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def _enrichr(self, category, background=None, verbose=True):
if background is None:
background = self.background
if isinstance(category, list):
gene_list = category
else:
assert category in ['up', 'down', 'all']
gene_list = list(self.rnadiff.dr_gene_lists[self.comparison][category])
enr = gseapy.enrichr(
gene_list=gene_list,
gene_sets=self.gene_sets,
verbose=verbose,
background=background,
outdir="test", no_plot=True)
return enr
# Load arguments
tad_gene_file = args.tad_genelist_file
output_name = args.output_name
output_dir = args.output_directory
gene_sets = args.gene_sets
# Save output file locations
out_dir = os.path.join(output_dir, output_name)
# Load data
gene_df = pd.read_table(tad_gene_file)
genes = gene_df.gene_name
output_results_file = "{}_enrichr_results.tsv".format(out_dir)
# Perform the enrichment analysis
enr = gp.enrichr(gene_list=genes,
description=output_name,
gene_sets=gene_sets,
outdir=out_dir,
cutoff=0.5)
# Output results
results_df = enr.results.sort_values(by='Combined Score', ascending=False)
results_df.to_csv(output_results_file, sep='\t', index=False)