#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")