def _output(section, subsection, valuef, dtype):
# fold change matrix
matrix, row_headers = buildMatrix(results, valuef=valuef, dtype=dtype)
outfile = getFileName(options,
go=test_ontology,
section=section,
set='%s_all' % subsection)
IOTools.writeMatrix(outfile,
matrix,
row_headers,
col_headers,
row_header="category")
outfile = getFileName(options,
go=test_ontology,
section=section,
set='%s_alldesc' % subsection)
IOTools.writeMatrix(
outfile,
matrix,
["%s:%s" % (x, go2info[x].mDescription) for x in row_headers],
col_headers,
row_header="category")
def _output(section, subsection, valuef, dtype):
# fold change matrix
matrix, row_headers = buildMatrix(results,
valuef=valuef,
dtype=dtype)
outfile = getFileName(options,
go=test_ontology,
section=section,
set='%s_all' % subsection)
IOTools.writeMatrix(
outfile, matrix, row_headers, col_headers, row_header="category")
outfile = getFileName(options,
go=test_ontology,
section=section,
set='%s_alldesc' % subsection)
IOTools.writeMatrix(outfile, matrix,
["%s:%s" % (x, go2info[x].mDescription)
for x in row_headers],
col_headers, row_header="category")
def buildGeneListMatrix(infiles, outfile):
'''build a gene list matrix for simple pathway analysis
based on hypergeometric test.
A gene list is derived from a gene set by
applying thresholds to the input data set. The
thresholds are defined in the configuration file.
'''
genesets = []
backgrounds = []
headers = []
for infile in infiles:
genelist = pandas.read_csv(
IOTools.openFile(infile),
index_col=0,
sep='\t')
track = P.snip(os.path.basename(infile), ".tsv.gz")
headers.append(track)
field = PARAMS[P.matchParameter("%s_foreground_field" % track)]
min_threshold = PARAMS[P.matchParameter(
"%s_foreground_min_threshold" % track)]
max_threshold = PARAMS[P.matchParameter(
"%s_foreground_max_threshold" % track)]
genesets.append(set(genelist[
(genelist[field] >= min_threshold) &
(genelist[field] <= max_threshold)].index))
E.info('%s: foreground: %f <= %s <= %f' % (track,
min_threshold,
field,
max_threshold))
field = PARAMS[P.matchParameter("%s_background_field" % track)]
min_threshold = PARAMS[P.matchParameter(
"%s_background_min_threshold" % track)]
max_threshold = PARAMS[P.matchParameter(
"%s_background_max_threshold" % track)]
E.info('%s: background: %f <= %s <= %f' % (track,
min_threshold,
field,
max_threshold))
backgrounds.append(set(genelist[
(genelist[field] >= min_threshold) &
(genelist[field] <= max_threshold)].index))
E.info("%s: fg=%i, bg=%i" % (track,
len(genesets[-1]),
len(backgrounds[-1])))
E.info("writing gene list matrix")
with IOTools.openFile(outfile, "w") as outf:
SetTools.writeSets(outf, genesets, labels=headers)
with IOTools.openFile(outfile + ".bg.tsv.gz", "w") as outf:
SetTools.writeSets(outf, backgrounds, labels=headers)
E.info("writing intersection/union matrix")
# build set intersection matrix
matrix = SetTools.unionIntersectionMatrix(genesets)
with IOTools.openFile(outfile + ".matrix.gz", "w") as outf:
IOTools.writeMatrix(outf, matrix, headers, headers)
matrix = SetTools.unionIntersectionMatrix(backgrounds)
with IOTools.openFile(outfile + ".bg.matrix.gz", "w") as outf:
IOTools.writeMatrix(outf, matrix, headers, headers)
def writeMatricesForSortOrder(features_per_interval, bins, foreground_track,
control_tracks, shifted, sort_order):
'''output one or more matrices for each sort sorder.
For each sort order output the forerground. If there
are additional controls and shifted section, output
these as well
The files will named:
matrix_<track>_<sortorder>
'''
if "name" in features_per_interval[0].interval:
names = [x.interval.name for x in features_per_interval]
else:
names = map(str, range(1, len(features_per_interval) + 1))
bins = ["%i" % x for x in bins]
sort_order = re.sub("-", "_", sort_order)
# write foreground
IOTools.writeMatrix(E.openOutputFile("matrix_%s_%s.gz" %
(foreground_track, sort_order)),
[x.foreground.counts for x in features_per_interval],
row_headers=names,
col_headers=bins,
row_header="name")
# write controls
for idx, track in enumerate(control_tracks):
IOTools.writeMatrix(
E.openOutputFile("matrix_%s_%s.gz" % (track, sort_order)),
[x.controls[idx].counts for x in features_per_interval],
row_headers=names,
col_headers=bins,
row_header="name")
# write shifted matrix
if shifted:
IOTools.writeMatrix(E.openOutputFile("matrix_shift_%s.gz" %
(sort_order)),
[x.shifted.counts for x in features_per_interval],
row_headers=names,
col_headers=bins,
row_header="name")
# output a combined matrix
if len(control_tracks) > 0 or shifted:
rows = []
for row in features_per_interval:
l = [row.foreground.counts]
l.extend(
[row.controls[x].counts for x in range(len(control_tracks))])
if shifted:
l.append(row.shifted.counts)
rows.append(numpy.concatenate(l))
n = 1 + len(control_tracks)
if shifted:
n += 1
# make column names unique and make sure they can be sorted
# lexicographically
all_bins = []
for x in range(n):
all_bins.extend(["%i:%s" % (x, b) for b in bins])
IOTools.writeMatrix(E.openOutputFile("matrix_sidebyside_%s.gz" %
(sort_order)),
rows,
row_headers=names,
col_headers=all_bins,
row_header="name")
def buildGeneListMatrix(infiles, outfile):
'''build a gene list matrix for simple pathway analysis
based on hypergeometric test.
A gene list is derived from a gene set by
applying thresholds to the input data set. The
thresholds are defined in the configuration file.
'''
genesets = []
backgrounds = []
headers = []
for infile in infiles:
genelist = pandas.read_csv(IOTools.openFile(infile),
index_col=0,
sep='\t')
track = P.snip(os.path.basename(infile), ".tsv.gz")
headers.append(track)
field = PARAMS[P.matchParameter("%s_foreground_field" % track)]
min_threshold = PARAMS[P.matchParameter("%s_foreground_min_threshold" %
track)]
max_threshold = PARAMS[P.matchParameter("%s_foreground_max_threshold" %
track)]
genesets.append(
set(genelist[(genelist[field] >= min_threshold)
& (genelist[field] <= max_threshold)].index))
E.info('%s: foreground: %f <= %s <= %f' %
(track, min_threshold, field, max_threshold))
field = PARAMS[P.matchParameter("%s_background_field" % track)]
min_threshold = PARAMS[P.matchParameter("%s_background_min_threshold" %
track)]
max_threshold = PARAMS[P.matchParameter("%s_background_max_threshold" %
track)]
E.info('%s: background: %f <= %s <= %f' %
(track, min_threshold, field, max_threshold))
backgrounds.append(
set(genelist[(genelist[field] >= min_threshold)
& (genelist[field] <= max_threshold)].index))
E.info("%s: fg=%i, bg=%i" %
(track, len(genesets[-1]), len(backgrounds[-1])))
E.info("writing gene list matrix")
with IOTools.openFile(outfile, "w") as outf:
SetTools.writeSets(outf, genesets, labels=headers)
with IOTools.openFile(outfile + ".bg.tsv.gz", "w") as outf:
SetTools.writeSets(outf, backgrounds, labels=headers)
E.info("writing intersection/union matrix")
# build set intersection matrix
matrix = SetTools.unionIntersectionMatrix(genesets)
with IOTools.openFile(outfile + ".matrix.gz", "w") as outf:
IOTools.writeMatrix(outf, matrix, headers, headers)
matrix = SetTools.unionIntersectionMatrix(backgrounds)
with IOTools.openFile(outfile + ".bg.matrix.gz", "w") as outf:
IOTools.writeMatrix(outf, matrix, headers, headers)
def writeMatricesForSortOrder(features_per_interval,
bins,
foreground_track,
control_tracks,
shifted,
sort_order):
'''output one or more matrices for each sort sorder.
For each sort order output the forerground. If there
are additional controls and shifted section, output
these as well
The files will named:
matrix_<track>_<sortorder>
'''
if "name" in features_per_interval[0].interval:
names = [x.interval.name for x in features_per_interval]
else:
names = map(str, range(1, len(features_per_interval) + 1))
bins = ["%i" % x for x in bins]
sort_order = re.sub("-", "_", sort_order)
# write foreground
IOTools.writeMatrix(
E.openOutputFile("matrix_%s_%s.gz" % (foreground_track, sort_order)),
[x.foreground.counts for x in features_per_interval],
row_headers=names,
col_headers=bins,
row_header="name")
# write controls
for idx, track in enumerate(control_tracks):
IOTools.writeMatrix(
E.openOutputFile("matrix_%s_%s.gz" % (track, sort_order)),
[x.controls[idx].counts for x in features_per_interval],
row_headers=names,
col_headers=bins,
row_header="name")
# write shifted matrix
if shifted:
IOTools.writeMatrix(
E.openOutputFile("matrix_shift_%s.gz" % (sort_order)),
[x.shifted.counts for x in features_per_interval],
row_headers=names,
col_headers=bins,
row_header="name")
# output a combined matrix
if len(control_tracks) > 0 or shifted:
rows = []
for row in features_per_interval:
l = [row.foreground.counts]
l.extend([row.controls[x].counts for x in
range(len(control_tracks))])
if shifted:
l.append(row.shifted.counts)
rows.append(numpy.concatenate(l))
n = 1 + len(control_tracks)
if shifted:
n += 1
# make column names unique and make sure they can be sorted
# lexicographically
all_bins = []
for x in range(n):
all_bins.extend(["%i:%s" % (x, b) for b in bins])
IOTools.writeMatrix(
E.openOutputFile("matrix_sidebyside_%s.gz" % (sort_order)),
rows,
row_headers=names,
col_headers=all_bins,
row_header="name")
