def run(self):
ext = 'pickle'
files = iseqlib.getfiles(self.indir, ext)
sizes = []
size2stats = {} #key = size, val = averStats
for file in files:
size = int(file.rstrip(ext).rstrip('.'))
sizes.append(size)
stats = pickle.load( gzip.open(os.path.join(self.indir, file), "rb") )
size2stats[size] = stats
sizes.sort()
#output summary file of the sample:
outfile = os.path.join( self.outdir, "%s.txt" %self.name )
f = open(outfile, 'w')
f.write("Index")
for s in sizes:
f.write("\t%d\tStd" %s)
metrics = self.metrics
metricsStd = [m + "Std" for m in metrics]
for i, metric in enumerate(metrics):
f.write("\n%s" %metric)
for size in sizes:
avr = size2stats[size][metric]
std = size2stats[size][metricsStd[i]]
f.write("\t%f\t%f" %(avr, std))
f.write("\n")
f.close()
#pickle size2stats to temporary output directory
picklefile = os.path.join(self.tempOutdir, "%s.pickle" % self.name)
pickle.dump( size2stats, gzip.open(picklefile, "wb") )
def run(self):
globalTempDir = self.getGlobalTempDir()
ext = "pickle"
files = iseqlib.getfiles(self.indir, ext)
samples = [file.split('.')[0] for file in files]
for sample in samples:
samplefile = os.path.join(self.indir, "%s.%s"%(sample, ext))
sampledir = os.path.join(globalTempDir, sample)
system("mkdir -p %s" %sampledir)
if self.options.sampling:
for i in xrange(self.options.numsam): #sampling a number of times
samplingdir = os.path.join(sampledir, "%d" %i)
system("mkdir -p %s" %samplingdir)
self.addChildTarget( Sampling(samplefile, samplingdir, self.options) )
else:
tempoutdir = os.path.join(sampledir, "0")
system("mkdir -p %s" %tempoutdir)
#filtering if selected Vs and/or selected Js were specified
if self.options.vs or self.options.js:
sampleObj = pickle.load( gzip.open(samplefile, "rb") )
subsample = iseqlib.filterSampleByGenes(sampleObj, self.options.vs, self.options.js)
system("rm %s" %samplefile)
pickle.dump( subsample, gzip.open(samplefile, "wb") )
self.addChildTarget( Analyses(samplefile, tempoutdir, self.options) )
#Calculate means & standard deviations of samplings
self.setFollowOnTarget( AverageResults(globalTempDir, self.options) )
def run(self):
#read input fasta files:
ext = 'fa'
files = iseqlib.getfiles(self.options.indir, ext)
globalTempDir = self.getGlobalTempDir()
for file in files:
filepath = os.path.join(self.options.indir, file)
self.addChildTarget( ReadFasta(filepath, globalTempDir, self.options.minReadCount) )
self.setFollowOnTarget( SamplingAndAnalyses(globalTempDir, self.options) )
def run(self):
singleOutdir = os.path.join(self.options.outdir, "diversity")
system("mkdir -p %s" %singleOutdir)
globalTempDir = self.getGlobalTempDir()
ext = 'pickle'
samples = iseqlib.getfiles(self.samdir, ext)
for sample in samples: #Each sample
samplename = sample.rstrip(ext).rstrip('.')
self.addChildTarget( SampleSingleAnalyses(globalTempDir, samplename, self.samdir, self.options, singleOutdir) )
# R --no-save --no-restore --args adapt16D-adapt11D.txt < diversityPlot.R
self.setFollowOnTarget( SummarySingle(globalTempDir, singleOutdir, self.options.diversityIndices) )
def filterSamples(indir, vs, js):
if not vs and not js:
return
ext = 'pickle'
files = iseqlib.getfiles(indir, ext)
for file in files:
filepath = os.path.join(indir, file)
sample = pickle.load( gzip.open(filepath, "rb") )
subsample = iseqlib.filterSampleByGenes(sample, vs, js)
system("rm %s" %filepath)
pickle.dump(subsample, gzip.open(filepath, "wb"))
return
def run(self):
globalTempDir = self.getGlobalTempDir()
ext = "pickle"
files = iseqlib.getfiles(self.indir, ext)
samples = [ '.'.join(file.split('.')[:-1]) for file in files ]
for sample in samples:
samplefile = os.path.join(self.indir, "%s.%s" %(sample, ext))
outfile = os.path.join(globalTempDir, "%s.pickle" %sample) #temp/sample.pickle
self.addChildTarget( Sampling(samplefile, outfile, self.options) )
self.setFollowOnTarget( Analyses(globalTempDir, self.outdir, self.options) )
def run(self):
ext = 'pickle'
files = iseqlib.getfiles(self.indir, ext)
sample2size2stats = {}
sizes = []
for file in files:
name = file.rstrip(ext).rstrip('.')
size2stats = pickle.load( gzip.open(os.path.join(self.indir, file), "rb") )
for size in size2stats:
if size not in sizes:
sizes.append(size)
sample2size2stats[name] = size2stats
sizes.sort()
#Print summary of each statistic to output files (1 file/statistic where row = samples, columns = sampling size)
metrics = self.metrics
metricsStd = [m + "Std" for m in metrics]
for i, metric in enumerate(metrics):
outfile = os.path.join(self.outdir, "%s.txt" %metric)
f = open(outfile, 'w')
f.write("Sample")
for size in sizes:
f.write("\t%d\tStd" %size)
for sample, size2stats in sample2size2stats.iteritems():
f.write("\n%s" %sample)
for size in sizes:
if size not in size2stats:
f.write("\tNA\tNA")
else:
s = size2stats[size]
f.write("\t%f\t%f" % (s[metric], s[metricsStd[i]]) )
f.write("\n")
f.close()
#Summary of all statictics for each sampling size (each file per sampling size, row=samples, columns = statistics
for size in sizes:
outfile = os.path.join(self.outdir, "%d.txt" %size)
f = open(outfile, 'w')
f.write("Sample")
for m in metrics:
f.write("\t%s\tStd" %m)
for sample, size2stats in sample2size2stats.iteritems():
f.write("\n%s" %sample)
for i, metric in enumerate(metrics):
if size not in size2stats:
f.write("\tNA\tNA")
else:
s = size2stats[size]
f.write("\t%f\t%f" %(s[metric], s[metricsStd[i]]) )
f.write("\n")
f.close()
def run(self):
ext = 'fa'
samples = iseqlib.getfiles(self.options.indir, ext)
globalTempDir = self.getGlobalTempDir()
#Read input fasta files and write pickle files into globalTempDir:
for sample in samples:
name = sample.rstrip(ext).rstrip('.')
infile = os.path.join(self.options.indir, sample)
self.addChildTarget( ReadFasta(infile, globalTempDir, name) )
#After done reading fastas, move to the analyses
self.setFollowOnTarget( Analyses(globalTempDir, self.options) )
def run(self):
pairOutdir = os.path.join(self.options.outdir, 'similarity')
system("mkdir -p %s" %pairOutdir)
globalTempDir = self.getGlobalTempDir()
ext = 'pickle'
samples = iseqlib.getfiles(self.samdir, ext)
samplenames = [s.rstrip(ext).rstrip('.') for s in samples]
for i in xrange( len(samples) - 1 ):
s1 = samples[i]
s1name = samplenames[i]
for j in xrange( i+1, len(samples) ):
s2 = samples[j]
s2name = samplenames[j]
self.addChildTarget( SamplePairAnalyses(globalTempDir, s1name, s2name, self.samdir, self.options, pairOutdir) )
self.setFollowOnTarget( SummaryPair(globalTempDir, pairOutdir, self.options.similarityIndices) )
def run(self):
ext = 'pickle'
picklefiles = iseqlib.getfiles( self.indir, ext )
statsList = []
for file in picklefiles:
stats = pickle.load( gzip.open(os.path.join(self.indir, file), "rb") )
statsList.append(stats)
avrstats = PairSamplingStats() #initialize avrstats
#Calculate mean and std using numpy
#metrics = ['bray', 'horn', 'mountford', 'chao']
#stds = ['brayStd', 'hornStd', 'mountfordStd', 'chaoStd']
metrics = self.metrics
stds = [m + "Std" for m in metrics]
for i in xrange( len(metrics) ):
vals = [s[metrics[i]] for s in statsList]
avrstats[ metrics[i] ] = np.mean( vals )
avrstats[ stds[i] ] = np.std( vals )
#Pickle the average stat of this sampling size
picklefile = os.path.join(self.outdir, "%d.pickle" %self.samplingsize)
pickle.dump( avrstats, gzip.open(picklefile, "wb") )
def run(self):
ext = 'pickle'
picklefiles = iseqlib.getfiles( self.indir, ext )
statsList = []
for file in picklefiles:
stats = pickle.load( gzip.open(os.path.join(self.indir, file), "rb") )
statsList.append(stats)
avrstats = SingleSamplingStats() #initialize avrstats
#Calculate mean and std using numpy
#metrics = ['uniqClones', 'simpson', 'invsimpson', 'shannon', 'fisherAlpha']
#stds = ['uniqClonesStd', 'simpsonStd', 'invsimpsonStd', 'shannonStd', 'fisherAlphaStd']
metrics = self.metrics
stds = [m + "Std" for m in metrics]
for i in xrange( len(metrics) ):
vals = [s[metrics[i]] for s in statsList]
avrstats[ metrics[i] ] = np.mean( vals )
avrstats[ stds[i] ] = np.std( vals )
#Pickle the average stat of this sampling size
picklefile = os.path.join(self.outdir, "%d.pickle" %self.samplingsize)
pickle.dump( avrstats, gzip.open(picklefile, "wb") )
def run(self):
ext = 'pickle'
files = iseqlib.getfiles(self.indir, ext)
sample2mate2size2stats = {}
sizes = []
for file in files:
name = file.rstrip(ext).rstrip('.')
samples = name.split('-')
size2stats = pickle.load( gzip.open(os.path.join(self.indir, file), "rb") )
for size in size2stats:
if size not in sizes:
sizes.append(size)
s1 = samples[0]
s2 = samples[1]
if s1 not in sample2mate2size2stats:
sample2mate2size2stats[s1] = { s2: size2stats }
else:
sample2mate2size2stats[s1][s2] = size2stats
if s2 not in sample2mate2size2stats:
sample2mate2size2stats[s2] = { s1: size2stats }
else:
sample2mate2size2stats[s2][s1] = size2stats
#for i, sample in enumerate(samples):
# if sample not in sample2mate2size2stats:
# sample2mate2size2stats[sample] = {samples[(i+1) %2]: size2stats}
# else:
# sample2mate2size2stats[sample][samples[(i+1)%2]] = size2stats
sizes.sort()
#Print summary of each statistic to output files (1 file/1 statistic, 1 sampling size where row = samples, cols = samples)
metrics = self.metrics
metricsStd = [m + "Std" for m in metrics]
samples = sorted(sample2mate2size2stats.keys())
for i, metric in enumerate(metrics):
for size in sizes:
outfile = os.path.join(self.outdir, "%s-%d.txt" %(metric, size))
f = open(outfile, 'w')
f.write("Sample")
for s in samples:
f.write("\t%s\tStd" %(s) )
for s in samples:
f.write("\n%s" %s)
for s2 in samples:
if s == s2:
f.write("\t-\t-")
else:
if s in sample2mate2size2stats and s2 in sample2mate2size2stats[s]:
size2stats = sample2mate2size2stats[s][s2]
if size not in size2stats:
f.write("\t-\t-")
else:
stat= size2stats[size]
f.write("\t%f\t%f" %(stat[metric], stat[ metricsStd[i] ] ))
else:
f.write("\t-\t-")
f.write("\n")
f.close()
