def main():
if len(sys.argv) != 2:
print "Usage:python %s mouse/tcga_cancer_index" % sys.argv[0]
exit()
data_type = sys.argv[1]
assert data_type == "mouse" or data_type.isdigit()
if data_type.isdigit():
data_type = file_operations.get_tcga_disease_list()[int(data_type)]
if data_type == "mouse":
pcc_threshold = 0.9
else:
# TCGA coefficients are worse.
pcc_threshold = 0.5
# Read in the tsv file.
gene_exp_dct = file_operations.get_gene_expression_dct(data_type)
high_std_genes = file_operations.get_high_std_genes(data_type)
gene_exp_matrix = create_gene_exp_matrix(gene_exp_dct, high_std_genes)
r, p = corrcoef(gene_exp_matrix)
out = open("./data/%s_data/high_std_network.txt" % data_type, "w")
for row_idx, row in enumerate(r):
for col_idx, pcc in enumerate(row):
if col_idx <= row_idx or pcc < pcc_threshold or pcc == 1:
continue
# if p[row_idx][col_idx] > P_VALUE_THRESOLD:
# continue
# Write out gene information.
gene_a, gene_b = (high_std_genes[row_idx], high_std_genes[col_idx])
out.write("%s\t%s\t%f\n" % (gene_a, gene_b, abs(pcc)))
out.close()
def get_go_domains(data_type):
'''
Returns three dictionaries, one corresponding to each GO domain. Keys are
genes, and values are GO terms.
'''
high_std_genes = file_operations.get_high_std_genes(data_type)
# bp = biological process, mf = molecular function.
bp_go_gene_dct = {}
mf_go_gene_dct = {}
if data_type == 'mouse':
f = open('./data/mouse_data/pantherGeneList_mouse.txt', 'r')
else:
f = open('./data/tcga_data/pantherGeneList_tcga.txt', 'r')
for line in f:
(gene_id, ensembl_id_list, ortholog, family, protein_class, species,
bp_term_list, mf_term_list) = line.split('\t')
# Process the GO terms.
bp_term_list = process_go_term_list(bp_term_list)
mf_term_list = process_go_term_list(mf_term_list)
# Associate each GO term list with the corresponding gene.
ensembl_id_list = ensembl_id_list.split(',')
for gene in ensembl_id_list:
assert ('ENSMUSG' in gene or 'ENSG' in gene)
if gene not in high_std_genes:
continue
add_to_dictionary(gene, bp_term_list, bp_go_gene_dct)
add_to_dictionary(gene, mf_term_list, mf_go_gene_dct)
f.close()
return bp_go_gene_dct, mf_go_gene_dct
def main():
if len(sys.argv) != 4:
print 'Usage:python %s data_type objective_function run_num' % sys.argv[0]
exit()
global data_type, objective_function, run_num
data_type = sys.argv[1]
assert data_type == 'mouse' or data_type.isdigit()
objective_function = sys.argv[2]
assert objective_function in ['oclode', 'schaeffer', 'wlogv', 'prosnet']
run_num = sys.argv[3]
assert run_num.isdigit()
if data_type.isdigit():
data_type = file_operations.get_tcga_disease_list()[int(data_type)]
go_dct_list = read_go_dictionaries()
global gene_universe
gene_universe = file_operations.get_high_std_genes(data_type)
# Get the overlapping MF and BP terms.
overlap_list = file_operations.read_go_overlap(data_type)
# Only evaluate on BP for now.
for domain_index in [0]:
go_dct = go_dct_list[domain_index]
# Remove the overlapping BP terms.
overlapping_go_terms = set([tup[domain_index] for tup in overlap_list])
for overlapping_go in overlapping_go_terms:
del go_dct[overlapping_go]
# No GO network.
no_go_cluster_fname = './results/%s_results/%s/clusters_no_go/clusters_no_go_%s.txt' % (
data_type, objective_function, run_num)
no_go_fname = './results/%s_results/%s/cluster_enrichment_terms_no_go/cluster_' % (
data_type, objective_function)
no_go_fname += 'enrichment_terms_no_go_%s_%d.txt' % (run_num,
domain_index)
write_enrichment_files(no_go_cluster_fname, no_go_fname, go_dct)
# GO network.
cluster_fname = './results/%s_results/%s/clusters_go/clusters_go_clean_%s_%d.txt' % (
data_type, objective_function, run_num, domain_index)
go_fname = './results/%s_results/%s/cluster_enrichment_terms_go/cluster_' % (
data_type, objective_function)
go_fname += 'enrichment_terms_go_%s_%d.txt' % (run_num, domain_index)
write_enrichment_files(cluster_fname, go_fname, go_dct)
