def __call__(self, **kw):
feature_type = int(kw.get('feature_type') or 0)
assembly_id = kw.get('assembly') or None
chrmeta = "guess"
if assembly_id:
assembly = genrep.Assembly(assembly_id)
chrmeta = assembly.chrmeta
genes = assembly.gene_track
exons = assembly.exon_track
elif not(feature_type == 2):
raise ValueError("Please specify an assembly")
signals = kw.get('signals', [])
if not isinstance(signals, list): signals = [signals]
snames = [os.path.splitext(os.path.basename(sig))[0] for sig in signals]
signals = [track(sig, chrmeta=chrmeta) for sig in signals]
if feature_type == 0: #bodies
features = genes
elif feature_type == 1: #promoters
prom_pars = {'before_start': int(kw.get('upstream') or prom_up_def),
'after_start': int(kw.get('downstream') or prom_down_def),
'on_strand': True}
features = lambda c: neighborhood(genes(c), **prom_pars)
elif feature_type == 2: #exons
features = exons
elif feature_type == 3: #custom track
_t = track(kw.get('features'), chrmeta=chrmeta)
chrmeta = _t.chrmeta
features = _t.read
else:
raise ValueError("Feature type not known: %i" % feature_type)
pdf = self.temporary_path(fname='plot_pairs.pdf')
narr = None
if int(kw['mode']) == 0: #correl
xarr = array(range(-cormax, cormax + 1))
srtdchrom = sorted(chrmeta.keys())
features = [x[:3] for chrom in srtdchrom
for x in sorted_stream(features(chrom))]
_f = ['chr', 'start', 'end', 'score']
narr = correlation([s.read(fields=_f) for s in signals],
features, (-cormax, cormax), True)
elif int(kw['mode']) == 1: #density
xarr = None
for chrom in chrmeta:
feat = features(chrom)
means = score_by_feature([s.read(chrom) for s in signals], feat)
mf = means.fields[len(feat.fields):]
_n, _l = score_array(means, mf)
if _n.size == 0: continue
if narr is None: narr = _n
else: narr = vstack((narr, _n))
else:
raise ValueError("Mode not implemented: %s" % kw['mode'])
if narr is None:
raise ValueError("No data")
pairs(narr, xarr, labels=snames, output=pdf)
self.new_file(pdf, 'plot_pairs')
return self.display_time()
def quantify(self,**kw):
feature_type = int(kw.get('feature_type', 0))
func = str(kw.get('score_op', 'mean'))
assembly_id = kw.get('assembly')
format = kw.get('format','sql')
chrmeta = "guess"
if assembly_id:
assembly = genrep.Assembly(assembly_id)
chrmeta = assembly.chrmeta
genes = assembly.gene_track
exons = assembly.exon_track
elif not(feature_type == 3):
raise ValueError("Please specify an assembly")
signals = kw.get('signals', [])
if not isinstance(signals, list): signals = [signals]
signals = [track(sig, chrmeta=chrmeta) for sig in signals]
if feature_type == 0:
features = genes
elif feature_type == 1:
prom_pars = {'before_start': int(kw.get('upstream') or prom_up_def),
'after_start': int(kw.get('downstream') or prom_down_def),
'on_strand': True}
features = lambda c: neighborhood(genes(c), **prom_pars)
elif feature_type == 2:
features = exons
elif feature_type == 3:
assert os.path.exists(str(kw.get('features'))), "Features file not found: '%s'" % kw.get("features")
_t = track(kw.get('features'), chrmeta=chrmeta)
chrmeta = _t.chrmeta
features = _t.read
else:
raise ValueError("Take feature_type in %s." %ftypes)
output = self.temporary_path(fname='features_quantification.'+format)
if len(signals) > 1:
_f = ["score" + str(i) for i in range(len(signals))]
else:
_f = ["score"]
tout = track(output, format, fields=['chr','start','end','name'] + _f,
chrmeta=chrmeta, info={'datatype':'qualitative'})
for chrom in chrmeta:
sread = [sig.read(chrom) for sig in signals]
tout.write(score_by_feature(sread, features(chrom), fn=func),
chrom=chrom, clip=True)
tout.close()
return output
def __call__(self, **kw):
feature_type = int(kw.get('feature_type', 0))
func = str(kw.get('score_op', 'mean'))
assembly_id = kw.get('assembly') or None
chrmeta = "guess"
if assembly_id:
assembly = genrep.Assembly(assembly_id)
chrmeta = assembly.chrmeta
genes = assembly.gene_track
elif not(feature_type == 2):
raise ValueError("Please specify an assembly")
signals = [track(sig, chrmeta=chrmeta) for sig in kw.get('signals', [])]
if feature_type == 0:
features = genes
elif feature_type == 1:
prom_pars = {'before_start': int(kw.get('upstream') or prom_up_def),
'after_start': int(kw.get('downstream') or prom_down_def),
'on_strand': True}
features = lambda c: neighborhood(genes(c), **prom_pars)
elif feature_type == 2:
_t = track(kw.get('features'), chrmeta=chrmeta)
chrmeta = _t.chrmeta
features = _t.read
else:
return 2
output = self.temporary_path(fname='features_quantification.sql')
if len(signals) > 1:
_f = ["score" + str(i) for i in range(len(signals))]
else:
_f = ["score"]
tout = track(output, format='sql', fields=['start', 'end', 'name'] + _f,
chrmeta=chrmeta, info={'datatype': 'qualitative'})
for chrom in chrmeta:
sread = [sig.read(chrom) for sig in signals]
tout.write(score_by_feature(sread, features(chrom), fn=func),
chrom=chrom, clip=True)
tout.close()
self.new_file(output, 'features_quantification')
return 1
def __call__(self, **kw):
feature_type = int(kw.get('feature_type', 0))
assembly_id = kw.get('assembly')
chrmeta = "guess"
if assembly_id:
assembly = genrep.Assembly(assembly_id)
chrmeta = assembly.chrmeta
genes = assembly.gene_track
exons = assembly.exon_track
elif not(feature_type == 3):
raise ValueError("Please specify an assembly")
if feature_type == 0:
features = genes
elif feature_type == 1:
prom_pars = {'before_start': int(kw.get('upstream') or prom_up_def),
'after_start': int(kw.get('downstream') or prom_down_def),
'on_strand': True}
features = lambda c: neighborhood(genes(c), **prom_pars)
elif feature_type == 2:
features = exons
elif feature_type == 3:
assert os.path.exists(kw.get('features'))
_t = track(kw.get('features'), chrmeta=chrmeta)
chrmeta = _t.chrmeta
features = _t.read
else:
return 2
signals = []
norm_factors = []
for sig in kw.get('signals', []):
assert os.path.exists(sig), "File not found: %s." % sig
_t = track(sig, chrmeta=chrmeta)
if 'normalization' in _t.info:
_nf = float(_t.info['normalization'])
elif 'nreads' in _t.info:
_nf = float(_t.info['nreads']) * 1e-7 / float(_t.info.get('read_extension', 1))
else:
_nf = 1
signals.append(_t)
norm_factors.append(_nf)
if len(signals) > 1:
_f = ["score" + str(i) for i in range(len(signals))]
else:
_f = ["score"]
de_list = []
for chrom in chrmeta:
sread = [sig.read(chrom) for sig in signals]
mread = score_by_feature(sread, features(chrom), fn='sum')
de_list.extend(list(mread))
name_idx = mread.fields.index("name")
# Turn all scores into integers
de_matrix = numpy.asarray([[int(s * norm_factors[k] + .5) for s in x[-len(_f):]]
for k, x in enumerate(de_list)], dtype=numpy.float)
rownames = numpy.asarray([x[name_idx] for x in de_list])
colnames = numpy.asarray([os.path.splitext(os.path.basename(s.path))[0]
for s in signals])
del de_list
output = self.temporary_path(fname='DE')
robjects.r.assign('Mdata', numpy2ri.numpy2ri(de_matrix))
robjects.r.assign('row_names', numpy2ri.numpy2ri(rownames))
robjects.r.assign('col_names', numpy2ri.numpy2ri(colnames))
robjects.r("""
Mdata <- as.data.frame(Mdata,row.names=row_names)
conds <- unlist(strsplit(col_names,".",fixed=T))
colnames(Mdata) <- conds
groups <- unique(conds)
couples <- combn(groups,2)
# Still need to check that replicates are not identical - lfproc would fail
if (any(table(conds)>1)){ method = 'normal' # if replicates
} else { method = 'blind' }
library(DESeq)
cds <- newCountDataSet(Mdata, conds)
cds <- estimateSizeFactors(cds)
cds <- estimateVarianceFunctions(cds,method='blind')
""")
groups = list(set(colnames))
couples = itertools.combinations(groups, 2)
for c in couples:
out = output + '_' + c[0] + '-' + c[1] + '.txt'
print out
r_cmd = """
res <- nbinomTest(cds, '%s', '%s')
res <- res[order(res[,8]),]
write.table(res, '%s', row.names=F)
""" % (c[0], c[1], out)
robjects.r(r_cmd)
self.new_file(out, 'differential_expression')
return 1
