def __init__(self,
region,
track,
track2,
numSubBins=10,
method='pearson',
tail='different',
**kwArgs):
assert method in ['pearson', 'spearman', 'kendall']
assert tail in ['more', 'less', 'different']
tailMapping = {
'more': "greater",
'less': "less",
'different': "two.sided"
}
if kwArgs.get('minimal') != True:
silenceRWarnings()
self._numSubBins = numSubBins
self._method = method
self._rTail = tailMapping[tail]
Statistic.__init__(self,
region,
track,
track2,
method=method,
tail=tail,
**kwArgs)
def _clusterAndPlotDendrogram(cls, figurePath, method, distVar, matrixVar,
rowNames):
##TEMP DEBUG..
#DebugRObj = GalaxyRunSpecificFile(['debug.obj'], galaxyFn)
#r('rsave <- function(f){save(d,file=f)}')
#r.rsave(DebugRObj.getDiskPath(True))
##TEMP DEBUG END
from proto.RSetup import r
silenceRWarnings()
r('library(flashClust)')
# A device is needed to calculate the correct width and height
r('pdf("%s", width=100, height=100)' % figurePath)
r.assign('extra_option', method)
r('hr <- hclust(%s, method=extra_option, members=NULL)' % distVar)
r('hr$height <- hr$height/max(hr$height)*10')
longestRowName = max(zip((len(_) for _ in rowNames), rowNames))[1]
r('pdf_width=dim(%s)[1] * (strheight("%s", units="inches") + (0.4/2.54)) + par("mai")[2] + par("mai")[4]'
% (matrixVar, longestRowName))
r('pdf_height=max(hr$height)/2.54 + strwidth("%s", units="inches") + par("mai")[1] + par("mai")[3]'
% longestRowName)
r('dev.off()')
r('pdf("%s" , width=pdf_width, height=pdf_height)' % figurePath)
r('plot(hr, ylab="Distance", hang=-1)')
r('dev.off()')
def _writeContent(self, resDictKey, fn):
from gold.application.RSetup import r
ensurePathExists(fn)
silenceRWarnings()
bmpFn = fn #+ '.png'
# r.png(filename=bmpFn, units='px', pointsize=self.POINT_SIZE, res=72)
width, height = self.getPlotDimensions(resDictKey)
# pdf test:
# self.LINE_HEIGHT = self.POINT_SIZE
# width, height = self.getPlotDimensions(resDictKey)
# r.pdf(bmpFn, height=height*1.0/72, width=width*1.0/72, pointsize=self.POINT_SIZE)
if any(x > 800 for x in [width, height]):
self.LINE_HEIGHT = self.POINT_SIZE
width, height = self.getPlotDimensions(resDictKey)
if self.HIGH_DEF_COLORS:
picType = 'png16m'
else:
picType = 'png256'
r.bitmap(bmpFn, height=height, width=width, units='px', type=picType, pointsize=self.POINT_SIZE)
else:
r.png(filename=bmpFn, height=height, width=width, units='px', pointsize=self.POINT_SIZE, res=72)
if resDictKey is not None:
xlab = self._results.getLabelHelpPair(resDictKey)[0]
else:
xlab = None
main = self._header
self._customRExecution(resDictKey, xlab, main)
#r.hist( , )
from gold.application.RSetup import r
r('dev.off()')
def __init__(self, region, track, track2, markType='number', **kwArgs):
self._markType = markType
#r('sink(file("/dev/null", open="wt"), type="message")')
if kwArgs.get('minimal') != True:
silenceRWarnings()
Statistic.__init__(self, region, track, track2, markType=markType, **kwArgs)
def __init__(self, region, track, track2, numSubBins=10, method='pearson', tail='different', **kwArgs):
assert method in ['pearson','spearman','kendall']
assert tail in ['more', 'less', 'different']
tailMapping = {'more': "greater", 'less': "less", 'different': "two.sided"}
silenceRWarnings()
self._numSubBins = numSubBins
self._method = method
self._rTail = tailMapping[tail]
Statistic.__init__(self, region, track, track2, method=method, tail=tail, **kwArgs)
def _init(self, distMethod='euclidean', clustMethod='complete', \
childStat=None, numClustersRows='1', numClustersCols='1', complete='False', **kwArgs):
assert childStat is not None
assert type(childStat) == str
from gold.statistic.AllStatistics import STAT_CLASS_DICT
self._childStat = STAT_CLASS_DICT[childStat]
self._distMethod = distMethod
self._clustMethod = clustMethod
self._numClustersRows = int(numClustersRows)
self._numClustersCols = int(numClustersCols)
assert complete in ['False', 'True']
self._complete = eval(complete)
silenceRWarnings()
def doAnalysis(analysisSpec, analysisBins, trackStructure):
'''Performs an analysis,
as specified by analysisSpec object,
in each bin specified by analysisBins,
on data sets specified in tracks.
Typical usage:
analysisSpec = AnalysisSpec(AvgSegLenStat)
analysisSpec.addParameter("withOverlaps","no")
analysisBins = GlobalBinSource('hg18')
tracks = [ Track(['Genes and gene subsets','Genes','Refseq']) ]
results = doAnalysis(analysisSpec, analysisBins, tracks)
'''
# TODO: handle multiple tracks analysis
# assert len(tracks) in [1,2] #for now..
# in an API setting, exceptions should not generally be hidden.
# Maybe this should be optional.
# setupDebugModeAndLogging()
silenceRWarnings()
silenceNumpyWarnings()
if isinstance(trackStructure, TrackStructureV2):
analysisDef = AnalysisDefHandler(analysisSpec.getDefAfterChoices())
statClass = analysisDef._statClassList[0]
validStatClass = wrapClass(
statClass,
keywords=analysisDef.getChoices(filterByActivation=True))
job = StatJob(analysisBins, trackStructure, validStatClass)
else:
tracks = trackStructure
if len(tracks) > 2:
from gold.util.CommonConstants import MULTIPLE_EXTRA_TRACKS_SEPARATOR
analysisSpec.addParameter(
'extraTracks',
MULTIPLE_EXTRA_TRACKS_SEPARATOR.join([
'^'.join([quote(part) for part in x.trackName])
for x in tracks[2:]
]))
job = AnalysisDefJob(analysisSpec.getDefAfterChoices(),
tracks[0].trackName,
tracks[1].trackName if len(tracks) > 1 else None,
analysisBins,
galaxyFn=None)
res = job.run(printProgress=False) # printProgress should be optional?
return res
def _writeContent(self, resDictKey, fn):
from proto.RSetup import r
ensurePathExists(fn)
silenceRWarnings()
self._setOutputDevice(fn, height=100, width=100)
width, height = self.getPlotDimensions(resDictKey)
r('dev.off()')
self._setOutputDevice(fn, height=height, width=width)
if resDictKey is not None:
xlab = self._results.getLabelHelpPair(resDictKey)[0]
else:
xlab = None
main = self._header
self._customRExecution(resDictKey, xlab, main)
r('dev.off()')
def _init(self, distMethod='euclidean', clustMethod='complete', \
childStat=None, numClustersRows='1', numClustersCols='1', complete='False', \
rowClustId='None', colClustId='None', **kwArgs):
assert childStat is not None
assert isinstance(childStat, basestring)
from gold.statistic.AllStatistics import STAT_CLASS_DICT
self._childStat = STAT_CLASS_DICT[childStat]
self._distMethod = distMethod
self._clustMethod = clustMethod
self._numClustersRows = int(numClustersRows)
self._numClustersCols = int(numClustersCols)
assert complete in ['False', 'True']
self._complete = ast.literal_eval(complete)
self._rowClustId = ast.literal_eval(rowClustId)
self._colClustId = ast.literal_eval(colClustId)
for id in [self._rowClustId, self._colClustId]:
if id is not None:
assert isinstance(id, int)
silenceRWarnings()
def _installAndCheckRLibrary(library, fn=''):
from gold.application.RSetup import r
from config.Config import HB_R_LIBS_DIR
from quick.util.CommonFunctions import silenceRWarnings, silenceROutput
silenceRWarnings()
silenceROutput()
try:
raise Exception
r("library('%s')" % library)
except:
try:
r("library('%s', lib.loc='%s')" % (library, HB_R_LIBS_DIR))
except:
try:
r("install.packages('%s', repos='http://cran.r-project.org', lib='%s')" \
% (library, HB_R_LIBS_DIR))
r("library('%s', lib.loc='%s')" % (library, HB_R_LIBS_DIR))
print "OK: Installed R package '%s'." % library
return
except Exception, e1:
try:
r("source('http://www.bioconductor.org/biocLite.R'); biocLite('%s', lib='%s')" \
% (library, HB_R_LIBS_DIR))
r("library('%s', lib.loc='%s')" % (library, HB_R_LIBS_DIR))
print "OK: Installed R package '%s'." % library
return
except Exception, e2:
try:
r("install.packages('%s', repos=NULL, lib='%s')" \
% (fn, HB_R_LIBS_DIR))
r("library('%s', lib.loc='%s')" % (library, HB_R_LIBS_DIR))
print "OK: Installed R package '%s'." % library
return
except Exception, e3:
print "FAILED: Did not find or manage to install R package '%s'. Error:" % library
print " " + str(e1).strip()
print " " + str(e2).strip()
sys.exit(1)
def executeSelfFeature(cls, genome, tracks, track_names, clusterMethod,
extra_option, feature, distanceType, kmeans_alg,
galaxyFn, regSpec, binSpec):
from proto.RSetup import r
#regSpec, binSpec = 'bed', '/usit/invitro/data/galaxy/galaxy-dist-hg-dev/./database/files/017/dataset_17084.dat'
silenceRWarnings()
jobFile = open(galaxyFn, 'w')
# print>>jobFile, 'PARAMS: ', dict(zip('genome, tracks, track_names, clusterMethod, extra_option, feature, distanceType, kmeans_alg, regSpec, binSpec'.split(','), [repr(v)+'<br>'for v in [genome, tracks, track_names, clusterMethod, extra_option, feature, distanceType, kmeans_alg,regSpec, binSpec]])), '<br><br>'
batchRun = GalaxyRunSpecificFile(['batch_run_job.txt'], galaxyFn)
print >> jobFile, '<h3>Results for the "similarity of positional distribution along the genome" way of clustering<h3/><br/><br/>'
with open(batchRun.getDiskPath(ensurePath=True), 'w') as batchFile:
print >> batchFile, '$clusterBySelfFeature', (genome, '$'.join([
':'.join(t) for t in tracks
]), ':'.join(track_names), clusterMethod, extra_option, feature,
distanceType,
kmeans_alg, regSpec,
binSpec)
print >> jobFile, batchRun.getLink(
'View batch script line for this analysis<br/>')
#print>>jobFile, 'Batch script syntax for this analysis:<br>$clusterBySelfFeature', (genome, '$'.join([':'.join(t) for t in tracks]), ':'.join(track_names) , clusterMethod, extra_option, feature, distanceType, kmeans_alg, regSpec, binSpec), '<br><br>'
#print>>jobFile, 'signature of method clusterBySelfFeature:<br>', 'clusterBySelfFeature(genome, tracksStr, track_namesStr, clusterMethod, extra_option, feature, distanceType, kmeans_alg, regSpec, binSpec):<br><br><br>'
prettyTrackNames = [
v[-1].replace('RoadMap_', '').replace('.H3K4me1', '')
for v in tracks
]
#prettyTrackNames = [prettyPrintTrackName(v, shortVersion=True) for v in tracks]
f_matrix = cls.construct_feature_matrix(genome, tracks, feature,
regSpec, binSpec)
#print>>jobFile, 'dir f_matrix: ', dir(f_matrix), regSpec, binSpec
userBinSource = GalaxyInterface._getUserBinSource(
regSpec, binSpec, genome)
binNames = [
str(bin)
for binIndex, bin in enumerate(sorted(list(userBinSource)))
]
if len(binNames) != f_matrix.shape[1]:
binNames = ['Microbin' + str(i) for i in range(f_matrix.shape[1])]
r.assign('bin_names', binNames)
r.assign('track_names', prettyTrackNames
) #use as track names, will be shown in clustering figure
r.assign('f_matrix', f_matrix)
r.assign('distanceType', distanceType)
r('row.names(f_matrix) <- track_names')
r('colnames(f_matrix) <- bin_names')
if clusterMethod == 'Hierarchical clustering' and extra_option != "--select--":
#print 'galaxyFn: ', galaxyFn
figure = GalaxyRunSpecificFile(
['cluster_tracks_result_figure.pdf'], galaxyFn)
figurepath = figure.getDiskPath(ensurePath=True)
r('d <- dist(f_matrix, method=distanceType)')
distTable = r('d')
distMatrix = GalaxyRunSpecificFile(['distance_matrix_result.txt'],
galaxyFn)
distMatrixPath = distMatrix.getDiskPath(True)
open(distMatrixPath, 'w').write(str(distTable))
print >> jobFile, distMatrix.getLink(
'View the distance matrix for this analysis <br>')
#with open(distMatrixPath,'w') as distObj:
# #distTable = d_matrix.tolist()
# core = HtmlCore()
# core.tableHeader(['']+track_names,firstRow=True)
# rowSize = len(track_names)
# index=0
# while index<len(distTable):
# core.tableLine([track_names[index % rowSize]]+[str(v) for v in distTable[index:index+rowSize]])
# #for index, row in enumerate(distTable):
# # core.tableLine([track_names[index]]+[str(v) for v in row])
# core.tableFooter()
# print>>distObj, str(core)
#print>>jobFile, distMatrix.getLink('View the distance matrix for this analysis <br>')
if True: #f_matrix.shape[1] <= 100:
r_f_matrixFile = GalaxyRunSpecificFile(['f-matrix.robj'],
galaxyFn)
#', '.join([str(v) for v in row])
r.assign('f_matrix_fn', r_f_matrixFile.getDiskPath(True))
r('dput(f_matrix, f_matrix_fn)')
#r_f_matrixFile.writeTextToFile(', '.join(cls.getFlattenedMatrix(f_matrix)) + '\n\nTrack names: '+', '.join(prettyTrackNames)+'\n\nNumber of tracks: '+str(len(prettyTrackNames))+'\n\nbins: +)
#r_f_matrixFile.writeTextToFile()
#r_f_matrixFile.writeTextToFile(str(f_matrix)+'\n\n'+str(r.d))
print >> jobFile, r_f_matrixFile.getLink(
'Access the R-representation of the Feature_matrix (text-file)'
), '<br/>'
cls._clusterAndPlotDendrogram(figurepath, extra_option, 'd',
'f_matrix', prettyTrackNames)
print >> jobFile, figure.getLink(
'View the clustering tree (dendrogram) for this analysis<br>')
if True: #f_matrix.shape[1] <= 100:
#heatmap = GalaxyRunSpecificFile(['heatmap_figure.pdf'], galaxyFn)
#baseDir = os.path.dirname(heatmap.getDiskPath(True))
resDict = Results([], [], '')
resDict.setGlobalResult({
'result': {
'Matrix': f_matrix,
'Rows': np.array(track_names),
'Cols': np.array(binNames),
'Significance': None,
'RowClust': r('hr'),
'ColClust': None
}
})
header = 'View the resulting heatmap plot <br>'
baseDir = GalaxyRunSpecificFile([], galaxyFn).getDiskPath()
heatPresenter = HeatmapFromNumpyPresenter(
resDict, baseDir, header, printDimensions=False)
print >> jobFile, heatPresenter.getReference('result')
#heatmap = GalaxyRunSpecificFile(['heatmap_figure.pdf'], galaxyFn)
#heatmap_path = heatmap.getDiskPath(True)
#r.pdf(heatmap_path)
##cm.colors(256)
#r.library("gplots")
#r('heatmap(f_matrix, col=redgreen(75), distfun=function(c) dist(c, method=distanceType), hclustfun=function(c) hclust(c, method=extra_option, members=NULL),Colv=NA, scale="none", xlab="", ylab="", cexRow=0.5, cexCol=0.5, margin=c(8,10))')#Features cluster tracks
#r('dev.off()')
##print>>jobFile, r('dimnames(f_matrix)')
#print>>jobFile, heatmap.getLink('View the resulting heatmap plot <br>')
else:
print >> jobFile, 'Heatmap not generated due to large size ', f_matrix.shape
elif clusterMethod == 'K-means clustering' and extra_option != "--select--" and kmeans_alg != "--select--":
textFile = GalaxyRunSpecificFile(
['result_of_kmeans_clustering.txt'], galaxyFn)
textFilePath = textFile.getDiskPath(True)
extra_option = int(extra_option)
r.assign('kmeans_alg', kmeans_alg)
r.assign('extra_option', extra_option)
r(
'hr <- kmeans(f_matrix,extra_option,algorithm=kmeans_alg)'
) #the number of cluster is gotten from clusterMethod+ tag, instead of 3 used here
r('hr$height <- hr$height/max(hr$height)*10')
kmeans_output = open(textFilePath, 'w')
clusterSizes = r('hr$size') #size of every cluster
withinSS = r('hr$withinss')
clusters = r('hr$cluster')
for index1 in range(
extra_option
): #extra_option actually the number of clusters
#trackInCluster = [k for k,val in clusters.items() if val == index1]
trackInCluster = [
k + 1 for k, val in enumerate(clusters)
if val == index1 + 1
] #IS THIS CORRECT, I.E. SAME AS ABOVE??
print >> kmeans_output, 'Cluster %i(%s objects) : ' % (
index1 + 1, str(clusterSizes[index1]))
for name in trackInCluster:
print >> kmeans_output, name, '(This result may be a bit shaky afters some changes in rpy access)'
print >> kmeans_output, 'Sum of square error for this cluster is : ' + str(
withinSS[index1]) + '\n'
kmeans_output.close()
print >> jobFile, textFile.getLink(
'Detailed result of kmeans clustering <br>')
#cls.print_data(f_matrix, jobFile)
'''
def executeReferenceTrack(cls,
genome,
tracks,
track_names,
clusterMethod,
extra_option,
distanceType,
kmeans_alg,
galaxyFn,
regSpec,
binSpec,
numreferencetracks=None,
refTracks=None,
refFeatures=None,
yesNo=None,
howMany=None,
upFlank=None,
downFlank=None):
from proto.RSetup import r
silenceRWarnings()
jobFile = open(galaxyFn, 'w')
print >> jobFile, '<h3>Results for the "similarity of relations to other sets of genomic features" way of clustering<h3/><br/><br/>'
# print>>jobFile, 'PARAMS: ', dict(zip('genome, tracks, track_names, clusterMethod, extra_option, distanceType, kmeans_alg, regSpec, binSpec'.split(','), [repr(v)+'<br>'for v in [genome, tracks, track_names, clusterMethod, extra_option, distanceType, kmeans_alg, regSpec, binSpec]])), '<br><br>'
batchRun = GalaxyRunSpecificFile(['batch_run_job.txt'], galaxyFn)
with open(batchRun.getDiskPath(ensurePath=True), 'w') as batchFile:
print >> batchFile, '$clusterByReference', (genome, '$'.join([
':'.join(t) for t in tracks
]), ':'.join(track_names), clusterMethod, extra_option,
distanceType,
kmeans_alg, regSpec,
binSpec,
numreferencetracks,
refTracks, refFeatures,
yesNo, howMany,
upFlank, downFlank)
print >> jobFile, batchRun.getLink(
'View batch script line for this analysis<br/>')
#print>>jobFile, 'Batch script syntax for this analysis:<br>', '$clusterByReference', (genome, '$'.join([':'.join(t) for t in tracks]), ':'.join(track_names) , clusterMethod, extra_option, distanceType, kmeans_alg, regSpec, binSpec,numreferencetracks, refTracks, refFeatures, yesNo, howMany, upFlank, downFlank), '<br><br>'
#print>>jobFile, 'signature of method clusterByReference:<br>', 'clusterByReference(genome, tracksStr, track_namesStr, clusterMethod, extra_option, distanceType, kmeans_alg, regSpec, binSpec, numreferencetracks=None, refTracks=None, refFeatures=None, yesNo=None, howMany=None, upFlank=None, downFlank=None)<br><br><br>'
prettyTrackNames = [
v[-1].replace("RoadMap_", "").replace('.H3K4me1', '')
for v in tracks
]
#prettyTrackNames = [prettyPrintTrackName(v) for v in tracks]
#paramNames = ['numreferencetracks', 'refTracks', 'refFeatures', 'yesNo', 'howMany', 'upFlank', 'downFlank']
#for index, value in enumerate([numreferencetracks, refTracks, refFeatures, yesNo, howMany, upFlank, downFlank]):
# if value != None:
# print paramNames[index]+'='+ str(value),
#print ''
reftrack_names = [
] #for use in creating the heatmap (as the column names)
options = [
] #for the case using refTracks, options contains feature for every refTrack, chosen by user.
if numreferencetracks:
for i in range(int(numreferencetracks)):
ref_i = refTracks[i].split(
":"
) #name of refTrack is being used to construct the name of expanded refTrack
#refTracks.append(ref_i) #put the refTrack into refTracks list
reftrack_names.append(ref_i[-1])
temp_opt1 = 'ref' + str(i) + 'feature'
options += [] if refFeatures[i] is None else [refFeatures[i]]
if yesNo and yesNo[
i] == "Yes" and howMany and howMany[i] != '--select--':
for expan in range(int(howMany[i])):
reftrack_names.append(ref_i[-1] + '_' +
upFlank[i][expan])
upFlank = int(upFlank[i][expan])
downFlank = int(downFlank[i][expan])
withinRunId = str(i + 1) + ' expansion ' + str(expan +
1)
outTrackName = GalaxyInterface.expandBedSegmentsFromTrackNameUsingGalaxyFn(
ref_i, genome, upFlank, downFlank, galaxyFn,
withinRunId) #outTrackName is unique for run
refTracks.append(
outTrackName
) #put the expanded track into refTracks list
options.append(
options[-1]
) # use chosen feature for refTack as valid feature for the expanded
for index, track in enumerate(refTracks):
#print track, '<br>'
if isinstance(track, basestring):
track = track.split(":")
refTracks[index] = track[:-1] if track[
-1] == "-- All subtypes --" else track
if len(refTracks) > 0:
trackFormats = [
TrackInfo(genome, track).trackFormatName for track in tracks
]
trackLen = len(tracks)
refLen = len(refTracks)
f_matrix = np.zeros((trackLen, refLen))
for i in range(trackLen):
for j in range(refLen):
#print 'len(options), refLen, len(tracks), trackLen, len(trackFormats):', len(options), refLen, len(tracks), trackLen, len(trackFormats)
f_matrix[i,
j] = cls.extract_feature(genome, tracks[i],
refTracks[j], options[j],
regSpec, binSpec,
trackFormats[i])
r.assign('track_names', prettyTrackNames
) #use as track names, will be shown in clustering figure
r.assign('reftrack_names', reftrack_names)
r.assign('f_matrix', f_matrix)
r.assign('distanceType', distanceType)
r('row.names(f_matrix) <- track_names')
r('colnames(f_matrix) <- reftrack_names')
if clusterMethod == 'Hierarchical clustering' and extra_option != "--select--":
figure = GalaxyRunSpecificFile(
['cluster_tracks_result_figure.pdf'], galaxyFn)
figurepath = figure.getDiskPath(True)
#r.pdf(figurepath, 8,8)
r('d <- dist(f_matrix, method=distanceType)')
distTable = r('d')
distMatrix = GalaxyRunSpecificFile(
['distance_matrix_result.txt'], galaxyFn)
distMatrixPath = distMatrix.getDiskPath(True)
open(distMatrixPath, 'w').write(str(distTable))
print >> jobFile, distMatrix.getLink(
'View the distance matrix for this analysis <br>')
#with open(distMatrixPath,'w') as distObj:
# #distTable = d_matrix.tolist()
# core = HtmlCore()
# core.tableHeader(['']+track_names,firstRow=True)
# rowSize = len(track_names)
# index=0
# while index<len(distTable):
# core.tableLine([track_names[index % rowSize]]+[str(v) for v in distTable[index:index+rowSize]])
# core.tableFooter()
# print>>distObj, str(core)
#print>>jobFile, distMatrix.getLink('View the distance matrix for this analysis <br>')
#print r.f_matrix
#print r.d
r_f_matrixFile = GalaxyRunSpecificFile(['f-matrix.robj'],
galaxyFn)
r.assign('f_matrix_fn', r_f_matrixFile.getDiskPath(True))
r('dput(f_matrix, f_matrix_fn)')
print >> jobFile, r_f_matrixFile.getLink(
'Access the R-representation of the Feature_matrix (text-file) <br>'
),
#r_f_matrixFile = GalaxyRunSpecificFile(['f-matrix.txt'], galaxyFn)
#r_f_matrixFile.writeTextToFile(str(f_matrix)+'\n\n'+str(r.d))
#print>>jobFile, r_f_matrixFile.getLink('r.f_matrix & r.d <br>')
cls._clusterAndPlotDendrogram(figurepath, extra_option, 'd',
'f_matrix', prettyTrackNames)
#r.assign('extra_option',extra_option)
#r('hr <- hclust(d, method=extra_option, members=NULL)')
#r('hr$height <- hr$height/max(hr$height)*10')
#r('plot(hr, ylab="Distance", hang=-1)')
#
#r('dev.off()')
print >> jobFile, figure.getLink(
'View the clustering tree (dendrogram) for this analysis<br>'
)
elif clusterMethod == 'K-means clustering' and extra_option != "--select--" and kmeans_alg != "--select--":
textFile = GalaxyRunSpecificFile(
['result_of_kmeans_clustering.txt'], galaxyFn)
textFilePath = textFile.getDiskPath(True)
extra_option = int(extra_option)
r.assign('extra_option', extra_option)
r.assign('kmeans_alg', kmeans_alg)
r(
'hr <- kmeans(f_matrix,extra_option,algorithm=kmeans_alg)'
) #the number of cluster is gotten from clusterMethod+ tag, instead of 3 used here
r('hr$height <- hr$height/max(hr$height)*10')
kmeans_output = open(textFilePath, 'w')
clusterSizes = r('hr$size') #size of every cluster
withinSS = r('hr$withinss')
clusters = np.array(
r('hr$cluster')
) #convert to array in order to handle the index more easily
track_names = np.array(track_names)
for index1 in range(
extra_option
): #extra_option actually the number of clusters
trackInCluster = [
k for k, val in clusters.items() if val == index1
]
print >> kmeans_output, 'Cluster %i(%s objects) : ' % (
index1 + 1, str(clusterSizes[index1]))
for name in trackInCluster:
print >> kmeans_output, name
print >> kmeans_output, 'Sum of square error for this cluster is : ' + str(
withinSS[index1]) + '\n'
kmeans_output.close()
print >> jobFile, textFile.getLink(
'Detailed result of kmeans clustering <br>')
#heatmap = GalaxyRunSpecificFile(['heatmap_figure.pdf'], galaxyFn)
#baseDir = os.path.dirname(heatmap.getDiskPath(True))
##r.png(heatmap_path, width=800, height=700)
resDict = Results([], [], 'ClusTrack')
resDict.setGlobalResult({
'result': {
'Matrix': f_matrix,
'Rows': np.array(track_names),
'Cols': np.array(reftrack_names),
'Significance': None,
'RowClust': r('hr'),
'ColClust': None
}
})
header = 'Heatmap of Feature matrix for "similarity of positional distribution along the genome" '
baseDir = GalaxyRunSpecificFile([], galaxyFn).getDiskPath()
heatPresenter = HeatmapFromNumpyPresenter(resDict,
baseDir,
header,
printDimensions=False)
print >> jobFile, heatPresenter.getReference('result')
#r.pdf(heatmap_path)
#r.library("gplots")
#r('heatmap(f_matrix, col=redgreen(75), Colv=NA, scale="none", xlab="", ylab="", margins=c(10,10))')#Features cluster tracks
#r('dev.off()')
#print>>jobFile, heatmap.getLink('View the resulting heatmap plot <br>')
#cls.print_data(f_matrix, jobFile)
else:
print 'Have to specify a set of refTracks'
def executePairDistance(cls, genome, tracks, track_names, clusterMethod,
extra_option, feature, extra_feature, galaxyFn,
regSpec, binSpec):
from proto.RSetup import r
silenceRWarnings()
#jobFile = galaxyFn
if feature is not None: # must use "" here because the '' does not work
l = len(tracks)
d_matrix = np.zeros((l, l))
for i in range(l):
for j in range(l):
if i < j:
if extra_feature == "1 minus the ratio":
d_matrix[
i,
j] = 1 - ClusteringExecution.computeDistance(
genome, tracks[i], tracks[j], feature,
regSpec, binSpec, galaxyFn)
d_matrix[j, i] = d_matrix[i, j]
elif extra_feature == "1 over the ratio":
d_matrix[
i,
j] = 1 / ClusteringExecution.computeDistance(
genome, tracks[i], tracks[j], feature,
regSpec, binSpec, galaxyFn)
d_matrix[j, i] = d_matrix[i, j]
else:
d_matrix[i,
j] = ClusteringExecution.computeDistance(
genome, tracks[i], tracks[j], feature,
regSpec, binSpec, galaxyFn)
d_matrix[j, i] = d_matrix[i, j]
jobFile = open(galaxyFn, 'w')
print >> jobFile, '<h3>Results for the "direct sequence-level similarity" way of clustering<h3/><br/><br/>'
figure = GalaxyRunSpecificFile(
['cluster_tracks_result_figure.pdf'], galaxyFn
) #this figure is runspecific and is put in the directory
distMatrix = GalaxyRunSpecificFile(['distance_matrix_result.html'],
galaxyFn)
distMatrixPath = distMatrix.getDiskPath(True)
with open(distMatrixPath, 'w') as distObj:
distTable = d_matrix.tolist()
core = HtmlCore()
core.tableHeader([''] + track_names, firstRow=True)
for index, row in enumerate(distTable):
core.tableLine([track_names[index]] +
[str(v) for v in row])
core.tableFooter()
print >> distObj, str(core)
figurepath = figure.getDiskPath(True)
#r.pdf(figurepath, 8, 8)
r.assign('track_names', track_names)
r.assign('d_matrix', d_matrix)
r('row.names(d_matrix) <- track_names')
r('d <- as.dist(d_matrix)')
if clusterMethod == 'Hierarchical clustering' and extra_option != "--select--":
cls._clusterAndPlotDendrogram(figurepath, extra_option, 'd',
'd_matrix', track_names)
#r.assign('extra_option',extra_option)
#r('hr <- hclust(d, method=extra_option, members=NULL)')
#r('hr$height <- hr$height/max(hr$height)*10')
#r('plot(hr, ylab="Distance", hang=-1)')
#r('dev.off()')
batchRun = GalaxyRunSpecificFile(['batch_run_job.txt'], galaxyFn)
with open(batchRun.getDiskPath(ensurePath=True), 'w') as batchFile:
print >> batchFile, '$clusterByPairDistance', (
genome, '$'.join([':'.join(t) for t in tracks
]), ':'.join(track_names), clusterMethod,
extra_option, feature, extra_feature, regSpec, binSpec)
print >> jobFile, batchRun.getLink(
'View batch script line for this analysis <br/>')
#print>>jobFile, 'Batch script syntax for this analysis:<br>$clusterByPairDistance', (genome, '$'.join([':'.join(t) for t in tracks]), ':'.join(track_names) , clusterMethod, extra_option, feature, extra_feature, regSpec, binSpec), '<br><br>'
print >> jobFile, figure.getLink(
'View the clustering tree (dendrogram) for this analysis <br>')
print >> jobFile, distMatrix.getLink(
'View the distance matrix for this analysis <br>')
def __init__(self, results, baseDir, header, historyFilePresenter):
GraphicsPresenter.__init__(self, results, baseDir, header)
self._historyFilePresenter = historyFilePresenter
silenceRWarnings()
def __init__(self, region, track, track2, markType='number', **kwArgs):
self._markType = markType
#r('sink(file("/dev/null", open="wt"), type="message")')
silenceRWarnings()
Statistic.__init__(self, region, track, track2, markType=markType, **kwArgs)
def __init__(self, results, baseDir, header, historyFilePresenter):
GraphicsPresenter.__init__(self, results, baseDir, header)
self._historyFilePresenter = historyFilePresenter
silenceRWarnings()
def setUp(self):
silenceRWarnings()
if self.SPLITTABLE:
gold.util.CompBinManager.COMP_BIN_SIZE = 100
self._ALLOW_COMP_BIN_SPLITTING = CompBinManager.ALLOW_COMP_BIN_SPLITTING
CompBinManager.ALLOW_COMP_BIN_SPLITTING = True
def execute(cls, choices, galaxyFn=None, username=''):
path = str(URL_PREFIX)
dataset = choices.dataset
genome = choices.genome
text = choices.newtrack
secondDataset = choices.newdataset
inputFile = open(ExternalTrackManager.extractFnFromGalaxyTN(dataset),
'r')
with inputFile as f:
data = [x for x in f.readlines()]
silenceRWarnings()
binSourceParam = '*'
regSourceParam = '*'
trackNamePrep = cls.preprocessTrack(genome, dataset)
if text == 'No':
figUrl = ''
if (len(data) > 30000):
core = HtmlCore()
core.styleInfoBegin(styleClass='debug')
figImage = GalaxyRunSpecificFile(['VizTrackOnGenome.png'],
galaxyFn)
analysisDef = ' [normalizeRows=%s] [centerRows=%s] -> RawVisualizationDataStat'
res = GalaxyInterface.runManual([trackNamePrep],
analysisDef,
regSourceParam,
binSourceParam,
genome,
username=username,
printResults=False,
printHtmlWarningMsgs=False)
core.styleInfoEnd()
core.line('')
core.tableHeader(None)
rScript = VisualizeTrackPresenceOnGenome.customRExecution(
res, figImage.getDiskPath(ensurePath=True), '')
figUrl = figImage.getURL()
print GalaxyInterface.getHtmlEndForRuns()
binSourceParam = '10m'
regSourceParam = '*'
cls.resultPrintGeneric(genome, binSourceParam, regSourceParam,
figUrl, path, trackNamePrep)
else:
if isinstance(trackNamePrep[0], (list, )):
numTracks = len(trackNamePrep[0])
firstTrack = cls.prepareTracknameForURL(trackNamePrep[0])
trackTitle = json.dumps(trackNamePrep[1])
cls.resultPrintGSuite(genome, binSourceParam,
regSourceParam, figUrl, path,
firstTrack, trackTitle, numTracks)
else:
firstTrack = cls.prepareTracknameForURL(trackNamePrep)
cls.resultPrintGeneric(genome, binSourceParam,
regSourceParam, figUrl, path,
firstTrack)
else:
trackName2 = cls.preprocessTrack(genome, secondDataset)
firstTrack = cls.prepareTracknameForURL(trackNamePrep)
secondTrack = cls.prepareTracknameForURL(trackName2)
cls.resultPrintOverlap(genome, binSourceParam, regSourceParam,
path, firstTrack, secondTrack)