def run(self, minThreshold, maxThreshold, stepSize, minGenomes,
mostSpecificRanks):
img = IMG()
trustedGenomeIds = img.trustedGenomes()
fout = open('./data/markerSetSize.tsv', 'w')
fout.write('Lineage\t# genomes')
for threshold in arange(maxThreshold, minThreshold, -stepSize):
fout.write('\t' + str(threshold))
fout.write('\n')
lineages = img.lineagesSorted(mostSpecificRanks)
for lineage in lineages:
genomeIds = img.genomeIdsByTaxonomy(lineage)
genomeIds = list(genomeIds.intersection(trustedGenomeIds))
if len(genomeIds) < minGenomes:
continue
print('\nLineage ' + lineage + ' contains ' + str(len(genomeIds)) +
' genomes.')
fout.write(lineage + '\t' + str(len(genomeIds)))
pfamTable = img.pfamTable(genomeIds)
for threshold in arange(maxThreshold, minThreshold, -stepSize):
markerSet = img.markerGenes(genomeIds, pfamTable,
threshold * len(genomeIds),
threshold * len(genomeIds))
fout.write('\t' + str(len(markerSet)))
print(' Threshold = %.2f, marker set size = %d' %
(threshold, len(markerSet)))
fout.write('\n')
fout.close()
def run(self, minThreshold, maxThreshold, stepSize, minGenomes, mostSpecificRanks):
img = IMG()
trustedGenomeIds = img.trustedGenomes()
fout = open("./data/markerSetSize.tsv", "w")
fout.write("Lineage\t# genomes")
for threshold in arange(maxThreshold, minThreshold, -stepSize):
fout.write("\t" + str(threshold))
fout.write("\n")
lineages = img.lineagesSorted(mostSpecificRanks)
for lineage in lineages:
genomeIds = img.genomeIdsByTaxonomy(lineage)
genomeIds = list(genomeIds.intersection(trustedGenomeIds))
if len(genomeIds) < minGenomes:
continue
print "\nLineage " + lineage + " contains " + str(len(genomeIds)) + " genomes."
fout.write(lineage + "\t" + str(len(genomeIds)))
pfamTable = img.pfamTable(genomeIds)
for threshold in arange(maxThreshold, minThreshold, -stepSize):
markerSet = img.markerGenes(
genomeIds, pfamTable, threshold * len(genomeIds), threshold * len(genomeIds)
)
fout.write("\t" + str(len(markerSet)))
print " Threshold = %.2f, marker set size = %d" % (threshold, len(markerSet))
fout.write("\n")
fout.close()
def run(self, ubiquityThreshold, singleCopyThreshold, minGenomes,
mostSpecificRank, minMarkers):
print('Ubiquity threshold: ' + str(ubiquityThreshold))
print('Single-copy threshold: ' + str(singleCopyThreshold))
print('Min. genomes: ' + str(minGenomes))
print('Most specific taxonomic rank: ' + str(mostSpecificRank))
img = IMG()
deltaMarkerSetSizes = []
lineages = img.lineagesByCriteria(minGenomes, mostSpecificRank)
lineages = ['prokaryotes'] + lineages
boxPlotLabels = []
for lineage in lineages:
genomeIds = img.genomeIdsByTaxonomy(lineage)
trusted = img.trustedGenomes()
genomeIds = list(genomeIds.intersection(trusted))
print('')
print('Lineage ' + lineage + ' contains ' + str(len(genomeIds)) +
' genomes.')
# get table of PFAMs and do some initial filtering to remove PFAMs that are
# clearly not going to pass the ubiquity and single-copy thresholds
pfamTable = img.pfamTable(genomeIds)
pfamTable = img.filterPfamTable(genomeIds, pfamTable,
ubiquityThreshold * 0.9,
singleCopyThreshold * 0.9)
markerSet = img.markerGenes(
genomeIds, pfamTable, ubiquityThreshold * (len(genomeIds) - 1),
singleCopyThreshold * (len(genomeIds) - 1))
fullMarkerSetSize = len(markerSet)
if fullMarkerSetSize < minMarkers:
continue
boxPlotLabels.append(
lineage.split(';')[-1].strip() + ' (' + str(len(genomeIds)) +
', ' + str(fullMarkerSetSize) + ')')
deltaMarkerSetSize = []
numGenomes = len(genomeIds) - 1
for loo in range(0, len(genomeIds)):
if loo != len(genomeIds) - 1:
genomeIdSubset = genomeIds[0:loo] + genomeIds[loo + 1:]
else:
genomeIdSubset = genomeIds[0:loo]
markerSet = img.markerGenes(
genomeIdSubset, pfamTable,
ubiquityThreshold * len(genomeIdSubset),
singleCopyThreshold * len(genomeIdSubset))
deltaMarkerSetSize.append(fullMarkerSetSize - len(markerSet))
if fullMarkerSetSize < len(markerSet):
print('[Warning] Unexpected!')
deltaMarkerSetSizes.append(deltaMarkerSetSize)
m = mean(deltaMarkerSetSize)
s = std(deltaMarkerSetSize)
print(' LOO Ubiquity >= ' +
str(int(ubiquityThreshold * numGenomes)) +
', LOO Single-copy >= ' +
str(int(singleCopyThreshold * numGenomes)))
print(' Delta Mean: %.2f +/- %.2f' % (m, s))
print(' Delta Min: %d, Delta Max: %d' %
(min(deltaMarkerSetSize), max(deltaMarkerSetSize)))
# plot data
boxPlot = BoxPlot()
plotFilename = './images/LOO.' + str(ubiquityThreshold) + '-' + str(
singleCopyThreshold) + '.boxplot.png'
title = 'Ubiquity = %.2f' % ubiquityThreshold + ', Single-copy = %.2f' % singleCopyThreshold
boxPlot.plot(plotFilename, deltaMarkerSetSizes, boxPlotLabels,
r'$\Delta$' + ' Marker Set Size', '', False, title)
def run(self, ubiquityThreshold, singleCopyThreshold, minGenomes, mostSpecificRank, minMarkers):
print 'Ubiquity threshold: ' + str(ubiquityThreshold)
print 'Single-copy threshold: ' + str(singleCopyThreshold)
print 'Min. genomes: ' + str(minGenomes)
print 'Most specific taxonomic rank: ' + str(mostSpecificRank)
img = IMG()
deltaMarkerSetSizes = []
lineages = img.lineagesByCriteria(minGenomes, mostSpecificRank)
lineages = ['prokaryotes'] + lineages
boxPlotLabels = []
for lineage in lineages:
genomeIds = img.genomeIdsByTaxonomy(lineage)
trusted = img.trustedGenomes()
genomeIds = list(genomeIds.intersection(trusted))
print ''
print 'Lineage ' + lineage + ' contains ' + str(len(genomeIds)) + ' genomes.'
# get table of PFAMs and do some initial filtering to remove PFAMs that are
# clearly not going to pass the ubiquity and single-copy thresholds
pfamTable = img.pfamTable(genomeIds)
pfamTable = img.filterPfamTable(genomeIds, pfamTable, ubiquityThreshold*0.9, singleCopyThreshold*0.9)
markerSet = img.markerGenes(genomeIds, pfamTable, ubiquityThreshold*(len(genomeIds)-1), singleCopyThreshold*(len(genomeIds)-1))
fullMarkerSetSize = len(markerSet)
if fullMarkerSetSize < minMarkers:
continue
boxPlotLabels.append(lineage.split(';')[-1].strip() + ' (' + str(len(genomeIds)) + ', ' + str(fullMarkerSetSize) + ')')
deltaMarkerSetSize = []
numGenomes = len(genomeIds)-1
for loo in xrange(0, len(genomeIds)):
if loo != len(genomeIds) - 1:
genomeIdSubset = genomeIds[0:loo] + genomeIds[loo+1:]
else:
genomeIdSubset = genomeIds[0:loo]
markerSet = img.markerGenes(genomeIdSubset, pfamTable, ubiquityThreshold*len(genomeIdSubset), singleCopyThreshold*len(genomeIdSubset))
deltaMarkerSetSize.append(fullMarkerSetSize - len(markerSet))
if fullMarkerSetSize < len(markerSet):
print '[Warning] Unexpected!'
deltaMarkerSetSizes.append(deltaMarkerSetSize)
m = mean(deltaMarkerSetSize)
s = std(deltaMarkerSetSize)
print ' LOO Ubiquity >= ' + str(int(ubiquityThreshold*numGenomes)) + ', LOO Single-copy >= ' + str(int(singleCopyThreshold*numGenomes))
print ' Delta Mean: %.2f +/- %.2f' % (m, s)
print ' Delta Min: %d, Delta Max: %d' % (min(deltaMarkerSetSize), max(deltaMarkerSetSize))
# plot data
boxPlot = BoxPlot()
plotFilename = './images/LOO.' + str(ubiquityThreshold) + '-' + str(singleCopyThreshold) + '.boxplot.png'
title = 'Ubiquity = %.2f' % ubiquityThreshold + ', Single-copy = %.2f' % singleCopyThreshold
boxPlot.plot(plotFilename, deltaMarkerSetSizes, boxPlotLabels, r'$\Delta$' + ' Marker Set Size', '', False, title)
