def testCPecanEmMultipleTrials(self):
"""Runs uns cPecanEm with multiple different trials.
"""
for seqFile1, seqFile2 in seqFilePairGenerator():
tempDir = getTempDirectory(rootDir=os.getcwd())
jobTreeDir = os.path.join(tempDir, "jobTree")
alignmentsFile = os.path.join(tempDir, "alignments.cigars")
computeAlignments(seqFile1, seqFile2, alignmentsFile)
logger.info("Computed alignments for seqs %s and %s" % (seqFile1, seqFile2))
outputModelFile = os.path.join(tempDir, "outputModel.txt")
outputModelXMLFile = os.path.join(tempDir, "outputModel.xml")
outputBlastFile = os.path.join(tempDir, "outputBlast.txt")
#First run the script to generate a model and do one iteration of EM to
#get the likelihood to compare with the final likelihood
trials=3
runCPecanEm(sequenceFiles=[ seqFile1, seqFile2 ],
alignmentsFile=alignmentsFile, outputModelFile=outputModelFile,
jobTreeDir=jobTreeDir,
trials=trials,
outputTrialHmms=True,
iterations=5, randomStart=True, logLevel=getLogLevelString(),
optionsToRealign="--diagonalExpansion=6 --splitMatrixBiggerThanThis=100",
outputXMLModelFile=outputModelXMLFile,
blastScoringMatrixFile=outputBlastFile)
trialHmms = [ Hmm.loadHmm(outputModelFile + ("_%i" % i)) for i in xrange(trials) ]
hmm = Hmm.loadHmm(outputModelFile)
node = ET.parse(outputModelXMLFile).getroot()
logger.info("After multiple trials and iterations of EM the best likelihood found was %s, the likelihoods of the variants were: %s" %
(hmm.likelihood, " ".join(map(lambda x : str(x.likelihood), trialHmms))))
matchProbs, gapOpen, gapExtend = makeBlastScoringMatrix(hmm, ("ACTG",))
logger.info("Gap open: %s, Gap extend: %s, Match probs %s" % (gapOpen, gapExtend, " ".join(map(str, matchProbs))))
self.assertTrue(float(node.attrib["maxLikelihood"]) == hmm.likelihood)
#Now use the blast file to compute a new matrix
computeAlignments(seqFile1, seqFile2, alignmentsFile, lastzArguments=("--ambiguous=iupac --scores=%s" % outputBlastFile))
#Run modifyHmm to check it works
system("cPecanModifyHmm %s %s --gcContent=0.5 --substitutionRate=0.05 --setFlatIndelEmissions" % (outputModelFile, outputModelFile))
hmm = Hmm.loadHmm(outputModelFile)
node = ET.parse(outputModelXMLFile).getroot()
system("rm -rf %s" % tempDir)
def testCPecanEm(self):
"""Runs cPecanEm.
"""
trial = 0
for modelType in ("fiveState", "fiveStateAsymmetric", "threeState", "threeStateAsymmetric"):
for seqFile1, seqFile2 in seqFilePairGenerator():
tempDir = getTempDirectory(rootDir=os.getcwd())
jobTreeDir = os.path.join(tempDir, "jobTree")
alignmentsFile = os.path.join(tempDir, "alignments.cigars")
computeAlignments(seqFile1, seqFile2, alignmentsFile)
logger.info("Computed alignments for seqs %s and %s" % (seqFile1, seqFile2))
outputModelFile = os.path.join(tempDir, "outputModel.txt")
#First run the script to generate a model and do one iteration of EM to
#get the likelihood to compare with the final likelihood
runCPecanEm(sequenceFiles=[ seqFile1, seqFile2 ],
alignmentsFile=alignmentsFile, outputModelFile=outputModelFile,
modelType=modelType,
jobTreeDir=jobTreeDir,
iterations=1, trials=1, randomStart=False, logLevel=getLogLevelString(),
setJukesCantorStartingEmissions=0.2,
#useDefaultModelAsStart=,
trainEmissions=True,
tieEmissions=True,
optionsToRealign="--diagonalExpansion=6 --splitMatrixBiggerThanThis=100")
hmm = Hmm.loadHmm(outputModelFile)
system("rm -rf %s" % jobTreeDir) #Cleanup the old jobTree
logger.info("For trial %s the likelihood after 1 iteration of EM is %s" % (trial, hmm.likelihood))
iterations = 5
runCPecanEm(sequenceFiles=[ seqFile1, seqFile2 ],
alignmentsFile=alignmentsFile, outputModelFile=outputModelFile, jobTreeDir=jobTreeDir,
optionsToRealign="--diagonalExpansion=6 --splitMatrixBiggerThanThis=100",
iterations=iterations, inputModelFile=outputModelFile, logLevel=getLogLevelString(),
maxAlignmentLengthPerJob=10000) #, updateTheBand=True)
hmm2 = Hmm.loadHmm(outputModelFile)
logger.info("For trial %s the likelihood after a further %s iterations of EM is %s" % (trial, iterations, hmm2.likelihood))
self.assertTrue(hmm.likelihood < hmm2.likelihood)
hmm2.normalise()
logger.info("Final transitions: %s" % " ".join(map(str, hmm2.transitions)))
logger.info("Final emissions: %s" % " ".join(map(str, hmm2.emissions)))
system("rm -rf %s" % tempDir)
trial += 1
def testCPecanEmMultipleTrials(self):
"""Runs uns cPecanEm with multiple different trials.
"""
for seqFile1, seqFile2 in seqFilePairGenerator():
tempDir = getTempDirectory(rootDir=os.getcwd())
jobTreeDir = os.path.join(tempDir, "jobTree")
alignmentsFile = os.path.join(tempDir, "alignments.cigars")
computeAlignments(seqFile1, seqFile2, alignmentsFile)
logger.info("Computed alignments for seqs %s and %s" %
(seqFile1, seqFile2))
outputModelFile = os.path.join(tempDir, "outputModel.txt")
outputModelXMLFile = os.path.join(tempDir, "outputModel.xml")
outputBlastFile = os.path.join(tempDir, "outputBlast.txt")
#First run the script to generate a model and do one iteration of EM to
#get the likelihood to compare with the final likelihood
trials = 3
runCPecanEm(
sequenceFiles=[seqFile1, seqFile2],
alignmentsFile=alignmentsFile,
outputModelFile=outputModelFile,
jobTreeDir=jobTreeDir,
trials=trials,
outputTrialHmms=True,
iterations=5,
randomStart=True,
logLevel=getLogLevelString(),
optionsToRealign=
"--diagonalExpansion=6 --splitMatrixBiggerThanThis=100",
outputXMLModelFile=outputModelXMLFile,
blastScoringMatrixFile=outputBlastFile)
trialHmms = [
Hmm.loadHmm(outputModelFile + ("_%i" % i))
for i in xrange(trials)
]
hmm = Hmm.loadHmm(outputModelFile)
node = ET.parse(outputModelXMLFile).getroot()
logger.info(
"After multiple trials and iterations of EM the best likelihood found was %s, the likelihoods of the variants were: %s"
% (hmm.likelihood, " ".join(
map(lambda x: str(x.likelihood), trialHmms))))
matchProbs, gapOpen, gapExtend = makeBlastScoringMatrix(
hmm, ("ACTG", ))
logger.info("Gap open: %s, Gap extend: %s, Match probs %s" %
(gapOpen, gapExtend, " ".join(map(str, matchProbs))))
self.assertTrue(
float(node.attrib["maxLikelihood"]) == hmm.likelihood)
#Now use the blast file to compute a new matrix
computeAlignments(seqFile1,
seqFile2,
alignmentsFile,
lastzArguments=("--ambiguous=iupac --scores=%s" %
outputBlastFile))
#Run modifyHmm to check it works
system(
"cPecanModifyHmm %s %s --gcContent=0.5 --substitutionRate=0.05 --setFlatIndelEmissions"
% (outputModelFile, outputModelFile))
hmm = Hmm.loadHmm(outputModelFile)
node = ET.parse(outputModelXMLFile).getroot()
system("rm -rf %s" % tempDir)
def testCPecanEm(self):
"""Runs cPecanEm.
"""
trial = 0
for modelType in ("fiveState", "fiveStateAsymmetric", "threeState",
"threeStateAsymmetric"):
for seqFile1, seqFile2 in seqFilePairGenerator():
tempDir = getTempDirectory(rootDir=os.getcwd())
jobTreeDir = os.path.join(tempDir, "jobTree")
alignmentsFile = os.path.join(tempDir, "alignments.cigars")
computeAlignments(seqFile1, seqFile2, alignmentsFile)
logger.info("Computed alignments for seqs %s and %s" %
(seqFile1, seqFile2))
outputModelFile = os.path.join(tempDir, "outputModel.txt")
#First run the script to generate a model and do one iteration of EM to
#get the likelihood to compare with the final likelihood
runCPecanEm(
sequenceFiles=[seqFile1, seqFile2],
alignmentsFile=alignmentsFile,
outputModelFile=outputModelFile,
modelType=modelType,
jobTreeDir=jobTreeDir,
iterations=1,
trials=1,
randomStart=False,
logLevel=getLogLevelString(),
setJukesCantorStartingEmissions=0.2,
#useDefaultModelAsStart=,
trainEmissions=True,
tieEmissions=True,
optionsToRealign=
"--diagonalExpansion=6 --splitMatrixBiggerThanThis=100")
hmm = Hmm.loadHmm(outputModelFile)
system("rm -rf %s" % jobTreeDir) #Cleanup the old jobTree
logger.info(
"For trial %s the likelihood after 1 iteration of EM is %s"
% (trial, hmm.likelihood))
iterations = 5
runCPecanEm(
sequenceFiles=[seqFile1, seqFile2],
alignmentsFile=alignmentsFile,
outputModelFile=outputModelFile,
jobTreeDir=jobTreeDir,
optionsToRealign=
"--diagonalExpansion=6 --splitMatrixBiggerThanThis=100",
iterations=iterations,
inputModelFile=outputModelFile,
logLevel=getLogLevelString(),
maxAlignmentLengthPerJob=10000) #, updateTheBand=True)
hmm2 = Hmm.loadHmm(outputModelFile)
logger.info(
"For trial %s the likelihood after a further %s iterations of EM is %s"
% (trial, iterations, hmm2.likelihood))
self.assertTrue(hmm.likelihood < hmm2.likelihood)
hmm2.normalise()
logger.info("Final transitions: %s" %
" ".join(map(str, hmm2.transitions)))
logger.info("Final emissions: %s" %
" ".join(map(str, hmm2.emissions)))
system("rm -rf %s" % tempDir)
trial += 1
