#Minimum reads for PCM: 100
crit_min=nsmd.crit_min_reads(fpcm,reg,gene=None,minreads=100)
#Normalized reads in pcm bigger than in wt.
crit_frac = nsmd.crit_frac_compare(fwt,fpcm,reg)
#Gene in expressed in pcm with alpha=1
crit_exp = nsmd.crit_expressed(fpcm,reg)
#Preliminary candidates satisfying the 3 criteria.
candidates = [g for g in nsmd.gene_loc if g in crit_min and g in crit_frac and g in crit_exp]
#Test for center of mass is more expensive. Run it only for candidates.
crit_cmass = nsmd.crit_cmass(fwt,fpcm,reg,gene=None,only=candidates,alpha=0.1)
#Save results
f = open(nsmd.full_path("candidates1.txt","results"),"w")
for g in crit_cmass:
f.write(g+"\n")
f.close()
#Save plots:
fignames = [g+".jpg" for g in crit_cmass]
nfignames = ["norm_"+n for n in fignames]
nsmd.plot_pileup(fwt,fpcm,reg,gene=crit_cmass,show=False,filename=fignames)
nsmd.plot_pileup(fwt,fpcm,reg,gene=crit_cmass,show=False,filename=nfignames,norm=True)
import os
os.chdir("..")
import nsmd
pre = "2L/"
list_cand = [pre+"candidates"+str(i)+".txt" for i in range(1,7)]
fout = pre+"candidates_ranking.txt"
c = {}
for fcand in list_cand:
f = open(nsmd.full_path(fcand,"results"),"r")
candidates = f.read().splitlines()
f.close()
for cc in candidates:
if cc not in c:
c[cc] = 1
else:
c[cc]+= 1
#Sort by value.
sc = sorted(c,key=c.get)
sc.reverse()
#Save resutls:
f = open(nsmd.full_path(fout,"results"),"w")
