def execute(choices, galaxyFn=None, username=''):
'''Is called when execute-button is pushed by web-user.
Should print output as HTML to standard out, which will be directed to a results page in Galaxy history.
If needed, StaticFile can be used to get a path where additional files can be put (e.g. generated image files).
choices is a list of selections made by web-user in each options box.
'''
if choices[2]=='Transfac TF ids':
mappingFn = 'pwm2TFids.shelf'
mapping = safeshelve.open(Tool1.MAPPING_SHELVES_PATH + os.sep + mappingFn )
elif choices[2]== 'Transfac TF readable names':
mappingFn = 'pwm2TFnamesNew.shelf'
mapping = safeshelve.open(Tool1.MAPPING_SHELVES_PATH + os.sep + mappingFn )
elif choices[2]== 'HGNC gene symbols':
mappingFn = 'PWM_to_HGNC.txt'
mapping = dict([line.strip().split() for line in open(Tool1.MAPPING_SHELVES_PATH + os.sep + mappingFn).readlines()])
else:
raise Exception(choices[2])
if galaxyFn==None:
for key in sorted(mapping.keys()):
print key + ':' + ','.join(mapping[key]) + os.linesep,
else:
mappingStaticFile = GalaxyRunSpecificFile(['mapping.txt'], galaxyFn)
f = mappingStaticFile.getFile()
for key in sorted(mapping.keys()):
if type(mapping[key]) in (list,tuple):
mapping[key] = ','.join(mapping[key])
f.write( key + ':' + mapping[key] + os.linesep )
f.close()
print mappingStaticFile.getLink('View/download mapping')
def handlePairDistance(self, genome, tracks, track_names, clusterMethod, extra_option):
from gold.application.RSetup import r
if self.params.has_key("pair_feature") : # must use "" here because the '' does not work
feature = self.params.get('pair_feature')
extra_feature = self.params.get('pair_feature+') #must be different from the text --select--
d_matrix = self.constructDistMatrix(genome, tracks, feature, extra_feature)
figure = GalaxyRunSpecificFile(['cluster_trakcs_result_figure.pdf'], self.jobFile) #this figure is runspecific and is put in the directory
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--" :
r.assign('extra_option',extra_option)
r('hr <- hclust(d, method=extra_option, members=NULL)')
r('plot(hr, ylab="Distance", hang=-1)')
r('dev.off()')
print figure.getLink('clustering results figure<br>')
else :
print 'A feature must be selected in order to compute the distance between tracks.'
def collectParamsIntoFile(self):
parameters = GalaxyRunSpecificFile(['run_parameters.html'],self.jobFile) #just collect the parametes used into a file
p_path = parameters.getDiskPath(True)
p_output = open(p_path,'w')
print>>p_output, '<html><body>'
print>>p_output, '<ol>'
for key in self.params.keys():
print>>p_output, '<li>%s:%s </li>'%(key,self.params[key])
print>>p_output, '</body></html>'
p_output.close()
print parameters.getLink('Parameters of this run')
def findTFsOccurringInRegions(cls, genome, tfSource, regionsBedFn, upFlankSize, downFlankSize, galaxyFn):
uniqueWebPath = getUniqueWebPath(extractIdFromGalaxyFn(galaxyFn))
#assert genome == 'hg18' #other genomes not supported. TF id links do not specify genome for pre-selection of analysis
tfTrackNameMappings = TfInfo.getTfTrackNameMappings(genome)
assert tfTrackNameMappings != {}, 'No TF info for genome: %s' % genome
tfTrackName = tfTrackNameMappings[tfSource]
if (upFlankSize == downFlankSize == 0):
flankedRegionsFn = regionsBedFn
else:
flankedRegionsFn= uniqueWebPath + os.sep + 'flankedRegs.bed'
GalaxyInterface.expandBedSegments(regionsBedFn, flankedRegionsFn, genome, upFlankSize, downFlankSize)
regSpec, binSpec = 'bed', flankedRegionsFn
res = cls._runCategoryPointCount(genome, regSpec, binSpec, tfTrackName)
tfNames = res.getResDictKeys()
#print 'RES: ', res.getGlobalResult()[tfNames[0]], type(res.getGlobalResult()[tfNames[0]])
import third_party.safeshelve as safeshelve
pwm2tfids = safeshelve.open(os.sep.join([HB_SOURCE_CODE_BASE_DIR,'data','pwm2TFids.shelf']), 'r')
tf2class = safeshelve.open(os.sep.join([HB_SOURCE_CODE_BASE_DIR,'data','TfId2Class.shelf']), 'r')
pwmName2id= safeshelve.open(os.sep.join([HB_SOURCE_CODE_BASE_DIR,'data','pwmName2id.shelf']), 'r')
#print tfNames[0],tfNames[1], ' VS ', pwm2tfids.keys()[0], len(pwm2tfids)
#tfs = list(reversed(sorted([(res.getGlobalResult()[tf], tf, '%s (%i hits (class %s))'%(tf, res.getGlobalResult()[tf]), '/'.join([tf2class[x] for x in pwm2tfids[tf]]) ) for tf in tfNames]))) #num hits, tfName, tfTextInclHits
tfs = list(reversed(sorted([(res.getGlobalResult()[tf], tf, '%s (%i hits )'%(tf, res.getGlobalResult()[tf]) + \
(' (class: %s)'%'/'.join(set([str(tf2class.get(x)) for x in pwm2tfids[pwmName2id[tf]] if x in tf2class]))\
if (tf in pwmName2id and pwmName2id[tf] in pwm2tfids and any([x in tf2class for x in pwm2tfids[pwmName2id[tf]]]))\
else '') ) \
for tf in tfNames])) ) #num hits, tfName, tfTextInclHits
tfsPlural = 's' if len(tfs)!=1 else ''
print '<p>There are %i TF%s targeting your regions of interest, using "%s" as source of TF occurrences.</p>' % (len(tfs), tfsPlural, tfSource)
expansionStr = ' flanked' if not (upFlankSize == downFlankSize == 0) else ''
idHtmlFileNamer = GalaxyRunSpecificFile(['allTfIds.html'],galaxyFn)
idHtmlFileNamer.writeTextToFile('<br>'.join(['<a href=/hbdev/hyper?track1=%s&track2=>%s</a>'%( quote(':'.join(tfTrackName+[tf[1]])), tf[2]) for tf in tfs]))
print '<p>', idHtmlFileNamer.getLink('Inspect html file'), ' of all TF IDs occurring 1 or more times within your%s regions of interest, with each TF ID linking to analysis with this TF pre-selected.</p>' % (expansionStr)
idFileNamer = GalaxyRunSpecificFile(['allTfIds.txt'],galaxyFn)
idFileNamer.writeTextToFile(os.linesep.join([tf[2] for tf in tfs]) + os.linesep)
print '<p>', idFileNamer.getLink('Inspect text file'), ' listing all TF IDs occurring 1 or more times within your%s regions of interest.</p>' % (expansionStr)
extractedTfbsFileNamer = GalaxyRunSpecificFile(['tfbsInGeneRegions.bed'],galaxyFn)
GalaxyInterface.extractTrackManyBins(genome, tfTrackName, regSpec, binSpec, True, 'bed', False, False, extractedTfbsFileNamer.getDiskPath(), True)
print '<p>', extractedTfbsFileNamer.getLoadToHistoryLink('Inspect bed-file'), 'of all TF binding sites occurring within your%s regions of interest.</p>' % (expansionStr)
for dummy,tf,dummy2 in tfs:
extractedTfbsFileNamer = GalaxyRunSpecificFile([tf+'_tfbsInGeneRegions.bed'],galaxyFn)
GalaxyInterface.extractTrackManyBins(genome, tfTrackName+[tf], regSpec, binSpec, True, 'bed', False, False, extractedTfbsFileNamer.getDiskPath())
print '<p>', extractedTfbsFileNamer.getLoadToHistoryLink('Binding sites of the TF %s' %tf, 'bed'), 'occurring within your%s regions of interest (bed-file).</p>' % (expansionStr)
def execute(choices, galaxyFn=None, username=''):
'''Is called when execute-button is pushed by web-user.
Should print output as HTML to standard out, which will be directed to a results page in Galaxy history.
If getOutputFormat is anything else than HTML, the output should be written to the file with path galaxyFn.
If needed, StaticFile can be used to get a path where additional files can be put (e.g. generated image files).
choices is a list of selections made by web-user in each options box.
'''
from time import time
startTime = time()
from quick.application.ExternalTrackManager import ExternalTrackManager
from quick.util.StaticFile import GalaxyRunSpecificFile
import os
motifFn = ExternalTrackManager.extractFnFromGalaxyTN( choices[0].split(':'))
observedFasta = ExternalTrackManager.extractFnFromGalaxyTN( choices[1].split(':'))
randomGalaxyTN = choices[2].split(':')
randomName = ExternalTrackManager.extractNameFromHistoryTN(randomGalaxyTN)
randomGalaxyFn = ExternalTrackManager.extractFnFromGalaxyTN( randomGalaxyTN)
randomStatic = GalaxyRunSpecificFile(['random'],randomGalaxyFn) #finds path to static file created for a previous history element (randomFn), and directs to a folder containing several files..
#print os.listdir(randomStatic.getDiskPath())
randomFastaPath = randomStatic.getDiskPath()
#motifFn, observedFasta, randomFastaPath = '/Users/sandve/egne_dokumenter/_faglig/NullModels/DnaSeqExample/liver.pwm', 'liver.fa', 'randomFastas'
testStatistic = choices[3]
if testStatistic == 'Average of max score per sequence':
scoreFunc = scoreMotifOnFastaAsAvgOfBestScores
elif testStatistic == 'Sum of scores across all positions of all sequences':
scoreFunc = scoreMotifOnFastaAsSumOfAllScores
elif testStatistic == 'Score of Frith et al. (2004)':
scoreFunc = lr4
elif testStatistic == 'Product of max per sequence':
scoreFunc = scoreMotifOnFastaAsProductOfBestScores
else:
raise
pvals = mcPvalFromMotifAndFastas(motifFn, observedFasta, randomFastaPath, scoreFunc)
print 'Pvals for motifs (%s) against observed (%s) vs random (%s - %s) sequences.' % (motifFn, observedFasta, randomName, randomFastaPath)
for motif,pval in sorted(pvals.items()):
print motif+'\t'+('%.4f'%pval)
from quick.util.StaticFile import GalaxyRunSpecificFile
from gold.application.RSetup import r, robjects
histStaticFile = GalaxyRunSpecificFile(['pvalHist.png'],galaxyFn)
#histStaticFile.openRFigure()
histStaticFile.plotRHist(pvals.values(), [x/40.0 for x in range(41)], 'Histogram of p-values', xlim=robjects.FloatVector([0.0, 1.0]))
#r.hist(robjects.FloatVector(pvals.values()), breaks=robjects.FloatVector([x/40.0 for x in range(41)]), xlim=robjects.FloatVector([0.0, 1.0]), main='Histogram of p-values' )
#histStaticFile.closeRFigure()
print histStaticFile.getLink('Histogram')
print 'Time (s):', time()-startTime
def findGeneTargets(genome, regionsTn, upFlankSize, downFlankSize, galaxyFn):
assert genome == 'hg18'
#tfTrackNameMappings = TfInfo.getTfTrackNameMappings(genome)
#tfTrackName = tfTrackNameMappings[tfSource] + [selectedTF]
geneIntersection = GeneIntersection(genome, 'Ensembl', regionsTn, galaxyFn)
geneIntersection.expandReferenceTrack(upFlankSize, downFlankSize)
expansionStr = ' flanked' if not (upFlankSize == downFlankSize == 0) else ''
#print '<p>There are %i Ensemble genes being targets of your selected TF (%s), based on intersecting TF target positions with%s %sgene regions.</p>' % (geneIntersection.getNumberOfIntersectedBins(), selectedTF, expansionStr, 'Ensembl')
print '<p>There are %i Ensemble genes being targets of your selected regions, based on intersecting your supplied regions with%s %sgene regions.</p>' % (geneIntersection.getNumberOfIntersectedBins(), expansionStr, 'Ensembl')
idFileNamer = geneIntersection.getGeneIdStaticFileWithContent()
print '<p>', idFileNamer.getLink('Download list'), ' of all Ensemble IDs with 1 or more hits.</p>'
regFileNamer = geneIntersection.getIntersectedRegionsStaticFileWithContent()
print '<p>', regFileNamer.getLink('Download bed-file'), ' of all Ensembl gene regions with 1 or more hits.</p>'
targetBins = geneIntersection.getIntersectedReferenceBins()
res = geneIntersection.getIntersectionResult()
resDictKey = geneIntersection.getUniqueResDictKey()
setOfNumOccurrences = set([res[bin][resDictKey] for bin in targetBins])
byNumOccurrencesStaticFile = GalaxyRunSpecificFile(['genes_by_num_occurrences.html'], galaxyFn)
f = byNumOccurrencesStaticFile.getFile()
for numOccurrences in reversed(sorted(setOfNumOccurrences)):
f.write('Gene regions having %i occurrences:<br>' % numOccurrences + '<br>' + os.linesep)
f.write(', '.join([ '<a href=http://www.ensembl.org/Homo_sapiens/Gene/Summary?g='+str(bin.val).split('|')[0]+'>'+str(bin.val).split('|')[0]+'</a>' for bin in targetBins if res[bin][resDictKey]==numOccurrences]) + '<br><br>' + os.linesep)
f.close()
print '</p>Inspect list of all intersected genes (by ID), ', byNumOccurrencesStaticFile.getLink('ordered by number of occurrences') + ' inside, and with links to gene descriptions.<br>'
def singleSimulation(self, numH0, numH1, replicateIndex, verbose=False):
tests = MultipleTestCollection(numH0, numH1, self._maxNumSamples, self._h, self._fdrThreshold,self._a,self._b)
tests.addSamples(self.NUM_SAMPLES_INITIALLY)
while not tests.allTestsAreDetermined():
tests.addSamples(self.NUM_SAMPLES_PER_CHUNK)
#if verbose:
#print tests.getTotalNumSamples()
#As sampling is now anyway over, we set fdrThreshold to a threshold used after computations are finished (i.e. affects final rejection/acception, but not stopping of samples)
tests.setFdrThresholdAtAllCounters(self._postFdrThreshold)
#print 'FINALLY, #samples: ',
if self._galaxyFn is not None:
if self._h is None:
scheme = 'Basic'
elif self._fdrThreshold is None:
scheme = 'Sequential'
else:
scheme = 'McFdr'
staticFile = GalaxyRunSpecificFile([scheme,str(numH1),str(replicateIndex),'PandQvals.txt'], self._galaxyFn)
tests.writeAllPandQVals(staticFile.getFile() )
linkToRaw = staticFile.getLink('Raw p and q-vals') + ' under %s scheme with %i true H1, (replication %i)' % (scheme, numH1, replicateIndex)
figStaticFile = GalaxyRunSpecificFile([scheme,str(numH1),str(replicateIndex),'PandQvals.png'], self._galaxyFn)
figStaticFile.openRFigure()
tests.makeAllPandQValsFigure()
figStaticFile.closeRFigure()
linkToFig = figStaticFile.getLink(' (p/q-figure) ') + '<br>'
figNumSamplesStaticFile = GalaxyRunSpecificFile([scheme,str(numH1),str(replicateIndex),'NumSamples.png'], self._galaxyFn)
figNumSamplesStaticFile.openRFigure()
tests.makeNumSamplesFigure()
figNumSamplesStaticFile.closeRFigure()
linkToNumSamplesFig = figNumSamplesStaticFile.getLink(' (numSamples-figure) ') + '<br>'
catalogStaticFile = GalaxyRunSpecificFile([str(numH1),'cat.html'], self._galaxyFn)
catalogStaticFile.writeTextToFile(linkToRaw + linkToFig + linkToNumSamplesFig, mode='a')
#if verbose:
#print sorted(tests.getFdrVals())
#print 'NumS ign Below 0.2: ', sum([1 if t<0.2 else 0 for t in tests.getFdrVals()])
#return tests.getTotalNumSamples(), tests.getTotalNumRejected()
return tests.getTotalNumSamples(), tests.getTotalNumRejected(), tests.getClassificationSummaries()
def analyzeNumRejectedDistribution(maxNumSamples,h, fdrThreshold, totalNumTests, totalNumH1Tests, numReplications,a,b, galaxyFn=None):
numRej = []
texts = []
#estimate time use:
print '(estimating run time..)'
prevTime= time.time()
#Experiment._analyzeNumRejectedDistribution(maxNumSamples, None, None, fdrThreshold, totalNumTests, totalNumH1Tests, 1,a,b, galaxyFn)
Experiment._analyzeNumRejectedDistribution(maxNumSamples, None, None, fdrThreshold, 1, 1, 1,a,b, galaxyFn)
baseMeasure = time.time() - prevTime
withOnlyMaxNumEstimate = baseMeasure * totalNumTests * numReplications
#print 'Estimated running time: between %i and %i seconds.' % (withOnlyMaxNumEstimate, withOnlyMaxNumEstimate*3)
print 'Estimated running time: around %i seconds. (%.1f hours)' % (withOnlyMaxNumEstimate, withOnlyMaxNumEstimate/3600.0)
for x,y,z,simult,text in [ [maxNumSamples, None, None,True,'Basic'], [maxNumSamples, h, None,True,'Sequential'], [maxNumSamples, h, fdrThreshold,True,'McFdr Simultanous'], [maxNumSamples, h, fdrThreshold,False,'McFdr Individual']]:
print text, ':'
MultipleTestCollection.SIMULTANOUS_FDR_STOPPING_CRITERION = simult
numRej.append( Experiment._analyzeNumRejectedDistribution(x,y,z, fdrThreshold, totalNumTests, totalNumH1Tests, numReplications,a,b, galaxyFn) )
texts.append(text)
plotStaticFile = GalaxyRunSpecificFile(['numRej.png'],galaxyFn)
plotStaticFile.plotRLines(range(len(numRej[0])), numRej, xlab='Sorted simulations', ylab='num Rejected', legend=texts)
print plotStaticFile.getLink('Cumulative distribution')
def executePairDistance(cls, genome, tracks, track_names, clusterMethod, extra_option, feature, extra_feature, galaxyFn, regSpec, binSpec):
from gold.application.RSetup import r
#jobFile = open(galaxyFn, 'w')
jobFile = galaxyFn
if feature is not None: # must use "" here because the '' does not work
l = len(tracks)
d_matrix = 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)
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)
d_matrix[j,i] = d_matrix[i,j]
else :
d_matrix[i,j] = ClusteringExecution.computeDistance(genome, tracks[i], tracks[j], feature, regSpec, binSpec)
d_matrix[j,i] = d_matrix[i,j]
figure = GalaxyRunSpecificFile(['cluster_trakcs_result_figure.pdf'], jobFile) #this figure is runspecific and is put in the directory
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--" :
r.assign('extra_option',extra_option)
r('hr <- hclust(d, method=extra_option, members=NULL)')
r('plot(hr, ylab="Distance", hang=-1)')
r('dev.off()')
print figure.getLink('clustering results figure<br>')
def retrieveBenchmarkSuiteAsZipFile(self, trackNames):
zipFile = GalaxyRunSpecificFile(['BenchmarkSuite.tar.gz'], self._galaxyFn)
path = zipFile.getDiskPath(True)
path = path[0:-len(path.split('/')[-1])]
# For every trackName, retrieve the trackName and copy it to a directory
for trackName in trackNames:
filePath = trackName.split(':')[2].split('/')
fileName = filePath[len(filePath)-1]
fastaFileName = GalaxyRunSpecificFile(['BenchmarkSuite/%s.fasta' % fileName.split('.')[0]], self._galaxyFn)
self.retrieveTrack(trackName, fastaFileName.getDiskPath(True))
# And finally create a zip file, and return a link pointing to it
os.system('tar -P -czvf %sBenchmarkSuite.tar.gz %sBenchmarkSuite/' % (path, path))
return zipFile.getLink("Download benchmark suite")
def execute(cls, choices, galaxyFn=None, username=''):
'''Is called when execute-button is pushed by web-user.
Should print output as HTML to standard out, which will be directed to a results page in Galaxy history.
If needed, StaticFile can be used to get a path where additional files can be put (e.g. generated image files).
choices is a list of selections made by web-user in each options box.
'''
genome = choices[0]
nmer = choices[1].lower()
regSpec = choices[2]
analysisRegions = parseRegSpec(regSpec, genome)
binSize = cls._calcBinSize(nmer, analysisRegions)
binSpec = '*' if binSize is None else str( binSize )
numBins = len( AutoBinner(analysisRegions, binSize) )
from quick.application.GalaxyInterface import GalaxyInterface
from quick.util.GenomeInfo import GenomeInfo
trackName1 = GenomeInfo.getPropertyTrackName(genome, 'nmer') + [str(len(nmer))+'-mers',nmer]
trackName2 = ['']
analysisDef = 'Counts: The number of track1-points -> CountPointStat'
#regSpec = '*'
#print 'Using binSize: ',binSpec
#print 'TN1: ',trackName1
from gold.result.HtmlCore import HtmlCore
print str(HtmlCore().styleInfoBegin(styleClass='debug'))
GalaxyInterface.run(trackName1, trackName2, analysisDef, regSpec, binSpec, genome, galaxyFn)
print str(HtmlCore().styleInfoEnd())
plotFileNamer = GalaxyRunSpecificFile(['0','CountPointStat_Result_gwplot.pdf'], galaxyFn)
textualDataFileNamer = GalaxyRunSpecificFile(['0','CountPointStat_Result.bedgraph'], galaxyFn)
core = HtmlCore()
core.paragraph('Inspect nmer frequency variation as a %s or as underlying %s.</p>' % ( plotFileNamer.getLink('plot'), textualDataFileNamer.getLink('textual data') ))
core.divider()
core.paragraph('The occurrence frequency of your specified nmer ("%s") has been computed along the genome, within your specified analysis region ("%s").' % (nmer, regSpec))
core.paragraph('The analysis region was divided into %i bins, based on calculations trying to find appropriate bin size (get enough data per bin and restrict maximum number of bins).' % numBins)
trackName1modified = trackName1[0:-2] + trackName1[-1:]
preSelectedAnalysisUrl = createHyperBrowserURL(genome, trackName1modified,[''], analysis='Counts',method='auto',region=regSpec, binsize=binSpec)
core.divider()
core.paragraph('If you do not find the inferred bin size to be appropriate, you can set this manually in a ' + str(HtmlCore().link('new analysis', preSelectedAnalysisUrl)) + '.')
print str(core)
def runBinaryClassificationSuiteEvaluation(self, algorithmNames, predictionTrackNames,
answerTrackNames, regionTrackNames, overlapAnalysisDef, ROCanalysisDef):
# Number of test sets and number of algorithms to evaluate
nTestSets = len(answerTrackNames)
nAlgorithms = len(predictionTrackNames)/nTestSets
# Initialize list data structures
resultFiles = []
globalOverlapResults = []
globalEqOverlapResults = []
globalRocResults = []
tmpAlgorithmNames = []
number = 1000000000000
statPlot = StatisticPlot()
globalResultFile = GalaxyRunSpecificFile(['globalResults.html'], self._galaxyFn)
# Initialize the global result lists, which collects localResults across all test sets
for i in range(0, nAlgorithms):
tmpAlgorithmNames.append(algorithmNames[i*nTestSets])
globalOverlapResults.append(OrderedDict(zip(['Neither','Only1','Only2','Both'] , (0,0,0,0))))
globalEqOverlapResults.append(OrderedDict(zip(['Neither','Only1','Only2','Both'] , (0,0,0,0))))
globalRocResults.append({'Result': []})
algorithmNames = tmpAlgorithmNames
# For all test sets...
for i in range(0, nTestSets):
# Create a result file for this test set
resultFile = GalaxyRunSpecificFile(['testset%d.html' % i], self._galaxyFn)
localOverlapResults = []
localRocResults = []
answerTrackName = answerTrackNames[i]
regionTrackName = regionTrackNames[i]
# Evaluate the predictions for every algorithm for this test set
for j in range(0, nAlgorithms):
predictionTrackName = predictionTrackNames[(j*nTestSets)+i]
# Run statistics for to compute overlap and ROC values
localOverlapResult = self._runSingleStatistic(regionTrackName, overlapAnalysisDef,
predictionTrackName, answerTrackName)
if self._isRocCurveCompatible(predictionTrackName):
localRocResult = self._runSingleStatistic(regionTrackName, ROCanalysisDef,
predictionTrackName, answerTrackName)
else:
localRocResult = None
# Collect the local results and global add to global results
localOverlapResults.append(localOverlapResult)
localRocResults.append(localRocResult)
globalOverlapResults[j]['Neither'] = globalOverlapResults[j]['Neither'] + localOverlapResult['Neither']
globalOverlapResults[j]['Only1'] = globalOverlapResults[j]['Only1'] + localOverlapResult['Only1']
globalOverlapResults[j]['Only2'] = globalOverlapResults[j]['Only2'] + localOverlapResult['Only2']
globalOverlapResults[j]['Both'] = globalOverlapResults[j]['Both'] + localOverlapResult['Both']
testSetLength = localOverlapResult['Neither'] + localOverlapResult['Only1'] + localOverlapResult['Only2'] + localOverlapResult['Both']
globalEqOverlapResults[j]['Neither'] = globalEqOverlapResults[j]['Neither'] + long(localOverlapResult['Neither']*number)/testSetLength
globalEqOverlapResults[j]['Only1'] = globalEqOverlapResults[j]['Only1'] + long(localOverlapResult['Only1']*number)/testSetLength
globalEqOverlapResults[j]['Only2'] = globalEqOverlapResults[j]['Only2'] + long(localOverlapResult['Only2']*number)/testSetLength
globalEqOverlapResults[j]['Both'] = globalEqOverlapResults[j]['Both'] + long(localOverlapResult['Both']*number)/testSetLength
if localRocResult != None:
globalRocResults[j]['Result'] = globalRocResults[j]['Result'] + localRocResult['Result']
# Create statistics for this test set
localStatisticsLink = statPlot.createBinaryClassificationStatistics(i,
algorithmNames, localOverlapResults, self._galaxyFn, 'Benchmark statistics')
totalPositives, totalNegatives = self._getTotalNegativesAndPositivesFromOverlapResults(localOverlapResults)
localRocCurveLink = statPlot.createROCCurve(i, algorithmNames,
totalPositives, totalNegatives, localRocResults, self._galaxyFn)
# Write statistical information for this test set to file
resultFile.writeTextToFile('%s</br>%s' % (localStatisticsLink, localRocCurveLink), 'w')
resultFiles.append(resultFile)
# Create statistics for all test sets
globalStatisticsLink = statPlot.createBinaryClassificationStatistics(nTestSets,
algorithmNames, globalOverlapResults, self._galaxyFn,
'Benchmark statistics (sum, longer test set has higher weight)')
globalEqStatisticsLink = statPlot.createBinaryClassificationStatistics(nTestSets+1,
algorithmNames, globalEqOverlapResults, self._galaxyFn,
'Benchmark statistics (same weight for each test set)')
totalPositives, totalNegatives = self._getTotalNegativesAndPositivesFromOverlapResults(globalOverlapResults)
globalRocCurveLink = statPlot.createROCCurve(nTestSets, algorithmNames,
totalPositives, totalNegatives, globalRocResults, self._galaxyFn)
# Write statistical information for all test sets to file
globalResultFile.writeTextToFile('%s</br>%s</br>%s' % (globalStatisticsLink, globalEqStatisticsLink, globalRocCurveLink), 'w')
# Add all result files to a result list, and return
results = []
results.append(globalResultFile.getLink('Global results\n\n'))
for i in range(0, len(resultFiles)):
results.append(resultFiles[i].getLink('Test set %d' % (i+1)))
return results
def compareCutoffSchemes(maxNumSamples, h, fdrThreshold, totalNumTests, stepSize, numReplications,a,b, galaxyFn=None):
print '<PRE>'
print 'Comparing cutoff schemes with parameters: maxNumSamples=%i, h=%i, fdrThreshold=%.2f, totalNumTests=%i, numReplications=%i' % (maxNumSamples, h, fdrThreshold, totalNumTests, numReplications)
print 'stepSize: ',stepSize
print 'H1 p-values drawn from beta with a=%.3f and b=%.3f' % (a,b)
print 'Minimum achieveable p-value is %.5f, which gives minimum Bonferroni-corrected p-value of %.5f (compares to a fdr threshold of %.2f)' % (1.0/maxNumSamples, (1.0/maxNumSamples)*totalNumTests, fdrThreshold)
#estimate time use:
prevTime= time.time()
Simulator(maxNumSamples, None, None,a,b,fdrThreshold).numSamplesAsFunctionOfNumH1( 1, 1, 1)
baseMeasure = time.time() - prevTime
if type(stepSize)==int:
numSteps = len(range(0,totalNumTests+1,stepSize))
elif type(stepSize)==list:
numSteps = len(stepSize)
withOnlyMaxNumEstimate = baseMeasure * totalNumTests * numSteps * numReplications
#print 'Estimated running time: between %i and %i seconds.' % (withOnlyMaxNumEstimate, withOnlyMaxNumEstimate*3)
print 'Estimated running time: around %i seconds. (%.1f hours)' % (withOnlyMaxNumEstimate, withOnlyMaxNumEstimate/3600.0)
sortedKeys, onlyMaxCutoff, onlyMaxNumRejected, onlyMaxType1Errors, onlyMaxType2Errors = Simulator(maxNumSamples, None, None,a,b,fdrThreshold, galaxyFn).numSamplesAsFunctionOfNumH1( totalNumTests, stepSize, numReplications)
sortedKeys, seqMcCutoff, seqMcNumRejected, seqMcType1Errors, seqMcType2Errors = Simulator(maxNumSamples, h, None,a,b,fdrThreshold, galaxyFn).numSamplesAsFunctionOfNumH1(totalNumTests, stepSize, numReplications)
sortedKeys, mcFdrCutoff, mcFdrNumRejected, mcFdrType1Errors, mcFdrType2Errors = Simulator(None, h, fdrThreshold,a,b,fdrThreshold, galaxyFn).numSamplesAsFunctionOfNumH1(totalNumTests, stepSize, numReplications)
maxY = max( max(s) for s in [onlyMaxCutoff, seqMcCutoff, mcFdrCutoff])
#minY = min( min(s) for s in [onlyMaxCutoff, seqMcCutoff, McFdrCutoff])
minY=0
print 'Time spent: ',time.time() - prevTime, ' secs'
print '</PRE>'
#plotStaticFile.getDiskPath(True)
if galaxyFn is not None:
#print 'Generating aggregate McFdr simulation figures'
plotStaticFile = GalaxyRunSpecificFile(['mainPlot.png'],galaxyFn)
if type(stepSize) is int:
allNumH1s = range(0,totalNumTests+1,stepSize)
elif type(stepSize) is list:
allNumH1s = stepSize
for numH1 in allNumH1s:
catalogStaticFile = GalaxyRunSpecificFile([str(numH1),'cat.html'], galaxyFn)
print catalogStaticFile.getLink( 'Tests with #True H1s=%i' % numH1 ), '<br>'
#plotStaticFile.openRFigure()
#r.png(filename=plotFn, height=600, width=800, units='px', pointsize=12, res=72)
#r.plot(r.unlist(sortedKeys), r.unlist(onlyMaxCutoff), ylim=r.unlist([minY,maxY]), type='l', xlab='Number of true H1s', ylab='Total MC samples' , col='black')
#r.lines(r.unlist(sortedKeys), r.unlist(seqMcCutoff), col='red' )
#r.lines(r.unlist(sortedKeys), r.unlist(mcFdrCutoff), col='green' )
#r.legend('topleft',['BasicMc','SeqMc','McFdr'],col=['black','red','green'],lty=1)
plotStaticFile.plotRLines(sortedKeys, [onlyMaxCutoff,seqMcCutoff,mcFdrCutoff], xlab='Number of true H1s', ylab='Total MC samples', legend=['BasicMc','SeqMc','McFdr'])
#r('dev.off()')
#plotStaticFile.closeRFigure()
print plotStaticFile.getLink('View main plot') + ' of sumSamples as function of #H1s.', '<br>'
numRejectedPlotStaticFile = GalaxyRunSpecificFile(['secondaryPlot.png'],galaxyFn)
numRejectedPlotStaticFile.plotRLines(sortedKeys, [onlyMaxNumRejected,seqMcNumRejected,mcFdrNumRejected],xlab='Number of true H1s', ylab='Num rejected tests',legend=['BasicMc','SeqMc','McFdr'])
#numRejectedPlotStaticFile.openRFigure()
#r.png(filename=plotFn, height=600, width=800, units='px', pointsize=12, res=72)
#r.plot(r.unlist(sortedKeys), r.unlist(onlyMaxNumRejected), ylim=r.unlist([0,totalNumTests]), type='l', xlab='Number of true H1s', ylab='Num rejected tests',col='black' )
#r.lines(r.unlist(sortedKeys), r.unlist(seqMcNumRejected), col='red' )
#r.lines(r.unlist(sortedKeys), r.unlist(mcFdrNumRejected), col='green' )
#r.lines(r.unlist(sortedKeys), r.unlist(sortedKeys), col='black', lty='dotted' ) #As this corresponds to perfect estimation..
#r.legend('topleft',['BasicMc','SeqMc','McFdr','NumFromH1'],col=['black','red','green','black'],lty=[1,1,1,2])
#r('dev.off()')
#numRejectedPlotStaticFile.closeRFigure()
print numRejectedPlotStaticFile.getLink('View secondary plot') + ' of #true H1s vs #tests rejected.', '<br>'
#Classification errors
classificationErrorPlotStaticFile = GalaxyRunSpecificFile(['errors.png'],galaxyFn)
classificationErrorPlotStaticFile.openRFigure()
yMax = max( max(x) for x in [mcFdrType2Errors,mcFdrType1Errors,seqMcType2Errors,seqMcType1Errors,onlyMaxType2Errors,onlyMaxType1Errors ])
#r.png(filename=plotFn, height=600, width=800, units='px', pointsize=12, res=72)
r.plot(r.unlist(sortedKeys), r.unlist(onlyMaxType1Errors), ylim=r.unlist([0,yMax]), type='l', xlab='Number of true H1s', ylab='Type 1/2 errors',col='black' )
r.lines(r.unlist(sortedKeys), r.unlist(onlyMaxType2Errors), col='black', lty='dotted' )
r.lines(r.unlist(sortedKeys), r.unlist(seqMcType1Errors), col='red' )
r.lines(r.unlist(sortedKeys), r.unlist(seqMcType2Errors), col='red', lty='dotted' )
r.lines(r.unlist(sortedKeys), r.unlist(mcFdrType1Errors), col='green' )
r.lines(r.unlist(sortedKeys), r.unlist(mcFdrType2Errors), col='green', lty='dotted' )
rpy1.legend('topleft',['BasicMcType1','SeqMcType1','McFdrType1','BasicMcType2','SeqMcType2','McFdrType2'],col=['black','red','green','black','red','green'],lty=[1,1,1,2,2,2])
#r('dev.off()')
classificationErrorPlotStaticFile.closeRFigure()
print classificationErrorPlotStaticFile.getLink('View Type 1/2 error plot') + ' as function of number of true H1.', '<br>'
#Classification errors
onlyMaxAccuracy = [ sum(errors)*1.0/totalNumTests for errors in zip(onlyMaxType1Errors, onlyMaxType2Errors)]
seqMcAccuracy = [ sum(errors)*1.0/totalNumTests for errors in zip(seqMcType1Errors, seqMcType2Errors)]
mcFdrAccuracy = [ sum(errors)*1.0/totalNumTests for errors in zip(mcFdrType1Errors, mcFdrType2Errors)]
accuracyPlotStaticFile = GalaxyRunSpecificFile(['accuracy.png'],galaxyFn)
accuracyPlotStaticFile.openRFigure()
yMax = 0.2 #just set ad hoc here..
#r.png(filename=plotFn, height=600, width=800, units='px', pointsize=12, res=72)
r.plot(r.unlist(sortedKeys), r.unlist(onlyMaxAccuracy), ylim=r.unlist([0,yMax]), type='l', xlab='Number of true H1s', ylab='Accuracy',col='black' )
r.lines(r.unlist(sortedKeys), r.unlist(seqMcAccuracy), col='red' )
r.lines(r.unlist(sortedKeys), r.unlist(mcFdrAccuracy), col='green' )
rpy1.legend('topleft',['BasicMc','SeqMc','McFdr','NumFromH1'],col=['black','red','green'],lty=[1,1,1])
#r('dev.off()')
accuracyPlotStaticFile.closeRFigure()
print accuracyPlotStaticFile.getLink('View accuracy plot') + ' as function of number of true H1.', '<br>'
#False positive rates
onlyMaxFpr= [ float(fp)/pos if pos!=0 else 0 for fp,pos in zip(onlyMaxType1Errors, onlyMaxNumRejected)]
seqMcFpr= [ float(fp)/pos if pos!=0 else 0 for fp,pos in zip(seqMcType1Errors, seqMcNumRejected)]
mcFdrFpr= [ float(fp)/pos if pos!=0 else 0 for fp,pos in zip(mcFdrType1Errors, mcFdrNumRejected)]
fprPlotStaticFile = GalaxyRunSpecificFile(['fpr.png'],galaxyFn)
fprPlotStaticFile.plotRLines(sortedKeys, [onlyMaxFpr, seqMcFpr, mcFdrFpr], legend=['BasicMc','SeqMc','McFdr'])
print fprPlotStaticFile.getLink('View FPR plot') + ' as function of number of true H1.', '<br>'
def executeSelfFeature(cls, genome, tracks, track_names, clusterMethod, extra_option, feature, distanceType, kmeans_alg, galaxyFn, regSpec, binSpec):
from gold.application.RSetup import r
#regSpec, binSpec = 'bed', '/usit/invitro/data/galaxy/galaxy-dist-hg-dev/./database/files/017/dataset_17084.dat'
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]]))
print>>jobFile, '<br><br>To run:<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)
r.assign('bin_names',[str(bin) for binIndex, bin in enumerate(sorted(list(userBinSource)))])
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(True)
r.pdf(figurepath)
r('d <- dist(f_matrix, method=distanceType)')
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('feature_matrix')
r.assign('extra_option',extra_option)
r('hr <- hclust(d, method=extra_option, members=NULL)')
r('plot(hr, ylab="Distance", hang=-1)')
r('dev.off()')
print>>jobFile, figure.getLink('clustering results figure<br>')
heatmap = GalaxyRunSpecificFile(['heatmap_figure.pdf'], galaxyFn)
heatmap_path = heatmap.getDiskPath(True)
r.pdf(heatmap_path)
r('heatmap(f_matrix, col=cm.colors(256), 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('heatmap figure <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('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
kmeans_output = open(textFilePath,'w')
clusterSizes = r('hr$size') #size of every cluster
withinSS = r('hr$withinss')
clusters = r('hr$cluster')
for index1 in range(extra_option) : #extra_option actually the number of clusters
#trackInCluster = [k for k,val in clusters.items() if val == index1]
trackInCluster = [k+1 for k,val in enumerate(clusters) if val == index1+1] #IS THIS CORRECT, I.E. SAME AS ABOVE??
print>>kmeans_output, 'Cluster %i(%s objects) : ' % (index1+1, str(clusterSizes[index1]))
for name in trackInCluster :
print>>kmeans_output, name, '(This result may be a bit shaky afters some changes in rpy access)'
print>>kmeans_output, 'Sum of square error for this cluster is : '+str(withinSS[index1])+'\n'
kmeans_output.close()
print>>jobFile, textFile.getLink('Detailed result of kmeans clustering <br>')
cls.print_data(f_matrix, jobFile)
'''
def executeReferenceTrack(cls, genome, tracks, track_names, clusterMethod, extra_option, distanceType, kmeans_alg, galaxyFn, regSpec, binSpec, numreferencetracks=None, refTracks=None, refFeatures=None, yesNo=None, howMany=None, upFlank=None, downFlank=None):
from gold.application.RSetup import r
jobFile = open(galaxyFn, 'w')
print>>jobFile, 'PARAMS: ', dict(zip('genome, tracks, track_names, clusterMethod, extra_option, distanceType, kmeans_alg, regSpec, binSpec'.split(','), [repr(v)+'<br>'for v in [genome, tracks, track_names, clusterMethod, extra_option, distanceType, kmeans_alg, regSpec, binSpec]]))
print>>jobFile, '<br><br>To run:<br>', '$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] == None else [refFeatures[i]]
if yesNo[i] == "Yes" 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 type(track) == str :
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 = 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)')
#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('feature_matrix')
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')
r.assign('extra_option',extra_option)
r('hr <- hclust(d, method=extra_option, members=NULL)')
r('plot(hr, ylab="Distance", hang=-1)')
r('dev.off()')
print>>jobFile, figure.getLink('clustering results figure<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
kmeans_output = open(textFilePath,'w')
clusterSizes = r('hr$size') #size of every cluster
withinSS = r('hr$withinss')
clusters = array(r('hr$cluster')) #convert to array in order to handle the index more easily
track_names = 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.png'], galaxyFn)
heatmap_path = heatmap.getDiskPath(True)
r.png(heatmap_path, width=800, height=700)
r('heatmap(f_matrix, col=cm.colors(256), Colv=NA, scale="none", xlab="", ylab="", margins=c(10,10))')#Features cluster tracks
r('dev.off()')
print>>jobFile, heatmap.getLink('heatmap figure <br>')
cls.print_data(f_matrix, jobFile)
else :
print 'Have to specify a set of refTracks'
def execute(cls, choices, galaxyFn=None, username=""):
from quick.application.GalaxyInterface import GalaxyInterface
fileformat = choices[6]
outputFile = open(galaxyFn, "w")
if fileformat == "html":
print GalaxyInterface.getHtmlBeginForRuns(galaxyFn)
print GalaxyInterface.getHtmlForToggles(withRunDescription=False)
t = calendar.timegm(time.gmtime())
htmlfile = GalaxyRunSpecificFile(["fet", str(t)], galaxyFn)
genome = choices[0]
track1 = choices[1].split(":")
track2 = choices[2].split(":")
tn1 = ExternalTrackManager.getPreProcessedTrackFromGalaxyTN(genome, track1)
tn2 = ExternalTrackManager.getPreProcessedTrackFromGalaxyTN(genome, track2)
windowSize = int(choices[3])
windowStep = int(choices[4])
percentile = float(choices[5])
# results = {}
# TODO: why this?
# tr = Track(tn1)
# tr.addFormatReq(TrackFormatReq(dense=False, allowOverlaps=True))
outputFile.write("#seqid\tstart\tscore\tstddev\n")
if fileformat == "html":
text = "#seqid\tstart\tscore\tstddev\n"
print "chrs:", str(GenomeInfo.getChrList(genome))
reg = "*"
bins = "*"
analysisDef = "Dummy: dummy name ([wStep=%g] [wSize=%g] [percentile=%g])-> FisherExactScoreStat" % (
windowStep,
windowSize,
percentile,
)
userBinSource = GalaxyInterface._getUserBinSource(reg, bins, genome)
result = GalaxyInterface.runManual([tn1, tn2], analysisDef, reg, bins, genome, galaxyFn=galaxyFn)
for key in result.getAllRegionKeys():
chrom = str(key).split(":")[0]
r = result[key]
if "Result" not in r.keys():
print "skipping chr:", chrom, r
continue
r = r["Result"]
scores = r[0]
stddev = r[1]
for i in range(len(scores)):
if scores[i] != 0:
pos = i * windowStep
# if choices[5] == "html":
# print "%s\t%s\t%s\t%s\n" % (str(chrom), pos, str(scores[i]), str(stddev[i]))
if fileformat == "tabular":
outputFile.write("%s\t%s\t%s\t%s\n" % (str(chrom), pos, str(scores[i]), str(stddev[i])))
else:
text += "%s\t%s\t%s\t%s\n" % (str(chrom), pos, str(scores[i]), str(stddev[i]))
if fileformat == "html":
htmlfile.writeTextToFile(text)
print htmlfile.getLink("Result file")
print GalaxyInterface.getHtmlEndForRuns()
outputFile.close()
def execute(choices, galaxyFn=None, username=''):
'''Is called when execute-button is pushed by web-user.
Should print output as HTML to standard out, which will be directed to a results page in Galaxy history.
If getOutputFormat is anything else than HTML, the output should be written to the file with path galaxyFn.
If needed, StaticFile can be used to get a path where additional files can be put (e.g. generated image files).
choices is a list of selections made by web-user in each options box.
'''
import subprocess
import os
from quick.util.StaticFile import GalaxyRunSpecificFile
from config.Config import HB_SOURCE_CODE_BASE_DIR
from quick.application.ExternalTrackManager import ExternalTrackManager
tempInStaticFile = GalaxyRunSpecificFile(['tempIn.txt'], galaxyFn)
outStaticFile = GalaxyRunSpecificFile(['tempOut.fasta'], galaxyFn)
#print os.getcwd()
inFn = ExternalTrackManager.extractFnFromGalaxyTN( choices[0].split(':') )
#print inFn
tempOutFn = outStaticFile.getDiskPath(True)
#print tempOutFn
os.chdir(HB_SOURCE_CODE_BASE_DIR + '/third_party/nonpython')
#print outStaticFile.getLink('output')
markovOrder = int(choices[1])
seqs = []
for line in open(inFn):
if line.startswith('>'):
seqs.append( [line[1:].strip(),[]] )
else:
seqs[-1][1].append(line.strip())
for seq in seqs:
seq[1] = ''.join(seq[1])
pureSequence = ''.join( [seq[1] for seq in seqs])
totalSeqLen = len(pureSequence)
#pureSequence = ''.join([line.replace('\n','') for line in open(inFn) if not line.startswith('>')])
tempInStaticFile.writeTextToFile(pureSequence)
numSamples = int(choices[2])
if numSamples>1:
zipOutStatic = GalaxyRunSpecificFile(['randomFastas.zip'], galaxyFn)
zipOut = zipfile.ZipFile(zipOutStatic.getDiskPath(True),'w')
for iteration in range(numSamples):
if numSamples>1:
fastaOutStatic = GalaxyRunSpecificFile(['random','s%s.fa'%iteration], galaxyFn)
fastaOutFn = fastaOutStatic.getDiskPath(True)
else:
fastaOutFn = galaxyFn
#fastaOutStatic = GalaxyRunSpecificFile(['random%s'%iteration], galaxyFn)
#subprocess.call('javac',shell=True)
#subprocess.call('javac',shell=False)
#subprocess.call('javac MarkovModel.java',shell=True)
subprocess.call('java MarkovModel %s %s %s >%s' % (tempInStaticFile.getDiskPath(), markovOrder, totalSeqLen, tempOutFn), shell=True )
#subprocess.call('javac third_party/nonpython/MarkovModel.java')
#subprocess.call('java third_party/nonpython/MarkovModel.java')
pureMarkovSequence = open(tempOutFn).readline().strip()
pmsIndex = 0
fastaOutF = open(fastaOutFn,'w')
for seq in seqs:
fastaOutF.write('>'+seq[0]+os.linesep)
nextPmsIndex = pmsIndex+len(seq[1])
#seq.append(pureMarkovSequence[pmsIndex:nextPmsIndex])
fastaOutF.write( pureMarkovSequence[pmsIndex:nextPmsIndex] + os.linesep)
pmsIndex = nextPmsIndex
fastaOutF.close()
assert pmsIndex == totalSeqLen == len(pureMarkovSequence), (pmsIndex, totalSeqLen , len(pureMarkovSequence))
if numSamples>1:
#print 'Adding %s to archive' % fastaOutFn.split('/')[-1]
zipOut.write(fastaOutFn, fastaOutFn.split('/')[-1])
if numSamples>1:
zipOut.close()
print zipOutStatic.getLink('Zipped random sequences')
def findTFsTargetingGenes(cls, genome, tfSource, ensembleGeneIdList,upFlankSize, downFlankSize, geneSource, galaxyFn):
#galaxyFn = '/usit/insilico/web/lookalike/galaxy_dist-20090924-dev/database/files/003/dataset_3347.dat'
#print 'overriding galaxyFN!: ', galaxyFn
uniqueWebPath = getUniqueWebPath(extractIdFromGalaxyFn(galaxyFn))
assert genome in ['mm9','hg18'] #other genomes not supported. TF id links do not specify genome for pre-selection of analysis
#if tfSource == 'UCSC tfbs conserved':
# tfTrackName = ['Gene regulation','TFBS','UCSC prediction track']
#else:
# raise
tfTrackNameMappings = TfInfo.getTfTrackNameMappings(genome)
tfTrackName = tfTrackNameMappings[tfSource]
#Get gene track
#targetGeneRegsTempFn = uniqueWebPath + os.sep + 'geneRegs.bed'
#geneRegsTrackName = GenomeInfo.getStdGeneRegsTn(genome)
#geneRegsFn = getOrigFn(genome, geneRegsTrackName, '.category.bed')
#GalaxyInterface.getGeneTrackFromGeneList(genome, geneRegsTrackName, ensembleGeneIdList, targetGeneRegsTempFn )
if not (upFlankSize == downFlankSize == 0):
unflankedGeneRegsTempFn = uniqueWebPath + os.sep + '_geneRegs.bed'
flankedGeneRegsTempFn = uniqueWebPath + os.sep + 'flankedGeneRegs.bed'
geneRegsTrackName = GenomeInfo.getStdGeneRegsTn(genome)
#geneRegsFn = getOrigFn(genome, geneRegsTrackName, '.category.bed')
GalaxyInterface.getGeneTrackFromGeneList(genome, geneRegsTrackName, ensembleGeneIdList, unflankedGeneRegsTempFn )
GalaxyInterface.expandBedSegments(unflankedGeneRegsTempFn, flankedGeneRegsTempFn, genome, upFlankSize, downFlankSize)
#flankedGeneRegsExternalTN = ['external'] +galaxyId + [flankedGeneRegsTempFn]
regSpec, binSpec = 'file', flankedGeneRegsTempFn
else:
regSpec, binSpec = '__genes__', ','.join(ensembleGeneIdList)
res = cls._runCategoryPointCount(genome, regSpec, binSpec, tfTrackName)
#trackName1 = tfTrackName
#
#analysisDef = 'Category point count: Number of elements each category of track1 (with overlaps)'+\
# '[tf1:=SegmentToStartPointFormatConverter:]'+\
# '-> FreqByCatStat'
##assert len(ensembleGeneIdList)==1
##geneId = ensembleGeneIdList[0]
#
#print '<div class="debug">'
#userBinSource, fullRunArgs = GalaxyInterface._prepareRun(trackName1, None, analysisDef, regSpec, binSpec, genome)
#res = AnalysisDefJob(analysisDef, trackName1, None, userBinSource, **fullRunArgs).run()
#
#print res
##GalaxyInterface._viewResults([res], galaxyFn)
#print '</div>'
tfs = res.getResDictKeys()
genesPlural = 's' if len(ensembleGeneIdList)>1 else ''
tfsPlural = 's' if len(tfs)!=1 else ''
print '<p>There are %i TF%s targeting your gene%s of interest (%s), using "%s" as source of TF occurrences.</p>' % (len(tfs), tfsPlural, genesPlural, ','.join(ensembleGeneIdList), tfSource)
expansionStr = ' flanked' if not (upFlankSize == downFlankSize == 0) else ''
idHtmlFileNamer = GalaxyRunSpecificFile(['allTfIds.html'],galaxyFn)
idHtmlFileNamer.writeTextToFile('<br>'.join(['<a href=%s/hyper?dbkey=%s&track1=%s&track2=>%s</a>'%(URL_PREFIX, genome, quote(':'.join(tfTrackName+[tf])), tf) for tf in tfs]))
#idHtmlFileNamer.writeTextToFile('<br>'.join(['<a href=/hbdev/hyper?track1=%s&track2=>%s</a>'%( ':'.join(tfTrackName+[tf]), tf) for tf in tfs]))
print '<p>', idHtmlFileNamer.getLink('Inspect html file'), ' of all TF IDs occurring 1 or more times within your%s gene region%s of interest, with each TF ID linking to analysis with this TF pre-selected.</p>' % (expansionStr, genesPlural)
idFileNamer = GalaxyRunSpecificFile(['allTfIds.txt'],galaxyFn)
idFileNamer.writeTextToFile(os.linesep.join(tfs) + os.linesep)
print '<p>', idFileNamer.getLink('Inspect text file'), ' listing all TF IDs occurring 1 or more times within your%s gene region%s of interest.</p>' % (expansionStr, genesPlural)
extractedTfbsFileNamer = GalaxyRunSpecificFile(['tfbsInGeneRegions.bed'],galaxyFn)
GalaxyInterface.extractTrackManyBins(genome, tfTrackName, regSpec, binSpec, True, 'bed', False, False, extractedTfbsFileNamer.getDiskPath())
print '<p>', extractedTfbsFileNamer.getLink('Inspect bed-file'), 'of all TF binding sites occurring within your%s gene region%s of interest.</p>' % (expansionStr, genesPlural)
#idFile = idFileNamer.getFile()
#idFile.write(', '.join([str(bin.val) for bin in targetBins if res[bin][resDictKey]>0]) + os.sep)
#idFile.close()
#print idFileNamer.getLink('Text file'), ' of TF IDs'
#GalaxyInterface.run(tfTrackName, tcGeneRegsExternalTN, analysisDef, regSpec, binSpec, genome, galaxyFn)
#GalaxyInterface.run(':'.join(tfTrackName), ':'.join(tcGeneRegsExternalTN), analysisDef, regSpec, binSpec, genome, galaxyFn)
def execute(cls, choices, galaxyFn=None, username=''):
from quick.application.GalaxyInterface import GalaxyInterface
fileformat = choices[9];
outputFile = open(galaxyFn, "w")
if fileformat == "html":
print GalaxyInterface.getHtmlBeginForRuns(galaxyFn)
print GalaxyInterface.getHtmlForToggles(withRunDescription=False)
t = calendar.timegm(time.gmtime())
htmlfile = GalaxyRunSpecificFile(["css", str(t)], galaxyFn);
genome = choices[0]
track1 = choices[1].split(":")
track2 = choices[2].split(":")
tn1 = ExternalTrackManager.getPreProcessedTrackFromGalaxyTN(genome, track1)
tn2 = ExternalTrackManager.getPreProcessedTrackFromGalaxyTN(genome, track2)
compare = choices[3] != "Count individual SNP-differences in window"
if choices[4] == "Classical MDS":
mds = 0;
elif choices[4] == "SMACOF":
mds = 1;
else:
mds = 2;
windowSize = int(choices[5])
windowStep = int(choices[6])
mcTreshold = int(choices[7])
mcRuns = int(choices[8])
outputFile.write("#seqid\tstart\tscore\tp\n")
if fileformat == "html":
text = "#seqid\tstart\tscore\tp\n";
print "chrs:"+str(GenomeInfo.getChrList(genome))
reg = "*"
bins = "*"
analysisDef = "Dummy: dummy name ([wStep=%g] [wSize=%s] [func=%s] [mds=%s] [mcT=%s] [mcR=%s])-> CategoryClusterSeparationStat" % (windowStep, windowSize, compare, mds, mcTreshold, mcRuns)
userBinSource = GalaxyInterface._getUserBinSource(reg, bins, genome)
result = GalaxyInterface.runManual([tn1, tn2], analysisDef, reg, bins, genome, galaxyFn=galaxyFn)
for key in result.getAllRegionKeys():
chrom = str(key).split(":")[0];
r = result[key];
if 'Result' not in r.keys():
print "skipping chr:", chrom, r;
continue;
r = r['Result'];
scores = r[0];
stddev = r[1];
for i in range(len(scores)):
if scores[i] != 0:
pos = i*windowStep;
if fileformat == "tabular":
outputFile.write("%s\t%s\t%s\t%s\n" % (str(chrom), pos, str(scores[i]), str(stddev[i])))
else:
text += "%s\t%s\t%s\t%s\n" % (str(chrom), pos, str(scores[i]), str(stddev[i]));
if fileformat == "html":
htmlfile.writeTextToFile(text);
print htmlfile.getLink("Result file");
print GalaxyInterface.getHtmlEndForRuns()
outputFile.close();
def execute(choices, galaxyFn=None, username=''):
'''Is called when execute-button is pushed by web-user.
Should print output as HTML to standard out, which will be directed to a results page in Galaxy history. If getOutputFormat is anything else than HTML, the output should be written to the file with path galaxyFn.gtr
If needed, StaticFile can be used to get a path where additional files can be put (e.g. generated image files).
choices is a list of selections made by web-user in each options box.
'''
print 'temporarily overriding tool, running McFdr2 simulation..'
from test.sandbox.aux.McFdr2 import analyzeSampleNumAccuracy
for numSamples in [100,1000,10000]:
print ''
print 'numSamples %s: ' % numSamples,
for i in range(3):
print analyzeSampleNumAccuracy(numSamples),
return
from gold.application.RSetup import r
from numpy import array,minimum
pVal,minNumSamples,maxNumSamples,chunkSize,numTests = [float(x) for x in choices[:-1]]
print 'pVal:%.2f, minNumSamples:%i, maxNumSamples:%i, chunkSize:%i, numTests:%i' % (pVal,minNumSamples,maxNumSamples,chunkSize,numTests)
assert (maxNumSamples-minNumSamples)%chunkSize == 0
assert numTests == 1 #More not yet supported. Should in McFdr be something like the min-max, i.e. the minimum across iterations of the maximum p-value across tests..
pValEstimation = choices[-1]
assert pValEstimation in ['Davison','ML']
if pValEstimation=='Davison':
pFunc = lambda k,n:1.0*(k+1)/(n+1)
else:
pFunc = lambda k,n:1.0*(k)/n
numRepl = 10**4
stdAtMin = [pFunc(k,minNumSamples) for k in r.rbinom(numRepl,minNumSamples,pVal)]
stdAtMax = [pFunc(k,maxNumSamples) for k in r.rbinom(numRepl,maxNumSamples,pVal)]
mcFdrBestPVals = array([1.0]*numRepl)
mcFdrSamples = minNumSamples #array([minNumSamples]*numRepl)
mcFdrExtremes = array(r.rbinom(numRepl,minNumSamples,pVal))
while mcFdrSamples<maxNumSamples:
tempMcFdrPVals = pFunc(mcFdrExtremes,mcFdrSamples)
mcFdrBestPVals = minimum(mcFdrBestPVals,tempMcFdrPVals)
mcFdrSamples += chunkSize
mcFdrExtremes += array(r.rbinom(numRepl,chunkSize,pVal))
tempMcFdrPVals = pFunc(mcFdrExtremes,mcFdrSamples)
mcFdrBestPVals = minimum(mcFdrBestPVals,tempMcFdrPVals)
assert mcFdrSamples == maxNumSamples
print 'Mean values<br>'
print 'AtMin:%.7f, AtMax:%.7f, McFdr:%.7f' % tuple([array(x).mean() for x in [stdAtMin, stdAtMax, mcFdrBestPVals]])
breaks = [pVal*2*x/100.0 for x in range(0,101)] +[1.0]
histRes = r.hist(stdAtMin,breaks=breaks,plot=False)
xVals = histRes['mids']
yValsStdAtMin = histRes['density']
histRes = r.hist(stdAtMax,breaks=breaks,plot=False)
assert xVals == histRes['mids']
yValsStdAtMax = histRes['density']
histRes = r.hist(mcFdrBestPVals,breaks=breaks,plot=False)
assert xVals == histRes['mids']
yValsMcFdr = histRes['density']
staticFile = GalaxyRunSpecificFile(['pDistr.png'],galaxyFn)
staticFile.openRFigure()
staticFile.plotRLines(xVals, [yValsStdAtMin, yValsStdAtMax, yValsMcFdr],alsoOpenAndClose=False,xlab='p-value',ylab='density',xlim=[0,2*pVal])
r.abline(v=pVal,lty='dotted',col='yellow')
staticFile.closeRFigure()
print staticFile.getLink('View estimated pval distribution')
