def drawClonesizeDist(outdir, options, sample2data):
options.out = os.path.join( outdir, "cloneVsRead" )
fig, pdf = iseqlib.initImage( 10.0, 8.0, options )
axes = fig.add_axes( [0.12, 0.15, 0.85, 0.75] )
lines = []
sampleNames = sorted(sample2data.keys())
#draw
colors = iseqlib.getColors6()
lightColors = iseqlib.getColors6light()
markers = ['o', '*', 's', 'd', '^', 'p', 'v']
c = 0
xdata = []
xticklabels = []
for sample in sampleNames:
#xticklabels, ydata = binData( sample2freqs[sample] )
(xticklabels, ydata) = sample2data[sample]
print sample
print xdata
print ydata
xdata = range(0, len(xticklabels), 1)
markersize = 10.0
m = markers[c]
if m == '*':
markersize = 12.0
elif m == 's':
markersize = 8.0
l = axes.plot(xdata, ydata, color=colors[c], marker=m, markeredgecolor=colors[c], markersize=markersize, linestyle='none')
axes.plot(xdata, ydata, color=lightColors[c], linestyle='-', linewidth=0.2)
lines.append(l)
c += 1
iseqlib.editSpine(axes)
axes.set_title('Clone Size Distribution', size='xx-large')
axes.set_xlabel('Clone size as percentage of total reads', size='large')
axes.set_ylabel('Percentage of total clones', size='large')
fontP = FontProperties()
fontP.set_size('medium')
legend = axes.legend( lines, sampleNames, numpoints=1, loc='best', ncol=1, prop=fontP)
legend._drawFrame = False
axes.xaxis.set_ticklabels( xticklabels )
axes.xaxis.set_ticks( [x - 0.5 for x in xdata] )
axes.set_xlim(-0.5, len(xdata) - 0.5)
axes.set_ylim(-0.5, 50)
for label in axes.get_xticklabels():
label.set_fontsize('medium')
label.set_rotation(45)
for label in axes.get_yticklabels():
label.set_fontsize('medium')
axes.yaxis.grid(b=True, color="#BDBDBD", linestyle='-', linewidth=0.005)
axes.xaxis.grid(b=True, color="#BDBDBD", linestyle='-', linewidth=0.005)
iseqlib.writeImage(fig, pdf, options)
def drawVJdata( fig, axes, sample, vgenes, jgenes, options, minvj, maxvj ):
#Draw heatmap:
#data = sample.normalizeVJusage()
if options.abs:
data = sample.intersectVJusage
else:
data = sample.normIntersectVJusage
#Normalize data to the range minvj-maxvj:
if options.heatmapNoScale:
hmaxes = axes.imshow( data, interpolation='nearest' )
else:
norm = mpl.colors.Normalize(vmin=minvj, vmax=maxvj)
cmap = mpl.cm.get_cmap('rainbow', 20)
hmaxes = axes.imshow( data, interpolation='nearest', cmap=cmap, norm=norm )
#Colorbar:
#from mpl_toolkits.axes_grid1.inset_locator import inset_axes
#axins = inset_axes(axes, width="5%", height="10%", loc=3, bbox_to_anchor=(1.05, 0, 1, 1), bbox_transform=axes.transAxes, borderpad=0)
#matplotlib.pyplot.colorbar(hmaxes, cax=axins, ticks=[0, 0.5, 1])
cbar = fig.colorbar(hmaxes, shrink=0.3)
#cbar.ax.set_yticklabels(['0', '0.5', '1'])
immunoseqLib.editSpine( axes )
axes.set_title( "VJ usage of sample %s" % immunoseqLib.properName(sample.name) )
xticks = [ x for x in range( len(jgenes) ) ]
axes.xaxis.set_ticks( xticks )
for i, xlabel in enumerate(jgenes):
items = xlabel.split('|')
items = [item.lstrip('TRB') for item in items]
jgenes[i] = '|'.join(items)
axes.xaxis.set_ticklabels( jgenes )
yticks = [ y for y in range( len(vgenes) ) ]
axes.yaxis.set_ticks( yticks )
for i, ylabel in enumerate(vgenes):
items = ylabel.split('|')
items = [item.lstrip('TRB') for item in items]
vgenes[i] = '|'.join(items)
axes.yaxis.set_ticklabels( vgenes )
textsize = 'x-small'
for label in axes.get_xticklabels():
label.set_fontsize( textsize )
label.set_rotation( 80 )
for label in axes.get_yticklabels():
label.set_fontsize( textsize )
def drawDistData(axes, sam2nt2aa):
#sam2nt2aa = getNumNtPerAa(samples)
lines = []
labels = []
colors = iseqlib.getColors6()
if len(colors) < len(sam2nt2aa.keys()):
colors.extend( iseqlib.getColors6light() )
if len(colors) < len(sam2nt2aa.keys()):
colors.extend( iseqlib.getColors6dark() )
#colors = iseqlib.getColors0()
#lightColors = getColors6light()
markersize = 10.0
c = -1
axes.set_yscale('log')
for s in sorted( sam2nt2aa.keys() ):
nt2aa = sam2nt2aa[s]
c += 1
xdata = sorted( nt2aa.keys() )
ydata = [ nt2aa[x] for x in xdata ]
line = axes.plot(xdata, ydata, color=colors[c], marker='o', markeredgecolor=colors[c], markersize = markersize, linestyle='-', linewidth=2)
#axes.plot(xdata, ydata, color=lightColors[c], linestyle='-', linewidth=0.5)
lines.append(line)
labels.append(s)
print s
print xdata
print ydata
xticks = xrange(0,8,1)
xticklabels = [ str(x) for x in xticks]
axes.xaxis.set_ticks(xticks)
axes.xaxis.set_ticklabels( xticklabels )
#axes.set_xlim(0.5, 5.5)
#axes.set_ylim(-0.1, 40)
axes.set_title('Nucleotide sequences to amino acid sequence', size="xx-large")
iseqlib.editSpine( axes )
axes.set_xlabel("Number of nucleotide sequences", size='x-large')
#axes.set_ylabel("Number of amino acid sequences", size='x-large')
axes.set_ylabel("Percentage of amino acid sequences", size='x-large')
legend = axes.legend( lines, labels, numpoints=1, loc='best', ncol=1)
legend.__drawFrame = False
axes.yaxis.grid(b=True, color="#BDBDBD", linestyle='-', linewidth=0.005)
axes.xaxis.grid(b=True, color="#BDBDBD", linestyle='-', linewidth=0.005)
def drawDistData(axes, sample2dist):
#sample2dist = getSharedSeqDist(samples, uniq)
lines = []
labels = []
colors = iseqlib.getColors6()
#lightColors = getColors6light()
markersize = 10.0
c = -1
xmax = 0
for s in sorted( sample2dist.keys() ):
numsam2count = sample2dist[s]
c += 1
xdata = sorted( numsam2count.keys() )
xmax = max([xmax, max(xdata)])
ydata = [ numsam2count[x] for x in xdata ]
totaly = sum(ydata)
pcydata = [(100.0*y)/totaly for y in ydata]
#line = axes.plot(xdata, ydata, color=colors[c], marker='o', markeredgecolor=colors[c], markersize = markersize, linestyle='-', linewidth=2)
line = axes.plot(xdata, pcydata, color=colors[c], marker='o', markeredgecolor=colors[c], markersize = markersize, linestyle='-', linewidth=2)
#axes.plot(xdata, ydata, color=lightColors[c], linestyle='-', linewidth=0.5)
lines.append(line)
labels.append(s)
print s
print xdata
print ydata
print pcydata
axes.set_yscale('log')
axes.set_xlim(0.8, xmax + 0.2)
xticks = xrange(1, xmax + 1)
xticklabels = [ str(x) for x in xticks ]
axes.xaxis.set_ticks(xticks)
axes.xaxis.set_ticklabels( xticklabels )
axes.set_title('Shared sequences', size="xx-large")
iseqlib.editSpine( axes )
axes.set_xlabel("Number of samples", size='x-large')
axes.set_ylabel("Number of clones", size='x-large')
legend = axes.legend( lines, labels, numpoints=1, loc='best', ncol=1)
legend.__drawFrame = False
axes.yaxis.grid(b=True, color="#BDBDBD", linestyle='-', linewidth=0.005)
axes.xaxis.grid(b=True, color="#BDBDBD", linestyle='-', linewidth=0.005)
def drawAll(options, outdir, rowname2cells, index2colname):
if options.infile == '-':
outname = 'all'
else:
outname = os.path.basename(options.infile).split('.')[0]
options.out = os.path.join(outdir, outname)
fig, pdf = iseqlib.initImage(10.0, 8.0, options)
axes = fig.add_axes( [0.12, 0.15, 0.85, 0.75] )
lines = []
#rownames = sorted( rowname2cells.keys() )
rownames = ['as11D', 'as16D', 'as1D', 'asBD', 'as20D', 'as15D', 'as8D']
name2color = sample2color(rownames)
#xdata = sorted( [ int(x) for x in colname2index.keys() ] )
xmax = 0.0
xmin = float('inf')
#ymax = 0
markersize = 12.0
xindices = []
#for rowname in rownames:
r = 0
while r < len(rownames):
rowname = rownames[r]
row = rowname2cells[rowname]
means = []
stds = []
xdata = []
for i, m in enumerate( row ):
if i % 2 == 0 and m != 'NA' and m != '' and m != '-':
colname = index2colname[i]
try:
colname = int(colname)
xdata.append( colname )
except:
xdata.append(i/2)
if i not in xindices:
xindices.append(i)
means.append( float(m) )
elif i%2 == 1 and m != 'NA' and m != '' and m != '-':
stds.append( float(m) )
xmax = max([xmax, max(xdata)])
xmin = min([xmin, min(xdata)])
#HACK
#if rowname == 'uniqClones':
#if rowname == 'mountford' or rowname == 'horn':
#if rowname != 'horn':
#if rowname == 'manhattan' or rowname == 'euclidean' or rowname == 'binomial':
exceptions = ['manhattan', 'euclidean', 'binomial', 'kulczynski', 'canberra', 'jaccard']
if rowname in exceptions:
rownames.remove(rowname)
continue
#means = [m/min(means) for m in means]
#stds = [0.0 for s in stds]
#END HACK
color = name2color[rowname]
axes.errorbar(xdata, means, yerr=stds, color=color, markeredgecolor=color, markersize=markersize, fmt='.')
line = axes.plot(xdata, means, color=color, linestyle='-', linewidth=4.0)
lines.append(line)
r += 1
#axes.set_xscale('log')
#axes.set_yscale('log')
iseqlib.editSpine(axes)
#axes.set_title("%s index across different sampling sizes" %outname, size='xx-large')
#axes.set_xlabel("Sampling size (number of reads)", size='large' )
#axes.set_ylabel("%s index" %outname, size='large')
#HACK
axes.set_title("Sequencing Saturation", size='xx-large', weight='bold')
axes.set_xlabel("Sampling size (number of sequences, in millions) ", size='x-large', weight='bold' )
#axes.set_xlabel("Sampling size (number of sequences, in thousands) ", size='x-large', weight='bold' )
axes.set_ylabel("Number of clones (in thousands)", size='x-large', weight='bold')
fontP = FontProperties()
fontP.set_size('medium')
#rownames = ["A", "B"]
rownames = [iseqlib.properName(n) for n in rownames]
legend = axes.legend( lines, rownames, numpoints = 1, loc='best', ncol = 1, prop=fontP)
legend._drawFrame = False
if len(xindices) > 0:
xticklabels = []
for i in sorted(xindices):
xticklabels.append( index2colname[i] )
axes.xaxis.set_ticks( xrange(len(xticklabels)) )
axes.xaxis.set_ticklabels( xticklabels )
#HACK:
#xticks = [ 10000, 50000, 100000, 200000, 300000, 400000, 500000]
#xticklabels = [ str(x/1000) for x in xticks]
xticks = [0, 1000000,2000000,3000000,4000000,5000000,6000000,7000000,8000000,9000000]
xticklabels = [ str(x) for x in xrange(0, 10) ]
axes.xaxis.set_ticks(xticks)
axes.xaxis.set_ticklabels( xticklabels )
#yticks = xrange(0, 121000, 20000)
#yticklabels = [ str(y) for y in xrange(0, 121, 20) ]
yticks = xrange(0, 250000, 50000)
yticklabels = [ str(y) for y in xrange(0, 250, 50) ]
axes.yaxis.set_ticks(yticks)
axes.yaxis.set_ticklabels(yticklabels)
for label in axes.get_xticklabels():
label.set_fontsize('large')
label.set_fontweight ('bold')
#label.set_rotation(45)
for label in axes.get_yticklabels():
label.set_fontsize('large')
label.set_fontweight ('bold')
axes.xaxis.grid(b=True, color='#3F3F3F', linestyle='-', linewidth=0.05)
axes.yaxis.grid(b=True, color='#3F3F3F', linestyle='-', linewidth=0.05)
#HACK
#axes.set_ylim(0.996, 1)
xspan = xmax - xmin
#axes.set_xlim(xmin - xspan*0.01, xmax + xspan*0.01)
#axes.set_ylim(10000, 121000)
#axes.set_xlim(-10, 110000)
#axes.set_ylim(-10, 60000)
axes.set_xlim(-10, 5100000)
axes.set_ylim(-10, 205000)
iseqlib.writeImage(fig, pdf, options)
def drawCloneVsReadData( axesList, samples, samplesPerPlot, options, isAbs ):
if len( samples ) <= 0:
return
colors = getColors6()
s2c, s2m, s2cLight = sample2color( colors )
#textsize = 'x-large'
textsize = 'medium'
fontP = FontProperties()
fontP.set_size(textsize)
linesDict = {}
labelsDict = {}
xticklabels = []
#get x location
#xdata = range( 0, len(xtickLabels), 1 )
maxy = 0
#markersize = 10.0
markersize = 8.0
name2line = {}
for i in range( len(axesList) ):
lines = []
sampleNames = []
axes = axesList[i]
#HACK
#if isAbs:
# axes.set_xscale('log')
startIndex = i*samplesPerPlot
endIndex = min( [startIndex + samplesPerPlot, len(samples)] )
for j in range( startIndex, endIndex ):
sample = samples[j]
if isAbs:
xtickLabels, xdata, ydata = getClonesVsReads( sample )
else:
xtickLabels, xdata, ydata = getClonesVsReadsRel( sample )
#HACK discrete:
discreteydata = [ydata[0]]
if not options.cumulative:
for k in xrange(1, len(ydata)):
discreteydata.append( ydata[k] - ydata[k-1] )
ydata = discreteydata
#sampleNames.append( "%s-%d" % (sample.name, sample.totalCount))
sampleNames.append( "%s" % (sample.name))
maxy = max( [maxy, max(ydata)] )
l = axes.plot( xdata, ydata, color=s2c[sample.name], marker=s2m[sample.name], markeredgecolor=s2c[sample.name], markersize=markersize, linestyle='none' )
axes.plot( xdata, ydata, color=s2cLight[sample.name], linestyle='-', linewidth=0.2 )
lines.append( l )
name2line[sample.name] = l
#if yaxisPcReads or yaxisPcClones:
# axes.plot( [-0.5, len(xtickLabels)], [90, 90], linestyle='-', linewidth=0.3, color="#CCCCCC" )
linesDict[i] = lines
labelsDict[i] = sampleNames
if options.cumulative:
#axes.set_title( 'Cumulative Distribution of 50 Largest Clones', size='xx-large', weight='bold' )
axes.set_title( 'C. Cumulative Distribution of 50 Largest Clones', size='large', weight='bold' )
else:
axes.set_title( 'Distribution of 50 Largest Clones', size='xx-large' )
if not options.cumulative:
axes.set_yscale('log')
for i in range( len(axesList) ):
axes = axesList[ i ]
#libplot.editSpine( axes )
iseqlib.editSpine( axes )
axes.set_xlabel('Number of top clones', size = textsize, weight='bold')
if not options.cumulative:
axes.set_xlabel('Clone rank', size = textsize, weight='bold')
if not isAbs:
axes.set_xlabel('Percentage of total clones', size = textsize, weight='bold')
axes.set_ylabel('Frequency (% of total sequences)', size = textsize, weight= 'bold')
#legend = pyplot.legend( linesDict[i], labelsDict[i], numpoints=1, prop=fontP, loc="best" )
##HACK
#labelorder = ['as15D', 'as20D', 'asBD', 'asBDdraw2', 'as8D', 'as16D', 'as1D', 'as11D']
#currlabels = []
#currlines = []
#for label in labelorder:
# if label in labelsDict[i]:
# currlabels.append( iseqlib.properName(label) )
# currlines.append(name2line[label])
##legend = pyplot.legend( currlines, currlabels, numpoints=1, prop=fontP, loc="upper right" )
#legend = pyplot.legend( currlines, currlabels, numpoints=1, prop=fontP, loc="best" )
##END HACK
#legend._drawFrame = False
axes.xaxis.set_ticklabels( xtickLabels )
axes.xaxis.set_ticks( xdata )
#axes.set_xlim(-0.5, len(xtickLabels))
for label in axes.get_xticklabels():
#label.set_fontsize( 'large' )
label.set_fontsize( 'small' )
#label.set_rotation( 90 )
for label in axes.get_yticklabels():
#label.set_fontsize( 'large' )
label.set_fontsize( 'small' )
#HACK: yaxis tick labels:
#ytickdata = [0.1, 1, 2.5, 5, 7.5, 10, 15, 20]
#for y in ytickdata:
# if y == 20:
# continue
# if y in [0.1, 1, 10, 100]:
# #axes.plot([0,50.5], [y, y], color='#838383', linestyle='-', linewidth=0.005)
# axes.plot([0,50.5], [y, y], color='#BCBCBC', linestyle='-', linewidth=0.005)
# else:
# axes.plot([0,50.5], [y, y], color='#BCBCBC', linestyle='-.', linewidth=0.005)
#axes.yaxis.set_ticks(ytickdata)
#axes.set_ylim(0, 21)
#axes.yaxis.set_ticklabels([str(y) for y in ytickdata] , size='medium')
#x ticks:
xticks = [1]
xticks.extend( xrange(5, 51, 5) )
axes.xaxis.set_ticks( xticks )
axes.xaxis.set_ticklabels( [str(x) for x in xticks] , size = 'large')
#x tick lines:
for x in xdata:
if x in xrange(0, 51, 5):
axes.plot([x,x], [0.02, maxy+5], color='#3F3F3F', linestyle='-', linewidth=0.05)
#else:
# axes.plot([x,x], [0.02, maxy+5], color='#BCBCBC', linestyle='-.', linewidth=0.005)
#END HACK
if isAbs:
#HACK
axes.set_xlim(0, 50.5)
#axes.set_xlim(0, 525)
#axes.set_xlim(0, 10500) #before hack
else:
axes.set_xlim(0, 101)
#axes.set_xlim(-1, 51)
#axes.set_ylim(0.02, maxy + 5)
axes.set_ylim(0, maxy + 2)
#HACK
#axes.set_ylim(-1, maxy + 2)
axes.yaxis.grid(b=True, color="#3F3F3F", linestyle='-', linewidth=0.05)
#axes.xaxis.grid(b=True, color="#3F3F3F", linestyle='-', linewidth=0.05)
return
def drawCloneSizeData( axesList, samples, samplesPerPlot, options, isAbs, yaxisPcReads, yaxisPcClones, cumulative ):
if len( samples ) <= 0:
return
colors = getColors6()
s2c, s2m, s2cLight = sample2color( colors )
#markers = ['o', '^']
#markers=['o', 'o', 'o', 'o', 'o', 'o', 'o', 'o', '^', '^']
#c = -1
#textsize = 'x-large'
textsize = 'medium'
fontP = FontProperties()
fontP.set_size(textsize)
linesDict = {}
labelsDict = {}
if isAbs:
xtickLabels = samples[0].countArr
else:
xtickLabels = samples[0].percentArr
#get x location
xdata = range( 0, len(xtickLabels), 1 )
#offset = 0.01
offset = 0.0
maxy = 0
for i in range( len(axesList) ):
lines = []
sampleNames = []
axes = axesList[i]
#HACK
#if not yaxisPcReads and not yaxisPcClones:
if not yaxisPcReads:
axes.set_yscale('log')
#axes.set_yscale('log')
startIndex = i*samplesPerPlot
endIndex = min( [startIndex + samplesPerPlot, len(samples)] )
for j in range( startIndex, endIndex ):
sample = samples[j]
#sampleNames.append( "%s" % (sample.name))
sampleNames.append( "%s" % (iseqlib.properName(sample.name)))
ydata = sample.clonesPerCount
if not yaxisPcReads and not isAbs:
ydata = sample.clonesPerPercent
elif yaxisPcReads and isAbs:
ydata = sample.readsPerCount
elif yaxisPcReads and not isAbs:
ydata = sample.readsPerPercent
if yaxisPcClones:
ydata = [ 100*y/sample.totalClones for y in ydata ]
maxy = max( [maxy, max(ydata)] )
#if (i*len(axesList) + j) %2 == 0:
# c += 1
#l = axes.plot( xdata, ydata, color=colors[c], marker=markers[(i*len(axesList) + j)%2], markersize=6.0, linestyle='none' )
#axes.plot( xdata, ydata, color=colors[c], linestyle='-', linewidth=0.01 )
currxdata = [x + offset*(j - startIndex) for x in xdata]
if isAbs:
markersize = 8.0
#markersize = 10.0
else:
markersize = 8.0
#markersize = 10.0
if s2m[sample.name] == '*':
markersize += 2.0
elif s2m[sample.name] == 's':
markersize -= 2.0
print sample.name
print ydata
#l = axes.plot( xdata, ydata, color=s2c[sample.name], marker=s2m[sample.name], markeredgecolor=s2c[sample.name], markersize=6.0, linestyle='none' )
l = axes.plot( currxdata, ydata, color=s2c[sample.name], marker=s2m[sample.name], markeredgecolor=s2c[sample.name], markersize=markersize, linestyle='none' )
#l = axes.plot( currxdata, ydata, color="#377EB8", marker='o', markeredgecolor=s2c[sample.name], markersize=markersize, linestyle='none' )
#axes.plot( xdata, ydata, color=s2cLight[sample.name], linestyle='-', linewidth=0.3 )
axes.plot( currxdata, ydata, color=s2cLight[sample.name], linestyle='-', linewidth=0.5 )
#axes.plot( currxdata, ydata, color="#377EB8", linestyle='-', linewidth=0.7 )
lines.append( l )
#if yaxisPcReads or yaxisPcClones:
# axes.plot( [-0.5, len(xtickLabels)], [90, 90], linestyle='-', linewidth=0.2, color="#CCCCCC" )
linesDict[i] = lines
labelsDict[i] = sampleNames
if cumulative:
#axes.set_title( 'Cumulative Distribution of Clones', size="xx-large", weight='bold' )
#axes.set_title( 'Cumulative Distribution of Clones', size="large", weight='bold' )
axes.set_title( 'A. Cumulative Distribution of Clones', size="large", weight='bold' )
if yaxisPcReads:
#axes.set_title( 'Cumulative Distribution of Sequences', size="xx-large", weight='bold')
axes.set_title( 'B. Cumulative Distribution of Sequences', size="large", weight='bold')
else:
axes.set_title( 'Clone Size Distribution', size="xx-large" )
for i in range( len(axesList) ):
axes = axesList[ i ]
#libplot.editSpine( axes )
iseqlib.editSpine( axes )
axes.set_xlabel('Clone size (number of sequences)', size = textsize)
if not isAbs:
axes.set_xlabel('Clone size (% of total sequences)', size = textsize, weight='bold')
axes.set_ylabel('Frequency (number of clones)', size=textsize)
if yaxisPcReads:
axes.set_ylabel('Frequency (% of total sequences)', size=textsize, weight='bold')
if yaxisPcClones:
axes.set_ylabel('Frequency (% of total clones)', size = textsize, weight='bold')
#Legend
#legend = pyplot.legend( linesDict[i], labelsDict[i], numpoints=1, prop=fontP, loc="best" )
#legend = axes.legend( linesDict[i], labelsDict[i], numpoints = 1, "upper right", ncol=3)
if yaxisPcClones and not yaxisPcReads:
legend = axes.legend( linesDict[i], labelsDict[i], numpoints = 1, loc="best", ncol=1, prop=fontP)
#for t in legend.get_texts():
# t.set_fontsize(textsize)
legend._drawFrame = False
#HACK
#if isAbs:
if False:
xtickdata = xrange(10, 101, 10)
#grid:
for x in xtickdata:
axes.plot([x,x], [0.0001, maxy], color='#3F3F3F', linestyle='-', linewidth=0.1)
axes.xaxis.set_ticks(xtickdata)
minx = 0
maxx = 101
axes.set_xlim(minx, maxx)
axes.xaxis.set_ticklabels([str(x) for x in xtickdata] , size='medium')
else:
axes.xaxis.set_ticklabels( xtickLabels, size='medium' )
axes.xaxis.set_ticks([x + 0.5*offset*(samplesPerPlot-1) - 0.5 for x in xdata] )
minx = -0.5
maxx = len(xtickLabels) - 0.5
xticks = [x + 0.5*offset*(samplesPerPlot-1) - 0.5 for x in xdata]
for xi, x in enumerate(xticks):
if xi == 0:
continue
axes.plot([x,x], [0.0001, maxy], color='#3F3F3F', linestyle='-', linewidth=0.1)
axes.set_xlim(minx, maxx)
#END HACK
#axes.xaxis.set_ticks( xdata )
#axes.xaxis.set_ticks([x + 0.5*offset*(samplesPerPlot-1) for x in xdata] )
#axes.set_xlim(-0.5, len(xtickLabels))
#numTicks = 20
#yticks = [ float(t)/numTicks for t in range(numTicks +1) ]
#ytickLabels = []
#for y in yticks:
# ytickLabels.append( "%d" %(y*100) )
if not yaxisPcReads and not yaxisPcClones:
#yticks = [100, 1000, 5000, 10000, 15000, 20000]
#ytickLabels = ["100", "1k", "5k", "10k", "15k", "20k"]
yticks = [1, 10, 100, 1000, 10000, 50000, 100000, 150000]
#ytickLabels = ["1", "10", "100", "1k", "10k", "50k", "100k", "150k"]
ytickLabels = ["1", "10", "100", "1k", "10k", "50k", "100k", "150k"]
axes.yaxis.set_ticklabels( ytickLabels )
axes.yaxis.set_ticks( yticks )
for y in yticks:
if y in [1, 10, 100, 1000, 10000, 100000]:
#if y in [100, 1000, 5000, 10000, 15000, 20000]:
axes.plot([minx, maxx], [y, y], color='#3F3F3F', linestyle='-', linewidth=0.1)
else:
axes.plot([minx, maxx], [y, y], color='#3F3F3F', linestyle='-.', linewidth=0.1)
axes.set_ylim(0.8, maxy + 15000)
elif yaxisPcClones:
#elif yaxisPcClones or yaxisPcReads:
#yticks = [0.001, 0.01, 0.1, 1, 10, 25, 50, 75, 100]
yticks = [0.001, 0.01, 0.1, 1, 10, 100]
#yticks = xrange(0, 101, 20)
ytickLabels = [str(y) for y in yticks]
axes.yaxis.set_ticks(yticks)
axes.yaxis.set_ticklabels( ytickLabels )
for y in yticks:
if y in [0.001, 0.01, 0.1, 1, 10, 100]:
#if y in xrange(0, 101, 20):
axes.plot([minx, maxx], [y, y], color='#3F3F3F', linestyle='-', linewidth=0.1)
else:
axes.plot([minx, maxx], [y, y], color='#3F3F3F', linestyle='-.', linewidth=0.1)
axes.set_ylim(0.0005, maxy + 25)
#axes.set_ylim(0.001, maxy + 25)
else:
yticks = xrange(0, 101, 20)
ytickLabels = [str(y) for y in yticks]
axes.yaxis.set_ticks(yticks)
axes.yaxis.set_ticklabels( ytickLabels )
for y in yticks:
axes.plot([minx, maxx], [y, y], color='#3F3F3F', linestyle='-', linewidth=0.1)
axes.set_ylim(-1, maxy + 1)
#axes.set_ylim(-1, maxy + 1)
#if isAbs: #HACK
# axes.set_ylim(-0.1, 15)
#axes.set_ylim(-0.1, 50) #HACK
for label in axes.get_xticklabels():
#label.set_fontsize( 'large' )
label.set_fontsize( 'small' )
#label.set_rotation( 45 )
for label in axes.get_yticklabels():
#label.set_fontsize( 'large' )
label.set_fontsize( 'small' )
#axes.yaxis.grid(b=True, color="#BCBCBC", linestyle='-', linewidth=0.005)
#axes.xaxis.grid(b=True, color="#CCCCCC", linestyle='-', linewidth=0.005)
return
def drawUsageData( axesList, samples, stds, options, genetype, intersectGenes ):
if len( samples ) <= 0:
return
samplesPerPlot = options.samplesPerPlot
#HACK COLORS
#colors = []
#if options.sampleColor:
# colors = options.sampleColor
#if options.sampleMarker:
# markers = options.sampleMarker
#if options.sampleOrder and not options.sampleColor:
# colors = [ "#E31A1C", "#FE8E8F"]*len(options.sampleOrder) + ["#377EB8", "#A6D7FE"]*(len(samples) - len(options.sampleOrder))
# markers = ["o"]*len(options.sampleOrder) + ["^"]*(len(samples) - len(options.sampleOrder))
#if not options.groupDrawInfo and len(colors) < len(samples):
# raise ValueError("Too many samples (%d), not enough colors (%d). Please specify the options sampleColor and sampleMarker to fix the problem." %(len(samples), len(colors)))
#END HACK COLORS
c = -2
textsize = 'large'
fontP = FontProperties()
fontP.set_size(textsize)
linesDict = {}
labelsDict = {}
xtickLabels = intersectGenes
#get x location
xdata = range( 0, len(xtickLabels), 1 )
maxy = 0
#HACK COLORS:
s2group = {'as1D': 'B27+,AS', 'as8D': 'B27+,AS', 'as11D': 'B27+,AS', 'as16D': 'B27+,AS',
'as15D': 'B27-,AS',
'asBD': 'B27+,Healthy', 'as20D': 'B27+,Healthy',
'adaptMA': 'B27-,Healthy', 'adaptM35': 'B27-,Healthy', 'adaptF57': 'B27-,Healthy', 'adaptAS': 'B27-,Healthy', 'adaptCD': 'B27-,Healthy', 'adaptF28':'B27-,Healthy',
'irep1D': 'B27+,AS', 'as10R': 'B27+,AS', 'as11R': 'B27+,AS', 'as12R': 'B27+,AS', 'as13R': 'B27+,AS', 'as1R': 'B27+,AS', 'irepBD': 'B27+,Healthy', 'asBR': 'B27+,Healthy',
'sameGroupShared': 'AS', 'diffGroupShared': 'Healthy', 'uniq': 'Unique',
'b27pos': 'B27+', 'b27neg':'B27-'}
#B27 +-
#g2c = {'B27+,AS': '#E31A1C', 'B27+,Healthy': '#E31A1C', 'B27-,AS':'#377EB8', 'B27-,Healthy':'#377EB8', 'B27+':'#E31A1C', 'B27-': '#377EB8'}
#g2lc = {'B27+,AS': '#FE8E8F', 'B27+,Healthy': '#FE8E8F', 'B27-,AS':'#A6D7FE', 'B27-,Healthy':'#A6D7FE', 'B27+': '#FE8E8F', 'B27-': '#A6D7FE'}
#g2m = {'B27+,AS': 'o', 'B27+,Healthy': '^', 'B27-,AS':'o', 'B27-,Healthy':'^', 'B27+':'o', 'B27-':'^'}
#AS vs Healthy
#g2c = {'B27+,AS': '#E31A1C', 'B27+,Healthy': '#9E1114', 'AS':'#377EB8', 'Healthy':'#275880', 'Unique': '#4DAF4A'}
#g2lc = {'B27+,AS': '#FE8E8F', 'B27+,Healthy': '#FE8E8F', 'AS':'#A6D7FE', 'Healthy':'#A6D7FE', 'Unique': '#B8FEB5'}
#g2m = {'B27+,AS': 'o', 'B27+,Healthy': '^', 'AS':'o', 'Healthy':'^', 'Unique': 's'}
#AS vs Healthy, no color (gray scale)
g2c = {'B27+,AS': '#353535', 'B27+,Healthy': '#848484', 'AS':'#353535', 'Healthy':'#848484', 'Unique': '#4DAF4A'}
g2lc = {'B27+,AS': '#BDBDBD', 'B27+,Healthy': '#BDBDBD', 'AS':'#BDBDBD', 'Healthy':'#BDBDBD', 'Unique': '#B8FEB5'}
g2m = {'B27+,AS': 'o', 'B27+,Healthy': '^', 'AS':'o', 'Healthy':'^', 'Unique': 's'}
#END HACK COLORS
if options.groupDrawInfo:
s2group = options.s2g
g2c = options.g2c
g2lc = options.g2lc
g2m = options.g2m
for i in range( len(axesList) ):
lines = []
sampleNames = []
axes = axesList[i]
startIndex = i*samplesPerPlot
endIndex = min( [startIndex + samplesPerPlot, len(samples)] )
groups = []
for j in range( startIndex, endIndex ):
sample = samples[j]
sampleNames.append( "%s" % (sample.name))
ydata = getGeneUsage( sample, genetype, xtickLabels, options.abs )
maxy = max( [maxy, max(ydata)] )
#c += 2
#l = axes.plot( xdata, ydata, color=colors[c], marker=markers[c/2], markersize=8.0, markeredgecolor=colors[c], linestyle='none' )
#axes.plot( xdata, ydata, color=colors[c +1], linestyle='-', linewidth=0.01 )
#lines.append( l )
group = s2group[sample.name]
l = axes.plot( xdata, ydata, color=g2c[group], marker=g2m[group], markersize=8.0, markeredgecolor=g2c[group], linestyle='none' )
axes.plot( xdata, ydata, color=g2lc[group], linestyle='-', linewidth=0.01 )
if group not in groups:
lines.append( l )
groups.append(group)
#Draw standard deviation if the data is available:
if sample.name in options.avr2std and options.avr2std[sample.name] in stds:
std = stds[ options.avr2std[sample.name] ]
higherYdata, lowerYdata = getStdData( std, sample, genetype, xtickLabels, options.abs )
if len(higherYdata) == 0 or len(lowerYdata) == 0:
continue
for i in xrange( len(ydata) ):
#axes.plot( [xdata[i], xdata[i]], [higherYdata[i], lowerYdata[i]], linestyle='-', marker='_', markersize=8.0, linewidth=0.02, color=colors[c+1] )
axes.plot( [xdata[i], xdata[i]], [higherYdata[i], lowerYdata[i]], linestyle='-', marker='_', markersize=8.0, linewidth=0.02, color=g2lc[group] )
maxy = max([maxy, max(higherYdata)])
linesDict[i] = lines
#labelsDict[i] = sampleNames
labelsDict[i] = groups
#axes.set_title( 'TRB%s Usage Distribution' % genetype.upper(), size='xx-large', weight='bold' )
#HACK
for i, xlabel in enumerate(xtickLabels):
if xlabel == 'TRBV6-5/TRBV6-6':
xtickLabels[i] = 'TRBV6-5/6-6'
xtickLabels = [xlabel.lstrip('TRB') for xlabel in xtickLabels]
for i, xlabel in enumerate(xtickLabels):
items = xlabel.split('|')
items = [item.lstrip('TRB') for item in items]
xtickLabels[i] = '|'.join(items)
#END HACK
for i in range( len(axesList) ):
axes = axesList[ i ]
immunoseqLib.editSpine( axes )
axes.set_xlabel('Gene', size = 'xx-large', weight='bold')
axes.set_ylabel('Frequency (% of total sequences)', size='xx-large', weight='bold')
#Legend
if not options.std:
#HACK
cat2index = {}
for j, label in enumerate(labelsDict[i]):
cat2index[label] = j
labels = cat2index.keys()
lines = [linesDict[i][cat2index[label]] for label in labels]
legend = pyplot.legend( lines, labels, numpoints=1, prop=fontP, loc="best" )
#END HACK
#legend = pyplot.legend( linesDict[i], labelsDict[i], numpoints=1, prop=fontP, loc="best" )
#legend = axes.legend( linesDict[i], labelsDict[i], numpoints = 1, "upper right", ncol=3)
#for t in legend.get_texts():
# t.set_fontsize('xx-small')
legend._drawFrame = False
axes.xaxis.set_ticklabels( xtickLabels )
axes.xaxis.set_ticks( xdata )
axes.set_xlim(-0.5, len(xtickLabels) + 0.5)
numTicks = 20
yticks = [ float(t)/numTicks for t in range(numTicks +1) ]
ytickLabels = []
for y in yticks:
ytickLabels.append( "%d" %(y*100) )
axes.yaxis.set_ticklabels( ytickLabels )
axes.yaxis.set_ticks( yticks )
axes.set_ylim(-0.01, maxy + 0.01)
#axes.set_ylim(-0.01, 0.22)
for label in axes.get_xticklabels():
label.set_fontsize( 'small' )
label.set_fontweight( 'bold' )
label.set_rotation( 75 )
for label in axes.get_yticklabels():
label.set_fontsize( textsize )
label.set_fontweight( 'bold' )
axes.yaxis.grid(b=True, color="#CCCCCC", linestyle='-', linewidth=0.005)
axes.xaxis.grid(b=True, color="#CCCCCC", linestyle='-', linewidth=0.005)
return
