def run(self, test, signLevel, statsResults, trials, bootstrapRep,
progress):
tableData = []
index = 0
for row in statsResults:
feature = row[0]
seq1 = row[1]
seq2 = row[2]
parentSeq1 = row[3]
parentSeq2 = row[4]
p1 = float(seq1) / parentSeq1
p2 = float(seq2) / parentSeq2
powerList = []
powerListLess5 = []
powerListGreater5 = []
for trial in xrange(0, trials):
if progress != '':
index += 1
progress.setValue(index)
progress.setLabelText(feature + ' - Trial = ' + str(trial))
power = 0
processedReplicates = 0
for dummy in xrange(0, bootstrapRep):
c1 = 0
c2 = 0
for dummy in xrange(0, parentSeq1):
rnd = random.random()
if rnd <= p1:
c1 += 1
for dummy in xrange(0, parentSeq2):
rnd = random.random()
if rnd <= p2:
c2 += 1
if c1 == 0 and c2 == 0:
# This is a special case that many hypothesis test will not handle correctly
# so we just ignore it. This will have little effect on the calculated power
# of a test.
continue
processedReplicates += 1
pValueOneSided, pValueTwoSided = test.hypothesisTest(
c1, c2, parentSeq1, parentSeq2)
if pValueTwoSided < signLevel:
power += 1
if processedReplicates > 0:
if min([seq1, seq2]) <= 5:
powerListLess5.append(
float(power) / processedReplicates)
else:
powerListGreater5.append(
float(power) / processedReplicates)
powerList.append(float(power) / processedReplicates)
row = []
row.append(feature)
row.append(seq1)
row.append(seq2)
row.append(parentSeq1)
row.append(parentSeq2)
row.append(float(seq1) / parentSeq1)
row.append(float(seq2) / parentSeq2)
row.append(mean(powerList))
row.append(stdDev(powerList))
if math.isnan(mean(powerListLess5)):
row.append('')
else:
row.append(mean(powerListLess5))
if math.isnan(stdDev(powerListLess5)):
row.append('')
else:
row.append(stdDev(powerListLess5))
if math.isnan(mean(powerListGreater5)):
row.append('')
else:
row.append(mean(powerListGreater5))
if math.isnan(stdDev(powerListGreater5)):
row.append('')
else:
row.append(stdDev(powerListGreater5))
tableData.append(row)
return tableData
def run(self, statTest, effectSizeMeasure, profile, progress=None):
self.results.data = []
self.results.test = statTest.name
self.results.profile = profile
if progress == 'Verbose':
print ' Processing feature:'
index = 0
for feature in profile.getFeatures():
if progress == 'Verbose':
print ' ' + feature
elif progress != None:
if progress.wasCanceled():
self.results.data = []
return
index += 1
progress.setValue(index)
seqCount = profile.getActiveFeatureCounts(feature)
parentCount = profile.getActiveParentCounts(feature)
data = profile.getActiveFeatureProportions(feature)
pValue, note = statTest.hypothesisTest(data)
effectSize = effectSizeMeasure.run(data)
row = [feature, float(pValue), float(pValue), effectSize, note]
for i in xrange(0, len(seqCount)):
propGroup = []
for j in xrange(0, len(seqCount[i])):
sc = seqCount[i][j]
pc = parentCount[i][j]
if pc > 0:
propGroup.append(sc * 100.0 / pc)
else:
propGroup.append(0.0)
meanGroup = mean(propGroup)
row.append(meanGroup)
row.append(stdDev(propGroup, meanGroup))
for i in xrange(0, len(seqCount)):
for j in xrange(0, len(seqCount[i])):
sc = seqCount[i][j]
pc = parentCount[i][j]
row.append(sc)
row.append(pc)
if pc > 0:
row.append(sc * 100.0 / pc)
else:
row.append(0.0)
self.results.data.append(row)
headingsSampleStats = []
for i in xrange(0, len(profile.activeSamplesInGroups)):
for sampleName in profile.activeSamplesInGroups[i]:
headingsSampleStats.append(sampleName)
headingsSampleStats.append(sampleName + ': parent seq. count')
headingsSampleStats.append(sampleName + ': rel. freq. (%)')
self.results.createTableHeadings(profile.activeGroupNames,
headingsSampleStats)
if len(self.results.data) >= 1:
# sort results according to p-values
self.results.data = TableHelper.SortTable(
self.results.data, [self.results.dataHeadings['pValues']])
if progress != None and progress != 'Verbose':
index += 1
progress.setValue(index)
def plot(self, profile, statsResults):
if len(profile.profileDict) <= 0:
self.emptyAxis()
return
if len(profile.profileDict) > 10000:
QtGui.QApplication.instance().setOverrideCursor(
QtGui.QCursor(QtCore.Qt.ArrowCursor))
reply = QtGui.QMessageBox.question(
self, 'Continue?', 'Profile contains ' +
str(len(profile.profileDict)) + ' features. ' +
'It may take several seconds to generate this plot. Exploring the data at a higher hierarchy level is recommended. '
+ 'Do you wish to continue?', QtGui.QMessageBox.Yes,
QtGui.QMessageBox.No)
QtGui.QApplication.instance().restoreOverrideCursor()
if reply == QtGui.QMessageBox.No:
self.emptyAxis()
return
# *** Colour of plot elements
axesColour = str(self.preferences['Axes colour'].name())
group1Colour = str(
self.preferences['Group colours'][profile.groupName1].name())
group2Colour = str(
self.preferences['Group colours'][profile.groupName2].name())
# *** Set sample names
self.groupName1 = profile.groupName1
self.groupName2 = profile.groupName2
# *** Create lists for each quantity of interest and calculate spread of data
groupData1, groupData2 = profile.getFeatureProportionsAll()
features = profile.getFeatures()
field1 = []
field2 = []
xSpread = []
ySpread = []
for i in xrange(0, len(groupData1)):
mean1 = mean(groupData1[i])
mean2 = mean(groupData2[i])
field1.append(mean1)
field2.append(mean2)
if self.spreadMethod == 'standard deviation':
xSpread.append([
max(mean1 - stdDev(groupData1[i], mean1), 0),
min(mean1 + stdDev(groupData1[i], mean1), 100)
])
ySpread.append([
max(mean2 - stdDev(groupData2[i], mean2), 0),
min(mean2 + stdDev(groupData2[i], mean2), 100)
])
elif self.spreadMethod == '2 * standard deviation':
xSpread.append([
max(mean1 - 2 * stdDev(groupData1[i], mean1), 0),
min(mean1 + 2 * stdDev(groupData1[i], mean1), 100)
])
ySpread.append([
max(mean2 - 2 * stdDev(groupData2[i], mean2), 0),
min(mean2 + 2 * stdDev(groupData2[i], mean2), 100)
])
elif self.spreadMethod == '25th and 75th percentile':
spread1 = mquantiles(groupData1[i], prob=[0.25, 0.75])
spread2 = mquantiles(groupData2[i], prob=[0.25, 0.75])
xSpread.append([max(spread1[0], 0), min(spread1[1], 100)])
ySpread.append([max(spread2[0], 0), min(spread2[1], 100)])
elif self.spreadMethod == '9th and 91st percentile':
spread1 = mquantiles(groupData1[i], prob=[0.09, 0.91])
spread2 = mquantiles(groupData2[i], prob=[0.09, 0.91])
xSpread.append([max(spread1[0], 0), min(spread1[1], 100)])
ySpread.append([max(spread2[0], 0), min(spread2[1], 100)])
elif self.spreadMethod == '2nd and 98th percentile':
spread1 = mquantiles(groupData1[i], prob=[0.02, 0.98])
spread2 = mquantiles(groupData2[i], prob=[0.02, 0.98])
xSpread.append([max(spread1[0], 0), min(spread1[1], 100)])
ySpread.append([max(spread2[0], 0), min(spread2[1], 100)])
elif self.spreadMethod == 'minimum and maximum':
xSpread.append([max(groupData1[i]), min(groupData1[i])])
ySpread.append([max(groupData2[i]), min(groupData2[i])])
# *** Set figure size
self.fig.clear()
self.fig.set_size_inches(self.figWidth, self.figHeight)
if self.bShowHistograms:
histogramSizeX = self.histogramSize / self.figWidth
histogramSizeY = self.histogramSize / self.figHeight
else:
histogramSizeX = 0.0
histogramSizeY = 0.0
padding = 0.1 # inches
xOffsetFigSpace = (0.4 + padding) / self.figWidth
yOffsetFigSpace = (0.3 + padding) / self.figHeight
axesScatter = self.fig.add_axes([
xOffsetFigSpace, yOffsetFigSpace, 1.0 - xOffsetFigSpace -
histogramSizeX - (2 * padding) / self.figWidth, 1.0 -
yOffsetFigSpace - histogramSizeY - (2 * padding) / self.figHeight
])
if self.bShowHistograms:
axesTopHistogram = self.fig.add_axes([
xOffsetFigSpace,
1.0 - histogramSizeY - padding / self.figHeight,
1.0 - xOffsetFigSpace - histogramSizeX -
(2 * padding) / self.figWidth, histogramSizeY
])
axesRightHistogram = self.fig.add_axes([
1.0 - histogramSizeX - padding / self.figWidth,
yOffsetFigSpace, histogramSizeX, 1.0 - yOffsetFigSpace -
histogramSizeY - (2 * padding) / self.figHeight
])
# *** Handle mouse events
tooltips = []
for i in xrange(0, len(field1)):
tooltip = features[i] + '\n\n'
tooltip += (self.groupName1 +
' mean proportion: %.3f' % field1[i]) + '\n'
tooltip += (self.groupName2 +
' mean proportion: %.3f' % field2[i]) + '\n\n'
tooltip += 'Difference between mean proportions (%): ' + (
'%.3f' % (field1[i] - field2[i])) + '\n'
if field2[i] != 0:
tooltip += 'Ratio of mean proportions: %.3f' % (field1[i] /
field2[i])
else:
tooltip += 'Ratio of mean proportions: undefined'
if statsResults.profile != None:
pValue = statsResults.getFeatureStatisticAsStr(
features[i], 'pValues')
pValueCorrected = statsResults.getFeatureStatisticAsStr(
features[i], 'pValuesCorrected')
tooltip += '\n\n'
tooltip += 'p-value: ' + pValue + '\n'
tooltip += 'Corrected p-value: ' + pValueCorrected
tooltips.append(tooltip)
self.plotEventHandler = PlotEventHandler(field1, field2, tooltips)
self.mouseEventCallback(self.plotEventHandler)
# *** Calculate R^2 value
slope, intercept, r_value, p_value, std_err = linregress(
field1, field2)
# *** Plot data
# set visual properties of all points
colours = []
highlightedField1 = []
highlightedField2 = []
highlighColours = []
for i in xrange(0, len(field1)):
if field1[i] > field2[i]:
colours.append(group1Colour)
else:
colours.append(group2Colour)
if features[i] in self.preferences['Highlighted group features']:
highlightedField1.append(field1[i])
highlightedField2.append(field2[i])
highlighColours.append(colours[i])
# scatter plot
axesScatter.scatter(field1,
field2,
c=colours,
s=self.markerSize,
zorder=5)
if len(highlightedField1) > 0:
axesScatter.scatter(highlightedField1,
highlightedField2,
c=highlighColours,
s=self.markerSize,
edgecolors='red',
linewidth=2,
zorder=10)
# plot CIs
if self.spreadMethod != 'None':
xlist = []
ylist = []
for i in xrange(0, len(field1)):
# horizontal CIs
xlist.append(xSpread[i][0])
xlist.append(xSpread[i][1])
xlist.append(None)
ylist.append(field2[i])
ylist.append(field2[i])
ylist.append(None)
# vertical CIs
xlist.append(field1[i])
xlist.append(field1[i])
xlist.append(None)
ylist.append(ySpread[i][0])
ylist.append(ySpread[i][1])
ylist.append(None)
axesScatter.plot(xlist,
ylist,
'-',
color='gray',
antialiased=False)
# plot y=x line
maxProportion = max(max(field1), max(field2)) * 1.05
axesScatter.plot([0, maxProportion], [0, maxProportion],
color=axesColour,
linestyle='dashed',
marker='',
zorder=1)
axesScatter.set_xlabel(self.groupName1 + ' (%)')
axesScatter.set_ylabel(self.groupName2 + ' (%)')
if self.bShowR2:
axesScatter.text(0.02,
0.98,
r'R$^2$ = ' + ('%0.3f' % r_value**2),
horizontalalignment='left',
verticalalignment='top',
transform=axesScatter.transAxes)
axesScatter.set_xlim(0, maxProportion)
axesScatter.set_ylim(0, maxProportion)
# *** Prettify scatter plot
for line in axesScatter.yaxis.get_ticklines():
line.set_color(axesColour)
for line in axesScatter.xaxis.get_ticklines():
line.set_color(axesColour)
for loc, spine in axesScatter.spines.iteritems():
spine.set_color(axesColour)
# plot histograms
if not self.bShowHistograms:
for a in axesScatter.yaxis.majorTicks:
a.tick1On = True
a.tick2On = False
for a in axesScatter.xaxis.majorTicks:
a.tick1On = True
a.tick2On = False
for line in axesScatter.yaxis.get_ticklines():
line.set_color(axesColour)
for line in axesScatter.xaxis.get_ticklines():
line.set_color(axesColour)
for loc, spine in axesScatter.spines.iteritems():
if loc in ['right', 'top']:
spine.set_color('none')
else:
spine.set_color(axesColour)
else: # show histograms
# plot top histogram
axesTopHistogram.xaxis.set_major_formatter(NullFormatter())
pdf, bins, patches = axesTopHistogram.hist(field1,
bins=self.numBins,
facecolor=group1Colour)
axesTopHistogram.set_xlim(axesScatter.get_xlim())
axesTopHistogram.set_yticks([0, max(pdf)])
axesTopHistogram.set_ylim([0, max(pdf) * 1.05])
# plot right histogram
axesRightHistogram.yaxis.set_major_formatter(NullFormatter())
pdf, bins, patches = axesRightHistogram.hist(
field2,
bins=self.numBins,
orientation='horizontal',
facecolor=group2Colour)
axesRightHistogram.set_ylim(axesScatter.get_ylim())
axesRightHistogram.set_xticks([0, max(pdf)])
axesRightHistogram.set_xlim([0, max(pdf) * 1.05])
# *** Prettify histogram plot
for a in axesTopHistogram.yaxis.majorTicks:
a.tick1On = True
a.tick2On = False
for a in axesTopHistogram.xaxis.majorTicks:
a.tick1On = True
a.tick2On = False
for line in axesTopHistogram.yaxis.get_ticklines():
line.set_color(axesColour)
for line in axesTopHistogram.xaxis.get_ticklines():
line.set_color(axesColour)
for loc, spine in axesTopHistogram.spines.iteritems():
if loc in ['right', 'top']:
spine.set_color('none')
else:
spine.set_color(axesColour)
for a in axesRightHistogram.yaxis.majorTicks:
a.tick1On = True
a.tick2On = False
for a in axesRightHistogram.xaxis.majorTicks:
a.tick1On = True
a.tick2On = False
for line in axesRightHistogram.yaxis.get_ticklines():
line.set_color(axesColour)
for line in axesRightHistogram.xaxis.get_ticklines():
line.set_color(axesColour)
for loc, spine in axesRightHistogram.spines.iteritems():
if loc in ['right', 'top']:
spine.set_color('none')
else:
spine.set_color(axesColour)
self.updateGeometry()
self.draw()
def run(self, statTest, effectSizeMeasure, profile, progress = None): self.results.data = [] self.results.test = statTest.name self.results.profile = profile if progress == 'Verbose': print ' Processing feature:' index = 0 for feature in profile.getFeatures(): if progress == 'Verbose': print ' ' + feature elif progress != None: if progress.wasCanceled(): self.results.data = [] return index += 1 progress.setValue(index) seqCount = profile.getActiveFeatureCounts(feature) parentCount = profile.getActiveParentCounts(feature) data = profile.getActiveFeatureProportions(feature) pValue, note = statTest.hypothesisTest(data) effectSize = effectSizeMeasure.run(data) row = [feature, float(pValue), float(pValue), effectSize, note] for i in xrange(0, len(seqCount)): propGroup = [] for j in xrange(0, len(seqCount[i])): propGroup.append(seqCount[i][j] * 100.0 / parentCount[i][j]) meanGroup = mean(propGroup) row.append(meanGroup) row.append(stdDev(propGroup, meanGroup)) for i in xrange(0, len(seqCount)): for j in xrange(0, len(seqCount[i])): row.append(seqCount[i][j]) row.append(parentCount[i][j]) row.append(seqCount[i][j] * 100.0 / parentCount[i][j]) self.results.data.append(row) headingsSampleStats = [] for i in xrange(0, len(profile.activeSamplesInGroups)): for sampleName in profile.activeSamplesInGroups[i]: headingsSampleStats.append(sampleName) headingsSampleStats.append(sampleName + ': parent seq. count') headingsSampleStats.append(sampleName + ': rel. freq. (%)') self.results.createTableHeadings(profile.activeGroupNames, headingsSampleStats) if len(self.results.data) >= 1: # sort results according to p-values self.results.data = TableHelper.SortTable(self.results.data, [self.results.dataHeadings['pValues']]) if progress != None and progress != 'Verbose': index += 1 progress.setValue(index)
results = [
coverageListDP, coverageListDPCC, coverageListNW, coverageListWoolf,
coverageListGart, coverageListRP
]
lengths = [
ciLengthDP, ciLengthDPCC, ciLengthNW, ciLengthWoolf, ciLengthGart,
ciLengthRP
]
methodNames = [
'DP: Asymptotic', 'DP: Asymptotic-CC', 'Newcombe-Wilson', 'Woolf', 'Gart',
'RP: Asympototic'
]
for i in xrange(0, len(results)):
coverageMeanStr = '%.2f' % mean(results[i])
coverageSdStr = '%.2f' % stdDev(results[i])
coverageMinStr = '%.2f' % min(results[i])
coverageMaxStr = '%.2f' % max(results[i])
lengthMeanStr = '%.2f' % mean(lengths[i])
lengthSdStr = '%.2f' % stdDev(lengths[i])
fout.write(methodNames[i] + '\n')
fout.write(coverageMeanStr + '+/-' + coverageSdStr + '[' + coverageMinStr +
';' + coverageMaxStr + ']\n')
fout.write(lengthMeanStr + '+/-' + lengthSdStr + '\n')
fout.write('\n')
fout.close()
#=======================================================================
def run(self, confIntervMethod, coverage, tables, trials, bootstrapRep,
progress):
tableData = []
index = 0
for row in tables:
feature = row[0]
seq1 = row[1]
seq2 = row[2]
parentSeq1 = row[3]
parentSeq2 = row[4]
lowerCI, upperCI, obsEffectSize = confIntervMethod.run(
seq1, seq2, parentSeq1, parentSeq2, coverage)
p1 = float(seq1) / parentSeq1
p2 = float(seq2) / parentSeq2
coverageList = []
coverageListLess5 = []
coverageListGreater5 = []
for trial in xrange(0, trials):
if progress != '':
index += 1
progress.setValue(index)
progress.setLabelText(feature + ' - Trial = ' + str(trial))
containedRep = 0
for dummy in xrange(0, bootstrapRep):
c1 = binomial(parentSeq1, p1)
c2 = binomial(parentSeq2, p2)
lowerCI, upperCI, effectSize = confIntervMethod.run(
c1, c2, parentSeq1, parentSeq2, coverage)
if obsEffectSize >= lowerCI and obsEffectSize <= upperCI:
containedRep += 1
if min([seq1, seq2]) <= 5:
coverageListLess5.append(
float(containedRep) / bootstrapRep)
else:
coverageListGreater5.append(
float(containedRep) / bootstrapRep)
coverageList.append(float(containedRep) / bootstrapRep)
row = []
row.append(feature)
row.append(seq1)
row.append(seq2)
row.append(parentSeq1)
row.append(parentSeq2)
row.append(float(seq1) / parentSeq1)
row.append(float(seq2) / parentSeq2)
row.append(mean(coverageList))
row.append(stdDev(coverageList))
if math.isnan(mean(coverageListLess5)):
row.append('')
else:
row.append(mean(coverageListLess5))
if math.isnan(stdDev(coverageListLess5)):
row.append('')
else:
row.append(stdDev(coverageListLess5))
if math.isnan(mean(coverageListGreater5)):
row.append('')
else:
row.append(mean(coverageListGreater5))
if math.isnan(stdDev(coverageListGreater5)):
row.append('')
else:
row.append(stdDev(coverageListGreater5))
tableData.append(row)
return tableData
def run(self, statTest, testType, confIntervMethod, coverage, profile, progress = None): self.results.test = statTest.name self.results.testType = testType self.results.alpha = 1.0 - coverage self.results.confIntervMethod = confIntervMethod self.results.profile = profile if progress == 'Verbose': print ' Processing feature:' self.results.data = [] index = 0 # calculate statistics seqsGroup1 = [] seqsGroup2 = [] parentSeqsGroup1 = [] parentSeqsGroup2 = [] pValues = [] lowerCIs = [] upperCIs = [] effectSizes = [] notes = [] if statTest.bSingleFeatureInterface: # process features one at a time for feature in profile.getFeatures(): if progress == 'Verbose': print ' ' + feature elif progress != None: if progress.wasCanceled(): self.results.data = [] return index += 1 progress.setValue(index) # get statistics seqGroup1, seqGroup2 = profile.getFeatureCounts(feature) parentSeqGroup1, parentSeqGroup2= profile.getParentFeatureCounts(feature) results = statTest.run(seqGroup1, seqGroup2, parentSeqGroup1, parentSeqGroup2, confIntervMethod, coverage) pValueOneSided, pValueTwoSided, lowerCI, upperCI, effectSize, note = results if testType == 'One-sided': pValue = pValueOneSided elif testType == 'Two-sided': pValue = pValueTwoSided else: print 'Error: Unknown test type.' # record results seqsGroup1.append(seqGroup1) seqsGroup2.append(seqGroup2) parentSeqsGroup1.append(parentSeqGroup1) parentSeqsGroup2.append(parentSeqGroup2) pValues.append(pValue) lowerCIs.append(lowerCI) upperCIs.append(upperCI) effectSizes.append(effectSize) notes.append(note) if progress != None and progress != 'Verbose': index += 1 progress.setValue(index) else: # process all features at once seqsGroup1, seqsGroup2 = profile.getFeatureCountsAll() parentSeqsGroup1, parentSeqsGroup2= profile.getParentFeatureCountsAll() pValuesOneSided, pValuesTwoSided, lowerCIs, upperCIs, effectSizes, notes = statTest.runAll(seqsGroup1, seqsGroup2, parentSeqsGroup1, parentSeqsGroup2, confIntervMethod, coverage, progress) if progress == 'Verbose': print ' Processing all features...' elif progress != None and progress.wasCanceled(): self.results.data = [] return if testType == 'One-sided': pValues = pValuesOneSided elif testType == 'Two-sided': pValues = pValuesTwoSided else: print 'Error: Unknown test type.' # record statistics features = profile.getFeatures() for i in xrange(0, len(features)): propGroup1 = [] for j in xrange(0, len(seqsGroup1[i])): sg1 = seqsGroup1[i][j] psg1 = parentSeqsGroup1[i][j] if psg1 > 0: propGroup1.append( sg1 * 100.0 / psg1 ) else: propGroup1.append( 0.0 ) propGroup2 = [] for j in xrange(0, len(seqsGroup2[i])): sg2 = seqsGroup2[i][j] psg2 = parentSeqsGroup2[i][j] if psg2 > 0: propGroup2.append( sg2 * 100.0 / psg2 ) else: propGroup2.append( 0.0 ) meanGroup1 = mean(propGroup1) meanGroup2 = mean(propGroup2) row = [features[i], meanGroup1, stdDev(propGroup1, meanGroup1), meanGroup2, stdDev(propGroup2, meanGroup2), float(pValues[i]),float(pValues[i]),float(effectSizes[i]), float(lowerCIs[i]),float(upperCIs[i]), notes[i]] for j in xrange(0, len(seqsGroup1[i])): row.append(seqsGroup1[i][j]) row.append(parentSeqsGroup1[i][j]) if parentSeqsGroup1[i][j] > 0: row.append(seqsGroup1[i][j] * 100.0 / parentSeqsGroup1[i][j]) else: row.append(0.0) for j in xrange(0, len(seqsGroup2[i])): row.append(seqsGroup2[i][j]) row.append(parentSeqsGroup2[i][j]) if parentSeqsGroup2[i][j] > 0: row.append(seqsGroup2[i][j] * 100.0 / parentSeqsGroup2[i][j]) else: row.append(0.0) self.results.data.append(row) headingsSampleStats = [] for sampleName in (profile.samplesInGroup1 + profile.samplesInGroup2): headingsSampleStats.append(sampleName) headingsSampleStats.append(sampleName + ': parent seq. count') headingsSampleStats.append(sampleName + ': rel. freq. (%)') self.results.createTableHeadings(profile.groupName1, profile.groupName2, headingsSampleStats) # sort results according to p-values if len(self.results.data) >= 1: self.results.data = TableHelper.SortTable(self.results.data, [self.results.dataHeadings['pValues']])
#=======================================================================
def run(self, test, signLevel, statsResults, trials, bootstrapRep, progress):
tableData = []
index = 0
for row in statsResults:
feature = row[0]
seq1 = row[1]
seq2 = row[2]
parentSeq1 = row[3]
parentSeq2 = row[4]
p1 = float(seq1) / parentSeq1
p2 = float(seq2) / parentSeq2
powerList = []
powerListLess5 = []
powerListGreater5 = []
for trial in xrange(0, trials):
if progress != '':
index += 1
progress.setValue(index)
progress.setLabelText(feature + ' - Trial = ' + str(trial))
power = 0
processedReplicates = 0
for dummy in xrange(0, bootstrapRep):
c1 = 0
c2 = 0
for dummy in xrange(0, parentSeq1):
rnd = random.random()
if rnd <= p1:
c1 += 1
for dummy in xrange(0, parentSeq2):
rnd = random.random()
if rnd <= p2:
c2 += 1
if c1 == 0 and c2 == 0:
# This is a special case that many hypothesis test will not handle correctly
# so we just ignore it. This will have little effect on the calculated power
# of a test.
continue
processedReplicates += 1
pValueOneSided, pValueTwoSided = test.hypothesisTest(c1, c2, parentSeq1, parentSeq2)
if pValueTwoSided < signLevel:
power += 1
if processedReplicates > 0:
if min([seq1,seq2]) <= 5:
powerListLess5.append(float(power) / processedReplicates)
else:
powerListGreater5.append(float(power) / processedReplicates)
powerList.append(float(power) / processedReplicates)
row = []
row.append(feature)
row.append(seq1)
row.append(seq2)
row.append(parentSeq1)
row.append(parentSeq2)
row.append(float(seq1) / parentSeq1)
row.append(float(seq2) / parentSeq2)
row.append(mean(powerList))
row.append(stdDev(powerList))
if math.isnan(mean(powerListLess5)):
row.append('')
else:
row.append(mean(powerListLess5))
if math.isnan(stdDev(powerListLess5)):
row.append('')
else:
row.append(stdDev(powerListLess5))
if math.isnan(mean(powerListGreater5)):
row.append('')
else:
row.append(mean(powerListGreater5))
if math.isnan(stdDev(powerListGreater5)):
row.append('')
else:
row.append(stdDev(powerListGreater5))
tableData.append(row)
return tableData
#=======================================================================
containedRepRP += 1
ciLengthRP.append(upperCI - lowerCI)
coverageListDP.append(float(containedRepDP) / replicates)
coverageListDPCC.append(float(containedRepDPCC) / replicates)
coverageListNW.append(float(containedRepNW) / replicates)
coverageListWoolf.append(float(containedRepWoolf) / replicates)
coverageListGart.append(float(containedRepGart) / replicates)
coverageListRP.append(float(containedRepRP) / replicates)
results = [coverageListDP, coverageListDPCC, coverageListNW, coverageListWoolf, coverageListGart, coverageListRP]
lengths = [ciLengthDP,ciLengthDPCC,ciLengthNW,ciLengthWoolf,ciLengthGart,ciLengthRP]
methodNames = ['DP: Asymptotic', 'DP: Asymptotic-CC', 'Newcombe-Wilson', 'Woolf', 'Gart', 'RP: Asympototic']
for i in xrange(0, len(results)):
coverageMeanStr = '%.2f' % mean(results[i])
coverageSdStr = '%.2f' % stdDev(results[i])
coverageMinStr = '%.2f' % min(results[i])
coverageMaxStr = '%.2f' % max(results[i])
lengthMeanStr = '%.2f' % mean(lengths[i])
lengthSdStr = '%.2f' % stdDev(lengths[i])
fout.write(methodNames[i] + '\n')
fout.write(coverageMeanStr + '+/-' + coverageSdStr + '[' + coverageMinStr + ';' + coverageMaxStr + ']\n')
fout.write(lengthMeanStr + '+/-' + lengthSdStr+ '\n')
fout.write('\n')
fout.close()
def run(self,
statTest,
testType,
confIntervMethod,
coverage,
profile,
progress=None):
self.results.test = statTest.name
self.results.testType = testType
self.results.alpha = 1.0 - coverage
self.results.confIntervMethod = confIntervMethod
self.results.profile = profile
if progress == 'Verbose':
print ' Processing feature:'
self.results.data = []
index = 0
# calculate statistics
seqsGroup1 = []
seqsGroup2 = []
parentSeqsGroup1 = []
parentSeqsGroup2 = []
pValues = []
lowerCIs = []
upperCIs = []
effectSizes = []
notes = []
if statTest.bSingleFeatureInterface:
# process features one at a time
for feature in profile.getFeatures():
if progress == 'Verbose':
print ' ' + feature
elif progress != None:
if progress.wasCanceled():
self.results.data = []
return
index += 1
progress.setValue(index)
# get statistics
seqGroup1, seqGroup2 = profile.getFeatureCounts(feature)
parentSeqGroup1, parentSeqGroup2 = profile.getParentFeatureCounts(
feature)
results = statTest.run(seqGroup1, seqGroup2, parentSeqGroup1,
parentSeqGroup2, confIntervMethod,
coverage)
pValueOneSided, pValueTwoSided, lowerCI, upperCI, effectSize, note = results
if testType == 'One-sided':
pValue = pValueOneSided
elif testType == 'Two-sided':
pValue = pValueTwoSided
else:
print 'Error: Unknown test type.'
# record results
seqsGroup1.append(seqGroup1)
seqsGroup2.append(seqGroup2)
parentSeqsGroup1.append(parentSeqGroup1)
parentSeqsGroup2.append(parentSeqGroup2)
pValues.append(pValue)
lowerCIs.append(lowerCI)
upperCIs.append(upperCI)
effectSizes.append(effectSize)
notes.append(note)
if progress != None and progress != 'Verbose':
index += 1
progress.setValue(index)
else:
# process all features at once
seqsGroup1, seqsGroup2 = profile.getFeatureCountsAll()
parentSeqsGroup1, parentSeqsGroup2 = profile.getParentFeatureCountsAll(
)
pValuesOneSided, pValuesTwoSided, lowerCIs, upperCIs, effectSizes, notes = statTest.runAll(
seqsGroup1, seqsGroup2, parentSeqsGroup1, parentSeqsGroup2,
confIntervMethod, coverage, progress)
if progress == 'Verbose':
print ' Processing all features...'
elif progress != None and progress.wasCanceled():
self.results.data = []
return
if testType == 'One-sided':
pValues = pValuesOneSided
elif testType == 'Two-sided':
pValues = pValuesTwoSided
else:
print 'Error: Unknown test type.'
# record statistics
features = profile.getFeatures()
for i in xrange(0, len(features)):
propGroup1 = []
for j in xrange(0, len(seqsGroup1[i])):
sg1 = seqsGroup1[i][j]
psg1 = parentSeqsGroup1[i][j]
if psg1 > 0:
propGroup1.append(sg1 * 100.0 / psg1)
else:
propGroup1.append(0.0)
propGroup2 = []
for j in xrange(0, len(seqsGroup2[i])):
sg2 = seqsGroup2[i][j]
psg2 = parentSeqsGroup2[i][j]
if psg2 > 0:
propGroup2.append(sg2 * 100.0 / psg2)
else:
propGroup2.append(0.0)
meanGroup1 = mean(propGroup1)
meanGroup2 = mean(propGroup2)
row = [
features[i], meanGroup1,
stdDev(propGroup1, meanGroup1), meanGroup2,
stdDev(propGroup2, meanGroup2),
float(pValues[i]),
float(pValues[i]),
float(effectSizes[i]),
float(lowerCIs[i]),
float(upperCIs[i]), notes[i]
]
for j in xrange(0, len(seqsGroup1[i])):
row.append(seqsGroup1[i][j])
row.append(parentSeqsGroup1[i][j])
if parentSeqsGroup1[i][j] > 0:
row.append(seqsGroup1[i][j] * 100.0 /
parentSeqsGroup1[i][j])
else:
row.append(0.0)
for j in xrange(0, len(seqsGroup2[i])):
row.append(seqsGroup2[i][j])
row.append(parentSeqsGroup2[i][j])
if parentSeqsGroup2[i][j] > 0:
row.append(seqsGroup2[i][j] * 100.0 /
parentSeqsGroup2[i][j])
else:
row.append(0.0)
self.results.data.append(row)
headingsSampleStats = []
for sampleName in (profile.samplesInGroup1 + profile.samplesInGroup2):
headingsSampleStats.append(sampleName)
headingsSampleStats.append(sampleName + ': parent seq. count')
headingsSampleStats.append(sampleName + ': rel. freq. (%)')
self.results.createTableHeadings(profile.groupName1,
profile.groupName2,
headingsSampleStats)
# sort results according to p-values
if len(self.results.data) >= 1:
self.results.data = TableHelper.SortTable(
self.results.data, [self.results.dataHeadings['pValues']])
def run(self, confIntervMethod, coverage, tables, trials, bootstrapRep, progress):
tableData = []
index = 0
for row in tables:
feature = row[0]
seq1 = row[1]
seq2 = row[2]
parentSeq1 = row[3]
parentSeq2 = row[4]
lowerCI, upperCI, obsEffectSize = confIntervMethod.run(seq1, seq2, parentSeq1, parentSeq2, coverage)
p1 = float(seq1) / parentSeq1
p2 = float(seq2) / parentSeq2
coverageList = []
coverageListLess5 = []
coverageListGreater5 = []
for trial in xrange(0, trials):
if progress != '':
index += 1
progress.setValue(index)
progress.setLabelText(feature + ' - Trial = ' + str(trial))
containedRep = 0
for dummy in xrange(0, bootstrapRep):
c1 = binomial(parentSeq1, p1)
c2 = binomial(parentSeq2, p2)
lowerCI, upperCI, effectSize = confIntervMethod.run(c1, c2, parentSeq1, parentSeq2, coverage)
if obsEffectSize >= lowerCI and obsEffectSize <= upperCI:
containedRep += 1
if min([seq1,seq2]) <= 5:
coverageListLess5.append(float(containedRep) / bootstrapRep)
else:
coverageListGreater5.append(float(containedRep) / bootstrapRep)
coverageList.append(float(containedRep) / bootstrapRep)
row = []
row.append(feature)
row.append(seq1)
row.append(seq2)
row.append(parentSeq1)
row.append(parentSeq2)
row.append(float(seq1) / parentSeq1)
row.append(float(seq2) / parentSeq2)
row.append(mean(coverageList))
row.append(stdDev(coverageList))
if math.isnan(mean(coverageListLess5)):
row.append('')
else:
row.append(mean(coverageListLess5))
if math.isnan(stdDev(coverageListLess5)):
row.append('')
else:
row.append(stdDev(coverageListLess5))
if math.isnan(mean(coverageListGreater5)):
row.append('')
else:
row.append(mean(coverageListGreater5))
if math.isnan(stdDev(coverageListGreater5)):
row.append('')
else:
row.append(stdDev(coverageListGreater5))
tableData.append(row)
return tableData
#=======================================================================
