def main(outdir=None, gpl_brief=None, gpl_data=None, study_data=None, varlist_fname=None, percentile=0.25):
assert outdir and gpl_brief and gpl_data and study_data
percentile = float(percentile)
assert percentile >= 0
if not os.path.exists(outdir):
print "Creating output directory %s..." % (outdir)
make_dir(outdir)
d = run(gpl_brief=gpl_brief, gpl_data=gpl_data, study_data=study_data, varlist_fname=varlist_fname, percentile=percentile)
idxs, gpl, M, varlist = d['idxs'], d['gpl'], d['M'], d['varlist']
# Output index results.
study_id = os.path.basename(gpl_data).partition('.')[0]
out_idx_fname = os.path.join(outdir, "%s.symbol_rownums.gt%.2f.txt" % (study_id, percentile))
print "Saving sorted idx list in line format '[row_num]\\t[probe ID]\\t[gene symbol]\\n' to %s" \
% (out_idx_fname)
fp = open(out_idx_fname, "w")
for i in idxs:
fp.write("%d\t%s\t%s\n" % (i, varlist[i], gpl.get_column(varlist[i], 'GENE_SYMBOL')))
fp.close()
# Save data array copy of only selected rows.
out_M_fname = os.path.join(outdir, "%s.gt%.2f.tab" % (os.path.basename(study_data), percentile))
print "Saving %d selected rows of data matrix as .tab format as %s" % (len(idxs), out_M_fname)
masked_npy_to_tab.npy_to_tab( \
M[idxs, :], open(out_M_fname, 'w'), varlist=[varlist[i] for i in idxs])
def main(pina_zip=None, outdir=None, gpl_brief=None, gpl_data=None, study_data=None, varlist_fname=None, percentile=0.25):
# Load files
assert outdir and gpl_brief and gpl_data and study_data and pina_zip
percentile = float(percentile)
assert percentile >= 0
if not os.path.exists(outdir):
print "Creating output directory %s..." % (outdir)
make_dir(outdir)
d = run(gpl_brief=gpl_brief, gpl_data=gpl_data, study_data=study_data, varlist_fname=varlist_fname, percentile=percentile)
# Load enrichment from single-file .zip archive
print "Loading PINA interaction file from zip archive %s." % (pina_zip)
zfp = zipfile.ZipFile(pina_zip, "r")
P = PINAEnriched(zfp.open(zfp.namelist()[0]))
print "Loaded %d interactions between %d unique genes" % (P.n_pairs, len(P.vars))
# Filter by symbols in enrichment list
idxs = []
for v, i in d['d_symbols'].items():
if P.is_in(v):
idxs.append(i)
idxs.sort()
print "Selected %d variables in PINA gene list." % len(idxs)
# Assert that selected rows are all in PINA list and are all unique.
print "Asserting that all row numbers correspond to unique gene symbols in PINA list."
sym_set = set()
for i in idxs:
s = d['gpl'].get_column(d['varlist'][i], 'GENE_SYMBOL')
assert P.is_in(s)
assert s not in sym_set
sym_set.add(s)
# Output index results.
study_id = os.path.basename(gpl_data).partition('.')[0]
out_idx_fname = os.path.join(outdir, "%s.symbol_rownums.gt%.2f.pina.txt" % (study_id, percentile))
print "Saving sorted idx list in line format '[row_num]\\t[probe ID]\\t[gene symbol]\\n' to %s" \
% (out_idx_fname)
fp = open(out_idx_fname, "w")
for i in idxs:
fp.write("%d\t%s\t%s\n" % (i, d['varlist'][i], d['gpl'].get_column(d['varlist'][i], 'GENE_SYMBOL')))
fp.close()
# Save data array copy of only selected rows.
out_M_fname = os.path.join(outdir, "%s.gt%.2f.pina.tab" % (os.path.basename(study_data), percentile))
print "Saving %d selected rows of data matrix as .tab format as %s" % (len(idxs), out_M_fname)
masked_npy_to_tab.npy_to_tab( \
d['M'][idxs, :], open(out_M_fname, 'w'), varlist=[d['varlist'][i] for i in idxs])