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 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 (cls, e.g. generated image files). choices is a list
of selections made by web-user in each options box.
Mandatory unless isRedirectTool() returns True.
"""
choices_gsuite = choices.chooseTrackFiles
choices_randType = choices.randType
choices_randAlg = choices.randAlg
choices_numberOfTimesToRandomize = choices.numberOfTimesToRandomize
assert choices_gsuite is not None
genome = choices.genome
assert genome is not None
analysisBins = UserBinMixin.getUserBinSource(choices)
gsuite = getGSuiteFromGalaxyTN(choices_gsuite)
for i, gsTrack in enumerate(gsuite.allTracks()):
assert gsTrack.trackName is not None, "gsuite track %s has track name None" % gsTrack
ts = getFlatTracksTS(genome, choices_gsuite)
cls.run_on_extracted_variables(ts, analysisBins, choices_numberOfTimesToRandomize, choices_randAlg,
choices_randType, galaxyFn, genome)
def execute(cls, choices, galaxyFn=None, username=''):
# DebugUtil.insertBreakPoint()
cls._setDebugModeIfSelected(choices)
choices_queryTrack = choices.queryTrack
choices_gsuite = choices.gsuite
genome = choices.genome
# queryTrackNameAsList = ExternalTrackManager.getPreProcessedTrackFromGalaxyTN(genome, choices.queryTrack,
# printErrors=False,
# printProgress=False)
# gsuite = getGSuiteFromGalaxyTN(choices.gsuite)
analysisBins = UserBinMixin.getUserBinSource(choices)
import quick.gsuite.GuiBasedTsFactory as factory
queryTS = factory.getSingleTrackTS(genome, choices_queryTrack)
refTS = factory.getFlatTracksTS(genome, choices_gsuite)
ts = TrackStructureV2()
realTS = TrackStructureV2()
realTS["query"] = queryTS
realTS["reference"] = refTS
randQueryTS = queryTS
randTvProvider = cls.createTrackViewProvider(choices, refTS, analysisBins, genome)
localAnalysis = randTvProvider.supportsLocalAnalysis()
randRefTS = getRandomizedVersionOfTs(refTS, randTvProvider)
hypothesisKeyList = [sts.metadata["title"] for sts in randRefTS.values()]
for hypothesisKey in hypothesisKeyList:
realTS = TrackStructureV2()
realTS["query"] = queryTS
realTS["reference"] = refTS[hypothesisKey]
randTS = TrackStructureV2()
randTS["query"] = randQueryTS
randTS["reference"] = randRefTS[hypothesisKey]
hypothesisTS = TrackStructureV2()
hypothesisTS["real"] = realTS
hypothesisTS["rand"] = randTS
ts[hypothesisKey] = hypothesisTS
analysisSpec = cls._prepareAnalysisWithHypothesisTests(choices, localAnalysis)
if DebugConfig.USE_PROFILING:
from gold.util.Profiler import Profiler
profiler = Profiler()
resDict = {}
profiler.run('resDict[0] = doAnalysis(analysisSpec, analysisBins, ts)', globals(), locals())
res = resDict[0]
result = res.getGlobalResult()['Result']
profiler.printStats()
if DebugConfig.USE_CALLGRAPH and galaxyFn:
profiler.printLinkToCallGraph(['profile_AnalysisDefJob'], galaxyFn)
else:
result = doAnalysis(analysisSpec, analysisBins, ts).getGlobalResult()["Result"]
for trackTitle, res in result.iteritems():
print '{}: {}<br>'.format(trackTitle, repr(res.getResult()))
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 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 numpy
numpy.seterr(all='raise')
cls._setDebugModeIfSelected(choices)
genome = choices.genome
analysisQuestion = choices.analysisName
similaryStatClassName = choices.similarityFunc if choices.similarityFunc else GSuiteStatUtils.T5_RATIO_OF_OBSERVED_TO_EXPECTED_OVERLAP
summaryFunc = choices.summaryFunc if choices.summaryFunc else 'average'
reverse = 'Yes' if choices.reversed else 'No'
gsuite = getGSuiteFromGalaxyTN(choices.gsuite)
analysisBins = UserBinMixin.getUserBinSource(choices)
# tracks = [Track(x.trackName, trackTitle=x.title) for x in gsuite.allTracks()]
trackTitles = CommonConstants.TRACK_TITLES_SEPARATOR.join(
[quote(x.title, safe='') for x in gsuite.allTracks()])
import quick.gsuite.GuiBasedTsFactory as factory
ts = factory.getFlatTracksTS(genome=genome,
guiSelectedGSuite=choices.gsuite)
additionalResultsDict = OrderedDict()
additionalAttributesDict = OrderedDict()
if analysisQuestion in [cls.Q1, cls.Q2, cls.Q3]:
additionalAttributesDict = cls.getSelectedAttributesForEachTrackDict(
choices.additionalAttributes, gsuite)
# additional analysis
stats = [CountStat, CountElementStat]
additionalResultsDict = runMultipleSingleValStatsOnTracks(
ts, stats, analysisBins)
if analysisQuestion == cls.Q1:
analysisSpec = AnalysisSpec(MultitrackSummarizedInteractionV2Stat)
analysisSpec.addParameter('multitrackSummaryFunc', 'raw')
analysisSpec.addParameter(
'pairwiseStatistic', GSuiteStatUtils.
PAIRWISE_STAT_LABEL_TO_CLASS_MAPPING[similaryStatClassName])
analysisSpec.addParameter(
'summaryFunc',
GSuiteStatUtils.SUMMARY_FUNCTIONS_MAPPER[summaryFunc])
analysisSpec.addParameter('reverse', reverse)
analysisSpec.addParameter('ascending', 'No')
analysisSpec.addParameter('trackTitles', trackTitles)
results = dictifyTSResult(
doAnalysis(analysisSpec, analysisBins,
ts).getGlobalResult()['Result'])
gsPerTrackResultsModel = GSuitePerTrackResultModel(
results,
['Similarity to rest of tracks in suite (%s)' % summaryFunc],
additionalResultsDict=additionalResultsDict,
additionalAttributesDict=additionalAttributesDict)
if choices.leadAttribute and choices.leadAttribute != GSuiteConstants.TITLE_COL:
columnTitles, decoratedResultsDict = \
gsPerTrackResultsModel.generateColumnTitlesAndResultsDict(choices.leadAttribute)
else:
columnTitles, decoratedResultsDict = \
gsPerTrackResultsModel.generateColumnTitlesAndResultsDict()
core = HtmlCore()
core.begin()
core.divBegin(divId='results-page')
core.divBegin(divClass='results-section')
core.header(analysisQuestion)
topTrackTitle = results.keys()[0]
core.paragraph('''
The track "%s" is the most representative track of the GSuite with %s %s similarity to the rest of the tracks
as measured by "%s" track similarity measure.
''' % (topTrackTitle, results[topTrackTitle], summaryFunc,
similaryStatClassName))
addTableWithTabularAndGsuiteImportButtons(
core,
choices,
galaxyFn,
cls.Q1_SHORT,
decoratedResultsDict,
columnTitles,
gsuite=gsuite,
results=results,
gsuiteAppendAttrs=['similarity_score'],
sortable=True)
# plot
columnInd = 0
if choices.leadAttribute and choices.leadAttribute != GSuiteConstants.TITLE_COL:
columnInd = 1
res = GSuiteTracksCoincidingWithQueryTrackTool.drawPlot(
results,
additionalResultsDict,
'Similarity to rest of tracks in suite (%s)' % summaryFunc,
columnInd=columnInd)
core.line(res)
core.divEnd()
core.divEnd()
core.end()
# elif analysisQuestion == cls.Q2:
# analysisSpec = AnalysisSpec(GSuiteRepresentativenessOfTracksRankingsWrapperStat)
# analysisSpec.addParameter('pairwiseStatistic', GSuiteStatUtils.PAIRWISE_STAT_LABEL_TO_CLASS_MAPPING[similaryStatClassName])
# analysisSpec.addParameter('summaryFunc', GSuiteStatUtils.SUMMARY_FUNCTIONS_MAPPER[summaryFunc])
# analysisSpec.addParameter('reverse', reverse)
# analysisSpec.addParameter('ascending', 'Yes')
# analysisSpec.addParameter('trackTitles', trackTitles)
# results = doAnalysis(analysisSpec, analysisBins, tracks).getGlobalResult()
#
# gsPerTrackResultsModel = GSuitePerTrackResultModel(
# results, ['Similarity to rest of tracks in suite (%s)' % summaryFunc],
# additionalResultsDict=additionalResultsDict,
# additionalAttributesDict=additionalAttributesDict)
# if choices.leadAttribute and choices.leadAttribute != GSuiteConstants.TITLE_COL:
# columnTitles, decoratedResultsDict = \
# gsPerTrackResultsModel.generateColumnTitlesAndResultsDict(choices.leadAttribute)
# else:
# columnTitles, decoratedResultsDict = \
# gsPerTrackResultsModel.generateColumnTitlesAndResultsDict()
#
# core = HtmlCore()
# core.begin()
# core.divBegin(divId='results-page')
# core.divBegin(divClass='results-section')
# core.header(analysisQuestion)
# topTrackTitle = results.keys()[0]
# core.paragraph('''
# The track "%s" is the most atypical track of the GSuite with %s %s similarity to the rest of the tracks
# as measured by the "%s" track similarity measure.
# ''' % (topTrackTitle, strWithNatLangFormatting(results[topTrackTitle]), summaryFunc, similaryStatClassName))
# # core.tableFromDictionary(results, columnNames=['Track title', 'Similarity to rest of tracks in suite (' + summaryFunc+')'], sortable=False)
#
# from quick.util import CommonFunctions
# rawDataURIList = CommonFunctions.getHyperlinksForRawTableData(
# dataDict=decoratedResultsDict, colNames=columnTitles,
# tableId="resultsTable", galaxyFn=galaxyFn)
# core.tableFromDictionary(decoratedResultsDict, columnNames=columnTitles, sortable=True,
# tableId='resultsTable', addInstruction=True,
# addRawDataSelectBox=True, rawDataURIList=rawDataURIList)
# # core.tableFromDictionary(decoratedResultsDict, columnNames=columnTitles, sortable=True, tableId='resultsTable')
#
# columnInd = 0
# if choices.leadAttribute and choices.leadAttribute != GSuiteConstants.TITLE_COL:
# columnInd = 1
# res = GSuiteTracksCoincidingWithQueryTrackTool.drawPlot(
# results, additionalResultsDict,
# 'Similarity to rest of tracks in suite (%s)' % summaryFunc,
# columnInd=columnInd)
# core.line(res)
# core.divEnd()
# core.divEnd()
# core.end()
#
# if choices.addResults == 'Yes':
# GSuiteStatUtils.addResultsToInputGSuite(
# gsuite, results, ['Similarity_score'],
# cls.extraGalaxyFn[GSUITE_EXPANDED_WITH_RESULT_COLUMNS_FILENAME])
elif analysisQuestion == cls.Q3:
q2TS = TrackStructureV2()
randTvProvider = cls.createTrackViewProvider(
choices, ts, analysisBins, genome)
localAnalysis = randTvProvider.supportsLocalAnalysis()
tsRand = getRandomizedVersionOfTs(ts, randTvProvider)
for key in ts.keys():
realTS = TrackStructureV2()
realTS['query'] = ts[key]
realTS['reference'] = FlatTracksTS(
dict([(refKey, refSTS)
for refKey, refSTS in ts.iteritems()
if refKey != key]))
randTS = TrackStructureV2()
randTS['query'] = tsRand[key]
randTS['reference'] = FlatTracksTS([
(refKey, refSTS) for refKey, refSTS in tsRand.iteritems()
if refKey != key
])
hypothesisTS = TrackStructureV2()
hypothesisTS['real'] = realTS
hypothesisTS['rand'] = randTS
q2TS[key] = hypothesisTS
mcfdrDepth = choices.mcfdrDepth if choices.mcfdrDepth else \
AnalysisDefHandler(REPLACE_TEMPLATES['$MCFDRv5$']).getOptionsAsText().values()[0][0]
analysisDefString = REPLACE_TEMPLATES[
'$MCFDRv5$'] + ' -> ' + ' -> MultipleRandomizationManagerStat'
analysisSpec = AnalysisDefHandler(analysisDefString)
analysisSpec.setChoice('MCFDR sampling depth', mcfdrDepth)
analysisSpec.addParameter(
'rawStatistic',
SummarizedInteractionWithOtherTracksV2Stat.__name__)
analysisSpec.addParameter(
'pairwiseStatistic', GSuiteStatUtils.
PAIRWISE_STAT_LABEL_TO_CLASS_MAPPING[similaryStatClassName])
analysisSpec.addParameter(
'summaryFunc',
GSuiteStatUtils.SUMMARY_FUNCTIONS_MAPPER[summaryFunc])
analysisSpec.addParameter('multitrackSummaryFunc', 'raw')
analysisSpec.addParameter('tail', 'right-tail')
analysisSpec.addParameter('evaluatorFunc',
'evaluatePvalueAndNullDistribution')
analysisSpec.addParameter('runLocalAnalysis',
'Yes' if localAnalysis else 'No')
results = doAnalysis(analysisSpec, analysisBins,
q2TS).getGlobalResult()
resultsTuples = []
for key, res in results['Result'].iteritems():
curRes = res.getResult()
curPval = curRes['P-value']
curTestStat = curRes[
'TSMC_' +
SummarizedInteractionWithOtherTracksV2Stat.__name__]
resultsTuples.append((key, [curTestStat, curPval]))
resultsDict = OrderedDict(
sorted(resultsTuples,
key=lambda t: (-t[1][1], t[1][0]),
reverse=True))
core = HtmlCore()
gsPerTrackResultsModel = GSuitePerTrackResultModel(
resultsDict, [
'Similarity to rest of tracks in suite (%s)' % summaryFunc,
'P-value'
],
additionalResultsDict=additionalResultsDict,
additionalAttributesDict=additionalAttributesDict)
if choices.leadAttribute and choices.leadAttribute != GSuiteConstants.TITLE_COL:
columnTitles, decoratedResultsDict = \
gsPerTrackResultsModel.generateColumnTitlesAndResultsDict(choices.leadAttribute)
else:
columnTitles, decoratedResultsDict = \
gsPerTrackResultsModel.generateColumnTitlesAndResultsDict()
core.begin()
core.divBegin(divId='results-page')
core.divBegin(divClass='results-section')
core.header(analysisQuestion)
topTrackTitle = resultsDict.keys()[0]
core.paragraph('''
The track "%s" has the lowest P-value of %s corresponding to %s %s similarity to the rest of the tracks
as measured by "%s" track similarity measure.
''' % (topTrackTitle,
strWithNatLangFormatting(resultsDict[topTrackTitle][1]),
strWithNatLangFormatting(resultsDict[topTrackTitle][0]),
summaryFunc, similaryStatClassName))
# core.tableFromDictionary(results, columnNames=['Track title', 'Similarity to rest of tracks in suite (' + summaryFunc+')', 'P-value'], sortable=False)
addTableWithTabularAndGsuiteImportButtons(
core,
choices,
galaxyFn,
cls.Q3_SHORT,
decoratedResultsDict,
columnTitles,
gsuite=gsuite,
results=resultsDict,
gsuiteAppendAttrs=['similarity_score', 'p_value'],
sortable=True)
core.divEnd()
core.divEnd()
core.end()
else: # Q4
# mcfdrDepth = choices.mcfdrDepth if choices.mcfdrDepth else \
# AnalysisDefHandler(REPLACE_TEMPLATES['$MCFDR$']).getOptionsAsText().values()[0][0]
# analysisDefString = REPLACE_TEMPLATES['$MCFDRv3$'] + ' -> CollectionSimilarityHypothesisWrapperStat'
# analysisSpec = AnalysisDefHandler(analysisDefString)
# analysisSpec.setChoice('MCFDR sampling depth', mcfdrDepth)
# analysisSpec.addParameter('assumptions', 'PermutedSegsAndIntersegsTrack')
# analysisSpec.addParameter('rawStatistic', 'MultitrackSummarizedInteractionV2Stat')
# analysisSpec.addParameter('pairwiseStatistic', GSuiteStatUtils.PAIRWISE_STAT_LABEL_TO_CLASS_MAPPING[similaryStatClassName])
# analysisSpec.addParameter('summaryFunc', GSuiteStatUtils.SUMMARY_FUNCTIONS_MAPPER[summaryFunc])
# analysisSpec.addParameter('multitrackSummaryFunc', 'avg') # should it be a choice?
# analysisSpec.addParameter('tail', 'right-tail')
# results = doAnalysis(analysisSpec, analysisBins, tracks).getGlobalResult()
mcfdrDepth = choices.mcfdrDepth if choices.mcfdrDepth else \
AnalysisDefHandler(REPLACE_TEMPLATES['$MCFDRv4$']).getOptionsAsText().values()[0][0]
analysisDefString = REPLACE_TEMPLATES[
'$MCFDRv4$'] + ' -> RandomizationManagerV3Stat'
analysisSpec = AnalysisDefHandler(analysisDefString)
analysisSpec.setChoice('MCFDR sampling depth', mcfdrDepth)
analysisSpec.addParameter('rawStatistic',
'MultitrackSummarizedInteractionV2Stat')
analysisSpec.addParameter(
'pairwiseStatistic', GSuiteStatUtils.
PAIRWISE_STAT_LABEL_TO_CLASS_MAPPING[similaryStatClassName]
) # needed for call of non randomized stat for assertion
analysisSpec.addParameter(
'summaryFunc',
GSuiteStatUtils.SUMMARY_FUNCTIONS_MAPPER[summaryFunc])
analysisSpec.addParameter('multitrackSummaryFunc',
'avg') # should it be a choice?
analysisSpec.addParameter('tail', 'right-tail')
analysisSpec.addParameter(
'tvProviderClass',
getClassName(
createTrackViewProvider(choices.randType,
choices.randAlg)))
results = doAnalysis(analysisSpec, analysisBins,
ts).getGlobalResult()
pval = results['P-value']
observed = results['TSMC_MultitrackSummarizedInteractionV2Stat']
significanceLevel = 'strong' if pval < 0.01 else (
'weak' if pval < 0.05 else 'no')
core = HtmlCore()
core.begin()
core.divBegin(divId='results-page')
core.divBegin(divClass='results-section')
core.header(analysisQuestion)
core.paragraph('''
The tracks in the suite show %s significance in their collective similarity
(average similarity of a track to the rest) of %s
and corresponding p-value of %s,
as measured by "%s" track similarity measure.
''' % (significanceLevel, strWithNatLangFormatting(observed),
strWithNatLangFormatting(pval), similaryStatClassName))
core.divEnd()
core.divEnd()
core.end()
print str(core)