def run(self):
sample1 = pickle.load( gzip.open(self.sampleobjfile1, "rb") )
sample2 = pickle.load( gzip.open(self.sampleobjfile2, "rb") )
print self.size, os.path.basename(self.sampleobjfile1).split('.')[0], sample1.total, os.path.basename(self.sampleobjfile2).split('.')[0], sample2.total
#sampling "size" sequences from sample:
subsample1 = iseqlib.samplingSample( sample1, self.size ) #sampled sample
subsample2 = iseqlib.samplingSample( sample2, self.size ) #sampled sample
#Get different statistics:
stats = PairSamplingStats() #initialize stats for the current subsample
#Find Sizes of the Union of clones between subsample1 and subsample2:
counts1 = []
counts2 = []
for header, seq in subsample1.seqs.iteritems():
counts1.append( seq.count )
if header in subsample2.seqs:
counts2.append( subsample2.seqs[header].count )
else:
counts2.append( 0 )
for header, seq in subsample2.seqs.iteritems():
if header not in subsample1.seqs:
counts1.append( 0 )
counts2.append( seq.count )
if len(counts1) != len(counts2):
raise ValueError("The count vectors of two compared samples must have equal length. Found %d and %d\n" %(len(counts1), len(counts2)))
## Initialize the R interface
import rpy2.rinterface as rinterface
rinterface.set_initoptions(('rpy2', '--no-save'))
rinterface.initr()
import rpy2.robjects as robjs
from rpy2.robjects.packages import importr
vegan = importr('vegan')
vegdist = vegan.vegdist
counts = counts1
counts.extend(counts2)
rcountsVec = robjs.IntVector( counts ) #convert python list into R vector
rcountsMatrix = robjs.r['matrix'](rcountsVec, nrow = 2, byrow=True)
#indices = ['bray', 'horn', 'mountford', 'chao']
indices = self.options.similarityIndices
#"manhattan", "euclidean", "canberra", "bray", "kulczynski", "jaccard", "gower", "morisita", "horn", "mountford", "raup" , "binomial" or "chao"
for index in indices:
disimilarity = vegdist( rcountsMatrix, method=index )
stats[index] = disimilarity[0]
#Write to temp file:
picklefile = os.path.join( self.outdir, "%d.pickle" % self.id)
pickle.dump( stats, gzip.open(picklefile, "wb") )
def run(self):
sample = pickle.load( gzip.open(self.samplefile, "rb") )
if self.options.uniq:
subsample = iseqlib.samplingSample_weightedUniq(sample, self.size)
#subsample = iseqlib.samplingSample_uniq(sample, self.size)
else:
subsample = iseqlib.samplingSample(sample, self.size)
#filtering if selected Vs and/or selected Js were specified
subsample = iseqlib.filterSampleByGenes(subsample, self.options.vs, self.options.js)
pickle.dump(subsample, gzip.open(self.outfile, "wb"))
def run(self):
globalTempDir = self.getGlobalTempDir()
#sampling
sample = pickle.load( gzip.open(self.samplefile, "rb") )
if self.options.uniq:
subsample = iseqlib.samplingSample_weightedUniq(sample, size)
else:
subsample = iseqlib.samplingSample(sample, self.size)
#filtering if selected Vs and/or selected Js were specified
subsample = iseqlib.filterSampleByGenes(subsample, self.options.vs, self.options.js)
picklefile = os.path.join(globalTempDir, "%s.pickle" %sample.name)
pickle.dump(subsample, gzip.open(picklefile, "wb"))
self.addChildTarget( Analyses(picklefile, self.outdir, self.options) )
def run(self):
sample = pickle.load( gzip.open(self.sampleobjfile, "rb") )
#sampling "size" sequences from sample:
subsample = iseqlib.samplingSample( sample, self.size ) #sampled sample
#Get different statistics:
stats = SingleSamplingStats() #initialize stats for the current subsample
#1/ Number of uniq clones:
stats.uniqClones = len( subsample.seqs )
#2/ Diversity Indices
counts = [ seq.count for seq in subsample.seqs.values() ] #list of clone sizes
## Initialize the R interface
import rpy2.rinterface as rinterface
rinterface.set_initoptions(('rpy2', '--no-save'))
rinterface.initr()
import rpy2.robjects as robjs
from rpy2.robjects.packages import importr
vegan = importr('vegan')
rcounts = robjs.IntVector( counts ) #convert python list into R vector
##Diversity indices (Simpson, InverseSimpson, Shannon)
indices = self.options.diversityIndices
#['simpson', 'invsimpson', 'shannon']
for index in indices:
if index == 'uniqClones' or index == 'fisherAlpha':
continue
rval = vegan.diversity( rcounts, index )
stats[index] = rval[0]
## Fisher Alpha:
rfisher = vegan.fisher_alpha( rcounts )
stats.fisherAlpha = rfisher[0]
#Write to temp file:
picklefile = os.path.join( self.outdir, "%d.pickle" % self.id)
pickle.dump( stats, gzip.open(picklefile, "wb") )
