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compHartParameters.py
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compHartParameters.py
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# obtain score <-> modFreq correlations for the hart2016 dataset using various filtering criteria
# a few different timepoints, various essential gene lists
# this was a try to figure out why the Hart correlations are sometimes better and sometimes worse against the
# the scores
import glob
import annotateOffs
from collections import defaultdict
from scipy.stats import pearsonr
import sys
seqToGene = {}
timeList = ["T12B", "T15B", "T18B"]
def parseTab(fname):
" parse an R dataframe, return seq -> dict timepoint -> float "
global seqToGene
ifh = open(fname)
print "reading", fname
headers = ifh.readline().strip("\n").split("\t")
tFields= [(x,y) for x, y in enumerate(headers) if y.startswith("T")]
timepoints= [x for x in headers if x.startswith("T")]
seqData = {}
for line in ifh:
fs = line.rstrip("\n").split("\t")
gene, seq = fs[0].split("_")
seqToGene[seq] = gene
rowDict = {}
for i, tName in tFields:
rowDict[tName] = float(fs[i])
seqData[seq] = rowDict
return seqData, timepoints
def parseGenes(fname):
# read the genes to filter on, return list
syms = set()
for line in open(fname):
syms.add(line.strip().split()[0])
return syms
def parseOnlyEffScores(inFname):
""" return a dict seq -> scoreType -> score
"""
print "reading %s" % inFname
scores = {}
#freqs = {}
for row in annotateOffs.iterTsvRows(inFname):
seq = row.seq
scores[seq] = {}
dataDict = row._asdict()
for st in annotateOffs.scoreTypes:
scores[seq][st] = float(dataDict[st])
guideSeq = row.seq[:20]
scores[seq]["finalGc6"] = int(annotateOffs.countFinalGc(guideSeq, 6)>=4)
scores[seq]["finalGg"] = int(guideSeq[-2:]=="GG")
scores[seq]["modFreq"] = float(row.modFreq)
#freq = float(row.modFreq)
#freqs[seq] = freq
assert(len(scores)!=0)
return scores
def addAvg(seqFoldChanges, timePoints):
# add "avg" to seqFoldChanges
for seq, timeToFold in seqFoldChanges.iteritems():
s = 0
for t in timePoints:
s += timeToFold[t]
timeToFold["avg"] = float(s)/len(timeList)
s = 0
for t in timePoints:
if t.startswith("T18"):
s += timeToFold[t]
timeToFold["AvgLate"] = float(s)/len(timeList)
return seqFoldChanges
def main():
#geneSets = []
#for fname in ["essentialSymsInFiveCellLines.txt", "core-essential-genes-sym_HGNCID", "essential_sym_hgnc.csv"]:
#geneSet = parseGenes("effData/hart2016/essGenes/"+fname)
#geneSets.append( (fname.split(".")[0]+"/"+str(len(geneSet)), geneSet) )
rows = []
for line in open("effData/hart2016/fileNames.txt"):
if "DLD" in line:
continue
#if not "HeLa" in line:
#continue
foldChangeFname, datasetName, readFname = line.strip().split()
print "dataset %s" % datasetName
seqFoldChanges, timePoints1 = parseTab("effData/hart2016/TKOFoldChange/"+foldChangeFname)
readCounts, timePoints2 = parseTab("effData/hart2016/readCounts/readcount-"+readFname)
print "possible time points in folds: %s, time points in reads: %s" % (timePoints1, timePoints2)
allTimePoints = set(timePoints1).intersection(timePoints2)
# add "avg" to seqFoldChanges and readCounts
seqFoldChanges = addAvg(seqFoldChanges, allTimePoints)
readCounts = addAvg(readCounts, allTimePoints)
print "all time points: ", allTimePoints
allTimePoints.add("Avg")
#headers = ["libraryName", "essentialGeneList", "minReads", "minFoldChange", "timepoint", "guideCountPass"]
#headers = ["libraryName", "minReads", "minFoldChange", "timepoint", "guideCountPass"]
headers = ["libraryName", "minReads", "timepoint", "guideCountPass"]
#for geneSetName, genes in geneSets:
for minReads in [0, 100, 300]:
#for minFoldChange in [0.0, 5.0]:
#for minFoldChange in [0.0, 5.0]:
#for timepoint in ["T12B", "T15B", "T18B", "avg"]:
for timepoint in allTimePoints:
seqPredScores = parseOnlyEffScores("effData/%s%s.scores.tab" % (datasetName, timepoint))
#row = [datasetName, geneSetName, minReads, minFoldChange, timepoint]
row = [datasetName, minReads, timepoint]
print "processing %s" % str(row)
predScores = defaultdict(list)
foldChanges = []
#for seq, thisPredScores in seqFoldChanges.iteritems():
for seq, thisPredScores in seqPredScores.iteritems():
# check gene
gene = seqToGene[seq]
#if gene not in genes:
#continue
# check foldChange
#foldChangeTimePoints = seqFoldChanges[seq]["modFreq"]
#if foldChangeTimePoints is None:
#print "%s has no fold change data but has been mapped" % seq
#continue
#foldChange = -foldChangeTimePoints[timepoint]
foldChange = seqPredScores[seq]["modFreq"]
#if foldChange < minFoldChange:
#continue
# check reads at T0
if int(readCounts[seq]["T0"]) < minReads:
continue
# seq is accepted:
# add all pred scores and the fold change
for scoreName, val in thisPredScores.iteritems():
predScores[scoreName].append(val)
foldChanges.append(foldChange)
if len(predScores)==0:
#print "no data for %s" % str(row)
continue
#assert(len(predScores)!=0)
#assert(len(foldChanges)==len(predScores))
# guideCountPass
# some of the filtering critera leave us with 5 genes.
# just skip these
if len(foldChanges)<300:
continue
row.append(len(foldChanges))
for scoreName, predScoreList in predScores.iteritems():
#print "XX", scoreName, len(predScoreList)
assert(len(predScoreList)==len(foldChanges))
corr, pVal = pearsonr(predScoreList, foldChanges)
if scoreName not in headers:
headers.append(scoreName)
corr = "%0.3f" % corr
row.append(corr)
row = [str(x) for x in row]
rows.append(row)
#print "\t".join(row)
#ofh = open(sys.argv[1], "w")
ofh = open("out/hartParams.tab", "w")
ofh.write( "\t".join(headers))
ofh.write( "\n")
for row in rows:
ofh.write( "\t".join(row))
ofh.write( "\n")
print "wrote to %s" % ofh.name
main()