run_all_442.py

import gsea_incontext_notk
import os
import numpy as np
from numpy.random import choice
from collections import defaultdict
import csv
import sys, logging

rnks = os.listdir('data/rnk_lists')
rnks = [r for r in rnks if r.startswith('GSE') and r.endswith('.rnk')]

for rnk in rnks:

	# Run GSEA preranked - Hallmarks
	prerank_results = gsea_incontext_notk.prerank(
		rnk='data/rnk_lists/' + rnk,
		gene_sets='data/gene_sets/hallmarks.gmt', 
		outdir='out/Prerank_Hallmarks/' + rnk[:-4], 
		permutation_num=100, 
		graph_num=20,
		processes=4
	)
	

	# Run GSEA-InContext - Kegg
	gseapen_results = gsea_incontext_notk.incontext(
		rnk='data/rnk_lists/' + rnk,
		gene_sets='data/gene_sets/hallmarks.gmt', 
		background_rnks='data/bg_rnk_lists/all_442_lists_permuted_x100.csv', 
		outdir='out/InContext_Hallmarks/' + rnk[:-4], 
		permutation_num=100,
		graph_num=20,
		processes=4
	)

	# Run GSEA preranked - Kegg
	prerank_results = gsea_incontext_notk.prerank(
		rnk='data/rnk_lists/' + rnk,
		gene_sets='data/gene_sets/c2.cp.kegg.v6.0.symbols.gmt', 
		outdir='out/Prerank_KEGG/' + rnk[:-4], 
		permutation_num=100, 
		graph_num=20,
		processes=4
	)
	

	# Run GSEA-InContext - Kegg
	gseapen_results = gsea_incontext_notk.incontext(
		rnk='data/rnk_lists/' + rnk,
		gene_sets='data/gene_sets/c2.cp.kegg.v6.0.symbols.gmt', 
		background_rnks='data/bg_rnk_lists/all_442_lists_permuted_x100.csv', 
		outdir='out/InContext_KEGG/' + rnk[:-4], 
		permutation_num=100,
		graph_num=20,
		processes=4
	)

print('Done!')