def getRunDescription(genome, trackNames, analysisDef, ubSource,
revEngBatchLine, urlForTrackAutoSelection, **kwArgs):
# genome = ubSource.genome
assert len(trackNames) == 3
core = HtmlCore()
analysis = Analysis(analysisDef, genome, trackNames[0], trackNames[1],
**kwArgs)
core.header('GENOME')
core.append(GenomeInfo(genome).mainInfo(printEmpty=False))
core.divider()
formatChoices = analysis.getFormatConverterChoicesAsText().items()
tr1FormatChoice, tr2FormatChoice = formatChoices if len(
formatChoices) == 2 else (None, None)
first = True
for tn,label,formatChoice in zip(trackNames, \
['TRACK 1', 'TRACK 2', 'INTENSITY TRACK'], \
[tr1FormatChoice, tr2FormatChoice, None]):
if tn in [None, []]:
continue
if not first:
core.divider()
core.header(label)
trackInfo = TrackInfo(genome, tn)
trackText = ''
if ExternalTrackManager.isHistoryTrack(tn):
assert len(
tn) >= 4, 'Length of external track name < 4: %s' % str(tn)
core.descriptionLine(
'Name',
ExternalTrackManager.extractNameFromHistoryTN(tn) +
' (from history)' + os.linesep)
else:
core.descriptionLine('Name', ':'.join(tn) + os.linesep)
core.append(trackInfo.mainInfo(printEmpty=False))
if formatChoice is not None:
core.descriptionLine('Treated as', formatChoice[1])
first = False
core.divider()
core.header('ANALYSIS')
core.paragraph(''.join(str(analysis).split(':')[1:]))
first = True
for label, choice in analysis.getInterfaceChoicesAsText().items():
if first:
core.divider()
core.header('OPTIONS')
core.descriptionLine(label, choice)
first = False
h0 = analysis.getH0()
if h0 is not None:
core.divider()
core.header('NULL HYPOTHESIS')
core.paragraph(h0)
h1 = analysis.getH1()
if h1 is not None:
core.divider()
core.header('ALTERNATIVE HYPOTHESIS')
core.paragraph(h1)
core.divider()
core.header('ANALYSIS REGIONS')
if hasattr(ubSource, 'description'):
core.paragraph(ubSource.description)
core.divider()
core.header('SOLUTION')
statClass = analysis.getStat()
#One alternative is to put getDescription in MagicStatFactory-hierarchy as class-method, and get real class behind partial-object.
#if isinstance(statClass, functools.partial):
#statClass = statClass.func
#core.paragraph( statClass.getDescription() )
#Chosen alternative is to Instantiate an object, which will automatically give object of real class..
#and then use the following two lines, which will get class in Statistic-hierarchy instead of MagicStatFactory-hierarchy ..
try:
reg = ubSource.__iter__().next()
except:
core.paragraph(
'Solution not relevant, as there are no specified analysis regions..'
)
else:
track1, track2 = analysis.getTracks()
if statClass is None:
core.paragraph(
'Solution not available, due to currently invalid analysis'
)
logMessage('Solution not available, with params: ' +
str([trackNames[0], trackNames[1], analysisDef]),
level=logging.WARN)
else:
statObj = statClass(reg, track1, track2)
statDescr = statObj.getDescription()
replPat = '<a href=' + os.sep.join(
[STATIC_REL_PATH, 'notes', 'stats', '']) + r'\1>note</a>'
statDescr = re.sub('<note>(.*)</note>', replPat, statDescr)
core.paragraph(statDescr)
core.divider()
core.header('TIME OF ANALYSIS')
core.paragraph('Analysis initiated at time: ' +
str(datetime.datetime.now()))
if urlForTrackAutoSelection not in [None, '']:
core.divider()
core.header('URL FOR TRACK AUTOSELECTION')
#urlOptions = '&'.join(['track1=' + quote(':'.join(trackName1)), 'track2=' + quote(':'.join(trackName2))])
#core.paragraph(URL_PREFIX + '/hyper?' + urlOptions)
core.styleInfoBegin(styleClass='break-word')
core.paragraph(urlForTrackAutoSelection)
core.styleInfoEnd()
if revEngBatchLine not in [None, '']:
core.divider()
core.header('CORRESPONDING BATCH COMMAND LINE')
#if any(ExternalTrackManager.isRedirectOrExternalTrack(tn) for tn in [trackName1, trackName2]):
#core.paragraph('Batch-run line not available with tracks from history')
#else:
core.styleInfoBegin(styleClass='break-word')
core.paragraph(revEngBatchLine)
core.styleInfoEnd()
core.divider()
core.header('REFERENCES')
core.paragraph(
'The HyperBrowser system is described in:<br>"Sandve et al., <a href="http://genomebiology.com/2010/11/12/R121/">The Genomic HyperBrowser: inferential genomics at the sequence level</a>, Genome Biol. 2010;11(12):R121'
)
from gold.statistic.RandomizationManagerStat import RandomizationManagerStat
if statClass is not None and RandomizationManagerStat.getMcSamplingScheme(
statClass.keywords) == 'MCFDR':
core.paragraph('The p-values of this analysis were computed using the MCFDR scheme for Monte Carlo based p-value computation'+\
', described in:<br>Sandve et al., <a href="http://bioinformatics.oxfordjournals.org/content/early/2011/10/13/bioinformatics.btr568.long">Sequential Monte Carlo multiple testing</a>, Bioinformatics 2011')
# description = \
#'''
#Run descriptions will be introduced in the next version of HB. <br>
#Below is an example run description, which is a static text unconnected to your choices. The purpose is to get feedback from you on what this should look like:<br>
#Track1 (refseg:genes): Unmarked points (converted from unmarked segments, taking midpoints)<br>
#Track2 (DNA melting:meltmap): Function<br>
#Bins: Chr1, divided into bins of 10 megabases<br>
#Question: Are track1-points occurring with different frequency inside track2-segment than outside?<br>
#Analysis:<br>
#The main result is a p-value resulting from a statistical test connected to the question.<br>
#The null-hypothesis assumes that the track1-points are randomly distributed according to a poisson-distribution, with the same number of points as in the original data. Track2-segment are assumed fixed as they are in the original data. This can be answered by a binomial test. The alternative hypothesis is then that the count of points inside segments has resulted from a different distribution of points, where the points are then either distributed more or less inside segments versus outside. See the note on this question in the user guide for further info.<br>
#'''
return str(core)
def getRunDescription(trackName1, trackName2, trackNameIntensity, analysisDef, ubSource, revEngBatchLine, \
urlForTrackAutoSelection, manualSeed, **kwArgs):
genome = ubSource.genome
core = HtmlCore()
analysis = Analysis(analysisDef, genome, trackName1, trackName2, **kwArgs)
core.header('GENOME')
core.append(GenomeInfo(genome).mainInfo(printEmpty=False))
core.divider()
formatChoices = analysis.getFormatConverterChoicesAsText().items()
tr1FormatChoice, tr2FormatChoice = formatChoices if len(formatChoices) == 2 else (None, None)
first = True
for tn,label,formatChoice in zip([trackName1,trackName2,trackNameIntensity], \
['TRACK 1','TRACK 2','INTENSITY TRACK'], \
[tr1FormatChoice,tr2FormatChoice,None]):
if tn in [None, []]:
continue
if not first:
core.divider()
core.header(label)
trackInfo = TrackInfo(genome, tn)
trackText = ''
if ExternalTrackManager.isHistoryTrack(tn):
assert len(tn)>=4, 'Length of external track name < 4: %s' % str(tn)
core.descriptionLine('Name', ExternalTrackManager.extractNameFromHistoryTN(tn) + ' (from history)' + os.linesep)
else:
core.descriptionLine('Name', ':'.join(tn) + os.linesep)
core.append(trackInfo.mainInfo(printEmpty=False))
if formatChoice is not None:
core.descriptionLine('Treated as', formatChoice[1])
first = False
core.divider()
core.header('ANALYSIS')
core.paragraph( ''.join(str(analysis).split(':')[1:]) )
first = True
for label,choice in analysis.getInterfaceChoicesAsText().items():
if first:
core.divider()
core.header('OPTIONS')
if manualSeed is not None and label == 'Random seed' and choice == 'Random':
choice = str(manualSeed)
core.descriptionLine(label, choice)
first = False
h0 = analysis.getH0()
if h0 is not None:
core.divider()
core.header('NULL HYPOTHESIS')
core.paragraph(h0)
h1 = analysis.getH1()
if h1 is not None:
core.divider()
core.header('ALTERNATIVE HYPOTHESIS')
core.paragraph(h1)
core.divider()
core.header('ANALYSIS REGIONS')
if hasattr(ubSource, 'description'):
core.paragraph(ubSource.description)
core.divider()
core.header('SOLUTION')
statClass = analysis.getStat()
#One alternative is to put getDescription in MagicStatFactory-hierarchy as class-method, and get real class behind partial-object.
#if isinstance(statClass, functools.partial):
#statClass = statClass.func
#core.paragraph( statClass.getDescription() )
#Chosen alternative is to Instantiate an object, which will automatically give object of real class..
#and then use the following two lines, which will get class in Statistic-hierarchy instead of MagicStatFactory-hierarchy ..
try:
reg = ubSource.__iter__().next()
except:
core.paragraph('Solution not relevant, as there are no specified analysis regions..')
else:
track1, track2 = analysis.getTracks()
if statClass is None:
core.paragraph('Solution not available, due to currently invalid analysis')
logMessage('Solution not available, with params: ' + str([trackName1, trackName2, analysisDef]), level=logging.WARN )
else:
statObj = statClass(reg,track1, track2)
statDescr = statObj.getDescription()
replPat = '<a href=' + os.sep.join([STATIC_REL_PATH,'notes','stats','']) + r'\1>note</a>'
statDescr = re.sub('<note>(.*)</note>', replPat, statDescr)
core.paragraph( statDescr )
core.divider()
core.header('TIME OF ANALYSIS')
core.paragraph('Analysis initiated at time: ' + str( datetime.datetime.now() ) )
if urlForTrackAutoSelection not in [None, '']:
core.divider()
core.header('URL FOR TRACK AUTOSELECTION')
#urlOptions = '&'.join(['track1=' + quote(':'.join(trackName1)), 'track2=' + quote(':'.join(trackName2))])
#core.paragraph(URL_PREFIX + '/hyper?' + urlOptions)
core.styleInfoBegin(styleClass='break-word')
core.paragraph(urlForTrackAutoSelection)
core.styleInfoEnd()
if revEngBatchLine not in [None, '']:
core.divider()
core.header('CORRESPONDING BATCH-RUN LINE')
#if any(ExternalTrackManager.isRedirectOrExternalTrack(tn) for tn in [trackName1, trackName2]):
#core.paragraph('Batch-run line not available with tracks from history')
#else:
core.styleInfoBegin(styleClass='break-word')
core.paragraph(revEngBatchLine)
core.styleInfoEnd()
core.divider()
core.header('REFERENCES')
core.paragraph('The HyperBrowser system is described in:<br>"Sandve et al., <a href="http://genomebiology.com/2010/11/12/R121/">The Genomic HyperBrowser: inferential genomics at the sequence level</a>, Genome Biol. 2010;11(12):R121')
from gold.statistic.RandomizationManagerStat import RandomizationManagerStat
if statClass is not None and RandomizationManagerStat.getMcSamplingScheme(statClass.keywords) == 'MCFDR':
core.paragraph('The p-values of this analysis were computed using the MCFDR scheme for Monte Carlo based p-value computation'+\
', described in:<br>Sandve et al., <a href="http://bioinformatics.oxfordjournals.org/content/early/2011/10/13/bioinformatics.btr568.long">Sequential Monte Carlo multiple testing</a>, Bioinformatics 2011')
# description = \
#'''
#Run descriptions will be introduced in the next version of HB. <br>
#Below is an example run description, which is a static text unconnected to your choices. The purpose is to get feedback from you on what this should look like:<br>
#Track1 (refseg:genes): Unmarked points (converted from unmarked segments, taking midpoints)<br>
#Track2 (DNA melting:meltmap): Function<br>
#Bins: Chr1, divided into bins of 10 megabases<br>
#Question: Are track1-points occurring with different frequency inside track2-segment than outside?<br>
#Analysis:<br>
#The main result is a p-value resulting from a statistical test connected to the question.<br>
#The null-hypothesis assumes that the track1-points are randomly distributed according to a poisson-distribution, with the same number of points as in the original data. Track2-segment are assumed fixed as they are in the original data. This can be answered by a binomial test. The alternative hypothesis is then that the count of points inside segments has resulted from a different distribution of points, where the points are then either distributed more or less inside segments versus outside. See the note on this question in the user guide for further info.<br>
#'''
return str(core)
