def distributionPlots(self, options):
"""Reference distribution plot command"""
self.logger.info(
'[CheckM - dist_plot] Creating GC, CD, and TD distribution plots.')
checkDirExists(options.bin_dir)
makeSurePathExists(options.output_dir)
binFiles = self.binFiles(options.bin_dir, options.extension)
genomicSignatures = GenomicSignatures(K=4, threads=1)
tetraSigs = genomicSignatures.read(options.tetra_profile)
plots = DistributionPlots(options)
filesProcessed = 1
for f in binFiles:
self.logger.info(
'Plotting reference distribution plots for %s (%d of %d)' %
(f, filesProcessed, len(binFiles)))
filesProcessed += 1
binId = binIdFromFilename(f)
plots.plot(f, tetraSigs, options.distributions)
outputFile = os.path.join(
options.output_dir,
binId) + '.ref_dist_plots.' + options.image_type
plots.savePlot(outputFile, dpi=options.dpi)
self.logger.info('Plot written to: ' + outputFile)
self.timeKeeper.printTimeStamp()
def identifyOutliers(self, outDir, binFiles, tetraProfileFile,
distribution, reportType, outputFile):
"""Identify sequences that are outliers."""
self.logger.info('Reading reference distributions.')
gcBounds = readDistribution('gc_dist')
cdBounds = readDistribution('cd_dist')
tdBounds = readDistribution('td_dist')
fout = open(outputFile, 'w')
fout.write(
'Bin Id\tSequence Id\tSequence length\tOutlying distributions')
fout.write(
'\tSequence GC\tMean bin GC\tLower GC bound (%s%%)\tUpper GC bound (%s%%)'
% (distribution, distribution))
fout.write('\tSequence CD\tMean bin CD\tLower CD bound (%s%%)' %
distribution)
fout.write('\tSequence TD\tMean bin TD\tUpper TD bound (%s%%)\n' %
distribution)
processedBins = 0
for binFile in binFiles:
binId = binIdFromFilename(binFile)
processedBins += 1
self.logger.info('Finding outliers in %s (%d of %d).' %
(binId, processedBins, len(binFiles)))
seqs = readFasta(binFile)
meanGC, deltaGCs, seqGC = self.gcDist(seqs)
genomicSig = GenomicSignatures(K=4, threads=1)
tetraSigs = genomicSig.read(tetraProfileFile)
binSig = self.binTetraSig(seqs, tetraSigs)
meanTD, deltaTDs = self.tetraDiffDist(seqs, genomicSig, tetraSigs,
binSig)
gffFile = os.path.join(outDir, 'bins', binId,
DefaultValues.PRODIGAL_GFF)
if not os.path.exists(gffFile):
self.logger.error(
'Missing gene feature file (%s). This plot if not compatible with the --genes option.\n'
% DefaultValues.PRODIGAL_GFF)
sys.exit(1)
prodigalParser = ProdigalGeneFeatureParser(gffFile)
meanCD, deltaCDs, CDs = self.codingDensityDist(
seqs, prodigalParser)
# find keys into GC and CD distributions
closestGC = findNearest(np.array(list(gcBounds.keys())), meanGC)
sampleSeqLen = list(gcBounds[closestGC].keys())[0]
d = gcBounds[closestGC][sampleSeqLen]
gcLowerBoundKey = findNearest(list(d.keys()),
(100 - distribution) / 2.0)
gcUpperBoundKey = findNearest(list(d.keys()),
(100 + distribution) / 2.0)
closestCD = findNearest(np.array(list(cdBounds.keys())), meanCD)
sampleSeqLen = list(cdBounds[closestCD].keys())[0]
d = cdBounds[closestCD][sampleSeqLen]
cdLowerBoundKey = findNearest(list(d.keys()),
(100 - distribution) / 2.0)
tdBoundKey = findNearest(
list(tdBounds[list(tdBounds.keys())[0]].keys()), distribution)
index = 0
for seqId, seq in seqs.items():
seqLen = len(seq)
# find GC, CD, and TD bounds
closestSeqLen = findNearest(list(gcBounds[closestGC].keys()),
seqLen)
gcLowerBound = gcBounds[closestGC][closestSeqLen][
gcLowerBoundKey]
gcUpperBound = gcBounds[closestGC][closestSeqLen][
gcUpperBoundKey]
closestSeqLen = findNearest(list(cdBounds[closestCD].keys()),
seqLen)
cdLowerBound = cdBounds[closestCD][closestSeqLen][
cdLowerBoundKey]
closestSeqLen = findNearest(list(tdBounds.keys()), seqLen)
tdBound = tdBounds[closestSeqLen][tdBoundKey]
outlyingDists = []
if deltaGCs[index] < gcLowerBound or deltaGCs[
index] > gcUpperBound:
outlyingDists.append('GC')
if deltaCDs[index] < cdLowerBound:
outlyingDists.append('CD')
if deltaTDs[index] > tdBound:
outlyingDists.append('TD')
if (reportType == 'any' and len(outlyingDists) >= 1) or (
reportType == 'all' and len(outlyingDists) == 3):
fout.write(binId + '\t' + seqId + '\t%d' % len(seq) +
'\t' + ','.join(outlyingDists))
fout.write('\t%.1f\t%.1f\t%.1f\t%.1f' %
(seqGC[index] * 100, meanGC * 100,
(meanGC + gcLowerBound) * 100,
(meanGC + gcUpperBound) * 100))
fout.write('\t%.1f\t%.1f\t%.1f' %
(CDs[index] * 100, meanCD * 100,
(meanCD + cdLowerBound) * 100))
fout.write('\t%.3f\t%.3f\t%.3f' %
(deltaTDs[index], meanTD, tdBound) + '\n')
index += 1
fout.close()
def identifyOutliers(self, outDir, binFiles, tetraProfileFile, distribution, reportType, outputFile):
"""Identify sequences that are outliers."""
self.logger.info(' Reading reference distributions.')
gcBounds = readDistribution('gc_dist')
cdBounds = readDistribution('cd_dist')
tdBounds = readDistribution('td_dist')
fout = open(outputFile, 'w')
fout.write('Bin Id\tSequence Id\tSequence length\tOutlying distributions')
fout.write('\tSequence GC\tMean bin GC\tLower GC bound (%s%%)\tUpper GC bound (%s%%)' % (distribution, distribution))
fout.write('\tSequence CD\tMean bin CD\tLower CD bound (%s%%)' % distribution)
fout.write('\tSequence TD\tMean bin TD\tUpper TD bound (%s%%)\n' % distribution)
self.logger.info('')
processedBins = 0
for binFile in binFiles:
binId = binIdFromFilename(binFile)
processedBins += 1
self.logger.info(' Finding outliers in %s (%d of %d).' % (binId, processedBins, len(binFiles)))
seqs = readFasta(binFile)
meanGC, deltaGCs, seqGC = self.gcDist(seqs)
genomicSig = GenomicSignatures(K=4, threads=1)
tetraSigs = genomicSig.read(tetraProfileFile)
binSig = self.binTetraSig(seqs, tetraSigs)
meanTD, deltaTDs = self.tetraDiffDist(seqs, genomicSig, tetraSigs, binSig)
gffFile = os.path.join(outDir, 'bins', binId, DefaultValues.PRODIGAL_GFF)
if not os.path.exists(gffFile):
self.logger.error(' [Error] Missing gene feature file (%s). This plot if not compatible with the --genes option.\n' % DefaultValues.PRODIGAL_GFF)
sys.exit(1)
prodigalParser = ProdigalGeneFeatureParser(gffFile)
meanCD, deltaCDs, CDs = self.codingDensityDist(seqs, prodigalParser)
# find keys into GC and CD distributions
closestGC = findNearest(np.array(gcBounds.keys()), meanGC)
sampleSeqLen = gcBounds[closestGC].keys()[0]
d = gcBounds[closestGC][sampleSeqLen]
gcLowerBoundKey = findNearest(d.keys(), (100 - distribution) / 2.0)
gcUpperBoundKey = findNearest(d.keys(), (100 + distribution) / 2.0)
closestCD = findNearest(np.array(cdBounds.keys()), meanCD)
sampleSeqLen = cdBounds[closestCD].keys()[0]
d = cdBounds[closestCD][sampleSeqLen]
cdLowerBoundKey = findNearest(d.keys(), (100 - distribution) / 2.0)
tdBoundKey = findNearest(tdBounds[tdBounds.keys()[0]].keys(), distribution)
index = 0
for seqId, seq in seqs.iteritems():
seqLen = len(seq)
# find GC, CD, and TD bounds
closestSeqLen = findNearest(gcBounds[closestGC].keys(), seqLen)
gcLowerBound = gcBounds[closestGC][closestSeqLen][gcLowerBoundKey]
gcUpperBound = gcBounds[closestGC][closestSeqLen][gcUpperBoundKey]
closestSeqLen = findNearest(cdBounds[closestCD].keys(), seqLen)
cdLowerBound = cdBounds[closestCD][closestSeqLen][cdLowerBoundKey]
closestSeqLen = findNearest(tdBounds.keys(), seqLen)
tdBound = tdBounds[closestSeqLen][tdBoundKey]
outlyingDists = []
if deltaGCs[index] < gcLowerBound or deltaGCs[index] > gcUpperBound:
outlyingDists.append('GC')
if deltaCDs[index] < cdLowerBound:
outlyingDists.append('CD')
if deltaTDs[index] > tdBound:
outlyingDists.append('TD')
if (reportType == 'any' and len(outlyingDists) >= 1) or (reportType == 'all' and len(outlyingDists) == 3):
fout.write(binId + '\t' + seqId + '\t%d' % len(seq) + '\t' + ','.join(outlyingDists))
fout.write('\t%.1f\t%.1f\t%.1f\t%.1f' % (seqGC[index] * 100, meanGC * 100, (meanGC + gcLowerBound) * 100, (meanGC + gcUpperBound) * 100))
fout.write('\t%.1f\t%.1f\t%.1f' % (CDs[index] * 100, meanCD * 100, (meanCD + cdLowerBound) * 100))
fout.write('\t%.3f\t%.3f\t%.3f' % (deltaTDs[index], meanTD, tdBound) + '\n')
index += 1
fout.close()
