#!/usr/bin/env python ''' Created on Jan 29, 2014 by Ferhat Ay Modified by Arya Kaul 2017-Present ''' import sys import math import time import numpy as np from scipy import * from scipy.interpolate import Rbf, UnivariateSpline from scipy import optimize import scipy.special as scsp import bisect import gzip from scipy.stats.mstats import mquantiles from scipy import stats try: from . import myStats from . import myUtils except: import myStats import myUtils from sklearn.isotonic import IsotonicRegression from sortedcontainers import SortedList import os import argparse import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt from matplotlib.ticker import ScalarFormatter, FormatStrFormatter, MaxNLocator from pylab import * dir = os.path.dirname(__file__) version_py = os.path.join(dir, "_version.py") exec(open(version_py).read()) #============================ # parse input arguments #============================ def parse_args(args): parser = argparse.ArgumentParser(description="Check the help flag") parser.add_argument("-i", "--interactions", dest="intersfile",\ help="REQUIRED: interactions between fragment pairs are \ read from INTERSFILE", required=True) parser.add_argument("-f", "--fragments", dest="fragsfile", \ help="REQUIRED: midpoints (or start indices) \ of the fragments are read from FRAGSFILE",\ required=True) parser.add_argument("-o", "--outdir", dest="outdir", \ help="REQUIRED: where the output files\ will be written", required=True) parser.add_argument("-r", "--resolution", dest="resolution", type=int, help="REQUIRED: If the files are fixed size, please \ supply the resolution of the dataset here; otherwise, \ please use a value of 0 if the data is not fixed size." \ , required=True) parser.add_argument("-t", "--biases", dest="biasfile",\ help="RECOMMENDED: biases calculated by\ ICE or KR norm for each locus are read from BIASFILE",\ required=False) parser.add_argument("-p", "--passes", dest="noOfPasses",type=int,\ help="OPTIONAL: number of spline passes to run\ Default is 1", required=False) parser.add_argument("-b", "--noOfBins", dest="noOfBins", type=int, \ help="OPTIONAL: number of equal-occupancy (count) \ bins. Default is 100", required=False) parser.add_argument("-m", "--mappabilityThres", dest="mappabilityThreshold",\ type=int, help="OPTIONAL: minimum number of hits per \ locus that has to exist to call it mappable. DEFAULT is 1.",\ required=False) parser.add_argument("-l", "--lib", dest="libname", help="OPTIONAL: Name of the\ library that is analyzed to be used for name of file prefixes \ . DEFAULT is fithic", required=False) parser.add_argument("-U", "--upperbound", dest="distUpThres", type=int, help="OPTIONAL: upper bound on the intra-chromosomal \ distance range (unit: base pairs). DEFAULT no limit. \ STRONGLY suggested to have a limit for large genomes,\ such as human/mouse. ex. '1000000, 5000000, etc.'", required=False) parser.add_argument("-L", "--lowerbound", dest="distLowThres", type=int, help="OPTIONAL: lower bound on the intra-chromosomal \ distance range (unit: base pairs). DEFAULT no limit. \ Suggested limit is 2x the resolution of the input files", required=False) parser.add_argument("-v", "--visual", action="store_true", dest="visual",\ help="OPTIONAL: use this flag for generating plots. \ DEFAULT is False.", required=False) parser.add_argument("-x", "--contactType", dest="contactType", help="OPTIONAL: use this flag to determine which chromosomal \ regions to study (intraOnly, interOnly, All) \ DEFAULT is intraOnly", required=False) parser.add_argument("-tL", "--biasLowerBound", dest="biasLowerBound", type=float, \ help="OPTIONAL: this flag is used to determine the lower bound\ of bias values to discard. DEFAULT is 0.5"\ , required=False) parser.add_argument("-tU", "--biasUpperBound", dest="biasUpperBound", type=float, \ help="OPTIONAL: this flag is used to determine the upper bound\ of bias values to discard. DEFAULT is 2"\ , required=False) parser.add_argument("-V", "--version", action="version",version="Fit-Hi-C {}".format(__version__)\ ,help="Print version and exit") return parser.parse_args() #============================ # main function #============================ def main(): args = parse_args(sys.argv[1:]) print("\n") print("GIVEN FIT-HI-C ARGUMENTS") print("=========================") ##PARSE REQUIRED ARGUMENTS## fragsFile = args.fragsfile if os.path.exists(fragsFile): print("Reading fragments file from: %s" % fragsFile) else: print("Fragment file not found") sys.exit(2) try: fragsF = gzip.open(fragsFile, 'r') fragsF.readline() except: print("Fragments file is not gzipped. Exiting now...") sys.exit(2) contactCountsFile = args.intersfile if os.path.isfile(contactCountsFile): print("Reading interactions file from: %s" % contactCountsFile) else: print("Interaction file not found") sys.exit(2) try: contactCountsF = gzip.open(contactCountsFile, 'r') contactCountsF.readline() except: print("Interactions file is not gzipped. Exiting now...") outputPath = args.outdir if not os.path.isdir(outputPath): os.makedirs(outputPath) print("Output path created %s" % outputPath) else: print("Output path being used from %s" % outputPath) resolution = args.resolution if args.resolution == 0: print("Fixed size data not being used.") elif args.resolution > 0: print("Fixed size option detected... Fast version of FitHiC will be used") print("Resolution is %s kb" % (resolution/1000)) else: print("INVALID RESOLUTION ARGUMENT DETECTED") print("Please make sure the given resolution is a positive number greater than zero") print("User-given resolution: %s" % resolution) sys.exit(2) ##PARSE OPTIONAL ARGUMENTS## if args.biasfile is not None: if os.path.isfile(args.biasfile): print("Reading bias file from: %s" % args.biasfile) else: print("Bias file not found") sys.exit(2) else: print("No bias file") biasFile = args.biasfile noOfPasses = 1 if args.noOfPasses: noOfPasses = args.noOfPasses print("The number of spline passes is %s" % noOfPasses) noOfBins = 100 if args.noOfBins: noOfBins = args.noOfBins print("The number of bins is %s" % noOfBins) global mappThres mappThres = 1 if args.mappabilityThreshold: mappThres = args.mappabilityThreshold print("The number of reads required to consider an interaction is %s" % mappThres) libName = "FitHiC" if args.libname: libName = args.libname print("The name of the library for outputted files will be %s" % libName) global distLowThres global distUpThres distUpThres = float("inf") distLowThres = 0 if args.distUpThres: distUpThres = args.distUpThres if args.distLowThres: distLowThres = args.distLowThres print("Upper Distance threshold is %s" % distUpThres) print("Lower Distance threshold is %s" % distLowThres) global visual visual = False if args.visual: #### matplotlib fontsize settings visual = True print("Graphs will be outputted") global interOnly global allReg chromosome_region=args.contactType if chromosome_region==None: chromosome_region='intraOnly' interOnly=False allReg=False if chromosome_region == "All": print("All genomic regions will be analyzed") allReg=True elif chromosome_region == "interOnly": print("Only inter-chromosomal regions will be analyzed") interOnly=True elif chromosome_region == "intraOnly": print("Only intra-chromosomal regions will be analyzed") interOnly=False allReg=False else: print("Invalid Option. Only options are 'All', 'interOnly', or 'intraOnly'") sys.exit(2) global biasLowerBound global biasUpperBound biasLowerBound = 0.5 biasUpperBound = 2 if args.biasLowerBound: biasLowerBound = args.biasLowerBound if args.biasUpperBound: biasUpperBound = args.biasUpperBound if biasLowerBound > biasUpperBound: print("Invalid Option. Bias lower bound is greater than bias upper bound. Please fix.") sys.exit(2) print("Lower bound of bias values is %s" % biasLowerBound) print("Upper bound of bias values is %s" % biasUpperBound) print("All arguments processed. Running FitHiC now...") print("=========================") print("\n") #########################PARSING COMPLETE############################################ possibleIntraInRangeCount=0 # count of all possible in range intra-chr fragpairs observedIntraInRangeCount=0 # count of obs. in range intra-chr frags based on intxn file observedIntraInRangeSum=0 # sum of all observed intra-chr read counts in range possibleIntraAllCount=0 # Same as above, but without range restriction observedIntraAllCount=0 observedIntraAllSum=0 possibleInterAllCount=0 # Same as above, note that the notion of distance thresholds # does not apply for interchr intxns observedInterAllCount=0 observedInterAllSum=0 global baselineIntraChrProb baselineIntraChrProb=0 # 1.0/possibleIntraAllCount global interChrProb interChrProb=0 # 1.0/possibleInterAllCount minObservedGenomicDist=float("inf") maxObservedGenomicDist=0 maxPossibleGenomicDist=0 #distScaling just avoids overflow - but is necessary for large genomes global distScaling distScaling=1000000.0 #RUNBY global toKb global toMb global toProb toKb=10**-3 toMb=10**-6 toProb=10**5 #intermediate values outputted here global logfile logfile = os.path.join(outputPath, libName+".fithic.log") ##maindic will be generated first using the interactions file only # given a distance this dictionary will return [Npairs,TotalContactCount] # for only those interactions present in the interactions file # applicable for intra chromosomal interactions mainDic={} # modification - sourya - returning "observedInterAllCount" argument (mainDic,observedInterAllCount,observedInterAllSum,observedIntraAllSum,observedIntraInRangeSum) = read_Interactions(contactCountsFile, biasFile) binStats = makeBinsFromInteractions(mainDic, noOfBins, observedIntraInRangeSum) #Enumerate (fast version) or generate (otherwise) all possible pairs of fragments within the range of interest. # modification - sourya - appended "observedInterAllCount" argument # and also added "observedInterAllSum" argument (binStats,noOfFrags, maxPossibleGenomicDist, possibleIntraInRangeCount, possibleInterAllCount, interChrProb, baselineIntraChrProb) = generate_FragPairs(observedInterAllCount, observedInterAllSum, binStats, fragsFile, resolution) #read and parse bias values for each locus from ICE or KR normalization output if biasFile: biasDic = read_biases(biasFile) else: biasDic = 0 #bin the data in desired number of bins, and for each bin, calculate the average genomic distance and average contact probability (x,y,yerr)= calculateProbabilities(mainDic, binStats,resolution,os.path.join(outputPath,libName+".fithic_pass1"), observedIntraInRangeSum) splinefit1st=time.time() print("Spline fit Pass 1 starting...") outliersline = SortedList() outliersdist = SortedList() #fit a smooth spline to the bin values, and compute and write p values/q values # modified - sourya - added the parameter observedInterAllCount # the parameter "observedInterAllSum" is already present splineXinit,splineYinit,residual,outliersline, outliersdist, FDRXinit, FDRYinit= fit_Spline(mainDic,x,y,yerr,contactCountsFile,os.path.join(outputPath,libName+".spline_pass1"),biasDic, outliersline, outliersdist, observedIntraInRangeSum, possibleIntraInRangeCount, possibleInterAllCount, observedInterAllCount, observedIntraAllSum, observedInterAllSum, biasLowerBound, biasUpperBound, resolution, 1) print("Number of outliers is... %s" % len(outliersline)) splinefit1en = time.time() print("Spline fit Pass 1 completed. Time took %s" % (splinefit1en-splinefit1st)) ### DO THE NEXT PASSES IF REQUESTED ### for i in range(2,1+noOfPasses): if interOnly: print("Extra spline fits will not help with interOnly spline fit... Bypassing option") break print("\n") print("\n") # modification - sourya - returning "observedInterAllCount" argument (mainDic,observedInterAllCount,observedInterAllSum,observedIntraAllSum,observedIntraInRangeSum) = read_Interactions(contactCountsFile, biasFile, outliersline) binStats = makeBinsFromInteractions(mainDic, noOfBins, observedIntraInRangeSum, outliersdist) # modification - sourya - appended "observedInterAllCount" argument # and also appended "observedInterAllSum" argument (binStats,noOfFrags, maxPossibleGenomicDist, possibleIntraInRangeCount,possibleInterAllCount, interChrProb, baselineIntraChrProb)= generate_FragPairs(observedInterAllCount, observedInterAllSum, binStats, fragsFile, resolution) (x,y,yerr)= calculateProbabilities(mainDic, binStats,resolution,os.path.join(outputPath,libName+".fithic_pass"+str(i)), observedIntraInRangeSum) #fit a smooth spline to the bin values, and compute and write p values/q values # modified - sourya - added the parameter observedInterAllCount # the parameter "observedInterAllSum" is already present splinefitst=time.time() print("Spline fit Pass %s starting..." % i) splineX,splineY,residual,outliersline, outliersdist, FDRX, FDRY= fit_Spline(mainDic,x,y,yerr,contactCountsFile,os.path.join(outputPath,libName+".spline_pass"+str(i)),biasDic, outliersline, outliersdist, observedIntraInRangeSum, possibleIntraInRangeCount, possibleInterAllCount, observedInterAllCount, observedIntraAllSum, observedInterAllSum, biasLowerBound, biasUpperBound, resolution, i) splinefiten = time.time() print("Spline fit Pass %s completed. Time took %s" % (i,(splinefit1en-splinefit1st))) if visual: compare_Spline_FDR(FDRXinit, FDRYinit, FDRX, FDRY, os.path.join(outputPath, libName+".spline_FDR_comparison"),str(i)) compareFits_Spline(splineXinit, splineYinit, splineX, splineY, os.path.join(outputPath,libName+".spline_comparison"), str(i)) print("=========================") print("Fit-Hi-C completed successfully") print("\n") ##FUNCTIONS START### #============================ # reading interactions from input file # parameters: # contactCountsFile: file storing the contact counts for interacting fragments # the format should be ch1,mid1,ch2,mid2,contactCount (tab delimited) #============================ def read_Interactions(contactCountsFile, biasFile, outliers=None): mainDic={} print("Reading the contact counts file to generate bins...") startT = time.time() observedInterAllSum=0 #used observedIntraAllSum=0 #used observedInterAllCount=0 observedIntraAllCount=0 #notused observedIntraInRangeSum=0 #used observedIntraInRangeCount=0 #notused minObservedGenomicDist=float('inf') #notused maxObservedGenomicDist=0 #notused linectr = 0 outlierposctr = 0 #Loop through every line in the contactCountsFile with gzip.open(contactCountsFile, 'rt') as f: for lines in f: if outliers != None and outlierposctr<len(outliers): if linectr == outliers[outlierposctr]: linectr+=1 outlierposctr+=1 continue ch1,mid1,ch2,mid2,contactCount=lines.split() #create the interaction contactCount=float(contactCount) interxn=myUtils.Interaction([ch1, int(mid1), ch2, int(mid2)]) interxn.setCount(contactCount) interactionType = interxn.getType(distLowThres,distUpThres) if interactionType=='inter': observedInterAllSum += interxn.getCount() observedInterAllCount +=1 else: # any type of intra observedIntraAllSum +=interxn.getCount() observedIntraAllCount +=1 if interactionType=='intraInRange': # only the intra chromosomal interactions within the specified distance thresholds # are put in the dictionary "mainDic" #interxn.setDistance(interxn.getDistance()+(1000-interxn.getDistance()) % 1000) minObservedGenomicDist=min(minObservedGenomicDist,interxn.getDistance()) maxObservedGenomicDist=max(maxObservedGenomicDist,interxn.getDistance()) # check if the specified distance of this interaction # exists in the dictionary containing interaction distances # otherwise append the current distance if interxn.getDistance() not in mainDic: # default entry of the dictionary: two zeros: Npairs and TotalContactCount mainDic[interxn.getDistance()] = [0,0] # add the contact count and the current pair information mainDic[interxn.getDistance()][1]+=interxn.getCount() observedIntraInRangeSum +=interxn.getCount() observedIntraInRangeCount +=1 linectr+=1 endT = time.time() print("Interactions file read. Time took %s" % (endT-startT)) with open(logfile, 'w') as log: log.write("\n\nInteractions file read successfully\n") log.write("------------------------------------------------------------------------------------\n") log.write("Observed, Intra-chr in range: pairs= "+str(observedIntraInRangeCount) +"\t totalCount= "+str(observedIntraInRangeSum)+"\n") log.write("Observed, Intra-chr all: pairs= "+str(observedIntraAllCount) +"\t totalCount= "+str(observedIntraAllSum)+"\n") log.write("Observed, Inter-chr all: pairs= "+str(observedInterAllCount) +"\t totalCount= "+str(observedInterAllSum)+"\n") log.write("Range of observed genomic distances [%s %s]" % (minObservedGenomicDist,maxObservedGenomicDist) + "\n"), log.write("\n") # modification - sourya - returning "observedInterAllCount" argument return (mainDic,observedInterAllCount,observedInterAllSum,observedIntraAllSum,observedIntraInRangeSum) # from read_Interactions #============================ # function to distribute input interactions into bins # Note: this is the equal occupancy binning function # parameters: # mainDic: dictionary containing different distance values and the number of interactions falling in each category # observedIntraInRangeSum: total contact count of all intra chromosomal interactions having distance within the specified distance range #============================ def makeBinsFromInteractions(mainDic,noOfBins, observedIntraInRangeSum, outliersdist=None): with open(logfile, 'a') as log: log.write("Making equal occupancy bins\n") log.write("------------------------------------------------------------------------------------\n") noPerBin = observedIntraInRangeSum/noOfBins log.write("Observed intra-chr read counts in range\t"+repr(observedIntraInRangeSum)+ "\nDesired number of contacts per bin\t" +repr(noPerBin)+",\nNumber of bins\t"+repr(noOfBins)+"\n") # the following five lists will be the print outputs interactionTotalForBinTermination=0 n=0 # bin counter so far totalInteractionCountSoFar=0 distsToGoInAbin=[] binFull=0 desiredPerBin=(observedIntraInRangeSum)/noOfBins bins = [] for i in sorted(mainDic.keys()): #everything here is inrange by definition totalInteractionCountSoFar+=mainDic[i][1] # if one distance has more than necessary counts to fill a bin if mainDic[i][1]>=desiredPerBin: distsToGoInAbin.append(i) interactionTotalForBinTermination=0 binFull=1 # if adding the next bin will fill the bin elif interactionTotalForBinTermination+mainDic[i][1] >= desiredPerBin: distsToGoInAbin.append(i) interactionTotalForBinTermination=0 binFull=1 # if adding the next bin will not fill the bin else: distsToGoInAbin.append(i) interactionTotalForBinTermination+=mainDic[i][1] # if bin is already full if binFull==1: noOfPairsForBin=0 interactionTotalForBin=0 avgDistance=0 # dynamically update the desiredPerBin after each bin is full n+=1 if n<noOfBins: desiredPerBin=1.0*(observedIntraInRangeSum-totalInteractionCountSoFar)/(noOfBins-n) bins.append(distsToGoInAbin) interactionTotalForBinTermination=0 binFull=0 distsToGoInAbin=[] #print(bins) binStats = {} for binIdx in range(len(bins)): ##binStats #0: range of distances in this bin #1: no. of possible pairs w/in this range of distances #2: sumoverallContactCounts #3: Sumoveralldistances in this bin in distScaling vals #4: avg CC #5: avg distance #6: bins if binIdx == 0: lb = 0 else: lb = max(bins[binIdx-1])+1 ub = bins[binIdx][-1] binStats[binIdx]=[(lb, ub), 0, 0, 0, 0, 0, bins[binIdx], 0] for dists in bins[binIdx]: binStats[binIdx][2]+=mainDic[dists][1] #binStats[binIdx][3]+=(dists/distScaling) if outliersdist != None: binTracker = 0 for i in range(len(outliersdist)): intxnDistance = outliersdist[i] currBin = binStats[binTracker] minOfBin = currBin[0][0] maxOfBin = currBin[0][1] while not (minOfBin<=intxnDistance<=maxOfBin): binTracker += 1 if binTracker not in binStats: binTracker-=1 currBin = binStats[binTracker] minOfBin = currBin[0][0] maxOfBin = currBin[0][1] break else: currBin = binStats[binTracker] minOfBin = currBin[0][0] maxOfBin = currBin[0][1] currBin[7]-=1 currBin[1]-=1 with open(logfile, 'a') as log: log.write("Equal occupancy bins generated\n") log.write("\n") return binStats #================================ # function to list all possible fragment pairs # irrespective of the presence or absence of contact counts #================================ # modification - sourya - appended "observedInterAllCount" argument # and also added "observedInterAllSum" argument def generate_FragPairs(observedInterAllCount, observedInterAllSum, binStats, fragsfile, resolution): if resolution: with open(logfile, 'a') as log: log.write("Looping through all possible fragment pairs in-range\n") log.write("------------------------------------------------------------------------------------\n"), else: with open(logfile, 'a') as log: log.write("Enumerating all possible fragment pairs in-range\n") log.write("------------------------------------------------------------------------------------\n"), startT = time.time() minPossibleGenomicDist = float("inf") maxPossibleGenomicDist = 0 possibleIntraAllCount = 0 possibleInterAllCount = 0 possibleIntraInRangeCount = 0 interChrProb = 0 baselineIntraChrProb = 0 allFragsDic={} with gzip.open(fragsfile,'rt') as infile: for line in infile: words=line.split() currChr=words[0] currMid=int(words[2]) currHit=int(words[3]) if currChr not in allFragsDic: allFragsDic[currChr]=[] if currHit>=mappThres: allFragsDic[currChr].append(currMid) if resolution: noOfFrags=0 maxFrags={} for ch in allFragsDic: maxFrags[ch]=max([int(i)-resolution/2 for i in allFragsDic[ch]]) noOfFrags+=len(allFragsDic[ch]) maxPossibleGenomicDist=max(maxPossibleGenomicDist,maxFrags[ch]) for ch in sorted(allFragsDic.keys()): maxFrag=maxFrags[ch] n=len(allFragsDic[ch]) d=0 binTracker = 0 possibleIntraInRangeCountPerChr = 0 for intxnDistance in range(0,int(maxFrag+1),resolution): npairs = n-d d+=1 if myUtils.in_range_check(intxnDistance,distLowThres,distUpThres): minPossibleGenomicDist = min(minPossibleGenomicDist, intxnDistance) possibleIntraInRangeCountPerChr += npairs else: continue # condition added - sourya if (len(binStats) > 0) and (binTracker in binStats): currBin = binStats[binTracker] minOfBin = currBin[0][0] maxOfBin = currBin[0][1] while not (minOfBin<=intxnDistance<=maxOfBin): binTracker += 1 if binTracker not in binStats: binTracker-=1 currBin = binStats[binTracker] minOfBin = currBin[0][0] maxOfBin = currBin[0][1] break else: currBin = binStats[binTracker] minOfBin = currBin[0][0] maxOfBin = currBin[0][1] currBin[7]+=npairs currBin[1]+=npairs currBin[3]+=(float(intxnDistance/distScaling)*npairs) possibleIntraInRangeCountPerChr += npairs # number of all possible inter-chromosomal fragment pairs # involving the current chromosome possibleInterAllCount+=n*(noOfFrags-n) # number of all possible intra-chromosomal fragment pairs for the current chromosome possibleIntraAllCount+=(n*(n+1))/2 # n(n-1) if excluding self with open(logfile, 'a') as log: log.write("Chromosome " +repr(ch) +",\t"+str(n) +" mappable fragments, \t"+str(possibleIntraInRangeCountPerChr)\ +" possible intra-chr fragment pairs in range,\t" + str((noOfFrags-n)*n) +" possible inter-chr fragment pairs\n") # accumulate the total number of intra-chromosomal contacts possible within this distance range # (possibleIntraInRangeCountPerChr) in the global variable "possibleIntraInRangeCount" possibleIntraInRangeCount += possibleIntraInRangeCountPerChr # after looping through all the chromosomes, total number of inter-chromosomal contacts # include each chromosome twice - so divide the "possibleInterAllCount" by 2 possibleInterAllCount/=2 # most important - sourya - modification # previously inter-chromosomal contact probability was computed by dividing the # oberved contact count sum with the number of possible inter-chromosomal fragment pairs # now the probability is computed by dividing with the number of observed inter-chromosomal contact count # "observedInterAllCount" try: # modification -sourya if 0: interChrProb=1.0/possibleInterAllCount else: # sourya - tried using inter-chromosomal locus pairs with nonzero contact count if 1: if (observedInterAllCount > 0): interChrProb=1.0/observedInterAllCount else: interChrProb=0 # sourya - now tried using obvserved inter-chromosomal all contact counts (sum) if 0: if (observedInterAllSum > 0): interChrProb=1.0/observedInterAllSum else: interChrProb=0 # end modification -sourya except: interChrProb = 0 # baseline intra-chromosomal contact probability is obtained by # dividing with respect to the number of possible intra-chromosomal contact count pairs # modification -sourya if (possibleIntraAllCount > 0): baselineIntraChrProb=1.0/possibleIntraAllCount else: baselineIntraChrProb=0 # end modification -sourya else: noOfFrags = 0 for ch in allFragsDic: noOfFrags += len(allFragsDic[ch]) for ch in sorted(allFragsDic.keys()): countIntraPairs = 0 fragsPerChr = sorted(allFragsDic[ch]) templen = len(fragsPerChr) possibleInterAllCount += (noOfFrags-templen)*templen possibleIntraInRangeCountPerChr = 0 for x in range(templen): binTracker = 0 d = 0 for y in range(x+1,templen): intxnDistance = abs(float(fragsPerChr[x])-float(fragsPerChr[y])) if myUtils.in_range_check(intxnDistance, distLowThres,distUpThres): possibleIntraInRangeCountPerChr += 1 else: continue maxPossibleGenomicDist = max(maxPossibleGenomicDist, intxnDistance) minPossibleGenomicDist = min(minPossibleGenomicDist, intxnDistance) npairs = templen-d d+=1 # condition added - sourya if (len(binStats) > 0) and (binTracker in binStats): currBin = binStats[binTracker] minOfBin = currBin[0][0] maxOfBin = currBin[0][1] while not (minOfBin<=intxnDistance<=maxOfBin): binTracker += 1 if binTracker not in binStats: binTracker-=1 currBin = binStats[binTracker] minOfBin = currBin[0][0] maxOfBin = currBin[0][1] break else: currBin = binStats[binTracker] minOfBin = currBin[0][0] maxOfBin = currBin[0][1] currBin[7]+=npairs currBin[1]+=1 currBin[3]+=float(intxnDistance/distScaling)*npairs possibleIntraAllCount += 1 with open(logfile, 'a') as log: log.write("Chromosome " +repr(ch) +",\t"+str(templen) +" mappable fragments, \t"+str(possibleIntraInRangeCountPerChr)\ +" possible intra-chr fragment pairs in range,\t" + str((noOfFrags-templen)*templen) +" possible inter-chr fragment pairs\n") # accumulate the total number of intra-chromosomal contacts possible within this distance range # (possibleIntraInRangeCountPerChr) in the global variable "possibleIntraInRangeCount" possibleIntraInRangeCount += possibleIntraInRangeCountPerChr # after looping through all the chromosomes, total number of inter-chromosomal contacts # include each chromosome twice - so divide the "possibleInterAllCount" by 2 possibleInterAllCount/=2 # most important - sourya - modification # previously inter-chromosomal contact probability was computed by dividing the # oberved contact count sum with the number of possible inter-chromosomal fragment pairs # now the probability is computed by dividing with the number of observed inter-chromosomal contact count # "observedInterAllCount" try: # modification -sourya if 0: interChrProb=1.0/possibleInterAllCount else: # sourya - tried using inter-chromosomal locus pairs with nonzero contact count if 1: if (observedInterAllCount > 0): interChrProb=1.0/observedInterAllCount else: interChrProb=0 # sourya - now tried using obvserved inter-chromosomal all contact counts (sum) if 0: if (observedInterAllSum > 0): interChrProb=1.0/observedInterAllSum else: interChrProb=0 # end modification -sourya except: interChrProb = 0 # baseline intra-chromosomal contact probability is obtained by # dividing with respect to the number of possible intra-chromosomal contact count pairs # modification -sourya if (possibleIntraAllCount > 0): baselineIntraChrProb=1.0/possibleIntraAllCount else: baselineIntraChrProb=0 # end modification -sourya endT = time.time() print("Fragments file read. Time took %s" % (endT-startT)) with open(logfile, 'a') as log: log.write("Number of all fragments= %s\n" % (noOfFrags)) log.write("Possible, Intra-chr in range: pairs= %s \n" % (possibleIntraInRangeCount)) log.write("Possible, Intra-chr all: pairs= %s \n" % (possibleIntraAllCount)) log.write("Possible, Inter-chr all: pairs= %s \n" % (possibleInterAllCount)) # modification - sourya log.write("Desired genomic distance range [%d %s] \n" % (distLowThres,distUpThres)), log.write("Range of possible genomic distances [%d %d] \n" % (minPossibleGenomicDist, maxPossibleGenomicDist)), log.write("Baseline intrachromosomal probability is %s \n" % (baselineIntraChrProb)), log.write("Interchromosomal probability is %s \n" % (interChrProb)), return (binStats,noOfFrags, maxPossibleGenomicDist, possibleIntraInRangeCount, possibleInterAllCount, interChrProb, baselineIntraChrProb) # return from generate_FragPairs #===================== # read bias information #===================== def read_biases(infilename): global biasLowerBound global biasUpperBound startt = time.time() biasDic={} rawBiases=[] with gzip.open(infilename, 'rt') as infile: for line in infile: words=line.rstrip().split() chrom=words[0]; midPoint=int(words[1]); bias=float(words[2]) if bias!=1.0: rawBiases.append(bias) botQ,med,topQ=mquantiles(rawBiases,prob=[0.05,0.5,0.95]) with open(logfile, 'a') as log: log.write("5th quantile of biases: "+str(botQ)+"\n") log.write("50th quantile of biases: "+str(med)+"\n") log.write("95th quantile of biases: "+str(topQ)+"\n") totalC=0 discardC=0 with gzip.open(infilename, 'rt') as infile: for line in infile: words=line.rstrip().split() chrom=words[0]; midPoint=int(words[1]); bias=float(words[2]); if bias<biasLowerBound or math.isnan(bias): bias=-1 #botQ discardC+=1 elif bias>biasUpperBound: bias=-1 #topQ discardC+=1 totalC+=1 if chrom not in biasDic: biasDic[chrom]={} if midPoint not in biasDic[chrom]: biasDic[chrom][midPoint]=bias with open(logfile, 'a') as log: log.write("Out of " + str(totalC) + " loci " +str(discardC) +" were discarded with biases not in range [0.5 2]\n\n" ) endt = time.time() print("Bias file read. Time took %s" % (endt-startt)) return biasDic # from read_biases #================================== # function to compute the contact probabilities # applied for intra-chromosomal interactions #================================== def calculateProbabilities(mainDic,binStats,resolution,outfilename,observedIntraInRangeSum): with open(logfile, 'a') as log: log.write("\nCalculating probability means and standard deviations of contact counts\n"), log.write("------------------------------------------------------------------------------------\n"), if resolution: nameoffile = (outfilename+'.res'+str(resolution)+'.txt') else: nameoffile = (outfilename+'.txt') outfile=open(nameoffile, 'w') x = [] y = [] yerr = [] pairCounts=[] interactionTotals=[] ##binStats #0: range of distances in this bin #1: no. of possible pairs w/in this range of distances #2: sumoverallContactCounts #3: Sumoveralldistances in this bin in distScaling vals #4: avg CC #5: avg distance #6: bins #7: no. of possible pairs w/ proper dist for i in range(len(binStats)): currBin = binStats[i] sumCC = currBin[2] sumDistB4Scaling = currBin[3] possPairsInRange = currBin[1] try: # modification -sourya if (possPairsInRange > 0) and (observedIntraInRangeSum > 0): avgCC = (1.0*sumCC/possPairsInRange)/observedIntraInRangeSum else: avgCC = 0 # end modification -sourya except: print("WARNING - Zero avg. contact in bin. Ensure interaction file is correct.") avgCC = 0 try: avgDist = distScaling*(sumDistB4Scaling/currBin[7]) except: print("WARNING - Zero avg. distance in bin. Ensure interaction file is correct.") avgDist = 0 currBin[4]=avgCC currBin[5]=avgDist y.append(avgCC) x.append(avgDist) """ meanCountPerPair = 0 M2 = 0 for dists in currBin[6]: #by definition not including the nonzero dists in this bin in this calc. delta = mainDic[dists][1]-meanCountPerPair meanCountPerPair += (delta*1.0)/possPairsInRange M2 += delta*(mainDic[dists][1]-meanCountPerPair) var = M2/(possPairsInRange-1) sd = math.sqrt(var) se = sd/math.sqrt(possPairsInRange) se_p = se/observedIntraInRangeSum #yerr.append(se_p) """ yerr.append(0) pairCounts.append(possPairsInRange) interactionTotals.append(sumCC) print("Writing %s" % nameoffile) outfile.write("avgGenomicDist\tcontactProbability\tstandardError\tnoOfLocusPairs\ttotalOfContactCounts\n") for i in range(len(x)): outfile.write("%d" % x[i] + "\t"+"%.2e" % y[i]+ "\t" + "%.2e" % yerr[i] + "\t" +"%d" % pairCounts[i] + "\t" +"%d" % interactionTotals[i]+"\n") outfile.close() with open(logfile, 'a') as log: log.write("Means and error written to %s\n" % (nameoffile)), log.write("\n"), return [x,y,yerr] # from calculateProbabilities #================================== # function to fit spline, apply statistical significance correction (q-value) # modified - sourya - added the parameter observedInterAllCount #================================== def fit_Spline(mainDic,x,y,yerr,infilename,outfilename,biasDic,outliersline,outliersdist,observedIntraInRangeSum, possibleIntraInRangeCount, possibleInterAllCount, observedInterAllCount, observedIntraAllSum, observedInterAllSum, biasLowerBound, biasUpperBound, resolution, passNo): with open(logfile, 'a') as log: log.write("\nFitting a univariate spline to the probability means\n"), log.write("------------------------------------------------------------------------------------\n"), splineX = None newSplineY = None residual = None FDRx = None FDRy = None if not interOnly: if outliersdist != None: y = [f for _, f in sorted(zip(x,y), key=lambda pair: pair[0])] x.sort() for i in range(1,len(x)): if x[i]<=x[i-1]: print("ERROR in spline fitting. Distances do not decrease across bins. Ensure interaction file is correct.") print("Avg. distance of bin(i-1)... %s" % x[i-1]) print("Avg. distance of bin(i)... %s" % x[i]) sys.exit(2) # maximum residual allowed for spline is set to min(y)^2 splineError=min(y)*min(y) # use fitpack2 method -fit on the real x and y from equal occupancy binning ius = UnivariateSpline(x, y, s=splineError) tempMaxX=max(x) tempMinX=min(x) tempList=sorted([dis for dis in mainDic]) splineX=[] ### The below for loop will make sure nothing is out of range of [min(x) max(x)] ### Therefore everything will be within the range where the spline is defined for i in tempList: if tempMinX<=i<=tempMaxX: splineX.append(i) splineY=ius(splineX) #print(splineY) #print(yerr) ir = IsotonicRegression(increasing=False) newSplineY = ir.fit_transform(splineX,splineY) #print(newSplineY) residual =sum([i*i for i in (y - ius(x))]) if visual==True: xi = np.linspace(min(x),max(x),5*len(x)) yi = ius(xi) print("Plotting %s" % (outfilename + ".png")) plt.clf() fig = plt.figure() ax = fig.add_subplot(2,1,1) plt.plot(myUtils.scale_a_list(splineX,toKb), myUtils.scale_a_list(newSplineY,toProb),'g-',label="spline-"+str(passNo),linewidth=2) plt.errorbar(myUtils.scale_a_list(x,toKb),myUtils.scale_a_list(y,toProb),myUtils.scale_a_list(yerr,toProb),fmt='r.',label="Mean with std. error",linewidth=2) #plt.ylabel('Contact probability (x10$^{-5}$)',fontsize='large') #plt.xlabel('Genomic distance (kb)',fontsize='large') plt.ylabel('Contact probability (x10$^{-5}$)') plt.xlabel('Genomic distance (kb)') if distLowThres>0 and distUpThres<float("inf"): plt.xlim(myUtils.scale_a_list([distLowThres, distUpThres],toKb)) plt.gca().yaxis.set_major_locator( MaxNLocator(nbins = 3, prune=None)) ax.legend(loc="upper right") ax = fig.add_subplot(2,1,2) plt.loglog(splineX,newSplineY,'g-') plt.errorbar(x, y, yerr=yerr, fmt='r.') # Data if distLowThres>0 and distUpThres<float("inf"): plt.xlim([distLowThres, distUpThres]) plt.ylabel('Contact probability (log-scale)') plt.xlabel('Genomic distance (log-scale)') plt.savefig(outfilename+'.png') # NOW write the calculated pvalues and corrected pvalues in a file infile = gzip.open(infilename, 'rt') intraInRangeCount=0 intraOutOfRangeCount=0 intraVeryProximalCount=0 interCount=0 discardCount=0 p_vals=[] q_vals=[] biasl=[] biasr=[] # add - sourya # declare list of expected contact counts expCC_List=[] # end add - sourya for line in infile: ch1,mid1,ch2,mid2,contactCount=line.rstrip().split() contactCount = float(contactCount) interxn=myUtils.Interaction([ch1, int(mid1), ch2, int(mid2)]) interxn.setCount(contactCount) mid1 = int(mid1); mid2 = int(mid2) interactionType = interxn.getType(distLowThres,distUpThres) bias1=1.0; bias2=1.0; # assumes there is no bias to begin with # if the biasDic is not null sets the real bias values if biasDic: if ch1 not in biasDic: print("Warning. Bias file does not contain chromosome %s. \ Please ensure you're using correct file. Fit-Hi-C will continue with\ bias = -1 for this locus" % ch1) bias1 = -1 else: if mid1 not in biasDic[ch1]: print("Error. Bias file does not contain midpoint %s within \ %s. Please ensure you're using the correct file and/or resolution \ argument. Fit-Hi-C will continue with bias = -1 for this locus" \ % (mid1, ch1)) bias1 = -1 else: bias1=biasDic[ch1][mid1] if ch2 not in biasDic: print("Warning. Bias file does not contain chromosome %s. \ Please ensure you're using correct file. Fit-Hi-C will continue with\ bias = -1 for this locus" % ch2) bias2 = -1 else: if mid2 not in biasDic[ch2]: print("Error. Bias file does not contain midpoint %s within \ %s. Please ensure you're using the correct file and/or resolution \ argument. Fit-Hi-C will continue with bias = -1 for this locus" \ % (mid2, ch2)) bias2 = -1 else: bias2=biasDic[ch2][mid2] biasl.append(bias1) biasr.append(bias2) if (bias1<0 or bias2<0) and interactionType !='inter': prior_p=1.0 p_val=1.0 discardCount+=1 # add - sourya # computing expected contact count expected_CC = 0 # end add - sourya elif interactionType=='intraInRange' and not interOnly: distToLookUp=max(interxn.getDistance(),min(x)) distToLookUp=min(distToLookUp,max(x)) i=min(bisect.bisect_left(splineX, distToLookUp),len(splineX)-1) prior_p=newSplineY[i]*(bias1*bias2) p_val=scsp.bdtrc(interxn.getCount()-1,observedIntraInRangeSum,prior_p) # add - sourya # computing expected contact count # if bias values both are positive then use the probability multiplied by the bias values # otherwise, use the probability value only if ((bias1 >= biasLowerBound) and (bias1 <= biasUpperBound) and (bias2 >= biasLowerBound) and (bias2 <= biasUpperBound)): expected_CC = (observedIntraInRangeSum * prior_p) else: expected_CC = 0 # end add - sourya intraInRangeCount +=1 elif interactionType =='intraShort' and not interOnly: prior_p=1.0 p_val=1.0 intraVeryProximalCount += 1 # add - sourya # computing expected contact count expected_CC = 0 # end add - sourya elif interactionType =='intraLong' and not interOnly: prior_p=1.0 #p_val=scsp.bdtrc(interxn.getCount()-1, observedIntraAllSum,prior_p) ##RUNBY p_val=1.0 intraOutOfRangeCount += 1 # add - sourya # computing expected contact count expected_CC = 0 # end add - sourya else: if allReg or interOnly: prior_p=interChrProb*(bias1*bias2) p_val=scsp.bdtrc(interxn.getCount()-1,observedInterAllSum,prior_p) interCount += 1 # add - sourya # computing expected contact count if ((bias1 >= biasLowerBound) and (bias1 <= biasUpperBound) and (bias2 >= biasLowerBound) and (bias2 <= biasUpperBound)): expected_CC = (observedInterAllSum * prior_p) else: expected_CC = 0 # end add - sourya else: p_val=1.0 #p_vals.append(p_val) # add - sourya # computing expected contact count expected_CC=0 # end add - sourya # after the iteration, add p-value in the final list p_vals.append(p_val) # add - sourya # after the iteration, add the expected contact count in the final list expCC_List.append(expected_CC) # end add - sourya infile.close() outlierThres = 0 # Do the BH FDR correction if allReg: # modified - sourya # previously all possible inter-chromosomal interactions were considered # now only observed inter-chromosomal interactions are considered if 0: outlierThres=1.0/(possibleIntraInRangeCount+possibleInterAllCount) q_vals=myStats.benjamini_hochberg_correction(p_vals, possibleInterAllCount+possibleIntraInRangeCount) else: # sourya - tried using observedInterAllCount if 1: outlierThres=1.0/(possibleIntraInRangeCount+observedInterAllCount) q_vals=myStats.benjamini_hochberg_correction(p_vals, observedInterAllCount+possibleIntraInRangeCount) # sourya - tried using observedInterAllSum if 0: outlierThres=1.0/(possibleIntraInRangeCount+observedInterAllSum) q_vals=myStats.benjamini_hochberg_correction(p_vals, observedInterAllSum+possibleIntraInRangeCount) elif interOnly and not allReg: # modified - sourya # previously all possible inter-chromosomal interactions were considered # now only observed inter-chromosomal interactions are considered if 0: outlierThres = 1.0/possibleInterAllCount q_vals=myStats.benjamini_hochberg_correction(p_vals, possibleInterAllCount) else: # sourya - tried using observedInterAllCount if 1: outlierThres = 1.0/observedInterAllCount q_vals=myStats.benjamini_hochberg_correction(p_vals, observedInterAllCount) # sourya - tried using observedInterAllSum if 0: outlierThres = 1.0/observedInterAllSum q_vals=myStats.benjamini_hochberg_correction(p_vals, observedInterAllSum) else: outlierThres = 1.0/possibleIntraInRangeCount q_vals=myStats.benjamini_hochberg_correction(p_vals, possibleIntraInRangeCount) print("Outlier threshold is... %s" % (outlierThres)) #now we write the values back to the file infile =gzip.open(infilename, 'rt') if resolution: outfile =gzip.open(outfilename+'.res'+str(resolution)+'.significances.txt.gz', 'wt') else: outfile =gzip.open(outfilename+'.significances.txt.gz', 'wt') print("Writing p-values and q-values to file %s" % (outfilename + ".significances.txt")) # modification - sourya # previously 9 fields were written # outfile.write("chr1\tfragmentMid1\tchr2\tfragmentMid2\tcontactCount\tp-value\tq-value\tbias1\tbias2\n") # now we write an additional field, named expected contact count outfile.write("chr1\tfragmentMid1\tchr2\tfragmentMid2\tcontactCount\tp-value\tq-value\tbias1\tbias2\tExpCC\n") # end modification - sourya count=0 for line in infile: words=line.rstrip().split() chr1=words[0] midPoint1=int(words[1]) chr2=words[2] midPoint2=int(words[3]) interactionCount=float(words[4]) p_val=p_vals[count] q_val=q_vals[count] bias1=biasl[count] bias2=biasr[count] # add - sourya # add the expected contact count expected_CC=expCC_List[count] # end add - sourya if (allReg or interOnly) and chr1!=chr2: # modification - sourya # previously 9 fields were written # outfile.write("%s\t%d\t%s\t%d\t%d\t%e\t%e\t%e\t%e\n" % (str(chr1), midPoint1, str(chr2), midPoint2, interactionCount, p_val, q_val, bias1, bias2)) # now we write an additional field, named expected contact count outfile.write("%s\t%d\t%s\t%d\t%d\t%e\t%e\t%e\t%e\t%f\n" % (str(chr1), midPoint1, str(chr2), midPoint2, interactionCount, p_val, q_val, bias1, bias2, expected_CC)) # end modification - sourya if (allReg or not interOnly) and chr1==chr2: interactionDistance = abs(midPoint1-midPoint2) if myUtils.in_range_check(interactionDistance,distLowThres, distUpThres): # modification - sourya # previously 9 fields were written # outfile.write("%s\t%d\t%s\t%d\t%d\t%e\t%e\t%e\t%e\n" % (str(chr1), midPoint1, str(chr2), midPoint2, interactionCount, p_val, q_val, bias1, bias2)) # now we write an additional field, named expected contact count outfile.write("%s\t%d\t%s\t%d\t%d\t%e\t%e\t%e\t%e\t%f\n" % (str(chr1), midPoint1, str(chr2), midPoint2, interactionCount, p_val, q_val, bias1, bias2, expected_CC)) # end modification - sourya if p_val<outlierThres: outliersline.add(count) outliersdist.add(abs(midPoint1-midPoint2)) count+=1 outfile.close() infile.close() if visual == True: print("Plotting q-values to file %s" % outfilename + ".qplot.png") minFDR=0.0 maxFDR=0.05 increment=0.001 FDRx,FDRy=plot_qvalues(q_vals,minFDR,maxFDR,increment,outfilename+".qplot") with open(logfile, 'a') as log: log.write("Spline successfully fit\n"), log.write("\n"), log.write("\n"), return [splineX, newSplineY, residual, outliersline, outliersdist, FDRx, FDRy] # from fit_Spline def plot_qvalues(q_values,minFDR,maxFDR,increment,outfilename): qvalTicks=np.arange(minFDR,maxFDR+increment,increment) significantTicks=[0 for i in range(len(qvalTicks))] qvalBins=[-1 for i in range(len(q_values))] for i, q in enumerate(q_values): if math.isnan(q): q=1 #make sure NaNs are set to 1 qvalBins[i]=int(math.floor(q/increment)) for i in range(len(qvalBins)): if qvalBins[i]>=len(qvalTicks): continue significantTicks[qvalBins[i]]+=1 # make it cumulative for i in range(1,len(significantTicks)): significantTicks[i]=significantTicks[i]+significantTicks[i-1] # shift them by 1 for i in range(1,len(significantTicks)): significantTicks[-1*i]=significantTicks[-1*i-1] significantTicks[0]=0 if visual==True: plt.clf() fig = plt.figure() ax = fig.add_subplot(1,1,1) plt.plot(qvalTicks,significantTicks, 'b*-') plt.xlabel('FDR threshold') plt.ylabel('Number of significant contacts') plt.savefig(outfilename+'.png') return [qvalTicks,significantTicks] def compare_Spline_FDR(splineFDRxinit,splineFDRyinit,splineFDRx,splineFDRy,figname,i): newlab = 'spline-' + str(i) plt.clf() fig = plt.figure() ax = fig.add_subplot(1,1,1) plt.plot(splineFDRx[1:],myUtils.scale_a_list(splineFDRy[1:],toKb), 'r+-',label=newlab) plt.plot(splineFDRxinit[1:],myUtils.scale_a_list(splineFDRyinit[1:],toKb), 'g.-',label='spline-1') plt.xlabel('FDR threshold') plt.ylabel('Significant contacts (x10$^{3}$)') plt.gca().yaxis.set_major_locator( MaxNLocator(prune='lower')) lg=ax.legend(loc="lower right") lg.draw_frame(False) plt.savefig(figname+'.png') def compareFits_Spline(splineXinit,splineYinit,splineX,splineY,figname,X): downsample=min(5000,len(splineXinit)) plt.clf() fig = plt.figure() ax = fig.add_subplot(1,1,1) x=splineXinit y=splineYinit col='g.-' lab='spline-1' randIndcs=[i for i in range(len(x))] randIndcs=np.random.choice(randIndcs,downsample) randIndcs=sorted(randIndcs) x=myUtils.scale_a_list([x[i] for i in randIndcs],toKb) y=myUtils.scale_a_list([y[i] for i in randIndcs],toProb) plt.plot(x,y,col,label=lab) if figname[-1]!='1': # meaning this is not the very first step x=splineX y=splineY col='r.-' lab='spline-'+X randIndcs=[i for i in range(len(x))] randIndcs=np.random.choice(randIndcs,downsample) randIndcs=sorted(randIndcs) x=myUtils.scale_a_list([x[i] for i in randIndcs],toKb) y=myUtils.scale_a_list([y[i] for i in randIndcs],toProb) plt.plot(x,y,col,label=lab) else: # plot only at a limited range and plot discrete binning if max(x)>1000: # if it's a big genome plt.xlim([500,1000]) plt.ylim([0,1.0]) else: # small genome plt.xlim([50,100]) plt.ylim([0,0.5]) ax.legend(loc="upper right") plt.xlabel('Genomic distance (kb)') plt.ylabel('Contact probability (x10$^{-5}$)') plt.gca().yaxis.set_major_locator( MaxNLocator(prune='lower')) plt.savefig(figname+'.png') if __name__ == "__main__": main()