def run(self):
AbstractAnalysis.run(self) #Call base method to do some logging
refSequences = getFastaDictionary(self.referenceFastaFile) #Hash of names to sequences
readSequences = getFastqDictionary(self.readFastqFile) #Hash of names to sequences
sam = pysam.Samfile(self.samFile, "r" )
indelCounters = map(lambda aR : IndelCounter(sam.getrname(aR.rname), refSequences[sam.getrname(aR.rname)], aR.qname, readSequences[aR.qname], aR), samIterator(sam)) #Iterate on the sam lines
sam.close()
#Write out the substitution info
if len(indelCounters) > 0:
indelXML = getAggregateIndelStats(indelCounters)
open(os.path.join(self.outputDir, "indels.xml"), "w").write(prettyXml(indelXML))
tmp = open(os.path.join(self.outputDir, "indels.tsv"), "w")
#build list of data as vectors
data_list = []
var = ["readInsertionLengths", "readDeletionLengths", "ReadSequenceLengths", "NumberReadInsertions", "NumberReadDeletions", "MedianReadInsertionLengths", "MedianReadDeletionLengths"]
for x in var:
data_list.append([x] + indelXML.attrib[x].split())
#transpose this list so R doesn't take hours to load it using magic
data_list = map(None, *data_list)
for line in data_list:
tmp.write("\t".join(map(str,line))); tmp.write("\n")
tmp.close()
system("Rscript nanopore/analyses/indelPlots.R {} {}".format(os.path.join(self.outputDir, "indels.tsv"), os.path.join(self.outputDir, "indel_plots.pdf")))
self.finish() #Indicates the batch is done
def run(self):
AbstractAnalysis.run(self) #Call base method to do some logging
refSequences = getFastaDictionary(self.referenceFastaFile) #Hash of names to sequences
readSequences = getFastqDictionary(self.readFastqFile) #Hash of names to sequences
sam = pysam.Samfile(self.samFile, "r" )
#The data we collect
avgPosteriorMatchProbabilityInCigar = []
alignedPairsInCigar = []
posteriorMatchProbabilities = []
for aR in samIterator(sam): #Iterate on the sam lines
#Exonerate format Cigar string
cigarString = getExonerateCigarFormatString(aR, sam)
#Temporary files
tempCigarFile = os.path.join(self.getLocalTempDir(), "rescoredCigar.cig")
tempRefFile = os.path.join(self.getLocalTempDir(), "ref.fa")
tempReadFile = os.path.join(self.getLocalTempDir(), "read.fa")
tempPosteriorProbsFile = os.path.join(self.getLocalTempDir(), "probs.tsv")
#Write the temporary files.
fastaWrite(tempRefFile, sam.getrname(aR.rname), refSequences[sam.getrname(aR.rname)])
fastaWrite(tempReadFile, aR.qname, aR.query)
#Trained hmm file to use.
hmmFile = os.path.join(pathToBaseNanoporeDir(), "nanopore", "mappers", "blasr_hmm_0.txt")
#Call to cactus_realign
system("echo %s | cactus_realign %s %s --rescoreByPosteriorProbIgnoringGaps --rescoreOriginalAlignment --diagonalExpansion=10 --splitMatrixBiggerThanThis=100 --outputPosteriorProbs=%s --loadHmm=%s > %s" % \
(cigarString, tempRefFile, tempReadFile, tempPosteriorProbsFile, hmmFile, tempCigarFile))
#Load the cigar and get the posterior prob
assert len([ pA for pA in cigarRead(open(tempCigarFile)) ]) > 0
assert len([ pA for pA in cigarRead(open(tempCigarFile)) ]) == 1
pA = [ i for i in cigarRead(open(tempCigarFile)) ][0]
avgPosteriorMatchProbabilityInCigar.append(pA.score)
#Calculate the number of aligned pairs in the cigar
alignedPairsInCigar.append(sum([ op.length for op in pA.operationList if op.type == PairwiseAlignment.PAIRWISE_MATCH ]))
assert alignedPairsInCigar[-1] == len([ readPos for readPos, refPos in aR.aligned_pairs if readPos != None and refPos != None ])
#Get the posterior probs
#posteriorMatchProbabilities += [ float(line.split()[2]) for line in open(tempPosteriorProbsFile) ]
sam.close()
#Write out the substitution info
node = ET.Element("alignmentUncertainty", {
"averagePosteriorMatchProbabilityPerRead":str(self.formatRatio(sum(avgPosteriorMatchProbabilityInCigar), len(avgPosteriorMatchProbabilityInCigar))),
"averagePosteriorMatchProbability":str(self.formatRatio(float(sum([ avgMatchProb*alignedPairs for avgMatchProb, alignedPairs in zip(avgPosteriorMatchProbabilityInCigar, alignedPairsInCigar) ])),sum(alignedPairsInCigar))),
"averagePosteriorMatchProbabilitesPerRead":",".join([ str(i) for i in avgPosteriorMatchProbabilityInCigar ]),
"alignedPairsInCigar":",".join([ str(i) for i in alignedPairsInCigar ]) })
open(os.path.join(self.outputDir, "alignmentUncertainty.xml"), "w").write(prettyXml(node))
if len(avgPosteriorMatchProbabilityInCigar) > 0:
outf = open(os.path.join(self.getLocalTempDir(), "tmp_uncertainty"), "w")
outf.write("\t".join([ str(i) for i in avgPosteriorMatchProbabilityInCigar ])); outf.write("\n")
outf.close()
system("Rscript nanopore/analyses/match_hist.R {} {}".format(os.path.join(self.getLocalTempDir(), "tmp_uncertainty"), os.path.join(self.outputDir, "posterior_prob_hist.pdf")))
#Indicate everything is all done
self.finish()
def run(self, globalAlignment=False):
AbstractAnalysis.run(self) #Call base method to do some logging
refSequences = getFastaDictionary(self.referenceFastaFile) #Hash of names to sequences
readSequences = getFastqDictionary(self.readFastqFile) #Hash of names to sequences
sam = pysam.Samfile(self.samFile, "r" )
readsToReadCoverages = {}
for aR in samIterator(sam): #Iterate on the sam lines
refSeq = refSequences[sam.getrname(aR.rname)]
readSeq = readSequences[aR.qname]
readAlignmentCoverageCounter = ReadAlignmentCoverageCounter(aR.qname, readSeq, sam.getrname(aR.rname), refSeq, aR, globalAlignment)
if aR.qname not in readsToReadCoverages:
readsToReadCoverages[aR.qname] = []
readsToReadCoverages[aR.qname].append(readAlignmentCoverageCounter)
sam.close()
#Write out the coverage info for differing subsets of the read alignments
if len(readsToReadCoverages.values()) > 0:
for readCoverages, outputName in [ (reduce(lambda x, y : x + y, readsToReadCoverages.values()), "coverage_all"), (map(lambda x : max(x, key=lambda y : y.readCoverage()), readsToReadCoverages.values()), "coverage_bestPerRead") ]:
parentNode = getAggregateCoverageStats(readCoverages, outputName, refSequences, readSequences, readsToReadCoverages, outputName)
open(os.path.join(self.outputDir, outputName + ".xml"), 'w').write(prettyXml(parentNode))
#this is a ugly file format with each line being a different data type - column length is variable
outf = open(os.path.join(self.outputDir, outputName + ".txt"), "w")
outf.write("MappedReadLengths " + parentNode.get("mappedReadLengths") + "\n")
outf.write("UnmappedReadLengths " + parentNode.get("unmappedReadLengths") + "\n")
outf.write("ReadCoverage " + parentNode.get("distributionreadCoverage") + "\n")
outf.write("MismatchesPerReadBase " + parentNode.get("distributionmismatchesPerReadBase") + "\n")
outf.write("ReadIdentity " + parentNode.get("distributionidentity") + "\n")
outf.write("InsertionsPerBase " + parentNode.get("distributioninsertionsPerReadBase") + "\n")
outf.write("DeletionsPerBase " + parentNode.get("distributiondeletionsPerReadBase") + "\n")
outf.close()
system("Rscript nanopore/analyses/coverage_plot.R {} {}".format(os.path.join(self.outputDir, outputName + ".txt"), os.path.join(self.outputDir, outputName + ".pdf")))
self.finish()
def run(self, globalAlignment=False):
AbstractAnalysis.run(self) #Call base method to do some logging
refSequences = getFastaDictionary(
self.referenceFastaFile) #Hash of names to sequences
readSequences = getFastqDictionary(
self.readFastqFile) #Hash of names to sequences
sam = pysam.Samfile(self.samFile, "r")
readsToReadCoverages = {}
for aR in samIterator(sam): #Iterate on the sam lines
refSeq = refSequences[sam.getrname(aR.rname)]
readSeq = readSequences[aR.qname]
readAlignmentCoverageCounter = ReadAlignmentCoverageCounter(
aR.qname, readSeq, sam.getrname(aR.rname), refSeq, aR,
globalAlignment)
if aR.qname not in readsToReadCoverages:
readsToReadCoverages[aR.qname] = []
readsToReadCoverages[aR.qname].append(readAlignmentCoverageCounter)
sam.close()
#Write out the coverage info for differing subsets of the read alignments
if len(readsToReadCoverages.values()) > 0:
for readCoverages, outputName in [
(reduce(lambda x, y: x + y,
readsToReadCoverages.values()), "coverage_all"),
(map(lambda x: max(x, key=lambda y: y.readCoverage()),
readsToReadCoverages.values()), "coverage_bestPerRead")
]:
parentNode = getAggregateCoverageStats(
readCoverages, outputName, refSequences, readSequences,
readsToReadCoverages, outputName)
open(os.path.join(self.outputDir, outputName + ".xml"),
'w').write(prettyXml(parentNode))
#this is a ugly file format with each line being a different data type - column length is variable
outf = open(os.path.join(self.outputDir, outputName + ".txt"),
"w")
outf.write("MappedReadLengths " +
parentNode.get("mappedReadLengths") + "\n")
outf.write("UnmappedReadLengths " +
parentNode.get("unmappedReadLengths") + "\n")
outf.write("ReadCoverage " +
parentNode.get("distributionreadCoverage") + "\n")
outf.write(
"MismatchesPerReadBase " +
parentNode.get("distributionmismatchesPerReadBase") + "\n")
outf.write("ReadIdentity " +
parentNode.get("distributionidentity") + "\n")
outf.write(
"InsertionsPerBase " +
parentNode.get("distributioninsertionsPerReadBase") + "\n")
outf.write("DeletionsPerBase " +
parentNode.get("distributiondeletionsPerReadBase") +
"\n")
outf.close()
system(
"Rscript nanopore/analyses/coverage_plot.R {} {}".format(
os.path.join(self.outputDir, outputName + ".txt"),
os.path.join(self.outputDir, outputName + ".pdf")))
self.finish()
def run(self, args=""):
tempSamFile = os.path.join(self.getLocalTempDir(), "temp.sam")
system("blasr %s %s -sam -clipping hard %s > %s" % (self.readFastqFile, self.referenceFastaFile, args, tempSamFile))
#Blasr seems to corrupt the names of read sequences, so lets correct them.
sam = pysam.Samfile(tempSamFile, "r" )
outputSam = pysam.Samfile(self.outputSamFile, "wh", template=sam)
readSequences = getFastqDictionary(self.readFastqFile) #Hash of names to sequences
for aR in sam: #Iterate on the sam lines and put into buckets by read
if aR.qname not in readSequences:
newName = '/'.join(aR.qname.split('/')[:-1])
if newName not in readSequences:
raise RuntimeError("Tried to deduce correct read name: %s, %s" % (newName, readSequences.keys()))
aR.qname = newName
outputSam.write(aR)
outputSam.close()
def run(self):
AbstractAnalysis.run(self)
readSequences = getFastqDictionary(self.readFastqFile)
nr = re.compile(r"channel_[0-9]+_read_[0-9]+")
per_channel_read_counts = Counter([int(x.split("_")[1]) for x in readSequences.iterkeys() if re.match(nr, x)])
sam = pysam.Samfile(self.samFile, "r")
mapped_read_counts = Counter([int(aR.qname.split("_")[1]) for aR in samIterator(sam) if re.match(nr, aR.qname) and aR.is_unmapped is False])
if len(mapped_read_counts) > 0 and len(per_channel_read_counts) > 0:
outf = open(os.path.join(self.outputDir, "channel_mappability.tsv"), "w")
outf.write("Channel\tReadCount\tMappableReadCount\n")
max_channel = max(513, max(per_channel_read_counts.keys())) #in case there are more than 512 in the future
for channel in xrange(1, max_channel):
outf.write("\t".join(map(str, [channel, per_channel_read_counts[channel], mapped_read_counts[channel]])))
outf.write("\n")
outf.close()
system("Rscript nanopore/analyses/channel_plots.R {} {} {} {} {}".format(os.path.join(self.outputDir, "channel_mappability.tsv"), os.path.join(self.outputDir, "channel_mappability.pdf"), os.path.join(self.outputDir, "channel_mappability_sorted.png"), os.path.join(self.outputDir, "mappability_levelplot.png"), os.path.join(self.outputDir, "mappability_leveplot_percent.png")))
self.finish()
def run(self, args=""):
tempSamFile = os.path.join(self.getLocalTempDir(), "temp.sam")
system(
"blasr %s %s -sam -clipping hard %s > %s" %
(self.readFastqFile, self.referenceFastaFile, args, tempSamFile))
#Blasr seems to corrupt the names of read sequences, so lets correct them.
sam = pysam.Samfile(tempSamFile, "r")
outputSam = pysam.Samfile(self.outputSamFile, "wh", template=sam)
readSequences = getFastqDictionary(
self.readFastqFile) #Hash of names to sequences
for aR in sam: #Iterate on the sam lines and put into buckets by read
if aR.qname not in readSequences:
newName = '/'.join(aR.qname.split('/')[:-1])
if newName not in readSequences:
raise RuntimeError(
"Tried to deduce correct read name: %s, %s" %
(newName, readSequences.keys()))
aR.qname = newName
outputSam.write(aR)
outputSam.close()
def run(self):
AbstractAnalysis.run(self) #Call base method to do some logging
refSequences = getFastaDictionary(
self.referenceFastaFile) #Hash of names to sequences
readSequences = getFastqDictionary(
self.readFastqFile) #Hash of names to sequences
sam = pysam.Samfile(self.samFile, "r")
indelCounters = map(lambda aR: IndelCounter(
sam.getrname(aR.rname), refSequences[sam.getrname(
aR.rname)], aR.qname, readSequences[aR.qname], aR),
samIterator(sam)) #Iterate on the sam lines
sam.close()
#Write out the substitution info
if len(indelCounters) > 0:
indelXML = getAggregateIndelStats(indelCounters)
open(os.path.join(self.outputDir, "indels.xml"),
"w").write(prettyXml(indelXML))
tmp = open(os.path.join(self.outputDir, "indels.tsv"), "w")
#build list of data as vectors
data_list = []
var = [
"readInsertionLengths", "readDeletionLengths",
"ReadSequenceLengths", "NumberReadInsertions",
"NumberReadDeletions", "MedianReadInsertionLengths",
"MedianReadDeletionLengths"
]
for x in var:
data_list.append([x] + indelXML.attrib[x].split())
#transpose this list so R doesn't take hours to load it using magic
data_list = map(None, *data_list)
for line in data_list:
tmp.write("\t".join(map(str, line)))
tmp.write("\n")
tmp.close()
system("Rscript nanopore/analyses/indelPlots.R {} {}".format(
os.path.join(self.outputDir, "indels.tsv"),
os.path.join(self.outputDir, "indel_plots.pdf")))
self.finish() #Indicates the batch is done
def run(self, kmer=5):
AbstractAnalysis.run(self) #Call base method to do some logging
refSequences = getFastaDictionary(self.referenceFastaFile) #Hash of names to sequences
readSequences = getFastqDictionary(self.readFastqFile) #Hash of names to sequences
sM = SubstitutionMatrix() #The thing to store the counts in
sam = pysam.Samfile(self.samFile, "r" )
for aR in samIterator(sam): #Iterate on the sam lines
for aP in AlignedPair.iterator(aR, refSequences[sam.getrname(aR.rname)], readSequences[aR.qname]): #Walk through the matches mismatches:
sM.addAlignedPair(aP.getRefBase(), aP.getReadBase())
sam.close()
#Write out the substitution info
open(os.path.join(self.outputDir, "substitutions.xml"), 'w').write(prettyXml(sM.getXML()))
bases = "ACGT"
outf = open(os.path.join(self.outputDir, "subst.tsv"), "w")
outf.write("A\tC\tG\tT\n")
for x in bases:
freqs = sM.getFreqs(x, bases)
outf.write("{}\t{}\n".format(x, "\t".join(map(str,freqs)), "\n"))
outf.close()
analysis = self.outputDir.split("/")[-2].split("_")[-1] + "_Substitution_Levels"
system("Rscript nanopore/analyses/substitution_plot.R {} {} {}".format(os.path.join(self.outputDir, "subst.tsv"), os.path.join(self.outputDir, "substitution_plot.pdf"), analysis))
self.finish()
def run(self):
AbstractAnalysis.run(self)
readSequences = getFastqDictionary(self.readFastqFile)
nr = re.compile(r"channel_[0-9]+_read_[0-9]+")
per_channel_read_counts = Counter([
int(x.split("_")[1]) for x in readSequences.iterkeys()
if re.match(nr, x)
])
sam = pysam.Samfile(self.samFile, "r")
mapped_read_counts = Counter([
int(aR.qname.split("_")[1]) for aR in samIterator(sam)
if re.match(nr, aR.qname) and aR.is_unmapped is False
])
if len(mapped_read_counts) > 0 and len(per_channel_read_counts) > 0:
outf = open(
os.path.join(self.outputDir, "channel_mappability.tsv"), "w")
outf.write("Channel\tReadCount\tMappableReadCount\n")
max_channel = max(513, max(per_channel_read_counts.keys())
) #in case there are more than 512 in the future
for channel in xrange(1, max_channel):
outf.write("\t".join(
map(str, [
channel, per_channel_read_counts[channel],
mapped_read_counts[channel]
])))
outf.write("\n")
outf.close()
system("Rscript nanopore/analyses/channel_plots.R {} {} {} {} {}".
format(
os.path.join(self.outputDir, "channel_mappability.tsv"),
os.path.join(self.outputDir, "channel_mappability.pdf"),
os.path.join(self.outputDir,
"channel_mappability_sorted.png"),
os.path.join(self.outputDir,
"mappability_levelplot.png"),
os.path.join(self.outputDir,
"mappability_leveplot_percent.png")))
self.finish()
def run(self):
#Call base method to do some logging
AbstractAnalysis.run(self)
#Hmm file
hmmFile = os.path.join(os.path.split(self.samFile)[0], "hmm.txt.xml")
if os.path.exists(hmmFile):
#Load the hmm
hmmsNode = ET.parse(hmmFile).getroot()
#Plot graphviz version of nanopore hmm, showing transitions and variances.
fH = open(os.path.join(self.outputDir, "hmm.dot"), 'w')
setupGraphFile(fH)
#Make states
addNodeToGraph("n0n", fH, "match")
addNodeToGraph("n1n", fH, "short delete")
addNodeToGraph("n2n", fH, "short insert")
addNodeToGraph("n3n", fH, "long insert")
addNodeToGraph("n4n", fH, "long delete")
#Make edges with labelled transition probs.
for transition in hmmsNode.findall("transition"):
if float(transition.attrib["avg"]) > 0.0:
addEdgeToGraph("n%sn" % transition.attrib["from"],
"n%sn" % transition.attrib["to"],
fH, dir="arrow", style='""',
label="%.3f,%.3f" % (float(transition.attrib["avg"]), float(transition.attrib["std"])))
#Finish up
finishGraphFile(fH)
fH.close()
#Plot match emission data
emissions = dict([ ((emission.attrib["x"], emission.attrib["y"]), emission.attrib["avg"]) \
for emission in hmmsNode.findall("emission") if emission.attrib["state"] == '0' ])
matchEmissionsFile = os.path.join(self.outputDir, "matchEmissions.tsv")
outf = open(matchEmissionsFile, "w")
bases = "ACGT"
outf.write("\t".join(bases) + "\n")
for base in bases:
outf.write("\t".join([ base] + map(lambda x : emissions[(base, x)], bases)) + "\n")
outf.close()
system("Rscript nanopore/analyses/substitution_plot.R %s %s %s" % (matchEmissionsFile, os.path.join(self.outputDir, "substitution_plot.pdf"), "Per-Base Substitutions after HMM"))
#Plot indel info
#Get the sequences to contrast the neutral model.
refSequences = getFastaDictionary(self.referenceFastaFile) #Hash of names to sequences
readSequences = getFastqDictionary(self.readFastqFile) #Hash of names to sequences
#Need to do plot of insert and deletion gap emissions
#Plot of insert and deletion gap emissions
insertEmissions = { "A":0.0, 'C':0.0, 'G':0.0, 'T':0.0 }
deleteEmissions = { "A":0.0, 'C':0.0, 'G':0.0, 'T':0.0 }
for emission in hmmsNode.findall("emission"):
if emission.attrib["state"] == '2':
insertEmissions[emission.attrib["x"]] += float(emission.attrib["avg"])
elif emission.attrib["state"] == '1':
deleteEmissions[emission.attrib["y"]] += float(emission.attrib["avg"])
#PLot insert and delete emissions
indelEmissionsFile = os.path.join(self.outputDir, "indelEmissions.tsv")
outf = open(indelEmissionsFile, "w")
outf.write("\t".join(bases) + "\n")
outf.write("\t".join(map(lambda x : str(insertEmissions[x]), bases)) + "\n")
outf.write("\t".join(map(lambda x : str(deleteEmissions[x]), bases)) + "\n")
outf.close()
###Here's where we do the plot..
system("Rscript nanopore/analyses/emissions_plot.R {} {}".format(indelEmissionsFile, os.path.join(self.outputDir, "indelEmissions_plot.pdf")))
#Plot convergence of likelihoods
outf = open(os.path.join(self.outputDir, "runninglikelihoods.tsv"), "w")
for hmmNode in hmmsNode.findall("hmm"): #This is a loop over trials
runningLikelihoods = map(float, hmmNode.attrib["runningLikelihoods"].split()) #This is a list of floats ordered from the first iteration to last.
outf.write("\t".join(map(str, runningLikelihoods))); outf.write("\n")
outf.close()
system("Rscript nanopore/analyses/running_likelihood.R {} {}".format(os.path.join(self.outputDir, "runninglikelihoods.tsv"), os.path.join(self.outputDir, "running_likelihood.pdf")))
self.finish() #Indicates the batch is done
def run(self):
AbstractAnalysis.run(self) #Call base method to do some logging
refSequences = getFastaDictionary(self.referenceFastaFile) #Hash of names to sequences
readSequences = getFastqDictionary(self.readFastqFile) #Hash of names to sequences
node = ET.Element("marginAlignComparison")
for hmmType in ("cactus", "trained_0", "trained_20", "trained_40"):
for coverage in (1000000, 120, 60, 30, 10):
for replicate in xrange(3 if coverage < 1000000 else 1): #Do replicates, unless coverage is all
sam = pysam.Samfile(self.samFile, "r" )
#Trained hmm file to use.q
hmmFile0 = os.path.join(pathToBaseNanoporeDir(), "nanopore", "mappers", "blasr_hmm_0.txt")
hmmFile20 = os.path.join(pathToBaseNanoporeDir(), "nanopore", "mappers", "blasr_hmm_20.txt")
hmmFile40 = os.path.join(pathToBaseNanoporeDir(), "nanopore", "mappers", "blasr_hmm_40.txt")
#Get substitution matrices
nullSubstitionMatrix = getNullSubstitutionMatrix()
flatSubstitutionMatrix = getJukesCantorTypeSubstitutionMatrix()
hmmErrorSubstitutionMatrix = loadHmmErrorSubstitutionMatrix(hmmFile20)
#Load the held out snps
snpSet = {}
referenceAlignmentFile = self.referenceFastaFile + "_Index.txt"
if os.path.exists(referenceAlignmentFile):
seqsAndMutatedSeqs = getFastaDictionary(referenceAlignmentFile)
count = 0
for name in seqsAndMutatedSeqs:
if name in refSequences:
count += 1
trueSeq = seqsAndMutatedSeqs[name]
mutatedSeq = seqsAndMutatedSeqs[name + "_mutated"]
assert mutatedSeq == refSequences[name]
for i in xrange(len(trueSeq)):
if trueSeq[i] != mutatedSeq[i]:
snpSet[(name, i)] = trueSeq[i]
else:
assert name.split("_")[-1] == "mutated"
assert count == len(refSequences.keys())
#The data we collect
expectationsOfBasesAtEachPosition = {}
frequenciesOfAlignedBasesAtEachPosition = {}
totalSampledReads = 0
totalAlignedPairs = 0
totalReadLength = 0
totalReferenceLength = sum(map(len, refSequences.values()))
#Get a randomised ordering for the reads
reads = [ aR for aR in samIterator(sam) ]
random.shuffle(reads)
for aR in reads: #Iterate on the sam lines
if totalReadLength/totalReferenceLength >= coverage: #Stop when coverage exceeds the quota
break
totalReadLength += len(readSequences[aR.qname])
totalSampledReads += 1
#Temporary files
tempCigarFile = os.path.join(self.getLocalTempDir(), "rescoredCigar.cig")
tempRefFile = os.path.join(self.getLocalTempDir(), "ref.fa")
tempReadFile = os.path.join(self.getLocalTempDir(), "read.fa")
tempPosteriorProbsFile = os.path.join(self.getLocalTempDir(), "probs.tsv")
#Ref name
refSeqName = sam.getrname(aR.rname)
#Sequences
refSeq = refSequences[sam.getrname(aR.rname)]
#Walk through the aligned pairs to collate the bases of aligned positions
for aP in AlignedPair.iterator(aR, refSeq, readSequences[aR.qname]):
totalAlignedPairs += 1 #Record an aligned pair
key = (refSeqName, aP.refPos)
if key not in frequenciesOfAlignedBasesAtEachPosition:
frequenciesOfAlignedBasesAtEachPosition[key] = dict(zip(bases, [0.0]*len(bases)))
readBase = aP.getReadBase() #readSeq[aP.readPos].upper() #Use the absolute read, ins
if readBase in bases:
frequenciesOfAlignedBasesAtEachPosition[key][readBase] += 1
#Write the temporary files.
readSeq = aR.query #This excludes bases that were soft-clipped and is always of positive strand coordinates
fastaWrite(tempRefFile, refSeqName, refSeq)
fastaWrite(tempReadFile, aR.qname, readSeq)
#Exonerate format Cigar string, which is in readSeq coordinates (positive strand).
assert aR.pos == 0
assert aR.qstart == 0
assert aR.qend == len(readSeq)
assert aR.aend == len(refSeq)
cigarString = getExonerateCigarFormatString(aR, sam)
#Call to cactus_realign
if hmmType == "trained_0":
system("echo %s | cactus_realign %s %s --diagonalExpansion=10 --splitMatrixBiggerThanThis=100 --outputAllPosteriorProbs=%s --loadHmm=%s > %s" % \
(cigarString, tempRefFile, tempReadFile, tempPosteriorProbsFile, hmmFile0, tempCigarFile))
elif hmmType == "trained_20":
system("echo %s | cactus_realign %s %s --diagonalExpansion=10 --splitMatrixBiggerThanThis=100 --outputAllPosteriorProbs=%s --loadHmm=%s > %s" % \
(cigarString, tempRefFile, tempReadFile, tempPosteriorProbsFile, hmmFile20, tempCigarFile))
elif hmmType == "trained_40":
system("echo %s | cactus_realign %s %s --diagonalExpansion=10 --splitMatrixBiggerThanThis=100 --outputAllPosteriorProbs=%s --loadHmm=%s > %s" % \
(cigarString, tempRefFile, tempReadFile, tempPosteriorProbsFile, hmmFile40, tempCigarFile))
else:
system("echo %s | cactus_realign %s %s --diagonalExpansion=10 --splitMatrixBiggerThanThis=100 --outputAllPosteriorProbs=%s > %s" % \
(cigarString, tempRefFile, tempReadFile, tempPosteriorProbsFile, tempCigarFile))
#Now collate the reference position expectations
for refPosition, readPosition, posteriorProb in map(lambda x : map(float, x.split()), open(tempPosteriorProbsFile, 'r')):
key = (refSeqName, int(refPosition))
if key not in expectationsOfBasesAtEachPosition:
expectationsOfBasesAtEachPosition[key] = dict(zip(bases, [0.0]*len(bases)))
readBase = readSeq[int(readPosition)].upper()
if readBase in bases:
expectationsOfBasesAtEachPosition[key][readBase] += posteriorProb
#Collate aligned positions from cigars
sam.close()
totalHeldOut = len(snpSet)
totalNotHeldOut = totalReferenceLength - totalHeldOut
class SnpCalls:
def __init__(self):
self.falsePositives = []
self.truePositives = []
self.falseNegatives = []
self.notCalled = 0
@staticmethod
def bucket(calls):
calls = calls[:]
calls.sort()
buckets = [0.0]*101
for prob in calls: #Discretize
buckets[int(round(prob*100))] += 1
for i in xrange(len(buckets)-2, -1, -1): #Make cumulative
buckets[i] += buckets[i+1]
return buckets
def getPrecisionByProbability(self):
tPs = self.bucket(map(lambda x : x[0], self.truePositives))
fPs = self.bucket(map(lambda x : x[0], self.falsePositives))
return map(lambda i : float(tPs[i]) / (tPs[i] + fPs[i]) if tPs[i] + fPs[i] != 0 else 0, xrange(len(tPs)))
def getRecallByProbability(self):
return map(lambda i : i/totalHeldOut if totalHeldOut != 0 else 0, self.bucket(map(lambda x : x[0], self.truePositives)))
def getTruePositiveLocations(self):
return map(lambda x : x[1], self.truePositives)
def getFalsePositiveLocations(self):
return map(lambda x : x[1], self.falsePositives)
def getFalseNegativeLocations(self):
return map(lambda x : x[0], self.falseNegatives)
#The different call sets
marginAlignMaxExpectedSnpCalls = SnpCalls()
marginAlignMaxLikelihoodSnpCalls = SnpCalls()
maxFrequencySnpCalls = SnpCalls()
maximumLikelihoodSnpCalls = SnpCalls()
#Now calculate the calls
for refSeqName in refSequences:
refSeq = refSequences[refSeqName]
for refPosition in xrange(len(refSeq)):
mutatedRefBase = refSeq[refPosition].upper()
trueRefBase = (mutatedRefBase if not (refSeqName, refPosition) in snpSet else snpSet[(refSeqName, refPosition)]).upper()
key = (refSeqName, refPosition)
#Get base calls
for errorSubstitutionMatrix, evolutionarySubstitutionMatrix, baseExpectations, snpCalls in \
((flatSubstitutionMatrix, nullSubstitionMatrix, expectationsOfBasesAtEachPosition, marginAlignMaxExpectedSnpCalls),
(hmmErrorSubstitutionMatrix, nullSubstitionMatrix, expectationsOfBasesAtEachPosition, marginAlignMaxLikelihoodSnpCalls),
(flatSubstitutionMatrix, nullSubstitionMatrix, frequenciesOfAlignedBasesAtEachPosition, maxFrequencySnpCalls),
(hmmErrorSubstitutionMatrix, nullSubstitionMatrix, frequenciesOfAlignedBasesAtEachPosition, maximumLikelihoodSnpCalls)):
if key in baseExpectations:
#Get posterior likelihoods
expectations = baseExpectations[key]
totalExpectation = sum(expectations.values())
if totalExpectation > 0.0: #expectationCallingThreshold:
posteriorProbs = calcBasePosteriorProbs(dict(zip(bases, map(lambda x : float(expectations[x])/totalExpectation, bases))), mutatedRefBase,
evolutionarySubstitutionMatrix, errorSubstitutionMatrix)
probs = [ posteriorProbs[base] for base in "ACGT" ]
#posteriorProbs.pop(mutatedRefBase) #Remove the ref base.
#maxPosteriorProb = max(posteriorProbs.values())
#chosenBase = random.choice([ base for base in posteriorProbs if posteriorProbs[base] == maxPosteriorProb ]).upper() #Very naive way to call the base
for chosenBase in "ACGT":
if chosenBase != mutatedRefBase:
maxPosteriorProb = posteriorProbs[chosenBase]
if trueRefBase != mutatedRefBase and trueRefBase == chosenBase:
snpCalls.truePositives.append((maxPosteriorProb, refPosition)) #True positive
else:
snpCalls.falsePositives.append((maxPosteriorProb, refPosition)) #False positive
"""
snpCalls.falseNegatives.append((refPosition, trueRefBase, mutatedRefBase, probs)) #False negative
if trueRefBase != mutatedRefBase:
if trueRefBase == chosenBase:
snpCalls.truePositives.append((maxPosteriorProb, refPosition)) #True positive
else:
snpCalls.falseNegatives.append((refPosition, trueRefBase, mutatedRefBase, probs)) #False negative
else:
snpCalls.falsePositives.append((maxPosteriorProb, refPosition)) #False positive
"""
else:
snpCalls.notCalled += 1
#Now find max-fscore point
for snpCalls, tagName in ((marginAlignMaxExpectedSnpCalls, "marginAlignMaxExpectedSnpCalls"),
(marginAlignMaxLikelihoodSnpCalls, "marginAlignMaxLikelihoodSnpCalls"),
(maxFrequencySnpCalls, "maxFrequencySnpCalls"),
(maximumLikelihoodSnpCalls, "maximumLikelihoodSnpCalls")):
recall = snpCalls.getRecallByProbability()
precision = snpCalls.getPrecisionByProbability()
assert len(recall) == len(precision)
fScore, pIndex = max(map(lambda i : (2 * recall[i] * precision[i] / (recall[i] + precision[i]) if recall[i] + precision[i] > 0 else 0.0, i), range(len(recall))))
truePositives = snpCalls.getRecallByProbability()[pIndex]
falsePositives = snpCalls.getPrecisionByProbability()[pIndex]
optimumProbThreshold = float(pIndex)/100.0
#Write out the substitution info
node2 = ET.SubElement(node, tagName + "_" + hmmType, {
"coverage":str(coverage),
"actualCoverage":str(float(totalAlignedPairs)/totalReferenceLength),
"totalAlignedPairs":str(totalAlignedPairs),
"totalReferenceLength":str(totalReferenceLength),
"replicate":str(replicate),
"totalReads":str(len(reads)),
"avgSampledReadLength":str(float(totalReadLength)/totalSampledReads),
"totalSampledReads":str(totalSampledReads),
"totalHeldOut":str(totalHeldOut),
"totalNonHeldOut":str(totalNotHeldOut),
"recall":str(recall[pIndex]),
"precision":str(precision[pIndex]),
"fScore":str(fScore),
"optimumProbThreshold":str(optimumProbThreshold),
"totalNoCalls":str(snpCalls.notCalled),
"recallByProbability":" ".join(map(str, snpCalls.getRecallByProbability())),
"precisionByProbability":" ".join(map(str, snpCalls.getPrecisionByProbability())) })
#"falsePositiveLocations":" ".join(map(str, snpCalls.getFalsePositiveLocations())),
#"falseNegativeLocations":" ".join(map(str, snpCalls.getFalseNegativeLocations())),
#"truePositiveLocations":" ".join(map(str, snpCalls.getTruePositiveLocations())) })
for refPosition, trueRefBase, mutatedRefBase, posteriorProbs in snpCalls.falseNegatives:
ET.SubElement(node2, "falseNegative_%s_%s" % (trueRefBase, mutatedRefBase), { "posteriorProbs":" ".join(map(str, posteriorProbs))})
for falseNegativeBase in bases:
for mutatedBase in bases:
posteriorProbsArray = [ posteriorProbs for refPosition, trueRefBase, mutatedRefBase, posteriorProbs in snpCalls.falseNegatives if (trueRefBase.upper() == falseNegativeBase.upper() and mutatedBase.upper() == mutatedRefBase.upper() ) ]
if len(posteriorProbsArray) > 0:
summedProbs = reduce(lambda x, y : map(lambda i : x[i] + y[i], xrange(len(x))), posteriorProbsArray)
summedProbs = map(lambda x : float(x)/sum(summedProbs), summedProbs)
ET.SubElement(node2, "combinedFalseNegative_%s_%s" % (falseNegativeBase, mutatedBase), { "posteriorProbs":" ".join(map(str, summedProbs))})
open(os.path.join(self.outputDir, "marginaliseConsensus.xml"), "w").write(prettyXml(node))
#Indicate everything is all done
self.finish()
def run(self):
AbstractAnalysis.run(self) #Call base method to do some logging
refSequences = getFastaDictionary(
self.referenceFastaFile) #Hash of names to sequences
readSequences = getFastqDictionary(
self.readFastqFile) #Hash of names to sequences
node = ET.Element("marginAlignComparison")
for hmmType in ("cactus", "trained_0", "trained_20", "trained_40"):
for coverage in (1000000, 120, 60, 30, 10):
for replicate in xrange(
3 if coverage < 1000000 else 1
): #Do replicates, unless coverage is all
sam = pysam.Samfile(self.samFile, "r")
#Trained hmm file to use.q
hmmFile0 = os.path.join(pathToBaseNanoporeDir(),
"nanopore", "mappers",
"blasr_hmm_0.txt")
hmmFile20 = os.path.join(pathToBaseNanoporeDir(),
"nanopore", "mappers",
"blasr_hmm_20.txt")
hmmFile40 = os.path.join(pathToBaseNanoporeDir(),
"nanopore", "mappers",
"blasr_hmm_40.txt")
#Get substitution matrices
nullSubstitionMatrix = getNullSubstitutionMatrix()
flatSubstitutionMatrix = getJukesCantorTypeSubstitutionMatrix(
)
hmmErrorSubstitutionMatrix = loadHmmErrorSubstitutionMatrix(
hmmFile20)
#Load the held out snps
snpSet = {}
referenceAlignmentFile = self.referenceFastaFile + "_Index.txt"
if os.path.exists(referenceAlignmentFile):
seqsAndMutatedSeqs = getFastaDictionary(
referenceAlignmentFile)
count = 0
for name in seqsAndMutatedSeqs:
if name in refSequences:
count += 1
trueSeq = seqsAndMutatedSeqs[name]
mutatedSeq = seqsAndMutatedSeqs[name +
"_mutated"]
assert mutatedSeq == refSequences[name]
for i in xrange(len(trueSeq)):
if trueSeq[i] != mutatedSeq[i]:
snpSet[(name, i)] = trueSeq[i]
else:
assert name.split("_")[-1] == "mutated"
assert count == len(refSequences.keys())
#The data we collect
expectationsOfBasesAtEachPosition = {}
frequenciesOfAlignedBasesAtEachPosition = {}
totalSampledReads = 0
totalAlignedPairs = 0
totalReadLength = 0
totalReferenceLength = sum(map(len, refSequences.values()))
#Get a randomised ordering for the reads
reads = [aR for aR in samIterator(sam)]
random.shuffle(reads)
for aR in reads: #Iterate on the sam lines
if totalReadLength / totalReferenceLength >= coverage: #Stop when coverage exceeds the quota
break
totalReadLength += len(readSequences[aR.qname])
totalSampledReads += 1
#Temporary files
tempCigarFile = os.path.join(self.getLocalTempDir(),
"rescoredCigar.cig")
tempRefFile = os.path.join(self.getLocalTempDir(),
"ref.fa")
tempReadFile = os.path.join(self.getLocalTempDir(),
"read.fa")
tempPosteriorProbsFile = os.path.join(
self.getLocalTempDir(), "probs.tsv")
#Ref name
refSeqName = sam.getrname(aR.rname)
#Sequences
refSeq = refSequences[sam.getrname(aR.rname)]
#Walk through the aligned pairs to collate the bases of aligned positions
for aP in AlignedPair.iterator(
aR, refSeq, readSequences[aR.qname]):
totalAlignedPairs += 1 #Record an aligned pair
key = (refSeqName, aP.refPos)
if key not in frequenciesOfAlignedBasesAtEachPosition:
frequenciesOfAlignedBasesAtEachPosition[
key] = dict(zip(bases, [0.0] * len(bases)))
readBase = aP.getReadBase(
) #readSeq[aP.readPos].upper() #Use the absolute read, ins
if readBase in bases:
frequenciesOfAlignedBasesAtEachPosition[key][
readBase] += 1
#Write the temporary files.
readSeq = aR.query #This excludes bases that were soft-clipped and is always of positive strand coordinates
fastaWrite(tempRefFile, refSeqName, refSeq)
fastaWrite(tempReadFile, aR.qname, readSeq)
#Exonerate format Cigar string, which is in readSeq coordinates (positive strand).
assert aR.pos == 0
assert aR.qstart == 0
assert aR.qend == len(readSeq)
assert aR.aend == len(refSeq)
cigarString = getExonerateCigarFormatString(aR, sam)
#Call to cactus_realign
if hmmType == "trained_0":
system("echo %s | cactus_realign %s %s --diagonalExpansion=10 --splitMatrixBiggerThanThis=100 --outputAllPosteriorProbs=%s --loadHmm=%s > %s" % \
(cigarString, tempRefFile, tempReadFile, tempPosteriorProbsFile, hmmFile0, tempCigarFile))
elif hmmType == "trained_20":
system("echo %s | cactus_realign %s %s --diagonalExpansion=10 --splitMatrixBiggerThanThis=100 --outputAllPosteriorProbs=%s --loadHmm=%s > %s" % \
(cigarString, tempRefFile, tempReadFile, tempPosteriorProbsFile, hmmFile20, tempCigarFile))
elif hmmType == "trained_40":
system("echo %s | cactus_realign %s %s --diagonalExpansion=10 --splitMatrixBiggerThanThis=100 --outputAllPosteriorProbs=%s --loadHmm=%s > %s" % \
(cigarString, tempRefFile, tempReadFile, tempPosteriorProbsFile, hmmFile40, tempCigarFile))
else:
system("echo %s | cactus_realign %s %s --diagonalExpansion=10 --splitMatrixBiggerThanThis=100 --outputAllPosteriorProbs=%s > %s" % \
(cigarString, tempRefFile, tempReadFile, tempPosteriorProbsFile, tempCigarFile))
#Now collate the reference position expectations
for refPosition, readPosition, posteriorProb in map(
lambda x: map(float, x.split()),
open(tempPosteriorProbsFile, 'r')):
key = (refSeqName, int(refPosition))
if key not in expectationsOfBasesAtEachPosition:
expectationsOfBasesAtEachPosition[key] = dict(
zip(bases, [0.0] * len(bases)))
readBase = readSeq[int(readPosition)].upper()
if readBase in bases:
expectationsOfBasesAtEachPosition[key][
readBase] += posteriorProb
#Collate aligned positions from cigars
sam.close()
totalHeldOut = len(snpSet)
totalNotHeldOut = totalReferenceLength - totalHeldOut
class SnpCalls:
def __init__(self):
self.falsePositives = []
self.truePositives = []
self.falseNegatives = []
self.notCalled = 0
@staticmethod
def bucket(calls):
calls = calls[:]
calls.sort()
buckets = [0.0] * 101
for prob in calls: #Discretize
buckets[int(round(prob * 100))] += 1
for i in xrange(len(buckets) - 2, -1,
-1): #Make cumulative
buckets[i] += buckets[i + 1]
return buckets
def getPrecisionByProbability(self):
tPs = self.bucket(
map(lambda x: x[0], self.truePositives))
fPs = self.bucket(
map(lambda x: x[0], self.falsePositives))
return map(
lambda i: float(tPs[i]) / (tPs[i] + fPs[i])
if tPs[i] + fPs[i] != 0 else 0,
xrange(len(tPs)))
def getRecallByProbability(self):
return map(
lambda i: i / totalHeldOut
if totalHeldOut != 0 else 0,
self.bucket(
map(lambda x: x[0], self.truePositives)))
def getTruePositiveLocations(self):
return map(lambda x: x[1], self.truePositives)
def getFalsePositiveLocations(self):
return map(lambda x: x[1], self.falsePositives)
def getFalseNegativeLocations(self):
return map(lambda x: x[0], self.falseNegatives)
#The different call sets
marginAlignMaxExpectedSnpCalls = SnpCalls()
marginAlignMaxLikelihoodSnpCalls = SnpCalls()
maxFrequencySnpCalls = SnpCalls()
maximumLikelihoodSnpCalls = SnpCalls()
#Now calculate the calls
for refSeqName in refSequences:
refSeq = refSequences[refSeqName]
for refPosition in xrange(len(refSeq)):
mutatedRefBase = refSeq[refPosition].upper()
trueRefBase = (
mutatedRefBase
if not (refSeqName, refPosition) in snpSet else
snpSet[(refSeqName, refPosition)]).upper()
key = (refSeqName, refPosition)
#Get base calls
for errorSubstitutionMatrix, evolutionarySubstitutionMatrix, baseExpectations, snpCalls in \
((flatSubstitutionMatrix, nullSubstitionMatrix, expectationsOfBasesAtEachPosition, marginAlignMaxExpectedSnpCalls),
(hmmErrorSubstitutionMatrix, nullSubstitionMatrix, expectationsOfBasesAtEachPosition, marginAlignMaxLikelihoodSnpCalls),
(flatSubstitutionMatrix, nullSubstitionMatrix, frequenciesOfAlignedBasesAtEachPosition, maxFrequencySnpCalls),
(hmmErrorSubstitutionMatrix, nullSubstitionMatrix, frequenciesOfAlignedBasesAtEachPosition, maximumLikelihoodSnpCalls)):
if key in baseExpectations:
#Get posterior likelihoods
expectations = baseExpectations[key]
totalExpectation = sum(
expectations.values())
if totalExpectation > 0.0: #expectationCallingThreshold:
posteriorProbs = calcBasePosteriorProbs(
dict(
zip(
bases,
map(
lambda x: float(
expectations[x]) /
totalExpectation,
bases))),
mutatedRefBase,
evolutionarySubstitutionMatrix,
errorSubstitutionMatrix)
probs = [
posteriorProbs[base]
for base in "ACGT"
]
#posteriorProbs.pop(mutatedRefBase) #Remove the ref base.
#maxPosteriorProb = max(posteriorProbs.values())
#chosenBase = random.choice([ base for base in posteriorProbs if posteriorProbs[base] == maxPosteriorProb ]).upper() #Very naive way to call the base
for chosenBase in "ACGT":
if chosenBase != mutatedRefBase:
maxPosteriorProb = posteriorProbs[
chosenBase]
if trueRefBase != mutatedRefBase and trueRefBase == chosenBase:
snpCalls.truePositives.append(
(maxPosteriorProb,
refPosition
)) #True positive
else:
snpCalls.falsePositives.append(
(maxPosteriorProb,
refPosition
)) #False positive
"""
snpCalls.falseNegatives.append((refPosition, trueRefBase, mutatedRefBase, probs)) #False negative
if trueRefBase != mutatedRefBase:
if trueRefBase == chosenBase:
snpCalls.truePositives.append((maxPosteriorProb, refPosition)) #True positive
else:
snpCalls.falseNegatives.append((refPosition, trueRefBase, mutatedRefBase, probs)) #False negative
else:
snpCalls.falsePositives.append((maxPosteriorProb, refPosition)) #False positive
"""
else:
snpCalls.notCalled += 1
#Now find max-fscore point
for snpCalls, tagName in (
(marginAlignMaxExpectedSnpCalls,
"marginAlignMaxExpectedSnpCalls"),
(marginAlignMaxLikelihoodSnpCalls,
"marginAlignMaxLikelihoodSnpCalls"),
(maxFrequencySnpCalls, "maxFrequencySnpCalls"),
(maximumLikelihoodSnpCalls,
"maximumLikelihoodSnpCalls")):
recall = snpCalls.getRecallByProbability()
precision = snpCalls.getPrecisionByProbability()
assert len(recall) == len(precision)
fScore, pIndex = max(
map(
lambda i:
(2 * recall[i] * precision[i] /
(recall[i] + precision[i])
if recall[i] + precision[i] > 0 else 0.0, i),
range(len(recall))))
truePositives = snpCalls.getRecallByProbability(
)[pIndex]
falsePositives = snpCalls.getPrecisionByProbability(
)[pIndex]
optimumProbThreshold = float(pIndex) / 100.0
#Write out the substitution info
node2 = ET.SubElement(
node, tagName + "_" + hmmType, {
"coverage":
str(coverage),
"actualCoverage":
str(
float(totalAlignedPairs) /
totalReferenceLength),
"totalAlignedPairs":
str(totalAlignedPairs),
"totalReferenceLength":
str(totalReferenceLength),
"replicate":
str(replicate),
"totalReads":
str(len(reads)),
"avgSampledReadLength":
str(
float(totalReadLength) /
totalSampledReads),
"totalSampledReads":
str(totalSampledReads),
"totalHeldOut":
str(totalHeldOut),
"totalNonHeldOut":
str(totalNotHeldOut),
"recall":
str(recall[pIndex]),
"precision":
str(precision[pIndex]),
"fScore":
str(fScore),
"optimumProbThreshold":
str(optimumProbThreshold),
"totalNoCalls":
str(snpCalls.notCalled),
"recallByProbability":
" ".join(
map(str,
snpCalls.getRecallByProbability())),
"precisionByProbability":
" ".join(
map(str,
snpCalls.getPrecisionByProbability()))
})
#"falsePositiveLocations":" ".join(map(str, snpCalls.getFalsePositiveLocations())),
#"falseNegativeLocations":" ".join(map(str, snpCalls.getFalseNegativeLocations())),
#"truePositiveLocations":" ".join(map(str, snpCalls.getTruePositiveLocations())) })
for refPosition, trueRefBase, mutatedRefBase, posteriorProbs in snpCalls.falseNegatives:
ET.SubElement(
node2, "falseNegative_%s_%s" %
(trueRefBase, mutatedRefBase), {
"posteriorProbs":
" ".join(map(str, posteriorProbs))
})
for falseNegativeBase in bases:
for mutatedBase in bases:
posteriorProbsArray = [
posteriorProbs for refPosition,
trueRefBase, mutatedRefBase, posteriorProbs
in snpCalls.falseNegatives
if (trueRefBase.upper() ==
falseNegativeBase.upper()
and mutatedBase.upper() ==
mutatedRefBase.upper())
]
if len(posteriorProbsArray) > 0:
summedProbs = reduce(
lambda x, y: map(
lambda i: x[i] + y[i],
xrange(len(x))),
posteriorProbsArray)
summedProbs = map(
lambda x: float(x) / sum(summedProbs),
summedProbs)
ET.SubElement(
node2, "combinedFalseNegative_%s_%s" %
(falseNegativeBase, mutatedBase), {
"posteriorProbs":
" ".join(map(str, summedProbs))
})
open(os.path.join(self.outputDir, "marginaliseConsensus.xml"),
"w").write(prettyXml(node))
#Indicate everything is all done
self.finish()
def run(self):
#Call base method to do some logging
AbstractAnalysis.run(self)
#Hmm file
hmmFile = os.path.join(os.path.split(self.samFile)[0], "hmm.txt.xml")
if os.path.exists(hmmFile):
#Load the hmm
hmmsNode = ET.parse(hmmFile).getroot()
#Plot graphviz version of nanopore hmm, showing transitions and variances.
fH = open(os.path.join(self.outputDir, "hmm.dot"), 'w')
setupGraphFile(fH)
#Make states
addNodeToGraph("n0n", fH, "match")
addNodeToGraph("n1n", fH, "short delete")
addNodeToGraph("n2n", fH, "short insert")
addNodeToGraph("n3n", fH, "long insert")
addNodeToGraph("n4n", fH, "long delete")
#Make edges with labelled transition probs.
for transition in hmmsNode.findall("transition"):
if float(transition.attrib["avg"]) > 0.0:
addEdgeToGraph("n%sn" % transition.attrib["from"],
"n%sn" % transition.attrib["to"],
fH,
dir="arrow",
style='""',
label="%.3f,%.3f" %
(float(transition.attrib["avg"]),
float(transition.attrib["std"])))
#Finish up
finishGraphFile(fH)
fH.close()
#Plot match emission data
emissions = dict([ ((emission.attrib["x"], emission.attrib["y"]), emission.attrib["avg"]) \
for emission in hmmsNode.findall("emission") if emission.attrib["state"] == '0' ])
matchEmissionsFile = os.path.join(self.outputDir,
"matchEmissions.tsv")
outf = open(matchEmissionsFile, "w")
bases = "ACGT"
outf.write("\t".join(bases) + "\n")
for base in bases:
outf.write("\t".join(
[base] + map(lambda x: emissions[(base, x)], bases)) +
"\n")
outf.close()
system("Rscript nanopore/analyses/substitution_plot.R %s %s %s" %
(matchEmissionsFile,
os.path.join(self.outputDir, "substitution_plot.pdf"),
"Per-Base Substitutions after HMM"))
#Plot indel info
#Get the sequences to contrast the neutral model.
refSequences = getFastaDictionary(
self.referenceFastaFile) #Hash of names to sequences
readSequences = getFastqDictionary(
self.readFastqFile) #Hash of names to sequences
#Need to do plot of insert and deletion gap emissions
#Plot of insert and deletion gap emissions
insertEmissions = {"A": 0.0, 'C': 0.0, 'G': 0.0, 'T': 0.0}
deleteEmissions = {"A": 0.0, 'C': 0.0, 'G': 0.0, 'T': 0.0}
for emission in hmmsNode.findall("emission"):
if emission.attrib["state"] == '2':
insertEmissions[emission.attrib["x"]] += float(
emission.attrib["avg"])
elif emission.attrib["state"] == '1':
deleteEmissions[emission.attrib["y"]] += float(
emission.attrib["avg"])
#PLot insert and delete emissions
indelEmissionsFile = os.path.join(self.outputDir,
"indelEmissions.tsv")
outf = open(indelEmissionsFile, "w")
outf.write("\t".join(bases) + "\n")
outf.write(
"\t".join(map(lambda x: str(insertEmissions[x]), bases)) +
"\n")
outf.write(
"\t".join(map(lambda x: str(deleteEmissions[x]), bases)) +
"\n")
outf.close()
###Here's where we do the plot..
system("Rscript nanopore/analyses/emissions_plot.R {} {}".format(
indelEmissionsFile,
os.path.join(self.outputDir, "indelEmissions_plot.pdf")))
#Plot convergence of likelihoods
outf = open(os.path.join(self.outputDir, "runninglikelihoods.tsv"),
"w")
for hmmNode in hmmsNode.findall(
"hmm"): #This is a loop over trials
runningLikelihoods = map(
float, hmmNode.attrib["runningLikelihoods"].split()
) #This is a list of floats ordered from the first iteration to last.
outf.write("\t".join(map(str, runningLikelihoods)))
outf.write("\n")
outf.close()
system(
"Rscript nanopore/analyses/running_likelihood.R {} {}".format(
os.path.join(self.outputDir, "runninglikelihoods.tsv"),
os.path.join(self.outputDir, "running_likelihood.pdf")))
self.finish() #Indicates the batch is done