def printClusterPlots(cls, correlationMatrix, linkageMatrix, galaxyFn, distanceMeasure, labels, htmlCore):
from numpy import amax, amin, isnan
maxVal = amax(correlationMatrix)
minVal = amin(correlationMatrix)
seabornFile = GalaxyRunSpecificFile(['Image', distanceMeasure + 'seabornHeatmap.pdf'], galaxyFn)
dendrogramFile = GalaxyRunSpecificFile(['Image', distanceMeasure + 'dendrogram.pdf'], galaxyFn)
if minVal < 0 or isnan(minVal):
MatplotlibPlots.seabornHeatmapPlot(
correlationMatrix,
labels,
max=maxVal if maxVal >= 1 else 1,
min=minVal if minVal <= -1 else -1,
fileName=seabornFile,
cmap="RdBu_r"
)
else:
MatplotlibPlots.seabornHeatmapPlot(
correlationMatrix,
labels,
max=maxVal if maxVal >= 1 else 1,
min=minVal if minVal <= 0 else 0,
fileName=seabornFile
)
MatplotlibPlots.dendrogramClusteringPlot(linkageMatrix, labels, dendrogramFile)
htmlCore.line(seabornFile.getEmbeddedImage())
htmlCore.link('PDF of similarity matrix', seabornFile.getURL())
htmlCore.line(dendrogramFile.getEmbeddedImage())
htmlCore.link('PDF of dendrogram', dendrogramFile.getURL())
def plotDistances(cls, distances, galaxyFn, bins, r2, htmlCore):
distanceBins, occurrences = cls.standardizeLineGraph(distances)
distFile = GalaxyRunSpecificFile(['distancegraph.pdf'], galaxyFn)
MatplotlibPlots.pointGraph(x=distanceBins[1:], y=occurrences, fileLocation=distFile,
xlabel='Distances between SNP pairs in LD', ylabel='LD-pair count')
dist = sorted(distances)
histFile = GalaxyRunSpecificFile(['histogram.pdf'], galaxyFn)
MatplotlibPlots.histogramPlot(dist, bins, histFile, 'Distances between SNP pairs in LD')
htmlCore.divider(True)
htmlCore.header('Line plot with distribution of distances between LD pairs, r<sup>2</sup> >= ' + str(r2))
htmlCore.line(distFile.getEmbeddedImage())
htmlCore.link('PDF of distances between tracks here', distFile.getURL())
htmlCore.divider(True)
htmlCore.header('Histogram with distribution of distances between LD pairs, r<sup>2</sup> >= ' + str(r2))
htmlCore.line(histFile.getEmbeddedImage())
htmlCore.link('PDF histogram of distances here', histFile.getURL())
def printTextMatrixes(cls, correlationMatrix, linkageMatrix, distanceMatrix, galaxyFn, filename, htmlCore):
# Print correlation matrix
corrMatrixFile = GalaxyRunSpecificFile(['corr_matrix_result_' + filename + '.txt'], galaxyFn)
corrMatrixPath = corrMatrixFile.getDiskPath(True)
open(corrMatrixPath, 'w').write(str(correlationMatrix))
htmlCore.link('<br><br>View the raw text similarity/correlation matrix for this analysis',
corrMatrixFile.getURL())
# Print distance matrix
distMatrixFile = GalaxyRunSpecificFile(['dist_matrix_result_' + filename + '.txt'], galaxyFn)
distMatrixPath = distMatrixFile.getDiskPath(True)
open(distMatrixPath, 'w').write(str(distanceMatrix))
htmlCore.link('<br><br>View the raw text triangular distance matrix for this analysis', distMatrixFile.getURL())
# Print linkage matrix
linkMatrixFile = GalaxyRunSpecificFile(['linkage_matrix_result_' + filename + '.txt'], galaxyFn)
linkMatrixPath = linkMatrixFile.getDiskPath(True)
open(linkMatrixPath, 'w').write(str(linkageMatrix))
htmlCore.link('<br><br>View the raw text linkage matrix for this analysis', linkMatrixFile.getURL())
def getSNPFrequencyStats(cls, bins, gSuite, galaxyFn, htmlCore):
rsIDs = set()
snpCount = []
analysisSpec = AnalysisSpec(UniquePointTrackStat)
trackLabels = []
for gSuiteTrack in gSuite.allTracks():
track = Track(gSuiteTrack.trackName)
trackLabels.append(gSuiteTrack.title)
result = doAnalysis(analysisSpec, bins, [track])
if 'Result' in result.getGlobalResult():
observations = result.getGlobalResult()['Result']
snpCount.append(len(observations))
rsIDs.update(observations)
snpcountFile = GalaxyRunSpecificFile(['snpfrequencies.pdf'], galaxyFn)
MatplotlibPlots.pointGraphY(snpCount, snpcountFile, ylabel='SNP counts',
xticks=trackLabels)
snpdistributionFile = GalaxyRunSpecificFile(['snpfreqhistogram.pdf'], galaxyFn)
MatplotlibPlots.histogramRugPlot(snpCount, 10, snpdistributionFile, 'SNP counts')
totalSNPCount = sum(snpCount)
cls.printStats(snpCount, 'track', htmlCore)
htmlCore.line('Total number of SNPs: ' + str(totalSNPCount))
htmlCore.line('Unique SNPs: ' + str(len(rsIDs)))
htmlCore.line('Overlapping rsIDs: ' + str(totalSNPCount - len(rsIDs)))
htmlCore.divider(True)
htmlCore.header('Graph of SNP frequencies in GSuite tracks')
htmlCore.line(snpcountFile.getEmbeddedImage())
htmlCore.link('PDF of SNP frequency graph', snpcountFile.getURL())
htmlCore.divider(True)
htmlCore.header('Histogram of SNP frequencies in GSuite tracks')
htmlCore.line(snpdistributionFile.getEmbeddedImage())
htmlCore.link('PDF of SNP frequency histogram', snpdistributionFile.getURL())
cls.getInteractiveColumnChartWithLabels(snpCount, trackLabels, htmlCore)
def plotDistances(cls, distances, galaxyFn, distCase, htmlCore):
# Plot distance graph
xdata, ydata = cls.standardizeLineGraph(distances)
distFile = GalaxyRunSpecificFile(['distancegraph.pdf'], galaxyFn)
MatplotlibPlots.pointGraph(xdata[1:], ydata, distFile, 'Smallest distance for each point',
'Distance point count')
# Write distance graph
htmlCore.divider(True)
htmlCore.header('Graph of smallest distances for all points ' + distCase + ' tracks in GSuite')
htmlCore.line(distFile.getEmbeddedImage())
htmlCore.link('PDF of distance graph', distFile.getURL())
# Plot distance histograms
dist = sorted(distances)
bins = 20
histFile = GalaxyRunSpecificFile(['histogram.pdf'], galaxyFn)
loghistFile = GalaxyRunSpecificFile(['loghistogram.pdf'], galaxyFn)
MatplotlibPlots.histogramRugPlot(dist, bins, histFile, 'Distances')
MatplotlibPlots.histogramRugPlot(log(dist), bins, loghistFile, 'Log of distances')
helperText = 'The rugs/vertical lines at the bottom show the distribution of point distances.'
# Write distance histograms
htmlCore.divider(True)
htmlCore.header('Histogram of smallest distances for all points ' + distCase +
' tracks in GSuite')
htmlCore.line(helperText)
htmlCore.line(histFile.getEmbeddedImage())
htmlCore.link('PDF of distance histogram', histFile.getURL())
htmlCore.header('Histogram of log of smallest distances for all points ' + distCase +
' tracks in GSuite')
htmlCore.line(helperText)
htmlCore.line(loghistFile.getEmbeddedImage())
htmlCore.link('PDF of log distance histogram', loghistFile.getURL())
# Plot and write interactive bar chart
cls.getInteractiveColumnChart(dist, distCase, htmlCore)
def getLDDistancesOfMultipleRsquares(cls, ldGraphTrack, rSquareThresholds, galaxyFn, htmlCore):
graph = LDExpansions.createRSquareGraph(ldGraphTrack, 0)
positions = LDExpansions.createPositionDict(ldGraphTrack)
ldDistances = []
rSquareLabels = []
bins = []
for rSquare, isSet in rSquareThresholds.items():
if not isSet:
continue
rSquareLabels.append(rSquare)
rSquare = float(rSquare)
distances = cls.findAllDistancesInLD(graph, positions, rSquare, htmlCore)
bins, ldPairCount = cls.standardizeLineGraph(distances)
ldDistances.append(ldPairCount)
graphFile = GalaxyRunSpecificFile(['multipleLines.pdf'], galaxyFn)
MatplotlibPlots.multipleLineGraph(
bins[1:],
ldDistances,
rSquareLabels,
graphFile,
'Distance',
'LD-pair count'
)
if len(bins) == 0:
return
htmlCore.divider(True)
htmlCore.header('Distribution of distances between LD pairs with different thresholds of r<sup>2</sup>')
htmlCore.line(graphFile.getEmbeddedImage())
htmlCore.link('PDF of distances between tracks here', graphFile.getURL())
def MakeHeatmapFromTracks(cls, galaxyFn, **trKwArgs):
tr1 = trKwArgs.get('tr1')
tr2 = trKwArgs.get('tr2')
tr3 = trKwArgs.get('tr3')
tableRowEntryTemplate = """<tr><td>%s</td><td><a href="%s"><img src="%s" /></a></td></tr>"""
#htmlTemplate = '''<head><link rel="stylesheet" type="text/css" href="image_zoom/styles/stylesheet.css" /><script language="javascript" type="text/javascript" src="image_zoom/scripts/mootools-1.2.1-core.js"></script><script language="javascript" type="text/javascript" src="image_zoom/scripts/mootools-1.2-more.js"></script><script language="javascript" type="text/javascript" src="image_zoom/scripts/ImageZoom.js"></script>
# <script language="javascript" type="text/javascript" >
# liste = %s;
# function point_it(event){
# pos_x = event.offsetX?(event.offsetX):event.pageX-document.getElementById("zoomer_image").offsetLeft;
# pos_y = event.offsetY?(event.offsetY):event.pageY-document.getElementById("zoomer_image").offsetTop;
# pos_x = Math.floor(pos_x/10);
# pos_y = Math.floor(pos_y/10);
# alert("Hello World!, you clicked: " +liste[pos_y][pos_x]);
# }</script>
# </head><body><div id="container"><!-- Image zoom start --><div id="zoomer_big_container"></div><div id="zoomer_thumb"> <a href="%s" target="_blank" ><img src="%s" /></a></div><!-- Image zoom end --></div></body></html>'''
javaScriptCode = '''
liste = %s;
function point_it(event){
pos_x = event.offsetX?(event.offsetX):event.pageX-document.getElementById("zoomer_image").offsetLeft;
pos_y = event.offsetY?(event.offsetY):event.pageY-document.getElementById("zoomer_image").offsetTop;
pos_x = Math.floor(pos_x/10);
pos_y = Math.floor(pos_y/10);
alert("Hello World!, you clicked: " +liste[pos_y][pos_x]);
}
'''
ResultDicts = [cls.getValuesFromBedFile(tr1,colorPattern=(1,0,0))]
ResultDicts += [cls.getValuesFromBedFile(tr2,colorPattern=(0,1,0))] if tr2 else []
ResultDicts += [cls.getValuesFromBedFile(tr3,colorPattern=(0,0,1))] if tr3 else []
htmlTableContent = []
resultDict = cls.syncResultDict(ResultDicts)
for chrom, valList in resultDict.items():
areaList = []
#For doing recursive pattern picture
posMatrix = cls.getResult(len(valList), 2,2)
javaScriptList = [[0 for v in xrange(len(posMatrix[0])) ] for t in xrange(len(posMatrix))]
rowLen = len(posMatrix[0])
im = Image.new("RGB", (rowLen, len(posMatrix)), "white")
for yIndex, row in enumerate(posMatrix):
for xIndex, elem in enumerate(row):
im.putpixel((xIndex, yIndex), valList[elem])
region = yIndex*rowLen + xIndex
javaScriptList[yIndex][xIndex] = chrom+':'+str(elem*10)+'-'+str((elem+1)*10)+': '+repr([ round((255-v)/255.0 ,2 ) for v in valList[elem]])
#areaList.append(areaTemplate % (xIndex*10, yIndex*10, xIndex*11, yIndex*11, repr(valList[elem])))
im2 = im.resize((len(posMatrix[0])*10, len(posMatrix)*10))
origSegsFile = GalaxyRunSpecificFile([chrom+'smallPic.png'], galaxyFn)
origSegsFn = origSegsFile.getDiskPath(True)
bigSegsFile = GalaxyRunSpecificFile([chrom+'BigPic.png'], galaxyFn)
bigSegsFn = bigSegsFile.getDiskPath(True)
im.save(origSegsFn)
im2.save(bigSegsFn)
#open('Recursive/'+chrom+'Zooming.html','w').write(htmlTemplate % (str(javaScriptList), chrom+'Big.png',chrom+'.png'))
core = HtmlCore()
core.begin( extraJavaScriptFns=['mootools-1.2.1-core.js', 'mootools-1.2-more.js', 'ImageZoom.js'], extraJavaScriptCode=javaScriptCode % str(javaScriptList), extraCssFns=['image_zoom.css'] )
core.styleInfoBegin(styleId='container')
core.styleInfoBegin(styleId='zoomer_big_container')
core.styleInfoEnd()
core.styleInfoBegin(styleId='zoomer_thumb')
core.link(url=bigSegsFile.getURL(), text=str(HtmlCore().image(origSegsFile.getURL())), popup=True)
core.styleInfoEnd()
core.styleInfoEnd()
core.end()
htmlfile = GalaxyRunSpecificFile([chrom+'.html'], galaxyFn)
htmlfile.writeTextToFile(str(core))
htmlTableContent.append(tableRowEntryTemplate % (chrom, htmlfile.getURL(), origSegsFile.getURL()))
#return str(core) #htmlTemplate % (str(javaScriptList), bigSegsFn, origSegsFn)
#######
# FOr doing normal picture
#columns = int(round((len(valList)/1000)+0.5))
#im = Image.new("RGB", (1000, columns), "white")
#y=-1
#for index, valuTuple in enumerate(valList):
# x = index%1000
#
# if x == 0:
# y+=1
# try:
# im.putpixel((x, y), valuTuple)
# except:
# pass
#im.save(chrom+'.png')
#htmlTableContent.append(tableRowEntryTemplate % (chrom, chrom+'.png'))
htmlPageTemplate = """<html><body><table border="1">%s</table></body></html>"""
return htmlPageTemplate % ('\n'.join(htmlTableContent))
