def __call__(self,**kw):
assembly = kw.get('assembly') or 'guess'
t1 = track(kw['numerator'],chrmeta=assembly)
t2 = track(kw['denominator'],chrmeta=assembly)
format = kw.get('output') or t1.format
wsize = int(kw.get('window_size') or size_def)
self.log = kw.get('log',False)
if isinstance(self.log, basestring):
self.log = (self.log.lower() in ['1', 'true', 't','on'])
try:
self.pseudo = float(kw.get('pseudo'))
except:
self.pseudo = pseudo_def
self.baseline = -log(self.pseudo,2)
try:
self.threshold = float(kw.get('threshold'))
except:
self.threshold = threshold_def
distribution = kw.get('distribution',False)
if isinstance(distribution, basestring):
distribution = (distribution.lower() in ['1', 'true', 't','on'])
if distribution:
genome_length = sum((v['length'] for v in t1.chrmeta.values()))
self.shifts = list(poisson(float(genome_length)/float(self.sample_num),self.sample_num))
self.ratios = []
output = self.temporary_path(fname='ratios_%s-%s.%s'%(t1.name,t2.name,format))
with track(output, chrmeta=t1.chrmeta, fields=t1.fields,
info={'datatype': 'quantitative',
'log': self.log,
'pseudocounts': self.pseudo,
'threshold': self.threshold,
'window_size': wsize}) as tout:
for chrom,vchr in t1.chrmeta.iteritems():
if wsize > 1:
s1 = window_smoothing(t1.read(chrom),window_size=wsize,step_size=1,featurewise=False)
s2 = window_smoothing(t2.read(chrom),window_size=wsize,step_size=1,featurewise=False)
else:
s1 = t1.read(chrom)
s2 = t2.read(chrom)
s3 = merge_scores([s1,s2],method=self._divide)
if distribution:
s3 = FeatureStream(self._sample_stream(s3,vchr['length']),fields=s3.fields)
tout.write(s3, chrom=chrom, clip=True)
self.new_file(output, 'ratios')
if distribution:
pdf = self.temporary_path(fname='%s-%s_ratios_distribution.pdf'%(t1.name,t2.name))
density_boxplot(self.ratios,output=pdf,
name="%s/%s (median=%.2f)" %(t1.name,t2.name,median(self.ratios)))
self.new_file(pdf, 'boxplot')
return self.display_time()
def __call__(self, **kw):
tinput = track(kw.get('track'), chrmeta=kw.get('assembly') or None)
outformat = kw.get('format',tinput.format)
wsize = int(kw.get('window_size', size_def) or 10)
wstep = int(kw.get('window_step', step_def) or 1)
featurewise = kw.get('by_feature', False)
if isinstance(featurewise, basestring):
featurewise = (featurewise.lower() in ['1', 'true', 't','on'])
output = self.temporary_path(fname=tinput.name+'_smoothed', ext=outformat)
if featurewise:
outfields = tinput.fields
datatype = "qualitative"
else:
outfields = ["chr","start", "end", "score"]
datatype = "quantitative"
tout = track(output, format=outformat, fields=outfields, chrmeta=tinput.chrmeta, info={'datatype': datatype})
for chrom in tout.chrmeta.keys():
s = window_smoothing(
tinput.read(selection=chrom, fields=outfields),
window_size=wsize, step_size=wstep,
featurewise=featurewise)
tout.write(s, chrom=chrom, clip=True)
tout.close()
self.new_file(output, 'smoothed_track')
return self.display_time()
def __call__(self, **kw):
tinput = track(kw.get("track"), chrmeta=kw.get("assembly") or None)
outformat = kw.get("output", tinput.format)
wsize = int(kw.get("window_size", size_def) or 10)
wstep = int(kw.get("window_step", step_def) or 1)
featurewise = kw.get("by_feature", False)
if isinstance(featurewise, basestring):
featurewise = featurewise.lower() in ["1", "true", "t", "on"]
output = self.temporary_path(fname=tinput.name + "_smoothed", ext=outformat)
if featurewise:
outfields = tinput.fields
datatype = "qualitative"
else:
outfields = ["chr", "start", "end", "score"]
datatype = "quantitative"
tout = track(output, format=outformat, fields=outfields, chrmeta=tinput.chrmeta, info={"datatype": datatype})
for chrom in tout.chrmeta.keys():
s = window_smoothing(
tinput.read(selection=chrom, fields=outfields),
window_size=wsize,
step_size=wstep,
featurewise=featurewise,
)
tout.write(s, chrom=chrom, clip=True)
tout.close()
self.new_file(output, "smoothed_track")
return self.display_time()
def test_window_smoothing(self):
stream = fstream([('chr1', 4, 5, 10.)],
fields=['chr', 'start', 'end', 'score'])
res = list(window_smoothing(stream, window_size=2, step_size=1))
expected = [('chr1', 4, 5, 5.), ('chr1', 5, 6, 5.)]
self.assertListEqual(res, expected)
def test_window_smoothing(self):
stream = fstream([('chr1',4,5,10.)], fields=['chr','start','end','score'])
res = list(window_smoothing(stream, window_size=2, step_size=1))
expected = [('chr1',4,5,5.),('chr1',5,6,5.)]
self.assertListEqual(res,expected)
def __call__(self, **kw):
assembly = kw.get('assembly') or 'guess'
t1 = track(kw['numerator'], chrmeta=assembly)
t2 = track(kw['denominator'], chrmeta=assembly)
format = kw.get('format') or t1.format
wsize = int(kw.get('window_size') or size_def)
self.log = kw.get('log', False)
if isinstance(self.log, basestring):
self.log = (self.log.lower() in ['1', 'true', 't', 'on'])
try:
self.pseudo = float(kw.get('pseudo'))
except:
self.pseudo = pseudo_def
self.baseline = -log(self.pseudo, 2)
try:
self.threshold = float(kw.get('threshold'))
except:
self.threshold = threshold_def
distribution = kw.get('distribution', False)
if isinstance(distribution, basestring):
distribution = (distribution.lower() in ['1', 'true', 't', 'on'])
if distribution:
genome_length = sum((v['length'] for v in t1.chrmeta.values()))
self.shifts = list(
poisson(
float(genome_length) / float(self.sample_num),
self.sample_num))
self.ratios = []
output = self.temporary_path(fname='ratios_%s-%s.%s' %
(t1.name, t2.name, format))
with track(output,
chrmeta=t1.chrmeta,
fields=t1.fields,
info={
'datatype': 'quantitative',
'log': self.log,
'pseudocounts': self.pseudo,
'threshold': self.threshold,
'window_size': wsize
}) as tout:
for chrom, vchr in t1.chrmeta.iteritems():
if wsize > 1:
s1 = window_smoothing(t1.read(chrom),
window_size=wsize,
step_size=1,
featurewise=False)
s2 = window_smoothing(t2.read(chrom),
window_size=wsize,
step_size=1,
featurewise=False)
else:
s1 = t1.read(chrom)
s2 = t2.read(chrom)
s3 = merge_scores([s1, s2], method=self._divide)
if distribution:
s3 = FeatureStream(self._sample_stream(s3, vchr['length']),
fields=s3.fields)
tout.write(s3, chrom=chrom, clip=True)
self.new_file(output, 'ratios')
if distribution:
pdf = self.temporary_path(fname='%s-%s_ratios_distribution.pdf' %
(t1.name, t2.name))
density_boxplot(self.ratios,
output=pdf,
name="%s/%s (median=%.2f)" %
(t1.name, t2.name, median(self.ratios)))
self.new_file(pdf, 'boxplot')
return self.display_time()
