def normalize(trackList, method='total', field='score'):
"""Normalizes the scores in every stream from *trackList* using the given *method*.
It assumes that each of the streams represents the same features, i.e. the n-th element
of one stream corresponds to the n-th element of another.
[!] This function will temporarily store everything in memory.
:param trackList: FeatureStream, or list of FeatureStream objects.
:param method: normalization method:
* ``'total'`` divides every score vector by its sum (total number of reads) x 10^7 .
* ``'deseq'`` applies DESeq's normalization ("size factors") - considering every track
as belonging to a different group.
* ``'quantile'`` applies quantile normalization.
:param field: (str) name of the field containing the scores (must be the same for all streams).
"""
if not isinstance(trackList, (list, tuple)):
trackList = [trackList]
allcontents = [list(t) for t in trackList]
ncols = len(trackList)
nlines = len(allcontents[0])
assert all(
len(t) == nlines for t in
allcontents), "All streams must have the same number of elements."
# Build the matrix
allscores = zeros((ncols, nlines))
for n, content in enumerate(allcontents):
idx = trackList[n].fields.index(field)
allscores[n] = asarray([x[idx] for x in content])
# Normalize
allscores = common.normalize(asarray(allscores), method)
# Reinsert the new scores in the respective tracks
for n, content in enumerate(allcontents):
idx = trackList[n].fields.index(field)
for k, x in enumerate(content):
content[k] = x[:idx] + (allscores[n][k], ) + x[idx + 1:]
res = [
FeatureStream(t, fields=trackList[n].fields)
for n, t in enumerate(allcontents)
]
if len(trackList) == 1:
return res[0]
else:
return res
def __call__(self, **kw):
filename = kw.get('table')
assert os.path.exists(str(filename)), "File not found: '%s'" % filename
file = open(filename, 'r')
header = file.readline()
id = []
matrix = []
for line in file:
newline = line.split()
id.append(newline[0])
matrix.append(map(int, newline[1:len(header)]))
norm = common.normalize(asarray(matrix).transpose(), kw.get('method'))
output = self.temporary_path(fname='output.tab')
out = open(output, "w")
out.write(header)
for i in range(len(norm[0])):
out.write(str(id[i])+"\t"+str(map(lambda x: "%.2g" % x, list(norm.transpose()[i]))).replace("'","").replace("[","").replace("]","").replace(", ","\t")+"\n")
self.new_file(output, 'normalized')
return self.display_time()
def normalize(trackList, method="total", field="score"):
"""Normalizes the scores in every stream from *trackList* using the given *method*.
It assumes that each of the streams represents the same features, i.e. the n-th element
of one stream corresponds to the n-th element of another.
[!] This function will temporarily store everything in memory.
:param trackList: FeatureStream, or list of FeatureStream objects.
:param method: normalization method:
* ``'total'`` divides every score vector by its sum (total number of reads) x 10^7 .
* ``'deseq'`` applies DESeq's normalization ("size factors") - considering every track
as belonging to a different group.
* ``'quantile'`` applies quantile normalization.
:param field: (str) name of the field containing the scores (must be the same for all streams).
"""
if not isinstance(trackList, (list, tuple)):
trackList = [trackList]
allcontents = [list(t) for t in trackList]
ncols = len(trackList)
nlines = len(allcontents[0])
assert all(len(t) == nlines for t in allcontents), "All streams must have the same number of elements."
# Build the matrix
allscores = zeros((ncols, nlines))
for n, content in enumerate(allcontents):
idx = trackList[n].fields.index(field)
allscores[n] = asarray([x[idx] for x in content])
# Normalize
allscores = common.normalize(asarray(allscores), method)
# Reinsert the new scores in the respective tracks
for n, content in enumerate(allcontents):
idx = trackList[n].fields.index(field)
for k, x in enumerate(content):
content[k] = x[:idx] + (allscores[n][k],) + x[idx + 1 :]
res = [FeatureStream(t, fields=trackList[n].fields) for n, t in enumerate(allcontents)]
if len(trackList) == 1:
return res[0]
else:
return res
def __call__(self, **kw):
filename = kw.get('table')
assert os.path.exists(str(filename)), "File not found: '%s'" % filename
file = open(filename, 'r')
header = file.readline()
id = []
matrix = []
for line in file:
newline = line.split()
id.append(newline[0])
matrix.append(map(int, newline[1:len(header)]))
norm = common.normalize(asarray(matrix).transpose(), kw.get('method'))
output = self.temporary_path(fname='output.tab')
out = open(output, "w")
out.write(header)
for i in range(len(norm[0])):
out.write(
str(id[i]) + "\t" +
str(map(lambda x: "%.2g" % x, list(
norm.transpose()[i]))).replace("'", "").replace(
"[", "").replace("]", "").replace(", ", "\t") + "\n")
self.new_file(output, 'normalized')
return self.display_time()
