum_plasma = [0] * chr_length
prefix_count_plasma = [0] * chr_length
prefix_sum_ref = [0] * chr_length
prefix_count_ref = [0] * chr_length
#get GC content prefix sums from the reference
gen_pos = 0
keep_reading = True
while keep_reading:
line = seq_file.readline().strip().upper()
if len(line) == 0: break
if line[0] == '>': continue
for i in range(len(line)):
gc_sum[gen_pos] = gc_sum[max(gen_pos - 1, 0)]
if line[i] in 'GC': gc_sum[gen_pos] += 1
gen_pos += 1
if gen_pos >= chr_length:
keep_reading = False
break
seq_file.close()
last = 0
for line in plasma_doc_file:
row = map(int, line.split(' '))
prefix_sum_plasma[row[0]] = prefix_sum_plasma[last] + row[1]
prefix_count_plasma[row[0]] = prefix_count_plasma[last] + 1
last = row[0]
plasma_doc_file.close()
last = 0
for line in ref_doc_file:
row = map(int, line.split(' '))
prefix_sum_ref[row[0]] = prefix_sum_ref[last] + row[1]
prefix_count_ref[row[0]] = prefix_count_ref[last] + 1
last = row[0]
ref_doc_file.close()
snp_positions = []
ground_truth = []
target_file = open(target_file_name, 'r')
for line in target_file.readlines():
line = line.rstrip("\n").split("\t")
snp_positions.append(int(line[0]))
ground_truth.append(int(line[-1]))
target_file.close()
fcnv = cvrgHMM.coverageFCNV(snp_positions, prefix_sum_plasma, prefix_count_plasma, prefix_sum_ref, prefix_count_ref, gc_sum)
mix = 0.13 #proportion of fetal genome in plasma
#mix = fcnv.estimateMixture(samples, M, P)
print "Est. Mixture: ", mix
#res_file = open(out_file_name, 'w')
file_name_prefix = target_file_name.split('/')[-1].split('.')[0].replace(':', '-')
print "------------------ w/o TRAINING -------------------"
test(fcnv, snp_positions, mix, ground_truth, file_name_prefix)
def main():
#parse command line arguments
parser = argparse.ArgumentParser(
description=
'Performs fetal CNV analysis from maternal plasma and phased parental data.'
)
parser.add_argument(
'input',
type=str,
nargs=1,
help=
'path to input file with allele counts in plasma and parental haplotypes'
)
parser.add_argument('target',
type=str,
nargs=1,
help='path to file with background truth - "target"')
parser.add_argument(
'plasma',
type=str,
nargs=1,
help=
'path to file with plasma sequencing DOC for all chromosomal positions'
)
parser.add_argument(
'ref',
type=str,
nargs=1,
help=
'path to file with reference plasma sequencing DOC for all chromosomal positions'
)
parser.add_argument('seq',
type=str,
nargs=1,
help='path to ref. genomic sequence in fasta format')
parser.add_argument('--ff',
type=float,
help='fetal mixture ratio',
default=-1.)
parser.add_argument('--useCvrg',
help='use coverage flag',
action="store_true")
args = parser.parse_args()
in_file_name = args.input[0]
target_file_name = args.target[0]
plasma_doc_file = open(args.plasma[0], "r")
ref_doc_file = open(args.ref[0], "r")
seq_file = open(args.seq[0], "r")
if args.ff > 0: mix = args.ff
#print input info
print "------------------------------------------"
print "Running fCNV, input parameters:"
print "input:", in_file_name
print "target:", target_file_name
print "plasma:", plasma_doc_file
print "refDOC:", ref_doc_file
print "seq:", seq_file
print "--ff:", args.ff
print "--useCvrg:", args.useCvrg
print "------------------------------------------"
os.system("hostname")
#read the pre-processed input
snp_positions, samples, M, P, MSC, PSC = readInput(in_file_name)
#get genomic positions on the last lines of the pileup files to estimate the length of the chromosome
with open(args.plasma[0], 'rb') as fh:
fh.seek(-256, 2)
last_pos_plasma = int(fh.readlines()[-1].decode().split(' ')[0])
fh.close()
# with open(args.ref[0], 'rb') as fh:
# fh.seek(-256, 2)
# last_pos_ref = int(fh.readlines()[-1].decode().split(' ')[0])
# fh.close()
chr_length = last_pos_plasma + 4742
gc_sum = [0] * chr_length
prefix_sum_plasma = [0] * chr_length
prefix_count_plasma = [0] * chr_length
prefix_sum_ref = [0] * chr_length
prefix_count_ref = [0] * chr_length
#get GC content prefix sums from the reference
gen_pos = 0
keep_reading = True
while keep_reading:
line = seq_file.readline().strip().upper()
if len(line) == 0: break
if line[0] == '>': continue
for i in range(len(line)):
gc_sum[gen_pos] = gc_sum[max(gen_pos - 1, 0)]
if line[i] in 'GC': gc_sum[gen_pos] += 1
gen_pos += 1
if gen_pos >= chr_length:
keep_reading = False
break
seq_file.close()
last = 0
while True:
line = plasma_doc_file.readline()
if not line: break
row = map(int, line.split(' '))
if row[0] >= chr_length: break
prefix_sum_plasma[row[0]] = prefix_sum_plasma[last] + row[1]
prefix_count_plasma[row[0]] = prefix_count_plasma[last] + 1
last = row[0]
plasma_doc_file.close()
last = 0
while True:
line = ref_doc_file.readline()
if not line: break
row = map(int, line.split(' '))
if row[0] >= chr_length: break
prefix_sum_ref[row[0]] = prefix_sum_ref[last] + row[1]
prefix_count_ref[row[0]] = prefix_count_ref[last] + 1
last = row[0]
ref_doc_file.close()
#snp_positions = []
ground_truth = []
target_file = open(target_file_name, 'r')
while True:
line = target_file.readline()
if not line: break
line = line.rstrip("\n").split("\t")
#snp_positions.append(int(line[0]))
ground_truth.append(int(line[-1]))
target_file.close()
fcnv = fcnvHMM.FCNV(None, None, False)
mix, mix_median, ct = fcnv.estimateMixture(samples, M, P)
print "Est. Mixture: ", mix, mix_median, '(', ct, ')',
#mix = 0.13 #proportion of fetal genome in plasma
if args.ff > 0: mix = args.ff
print "used:", mix
cnv_prior = None
if args.useCvrg:
cvrg = cvrgHMM.coverageFCNV(snp_positions, prefix_sum_plasma,
prefix_count_plasma, prefix_sum_ref,
prefix_count_ref, gc_sum)
cvrg_posterior = cvrg.posteriorDecoding(mix)
# byLL = cvrg.likelihoodDecoding(mix)
del prefix_sum_plasma, prefix_count_plasma, prefix_sum_ref, prefix_count_ref, gc_sum
cnv_prior = [[0., 0., 0.] for x in range(len(snp_positions))]
for pos in range(len(cvrg_posterior)):
for cp_num_posterior in cvrg_posterior[pos]:
cnv_prior[pos][cp_num_posterior[1]] = cp_num_posterior[0]
del cvrg, cvrg_posterior
# ll_state = []
# ll_value = []
# for x in byLL[pos]:
# ll_state.append(x[1])
# ll_value.append(x[0])
#
# ll_str = ''
# for j in range(len(ll_state)):
# cvrg.logNormalize(ll_value)
# ll_str += "%.8f"%math.exp(ll_value[j])+' '+str(ll_state[j])+' | '
#
# posterior_str = ''
# for x in cvrg_posterior[pos]:
# posterior_str += "%.8f"%math.exp(x[0])+' '+str(x[1])+' | '
# posterior_str+='\t'
# print snp_positions[pos], 'PP:', posterior_str, 'LL:', ll_str
# del cvrg, cvrg_posterior, byLL
del fcnv
fcnv = fcnvHMM.FCNV(snp_positions, cnv_prior, args.useCvrg)
ground_truth = []
target_file = open(target_file_name, 'r')
for line in target_file.readlines():
line = line.rstrip("\n").split("\t")
ground_truth.append(int(line[-1]))
target_file.close()
#res_file = open(out_file_name, 'w')
file_name_prefix = target_file_name.split('/')[-1].split('.')[0].replace(
':', '-')
print "------------------ w/o TRAINING -------------------"
test(fcnv, snp_positions, samples, M, P, MSC, PSC, mix, ground_truth,
file_name_prefix)
#test(fcnv, snp_positions[:1000], samples[:1000], M[:1000], P[:1000], MSC[:1000], PSC[:1000], mix, ground_truth[:1000], file_name_prefix)
'''
def main():
#parse command line arguments
parser = argparse.ArgumentParser(description='Performs fetal CNV analysis from maternal plasma and phased parental data.')
parser.add_argument('input', type=str, nargs=1, help='path to input file with allele counts in plasma and parental haplotypes')
parser.add_argument('target', type=str, nargs=1, help='path to file with background truth - "target"')
parser.add_argument('plasma', type=str, nargs=1, help='path to file with plasma sequencing DOC for all chromosomal positions')
parser.add_argument('ref', type=str, nargs=1, help='path to file with reference plasma sequencing DOC for all chromosomal positions')
parser.add_argument('seq', type=str, nargs=1, help='path to ref. genomic sequence in fasta format')
parser.add_argument('--ff', type=float, help='fetal mixture ratio', default=-1.)
parser.add_argument('--useCvrg', help='use coverage flag', action="store_true")
args = parser.parse_args()
in_file_name = args.input[0]
target_file_name = args.target[0]
plasma_doc_file = open(args.plasma[0], "r")
ref_doc_file = open(args.ref[0], "r")
seq_file = open(args.seq[0], "r")
if args.ff > 0: mix = args.ff
#print input info
print "------------------------------------------"
print "Running fCNV, input parameters:"
print "input:", in_file_name
print "target:", target_file_name
print "plasma:", plasma_doc_file
print "refDOC:", ref_doc_file
print "seq:", seq_file
print "--ff:", args.ff
print "--useCvrg:", args.useCvrg
print "------------------------------------------"
os.system("hostname")
#read the pre-processed input
snp_positions, samples, M, P, MSC, PSC = readInput(in_file_name)
#get genomic positions on the last lines of the pileup files to estimate the length of the chromosome
with open(args.plasma[0], 'rb') as fh:
fh.seek(-256, 2)
last_pos_plasma = int(fh.readlines()[-1].decode().split(' ')[0])
fh.close()
# with open(args.ref[0], 'rb') as fh:
# fh.seek(-256, 2)
# last_pos_ref = int(fh.readlines()[-1].decode().split(' ')[0])
# fh.close()
chr_length = last_pos_plasma + 4742
gc_sum = [0] * chr_length
prefix_sum_plasma = [0] * chr_length
prefix_count_plasma = [0] * chr_length
prefix_sum_ref = [0] * chr_length
prefix_count_ref = [0] * chr_length
#get GC content prefix sums from the reference
gen_pos = 0
keep_reading = True
while keep_reading:
line = seq_file.readline().strip().upper()
if len(line) == 0: break
if line[0] == '>': continue
for i in range(len(line)):
gc_sum[gen_pos] = gc_sum[max(gen_pos - 1, 0)]
if line[i] in 'GC': gc_sum[gen_pos] += 1
gen_pos += 1
if gen_pos >= chr_length:
keep_reading = False
break
seq_file.close()
last = 0
while True:
line = plasma_doc_file.readline()
if not line: break
row = map(int, line.split(' '))
if row[0] >= chr_length: break
prefix_sum_plasma[row[0]] = prefix_sum_plasma[last] + row[1]
prefix_count_plasma[row[0]] = prefix_count_plasma[last] + 1
last = row[0]
plasma_doc_file.close()
last = 0
while True:
line = ref_doc_file.readline()
if not line: break
row = map(int, line.split(' '))
if row[0] >= chr_length: break
prefix_sum_ref[row[0]] = prefix_sum_ref[last] + row[1]
prefix_count_ref[row[0]] = prefix_count_ref[last] + 1
last = row[0]
ref_doc_file.close()
#snp_positions = []
ground_truth = []
target_file = open(target_file_name, 'r')
while True:
line = target_file.readline()
if not line: break
line = line.rstrip("\n").split("\t")
#snp_positions.append(int(line[0]))
ground_truth.append(int(line[-1]))
target_file.close()
fcnv = fcnvHMM.FCNV(None, None, False)
mix, mix_median, ct = fcnv.estimateMixture(samples, M, P)
print "Est. Mixture: ", mix, mix_median, '(', ct ,')',
#mix = 0.13 #proportion of fetal genome in plasma
if args.ff > 0: mix = args.ff
print "used:", mix
cnv_prior = None
if args.useCvrg:
cvrg = cvrgHMM.coverageFCNV(snp_positions, prefix_sum_plasma, prefix_count_plasma, prefix_sum_ref, prefix_count_ref, gc_sum)
cvrg_posterior = cvrg.posteriorDecoding(mix)
# byLL = cvrg.likelihoodDecoding(mix)
del prefix_sum_plasma, prefix_count_plasma, prefix_sum_ref, prefix_count_ref, gc_sum
cnv_prior = [ [0., 0., 0.] for x in range(len(snp_positions)) ]
for pos in range(len(cvrg_posterior)):
for cp_num_posterior in cvrg_posterior[pos]:
cnv_prior[pos][cp_num_posterior[1]] = cp_num_posterior[0]
del cvrg, cvrg_posterior
# ll_state = []
# ll_value = []
# for x in byLL[pos]:
# ll_state.append(x[1])
# ll_value.append(x[0])
#
# ll_str = ''
# for j in range(len(ll_state)):
# cvrg.logNormalize(ll_value)
# ll_str += "%.8f"%math.exp(ll_value[j])+' '+str(ll_state[j])+' | '
#
# posterior_str = ''
# for x in cvrg_posterior[pos]:
# posterior_str += "%.8f"%math.exp(x[0])+' '+str(x[1])+' | '
# posterior_str+='\t'
# print snp_positions[pos], 'PP:', posterior_str, 'LL:', ll_str
# del cvrg, cvrg_posterior, byLL
del fcnv
fcnv = fcnvHMM.FCNV(snp_positions, cnv_prior, args.useCvrg)
ground_truth = []
target_file = open(target_file_name, 'r')
for line in target_file.readlines():
line = line.rstrip("\n").split("\t")
ground_truth.append(int(line[-1]))
target_file.close()
#res_file = open(out_file_name, 'w')
file_name_prefix = target_file_name.split('/')[-1].split('.')[0].replace(':', '-')
print "------------------ w/o TRAINING -------------------"
test(fcnv, snp_positions, samples, M, P, MSC, PSC, mix, ground_truth, file_name_prefix)
#test(fcnv, snp_positions[:1000], samples[:1000], M[:1000], P[:1000], MSC[:1000], PSC[:1000], mix, ground_truth[:1000], file_name_prefix)
'''
