def main(segfn, bedfn, outfn, pval=0.5):
mysegs=sgh.read_in_segs(segfn)
# get rid of chr if necessary,
for s in mysegs:
s.chr = s.chr.replace("chr", "")
mysegs=sorted(mysegs, key=lambda x: (x.chr, x.start, x.end))
outfh=open(outfn, 'w')
outfh.write("chr\tstart\tend\tname\tLtotal\tLmax\tpreval\tprevalsd\n")
for l in open(bedfn, 'r'):
bed=bedmod.BedEntry(l)
seghist=get_best_overlapping_seghist(bed, mysegs)
if seghist != 0:
CNprofiles=seghist.CNprofiles
ltot=get_total_likelihood(CNprofiles)
else: ltot=0
if ltot > pval:
bestCN=sorted(CNprofiles, key=lambda x: x.likelihood, reverse=True)[0]
mypreval=bestCN.pvals[0]
myprevalsd=bestCN.pvalsd[0]
mylscore=bestCN.likelihood
else:
(mypreval, myprevalsd, mylscore)=(0,0, 0)
outfh.write("\t".join(map(str, [
bed.chr, bed.start, bed.end, bed.name,
ltot, mylscore, mypreval, myprevalsd]))+"\n")
def main(segfn, bedfn, outfn, pval=0.5):
mysegs = sgh.read_in_segs(segfn)
# get rid of chr if necessary,
for s in mysegs:
s.chr = s.chr.replace("chr", "")
mysegs = sorted(mysegs, key=lambda x: (x.chr, x.start, x.end))
outfh = open(outfn, 'w')
outfh.write("chr\tstart\tend\tname\tLtotal\tLmax\tpreval\tprevalsd\n")
for l in open(bedfn, 'r'):
bed = bedmod.BedEntry(l)
seghist = get_best_overlapping_seghist(bed, mysegs)
if seghist != 0:
CNprofiles = seghist.CNprofiles
ltot = get_total_likelihood(CNprofiles)
else:
ltot = 0
if ltot > pval:
bestCN = sorted(CNprofiles,
key=lambda x: x.likelihood,
reverse=True)[0]
mypreval = bestCN.pvals[0]
myprevalsd = bestCN.pvalsd[0]
mylscore = bestCN.likelihood
else:
(mypreval, myprevalsd, mylscore) = (0, 0, 0)
outfh.write("\t".join(
map(str, [
bed.chr, bed.start, bed.end, bed.name, ltot, mylscore,
mypreval, myprevalsd
])) + "\n")
def main(segfn, bedfn, useall, outfn):
mysegs=sgh.read_in_segs(segfn)
for s in mysegs:
s.chr = s.chr.replace("chr", "")
mysegs=sorted(mysegs, key=lambda x: (x.chr, x.start, x.end))
mybeds=read_in_bedfile(bedfn)
bestsegs=[]
outfh=open(outfn, 'w')
for s in mysegs:
label=label_segment(s, mybeds)
if not useall:
CNprofs=s.CNprofiles
bestcnp=sorted(CNprofs, key=lambda x: x.likelihood, reverse=True)[0]
s.CNprofiles=[bestcnp]
outstr=""
for line in str(s).split("\n"):
if line !="":
outstr+=line.strip() + "\t"+label+"\n"
outfh.write(outstr)
outfh.close()