def calculateGC(self, seqs, seqStats):
"""Calculate fraction of nucleotides that are G or C."""
totalGC = 0
totalAT = 0
gcPerSeq = []
for seqId, seq in seqs.iteritems():
a, c, g, t = baseCount(seq)
gc = g + c
at = a + t
totalGC += gc
totalAT += at
if (gc + at) > 0:
gcContent = float(gc) / (gc + at)
else:
gcContent = 0.0
seqStats[seqId]['GC'] = gcContent
if len(seq) > DefaultValues.MIN_SEQ_LEN_GC_STD:
gcPerSeq.append(gcContent)
if (totalGC + totalAT) > 0:
GC = float(totalGC) / (totalGC + totalAT)
else:
GC = 0.0
varGC = 0
if len(gcPerSeq) > 1:
varGC = mean(map(lambda x: (x - GC) ** 2, gcPerSeq))
return GC, math.sqrt(varGC)
def calculateGC(self, seqs, seqStats=None):
"""Calculate fraction of nucleotides that are G or C."""
totalGC = 0
totalAT = 0
gcPerSeq = []
for seqId, seq in seqs.items():
a, c, g, t = baseCount(seq)
gc = g + c
at = a + t
totalGC += gc
totalAT += at
if (gc + at) > 0:
gcContent = float(gc) / (gc + at)
else:
gcContent = 0.0
if seqStats:
seqStats[seqId]['GC'] = gcContent
if len(seq) > DefaultValues.MIN_SEQ_LEN_GC_STD:
gcPerSeq.append(gcContent)
if (totalGC + totalAT) > 0:
GC = float(totalGC) / (totalGC + totalAT)
else:
GC = 0.0
varGC = 0
if len(gcPerSeq) > 1:
varGC = mean(list(map(lambda x: (x - GC)**2, gcPerSeq)))
return GC, math.sqrt(varGC)
def testBaseCount(self):
"""Verify computation of base count on mixed-case sequence."""
a, c, g, t = baseCount('ACGTacgtNnUu')
self.assertEqual(a, 2)
self.assertEqual(c, 2)
self.assertEqual(g, 2)
self.assertEqual(t, 4)
def run(self, binFiles, seqFile, outSeqFile, outStatsFile, minSeqLen):
checkFileExists(seqFile)
# get list of sequences in bins
self.logger.info(' Reading binned sequences.')
binnedSeqs = {}
totalBinnedBases = 0
for binFile in binFiles:
seqs = readFasta(binFile)
binnedSeqs.update(seqs)
for seq in seqs.values():
totalBinnedBases += len(seq)
self.logger.info(' Read %d (%.2f Mbp) binned sequences.' % (len(binnedSeqs), float(totalBinnedBases) / 1e6))
# get list of all sequences
self.logger.info(' Reading all sequences.')
allSeqs = readFasta(seqFile)
totalBases = 0
for seq in allSeqs.values():
totalBases += len(seq)
self.logger.info(' Read %d (%.2f Mbp) sequences.' % (len(allSeqs), float(totalBases) / 1e6))
# write all unbinned sequences
self.logger.info(' Identifying unbinned sequences >= %d bp.' % minSeqLen)
seqOut = open(outSeqFile, 'w')
statsOut = open(outStatsFile, 'w')
statsOut.write('Sequence Id\tLength\tGC\n')
unbinnedCount = 0
unbinnedBases = 0
for seqId, seq in allSeqs.iteritems():
if seqId not in binnedSeqs:
if len(seq) >= minSeqLen:
unbinnedCount += 1
seqOut.write('>' + seqId + '\n')
seqOut.write(seq + '\n')
unbinnedBases += len(seq)
a, c, g, t = baseCount(seq)
statsOut.write('%s\t%d\t%.2f\n' % (seqId, len(seq), float(g + c) * 100 / (a + c + g + t)))
seqOut.close()
statsOut.close()
self.logger.info(' Identified %d (%.2f Mbp) unbinned sequences.' % (unbinnedCount, float(unbinnedBases) / 1e6))
self.logger.info('')
self.logger.info(' Percentage of unbinned sequences: %.2f%%' % (unbinnedCount * 100.0 / len(allSeqs)))
self.logger.info(' Percentage of unbinned bases: %.2f%%' % (unbinnedBases * 100.0 / totalBases))
def run(self, binFiles, seqFile, outSeqFile, outStatsFile, minSeqLen):
checkFileExists(seqFile)
# get list of sequences in bins
self.logger.info('Reading binned sequences.')
binnedSeqs = {}
totalBinnedBases = 0
for binFile in binFiles:
seqs = readFasta(binFile)
binnedSeqs.update(seqs)
for seq in seqs.values():
totalBinnedBases += len(seq)
self.logger.info(' Read %d (%.2f Mbp) binned sequences.' % (len(binnedSeqs), float(totalBinnedBases) / 1e6))
# get list of all sequences
self.logger.info('Reading all sequences.')
allSeqs = readFasta(seqFile)
totalBases = 0
for seq in allSeqs.values():
totalBases += len(seq)
self.logger.info(' Read %d (%.2f Mbp) sequences.' % (len(allSeqs), float(totalBases) / 1e6))
# write all unbinned sequences
self.logger.info('Identifying unbinned sequences >= %d bp.' % minSeqLen)
seqOut = open(outSeqFile, 'w')
statsOut = open(outStatsFile, 'w')
statsOut.write('Sequence Id\tLength\tGC\n')
unbinnedCount = 0
unbinnedBases = 0
for seqId, seq in allSeqs.iteritems():
if seqId not in binnedSeqs:
if len(seq) >= minSeqLen:
unbinnedCount += 1
seqOut.write('>' + seqId + '\n')
seqOut.write(seq + '\n')
unbinnedBases += len(seq)
a, c, g, t = baseCount(seq)
statsOut.write('%s\t%d\t%.2f\n' % (seqId, len(seq), float(g + c) * 100 / (a + c + g + t)))
seqOut.close()
statsOut.close()
self.logger.info(' Identified %d (%.2f Mbp) unbinned sequences.' % (unbinnedCount, float(unbinnedBases) / 1e6))
self.logger.info('Percentage of unbinned sequences: %.2f%%' % (unbinnedCount * 100.0 / len(allSeqs)))
self.logger.info('Percentage of unbinned bases: %.2f%%' % (unbinnedBases * 100.0 / totalBases))
def gcDist(self, seqs):
"""GC statistics for bin."""
GCs = []
gcTotal = 0
basesTotal = 0
for _, seq in seqs.items():
a, c, g, t = baseCount(seq)
gc = g + c
bases = a + c + g + t
GCs.append(float(gc) / (bases))
gcTotal += gc
basesTotal += bases
meanGC = float(gcTotal) / basesTotal
deltaGCs = np.array(GCs) - meanGC
return meanGC, deltaGCs, GCs
def gcDist(self, seqs):
"""GC statistics for bin."""
GCs = []
gcTotal = 0
basesTotal = 0
for _, seq in seqs.iteritems():
a, c, g, t = baseCount(seq)
gc = g + c
bases = a + c + g + t
GCs.append(float(gc) / (bases))
gcTotal += gc
basesTotal += bases
meanGC = float(gcTotal) / basesTotal
deltaGCs = np.array(GCs) - meanGC
return meanGC, deltaGCs, GCs
def plotOnAxes(self, fastaFile, distributionsToPlot, axesHist,
axesDeltaGC):
# Read reference distributions from file
dist = readDistribution('gc_dist')
# get GC for windows
seqs = readFasta(fastaFile)
data = []
seqLens = []
for _, seq in seqs.iteritems():
start = 0
end = self.options.gc_window_size
seqLen = len(seq)
seqLens.append(seqLen)
while (end < seqLen):
a, c, g, t = baseCount(seq[start:end])
try:
data.append(float(g + c) / (a + c + g + t))
except:
# it is possible to reach a long stretch of
# N's that causes a division by zero error
pass
start = end
end += self.options.gc_window_size
if len(data) == 0:
axesHist.set_xlabel(
'[Error] No seqs >= %d, the specified window size' %
self.options.gc_window_size)
return
# Histogram plot
bins = [0.0]
binWidth = self.options.gc_bin_width
binEnd = binWidth
while binEnd <= 1.0:
bins.append(binEnd)
binEnd += binWidth
axesHist.hist(data, bins=bins, normed=True, color=(0.5, 0.5, 0.5))
axesHist.set_xlabel('% GC')
axesHist.set_ylabel('% windows (' + str(self.options.gc_window_size) +
' bp)')
# Prettify plot
for a in axesHist.yaxis.majorTicks:
a.tick1On = True
a.tick2On = False
for a in axesHist.xaxis.majorTicks:
a.tick1On = True
a.tick2On = False
for line in axesHist.yaxis.get_ticklines():
line.set_color(self.axesColour)
for line in axesHist.xaxis.get_ticklines():
line.set_color(self.axesColour)
for loc, spine in axesHist.spines.iteritems():
if loc in ['right', 'top']:
spine.set_color('none')
else:
spine.set_color(self.axesColour)
# get GC bin statistics
binTools = BinTools()
meanGC, deltaGCs, _ = binTools.gcDist(seqs)
# Delta-GC vs Sequence length plot
axesDeltaGC.scatter(deltaGCs,
seqLens,
c=abs(deltaGCs),
s=10,
lw=0.5,
cmap='gray_r')
axesDeltaGC.set_xlabel(r'$\Delta$ GC (mean GC = %.1f%%)' %
(meanGC * 100))
axesDeltaGC.set_ylabel('Sequence length (kbp)')
_, yMaxSeqs = axesDeltaGC.get_ylim()
xMinSeqs, xMaxSeqs = axesDeltaGC.get_xlim()
# plot reference distributions
for distToPlot in distributionsToPlot:
closestGC = findNearest(np.array(dist.keys()), meanGC)
# find closest distribution values
sampleSeqLen = dist[closestGC].keys()[0]
d = dist[closestGC][sampleSeqLen]
gcLowerBoundKey = findNearest(d.keys(), (100 - distToPlot) / 2.0)
gcUpperBoundKey = findNearest(d.keys(), (100 + distToPlot) / 2.0)
xL = []
xU = []
y = []
for windowSize in dist[closestGC]:
xL.append(dist[closestGC][windowSize][gcLowerBoundKey])
xU.append(dist[closestGC][windowSize][gcUpperBoundKey])
y.append(windowSize)
# sort by y-values
sortIndexY = np.argsort(y)
xL = np.array(xL)[sortIndexY]
xU = np.array(xU)[sortIndexY]
y = np.array(y)[sortIndexY]
axesDeltaGC.plot(xL, y, 'r--', lw=0.5, zorder=0)
axesDeltaGC.plot(xU, y, 'r--', lw=0.5, zorder=0)
# ensure y-axis include zero and covers all sequences
axesDeltaGC.set_ylim([0, yMaxSeqs])
# ensure x-axis is set appropriately for sequences
axesDeltaGC.set_xlim([xMinSeqs, xMaxSeqs])
# draw vertical line at x=0
axesDeltaGC.vlines(0,
0,
yMaxSeqs,
linestyle='dashed',
color=self.axesColour,
zorder=0)
# Change sequence lengths from bp to kbp
yticks = axesDeltaGC.get_yticks()
kbpLabels = []
for seqLen in yticks:
label = '%.1f' % (float(seqLen) / 1000)
label = label.replace('.0', '') # remove trailing zero
kbpLabels.append(label)
axesDeltaGC.set_yticklabels(kbpLabels)
# Prettify plot
for a in axesDeltaGC.yaxis.majorTicks:
a.tick1On = True
a.tick2On = False
for a in axesDeltaGC.xaxis.majorTicks:
a.tick1On = True
a.tick2On = False
for line in axesDeltaGC.yaxis.get_ticklines():
line.set_color(self.axesColour)
for line in axesDeltaGC.xaxis.get_ticklines():
line.set_color(self.axesColour)
for loc, spine in axesDeltaGC.spines.iteritems():
if loc in ['right', 'top']:
spine.set_color('none')
else:
spine.set_color(self.axesColour)
def plotOnAxes(self, fastaFile, distributionsToPlot, axesHist, axesDeltaCD):
# parse Prodigal output
gffFile = os.path.join(self.options.out_folder, 'bins', binIdFromFilename(fastaFile), DefaultValues.PRODIGAL_GFF)
if not os.path.exists(gffFile):
print 'Missing gene feature file (%s). This plot if not compatible with the --genes option.' % DefaultValues.PRODIGAL_GFF
sys.exit()
prodigalParser = ProdigalGeneFeatureParser(gffFile)
# Read reference distributions from file
dist = readDistribution('cd_dist')
# get coding density for windows
seqs = readFasta(fastaFile)
data = []
seqLens = []
for seqId, seq in seqs.iteritems():
start = 0
end = self.options.cd_window_size
seqLen = len(seq)
seqLens.append(seqLen)
while(end < seqLen):
codingBases = prodigalParser.codingBases(seqId, start, end)
a, c, g, t = baseCount(seq[start:end])
data.append(float(codingBases) / (a + c + g + t))
start = end
end += self.options.cd_window_size
if len(data) == 0:
axesHist.set_xlabel('[Error] No seqs >= %d, the specified window size' % self.options.cd_window_size)
return
# Histogram plot
bins = [0.0]
binWidth = self.options.cd_bin_width
binEnd = binWidth
while binEnd <= 1.0:
bins.append(binEnd)
binEnd += binWidth
axesHist.hist(data, bins=bins, normed=True, color=(0.5, 0.5, 0.5))
axesHist.set_xlabel('% coding density')
axesHist.set_ylabel('% windows (' + str(self.options.cd_window_size) + ' bp)')
# Prettify plot
for a in axesHist.yaxis.majorTicks:
a.tick1On = True
a.tick2On = False
for a in axesHist.xaxis.majorTicks:
a.tick1On = True
a.tick2On = False
for line in axesHist.yaxis.get_ticklines():
line.set_color(self.axesColour)
for line in axesHist.xaxis.get_ticklines():
line.set_color(self.axesColour)
for loc, spine in axesHist.spines.iteritems():
if loc in ['right', 'top']:
spine.set_color('none')
else:
spine.set_color(self.axesColour)
# get CD bin statistics
binTools = BinTools()
meanCD, deltaCDs, _ = binTools.codingDensityDist(seqs, prodigalParser)
# Delta-CD vs sequence length plot
axesDeltaCD.scatter(deltaCDs, seqLens, c=abs(deltaCDs), s=10, lw=0.5, cmap=pylab.cm.Greys)
axesDeltaCD.set_xlabel(r'$\Delta$ CD (mean coding density = %.1f%%)' % (meanCD * 100))
axesDeltaCD.set_ylabel('Sequence length (kbp)')
_, yMaxSeqs = axesDeltaCD.get_ylim()
xMinSeqs, xMaxSeqs = axesDeltaCD.get_xlim()
# plot reference distributions
for distToPlot in distributionsToPlot:
closestCD = findNearest(np.array(dist.keys()), meanCD)
# find closest distribution values
sampleSeqLen = dist[closestCD].keys()[0]
d = dist[closestCD][sampleSeqLen]
cdLowerBoundKey = findNearest(d.keys(), (100 - distToPlot) / 2.0)
cdUpperBoundKey = findNearest(d.keys(), (100 + distToPlot) / 2.0)
xL = []
xU = []
y = []
for windowSize in dist[closestCD]:
xL.append(dist[closestCD][windowSize][cdLowerBoundKey])
xU.append(dist[closestCD][windowSize][cdUpperBoundKey])
y.append(windowSize)
# sort by y-values
sortIndexY = np.argsort(y)
xL = np.array(xL)[sortIndexY]
xU = np.array(xU)[sortIndexY]
y = np.array(y)[sortIndexY]
axesDeltaCD.plot(xL, y, 'r--', lw=0.5, zorder=0)
axesDeltaCD.plot(xU, y, 'r--', lw=0.5, zorder=0)
# ensure y-axis include zero and covers all sequences
axesDeltaCD.set_ylim([0, yMaxSeqs])
# ensure x-axis is set appropriately for sequences
axesDeltaCD.set_xlim([xMinSeqs, xMaxSeqs])
# draw vertical line at x=0
axesDeltaCD.vlines(0, 0, yMaxSeqs, linestyle='dashed', color=self.axesColour, zorder=0)
# Change sequence lengths from bp to kbp
yticks = axesDeltaCD.get_yticks()
kbpLabels = []
for seqLen in yticks:
label = '%.1f' % (float(seqLen) / 1000)
label = label.replace('.0', '') # remove trailing zero
kbpLabels.append(label)
axesDeltaCD.set_yticklabels(kbpLabels)
# Prettify plot
for a in axesDeltaCD.yaxis.majorTicks:
a.tick1On = True
a.tick2On = False
for a in axesDeltaCD.xaxis.majorTicks:
a.tick1On = True
a.tick2On = False
for line in axesDeltaCD.yaxis.get_ticklines():
line.set_color(self.axesColour)
for line in axesDeltaCD.xaxis.get_ticklines():
line.set_color(self.axesColour)
for loc, spine in axesDeltaCD.spines.iteritems():
if loc in ['right', 'top']:
spine.set_color('none')
else:
spine.set_color(self.axesColour)
def plotOnAxes(self, fastaFile, distributionsToPlot, axesHist, axesDeltaCD):
# parse Prodigal output
gffFile = os.path.join(self.options.results_dir, 'bins', binIdFromFilename(fastaFile), 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.' % DefaultValues.PRODIGAL_GFF)
sys.exit()
prodigalParser = ProdigalGeneFeatureParser(gffFile)
# Read reference distributions from file
dist = readDistribution('cd_dist')
# get coding density for windows
seqs = readFasta(fastaFile)
data = []
seqLens = []
for seqId, seq in seqs.iteritems():
start = 0
end = self.options.cd_window_size
seqLen = len(seq)
seqLens.append(seqLen)
while(end < seqLen):
codingBases = prodigalParser.codingBases(seqId, start, end)
a, c, g, t = baseCount(seq[start:end])
data.append(float(codingBases) / (a + c + g + t))
start = end
end += self.options.cd_window_size
if len(data) == 0:
axesHist.set_xlabel('[Error] No seqs >= %d, the specified window size' % self.options.cd_window_size)
return
# Histogram plot
bins = [0.0]
binWidth = self.options.cd_bin_width
binEnd = binWidth
while binEnd <= 1.0:
bins.append(binEnd)
binEnd += binWidth
axesHist.hist(data, bins=bins, normed=True, color=(0.5, 0.5, 0.5))
axesHist.set_xlabel('% coding density')
axesHist.set_ylabel('% windows (' + str(self.options.cd_window_size) + ' bp)')
# Prettify plot
for a in axesHist.yaxis.majorTicks:
a.tick1On = True
a.tick2On = False
for a in axesHist.xaxis.majorTicks:
a.tick1On = True
a.tick2On = False
for line in axesHist.yaxis.get_ticklines():
line.set_color(self.axesColour)
for line in axesHist.xaxis.get_ticklines():
line.set_color(self.axesColour)
for loc, spine in axesHist.spines.iteritems():
if loc in ['right', 'top']:
spine.set_color('none')
else:
spine.set_color(self.axesColour)
# get CD bin statistics
binTools = BinTools()
meanCD, deltaCDs, _ = binTools.codingDensityDist(seqs, prodigalParser)
# Delta-CD vs sequence length plot
axesDeltaCD.scatter(deltaCDs, seqLens, c=abs(deltaCDs), s=10, lw=0.5, cmap='gray_r')
axesDeltaCD.set_xlabel(r'$\Delta$ CD (mean coding density = %.1f%%)' % (meanCD * 100))
axesDeltaCD.set_ylabel('Sequence length (kbp)')
_, yMaxSeqs = axesDeltaCD.get_ylim()
xMinSeqs, xMaxSeqs = axesDeltaCD.get_xlim()
# plot reference distributions
for distToPlot in distributionsToPlot:
closestCD = findNearest(np.array(dist.keys()), meanCD)
# find closest distribution values
sampleSeqLen = dist[closestCD].keys()[0]
d = dist[closestCD][sampleSeqLen]
cdLowerBoundKey = findNearest(d.keys(), (100 - distToPlot) / 2.0)
cdUpperBoundKey = findNearest(d.keys(), (100 + distToPlot) / 2.0)
xL = []
xU = []
y = []
for windowSize in dist[closestCD]:
xL.append(dist[closestCD][windowSize][cdLowerBoundKey])
xU.append(dist[closestCD][windowSize][cdUpperBoundKey])
y.append(windowSize)
# sort by y-values
sortIndexY = np.argsort(y)
xL = np.array(xL)[sortIndexY]
xU = np.array(xU)[sortIndexY]
y = np.array(y)[sortIndexY]
axesDeltaCD.plot(xL, y, 'r--', lw=0.5, zorder=0)
axesDeltaCD.plot(xU, y, 'r--', lw=0.5, zorder=0)
# ensure y-axis include zero and covers all sequences
axesDeltaCD.set_ylim([0, yMaxSeqs])
# ensure x-axis is set appropriately for sequences
axesDeltaCD.set_xlim([xMinSeqs, xMaxSeqs])
# draw vertical line at x=0
axesDeltaCD.vlines(0, 0, yMaxSeqs, linestyle='dashed', color=self.axesColour, zorder=0)
# Change sequence lengths from bp to kbp
yticks = axesDeltaCD.get_yticks()
kbpLabels = []
for seqLen in yticks:
label = '%.1f' % (float(seqLen) / 1000)
label = label.replace('.0', '') # remove trailing zero
kbpLabels.append(label)
axesDeltaCD.set_yticklabels(kbpLabels)
# Prettify plot
for a in axesDeltaCD.yaxis.majorTicks:
a.tick1On = True
a.tick2On = False
for a in axesDeltaCD.xaxis.majorTicks:
a.tick1On = True
a.tick2On = False
for line in axesDeltaCD.yaxis.get_ticklines():
line.set_color(self.axesColour)
for line in axesDeltaCD.xaxis.get_ticklines():
line.set_color(self.axesColour)
for loc, spine in axesDeltaCD.spines.iteritems():
if loc in ['right', 'top']:
spine.set_color('none')
else:
spine.set_color(self.axesColour)
def plotOnAxes(self, binFile, coverageProfile, windowAxes, seqAxes):
# get GC for windows
seqs = readFasta(binFile)
gcProfile = {}
for seqId, seq in seqs.iteritems():
start = 0
end = self.options.window_size
windowGCs = []
while(end < len(seq)):
a, c, g, t = baseCount(seq[start:end])
windowGCs.append(float(g + c) / (a + c + g + t))
start = end
end += self.options.window_size
a, c, g, t = baseCount(seq)
seqGC = float(g + c) / (a + c + g + t)
gcProfile[seqId] = [seqGC, windowGCs]
# plot GC vs coverage for windows
gc = []
coverage = []
for seqId, gcInfo in gcProfile.iteritems():
gc += gcInfo[1]
coverage += coverageProfile[seqId][1]
windowAxes.scatter(gc, coverage, c=abs(array(coverage)), s=10, lw=0.5, cmap=pylab.cm.Greys)
windowAxes.set_xlabel('GC (mean = %.1f%%)' % (mean(gc)*100))
windowAxes.set_ylabel('Coverage (mean = %.1f)' % mean(coverage))
# plot linear regression line
if len(gc) > 1:
slope, inter = polyfit(gc, coverage,1)
fit_fn = poly1d([slope, inter]) # fit_fn is now a function which takes in x and returns an estimate for y
windowAxes.plot([min(gc), max(gc)], fit_fn([min(gc), max(gc)]), '--r', lw=0.5)
windowAxes.set_title('GC vs. Coverage\n(window size = %d bp, slope = %.2f)' % (self.options.window_size, slope))
else:
# not possible to calculate best fit line
windowAxes.set_title('GC vs. Coverage\n(window size = %d bp, no best fit line)' % self.options.window_size)
# Prettify plot
for a in windowAxes.yaxis.majorTicks:
a.tick1On=True
a.tick2On=False
for a in windowAxes.xaxis.majorTicks:
a.tick1On=True
a.tick2On=False
for line in windowAxes.yaxis.get_ticklines():
line.set_color(self.axesColour)
for line in windowAxes.xaxis.get_ticklines():
line.set_color(self.axesColour)
for loc, spine in windowAxes.spines.iteritems():
if loc in ['right','top']:
spine.set_color('none')
else:
spine.set_color(self.axesColour)
# plot GC vs coverage for entire sequences
gc = []
coverage = []
seqLen = []
for seqId, gcInfo in gcProfile.iteritems():
gc.append(gcInfo[0])
coverage.append(coverageProfile[seqId][0])
seqLen.append(len(seqs[seqId]))
# set marker size proportional to sequence length
markerSize = log(array(seqLen)) # log-scale
markerSize = (markerSize - min(markerSize)) / max(markerSize) # normalize between 0 and 1
markerSize = markerSize*200 + 10 # normalize between 10 and 200
seqAxes.scatter(gc, coverage, c=abs(array(coverage)), s=markerSize, lw=0.5, cmap=pylab.cm.Greys)
seqAxes.set_xlabel('GC (mean = %.1f%%)' % (mean(gc)*100))
seqAxes.set_ylabel('Coverage (mean = %.1f)' % mean(coverage))
seqAxes.set_title('GC vs. Coverage\nIndividual Sequences')
# Prettify plot
for a in seqAxes.yaxis.majorTicks:
a.tick1On=True
a.tick2On=False
for a in seqAxes.xaxis.majorTicks:
a.tick1On=True
a.tick2On=False
for line in seqAxes.yaxis.get_ticklines():
line.set_color(self.axesColour)
for line in seqAxes.xaxis.get_ticklines():
line.set_color(self.axesColour)
for loc, spine in seqAxes.spines.iteritems():
if loc in ['right','top']:
spine.set_color('none')
else:
spine.set_color(self.axesColour)
def plotOnAxes(self, fastaFile, distributionsToPlot, axesHist, axesDeltaGC):
# Read reference distributions from file
dist = readDistribution('gc_dist')
# get GC for windows
seqs = readFasta(fastaFile)
data = []
seqLens = []
for _, seq in seqs.iteritems():
start = 0
end = self.options.gc_window_size
seqLen = len(seq)
seqLens.append(seqLen)
while(end < seqLen):
a, c, g, t = baseCount(seq[start:end])
try:
data.append(float(g + c) / (a + c + g + t))
except:
# it is possible to reach a long stretch of
# N's that causes a division by zero error
pass
start = end
end += self.options.gc_window_size
if len(data) == 0:
axesHist.set_xlabel('[Error] No seqs >= %d, the specified window size' % self.options.gc_window_size)
return
# Histogram plot
bins = [0.0]
binWidth = self.options.gc_bin_width
binEnd = binWidth
while binEnd <= 1.0:
bins.append(binEnd)
binEnd += binWidth
axesHist.hist(data, bins=bins, normed=True, color=(0.5, 0.5, 0.5))
axesHist.set_xlabel('% GC')
axesHist.set_ylabel('% windows (' + str(self.options.gc_window_size) + ' bp)')
# Prettify plot
for a in axesHist.yaxis.majorTicks:
a.tick1On = True
a.tick2On = False
for a in axesHist.xaxis.majorTicks:
a.tick1On = True
a.tick2On = False
for line in axesHist.yaxis.get_ticklines():
line.set_color(self.axesColour)
for line in axesHist.xaxis.get_ticklines():
line.set_color(self.axesColour)
for loc, spine in axesHist.spines.iteritems():
if loc in ['right', 'top']:
spine.set_color('none')
else:
spine.set_color(self.axesColour)
# get GC bin statistics
binTools = BinTools()
meanGC, deltaGCs, _ = binTools.gcDist(seqs)
# Delta-GC vs Sequence length plot
axesDeltaGC.scatter(deltaGCs, seqLens, c=abs(deltaGCs), s=10, lw=0.5, cmap=pylab.cm.Greys)
axesDeltaGC.set_xlabel(r'$\Delta$ GC (mean GC = %.1f%%)' % (meanGC * 100))
axesDeltaGC.set_ylabel('Sequence length (kbp)')
_, yMaxSeqs = axesDeltaGC.get_ylim()
xMinSeqs, xMaxSeqs = axesDeltaGC.get_xlim()
# plot reference distributions
for distToPlot in distributionsToPlot:
closestGC = findNearest(np.array(dist.keys()), meanGC)
# find closest distribution values
sampleSeqLen = dist[closestGC].keys()[0]
d = dist[closestGC][sampleSeqLen]
gcLowerBoundKey = findNearest(d.keys(), (100 - distToPlot) / 2.0)
gcUpperBoundKey = findNearest(d.keys(), (100 + distToPlot) / 2.0)
xL = []
xU = []
y = []
for windowSize in dist[closestGC]:
xL.append(dist[closestGC][windowSize][gcLowerBoundKey])
xU.append(dist[closestGC][windowSize][gcUpperBoundKey])
y.append(windowSize)
# sort by y-values
sortIndexY = np.argsort(y)
xL = np.array(xL)[sortIndexY]
xU = np.array(xU)[sortIndexY]
y = np.array(y)[sortIndexY]
axesDeltaGC.plot(xL, y, 'r--', lw=0.5, zorder=0)
axesDeltaGC.plot(xU, y, 'r--', lw=0.5, zorder=0)
# ensure y-axis include zero and covers all sequences
axesDeltaGC.set_ylim([0, yMaxSeqs])
# ensure x-axis is set appropriately for sequences
axesDeltaGC.set_xlim([xMinSeqs, xMaxSeqs])
# draw vertical line at x=0
axesDeltaGC.vlines(0, 0, yMaxSeqs, linestyle='dashed', color=self.axesColour, zorder=0)
# Change sequence lengths from bp to kbp
yticks = axesDeltaGC.get_yticks()
kbpLabels = []
for seqLen in yticks:
label = '%.1f' % (float(seqLen) / 1000)
label = label.replace('.0', '') # remove trailing zero
kbpLabels.append(label)
axesDeltaGC.set_yticklabels(kbpLabels)
# Prettify plot
for a in axesDeltaGC.yaxis.majorTicks:
a.tick1On = True
a.tick2On = False
for a in axesDeltaGC.xaxis.majorTicks:
a.tick1On = True
a.tick2On = False
for line in axesDeltaGC.yaxis.get_ticklines():
line.set_color(self.axesColour)
for line in axesDeltaGC.xaxis.get_ticklines():
line.set_color(self.axesColour)
for loc, spine in axesDeltaGC.spines.iteritems():
if loc in ['right', 'top']:
spine.set_color('none')
else:
spine.set_color(self.axesColour)
def plotOnAxes(self, binFile, coverageProfile, windowAxes, seqAxes):
# get GC for windows
seqs = readFasta(binFile)
gcProfile = {}
for seqId, seq in seqs.items():
start = 0
end = self.options.window_size
windowGCs = []
while (end < len(seq)):
a, c, g, t = baseCount(seq[start:end])
windowGCs.append(float(g + c) / (a + c + g + t))
start = end
end += self.options.window_size
a, c, g, t = baseCount(seq)
seqGC = float(g + c) / (a + c + g + t)
gcProfile[seqId] = [seqGC, windowGCs]
# plot GC vs coverage for windows
gc = []
coverage = []
for seqId, gcInfo in gcProfile.items():
gc += gcInfo[1]
coverage += coverageProfile[seqId][1]
windowAxes.scatter(gc,
coverage,
c=abs(array(coverage)),
s=10,
lw=0.5,
cmap='gray_r')
windowAxes.set_xlabel('GC (mean = %.1f%%)' % (mean(gc) * 100))
windowAxes.set_ylabel('Coverage (mean = %.1f)' % mean(coverage))
# plot linear regression line
if len(gc) > 1:
slope, inter = polyfit(gc, coverage, 1)
fit_fn = poly1d(
[slope, inter]
) # fit_fn is now a function which takes in x and returns an estimate for y
windowAxes.plot([min(gc), max(gc)],
fit_fn([min(gc), max(gc)]),
'--r',
lw=0.5)
windowAxes.set_title(
'GC vs. Coverage\n(window size = %d bp, slope = %.2f)' %
(self.options.window_size, slope))
else:
# not possible to calculate best fit line
windowAxes.set_title(
'GC vs. Coverage\n(window size = %d bp, no best fit line)' %
self.options.window_size)
# Prettify plot
for a in windowAxes.yaxis.majorTicks:
a.tick1On = True
a.tick2On = False
for a in windowAxes.xaxis.majorTicks:
a.tick1On = True
a.tick2On = False
for line in windowAxes.yaxis.get_ticklines():
line.set_color(self.axesColour)
for line in windowAxes.xaxis.get_ticklines():
line.set_color(self.axesColour)
for loc, spine in windowAxes.spines.items():
if loc in ['right', 'top']:
spine.set_color('none')
else:
spine.set_color(self.axesColour)
# plot GC vs coverage for entire sequences
gc = []
coverage = []
seqLen = []
for seqId, gcInfo in gcProfile.items():
gc.append(gcInfo[0])
coverage.append(coverageProfile[seqId][0])
seqLen.append(len(seqs[seqId]))
# set marker size proportional to sequence length
markerSize = log(array(seqLen)) # log-scale
markerSize = (markerSize - min(markerSize)) / max(
markerSize) # normalize between 0 and 1
markerSize = markerSize * 200 + 10 # normalize between 10 and 200
seqAxes.scatter(gc,
coverage,
c=abs(array(coverage)),
s=markerSize,
lw=0.5,
cmap='gray_r')
seqAxes.set_xlabel('GC (mean = %.1f%%)' % (mean(gc) * 100))
seqAxes.set_ylabel('Coverage (mean = %.1f)' % mean(coverage))
seqAxes.set_title('GC vs. Coverage\nIndividual Sequences')
# Prettify plot
for a in seqAxes.yaxis.majorTicks:
a.tick1On = True
a.tick2On = False
for a in seqAxes.xaxis.majorTicks:
a.tick1On = True
a.tick2On = False
for line in seqAxes.yaxis.get_ticklines():
line.set_color(self.axesColour)
for line in seqAxes.xaxis.get_ticklines():
line.set_color(self.axesColour)
for loc, spine in seqAxes.spines.items():
if loc in ['right', 'top']:
spine.set_color('none')
else:
spine.set_color(self.axesColour)
