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45_two_gene_co-ocurrence.py
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45_two_gene_co-ocurrence.py
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#! /usr/bin/python3
#
# This source code is part of icgc, an ICGC processing pipeline.
#
# Icgc is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# Icgc is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see<http://www.gnu.org/licenses/>.
#
# Contact: [email protected]
#
from icgc_utils.common_queries import *
from icgc_utils.icgc_stats import *
from config import Config
from numpy import cumsum
from time import time
from math import log10
verbose = False
###################################
def main():
size_corrected = True
if len(sys.argv) < 3:
print("usage: %s <bg gene> <gene 1> [<gene 2> ...]" % sys.argv[0])
exit()
bg_gene = sys.argv[1].upper()
other_genes = [g.upper() for g in sys.argv[2:]]
# TODO: check that all gene names exist
# rbf is a small C program that runs the simulation
# to evaluate Fisher-like probabilities for bins of uneven size (i.e probaility of being chosen)
rbf = Config().rbf_path()
db = connect_to_mysql(Config.mysql_conf_file)
cursor = db.cursor()
#########################
# which simple somatic tables do we have
qry = "select table_name from information_schema.tables "
qry += "where table_schema='icgc' and table_name like '%_simple_somatic'"
tables = [field[0] for field in search_db(cursor,qry)]
for table in tables:
#print("checking/creating index on", table)
create_index(cursor, 'icgc', 'donor_gene_idx', table,['icgc_donor_id','gene_symbol'])
#########################
switch_to_db(cursor,"icgc")
pancan_donors = 0
pancan_other = 0
pancan_bg_gene = 0
pancan_cooc = 0
write_to_file = len(other_genes)==1
if write_to_file:
outf = open("{}_cooccurrence.tsv".format("_".join([bg_gene]+other_genes)),"w")
outf.write("\t".join(['cancer','donors', "mutations in %s"%bg_gene,
"mutations in %s"%other_genes[0], 'cooccurrence','expected',
'p smaller', 'p bigger'])+"\n")
pancan_mut_count_values = []
p_smaller_sc, p_bigger_sc = 0 ,0 # to make the code checker shut up
for table in tables:
tumor_short = table.split("_")[0]
# mut_count = mutation_count_per_donor(cursor, table)
mut_count = genes_per_patient_breakdown(cursor, table)
patients_with_muts_in_gene = patients_per_gene_breakdown(cursor, table)
if patients_with_muts_in_gene.get(bg_gene,0)==0: continue
no_mutant = True
for gene in other_genes:
if patients_with_muts_in_gene.get(gene,0)==0: continue
no_mutant = False
break
if no_mutant: continue
cumulative_size = [0]
total_patients = len(mut_count)
# cumulative_size.extend(cumsum(list(mut_count.values())))
# pancan_mut_count_values.extend(list(mut_count.values()))
# hypermutated samples completely skew the stats
# shaving off the peaks this way seems to work,
# in the sense that is does not heavily overestimate the expected overlap
# but it returns the results that look just like Fisher
# should I just mark the hypermutated cancers as unreliable?
# Jaime Iranzo, Iñigo Martincorena, and Eugene V. Koonin
# https://www.pnas.org/content/115/26/E6010
# consider a sample with >3,000 mutations in a coding region a hypermutator
# I think I should go even lower - to >1,000 mutations
# This correction downplays the probabilty of highly mutated samples.
# I am not sure what is the justification, but it does produce
# much more reasonable results than plain number of mutated genes (or plain number of mutations)
weights = [int(10*log10(m)) for m in mut_count.values()]
cumulative_size.extend(cumsum(weights))
pancan_mut_count_values.extend(weights)
print("=================================")
print(table)
print("donors: ", total_patients)
pancan_donors += total_patients
for gene in [bg_gene]+other_genes:
print(gene, patients_with_muts_in_gene.get(gene, 0))
bg_gene_mutated = patients_with_muts_in_gene.get(bg_gene,0)
other_mutated = patients_with_muts_in_gene_group(cursor, table, other_genes)
pancan_bg_gene += bg_gene_mutated
pancan_other += other_mutated
cooc = co_ocurrence_w_group_count(cursor, table, bg_gene, other_genes)
pancan_cooc += cooc
#
p_smaller, p_bigger = myfisher(total_patients, bg_gene_mutated, other_mutated, cooc)
#pval_lt, pval_gt = fisher(donors, gene_1_mutated, other_mutated, cooc)
expected = float(bg_gene_mutated)/total_patients*other_mutated
print("co-ocurrence:", cooc)
print(" expected: %.1f" % expected)
print(" p_smaller: %.2f" % p_smaller)
print(" p_bigger: %.2f" % p_bigger)
if size_corrected:
selection_sizes = [bg_gene_mutated, other_mutated, cooc]
print("----------------------")
p_smaller_sc, p_bigger_sc, expected_ovlp_sc = size_corrected_pvals_python(cumulative_size, selection_sizes)
print("sc expected (python): %.1f" % expected_ovlp_sc)
print("sc p_smaller (python): %.2f" % p_smaller_sc)
print("sc p_bigger (python): %.2f" % p_bigger_sc)
p_smaller_sc, p_bigger_sc, expected_ovlp_sc = size_corrected_pvals_C (rbf, cumulative_size, selection_sizes)
print("sc expected: %.1f" % expected_ovlp_sc)
print("sc p_smaller: %.2f" % p_smaller_sc)
print("sc p_bigger: %.2f" % p_bigger_sc)
print()
if write_to_file: outf.write("%s\t%d\t%d\t%d\t%d\t%.1f\t%.2f\t%.1f\n"%
(tumor_short,total_patients, patients_with_muts_in_gene.get(bg_gene, 0),
patients_with_muts_in_gene.get(other_genes[0], 0),
cooc,expected,p_smaller,p_bigger))
p_smaller, p_bigger = myfisher(pancan_donors, pancan_bg_gene, pancan_other, pancan_cooc)
print()
print("=================================")
print(other_genes)
print("total donors:", pancan_donors)
print(" other:", pancan_other)
print("%12s: %d" % (bg_gene, pancan_bg_gene))
print(" cooc:", pancan_cooc)
print(" expected: %.1f" % (float(pancan_bg_gene)/pancan_donors*pancan_other))
print(" p_smaller: %.1e" % p_smaller)
print(" p_bigger: %.1e" % p_bigger)
if size_corrected:
selection_sizes = [pancan_bg_gene, pancan_other, pancan_cooc]
print("----------------------")
# time0 = time()
# python version takes 0 as the first value in the cumulative soze array
# pancan_cumulative_size = [0]
# pancan_cumulative_size.extend(cumsum(pancan_mut_count_values))
# p_smaller_sc, p_bigger_sc, expected_ovlp_sc = \
# size_corrected_pvals_python(pancan_cumulative_size, selection_sizes, number_of_simulation_rounds=100)
# print("\t\t time for size corrected sim (python): %.1f mins"% (float(time()-time0)/60))
# print("sc expected: %.1f" % expected_ovlp_sc)
# print("sc p_smaller: %.2e" % p_smaller_sc)
# print("sc p_bigger: %.2e" % p_bigger_sc)
time0 = time()
pancan_cumulative_size = cumsum(pancan_mut_count_values)
p_smaller_sc, p_bigger_sc, expected_ovlp_sc = \
size_corrected_pvals_C(rbf, pancan_cumulative_size, selection_sizes, number_of_simulation_rounds=1.0e4)
print("\t\t time for size corrected sim: %.1f mins"% (float(time()-time0)/60))
print("sc expected: %.1f" % expected_ovlp_sc)
print("sc p_smaller: %.2e" % p_smaller_sc)
print("sc p_bigger: %.2e" % p_bigger_sc)
expected = (float(pancan_bg_gene)/pancan_donors*pancan_other)
if write_to_file: outf.write("%s\t%d\t%d\t%d\t%d\t%.1f\t%.1e\t%.1e\n"%
("total", pancan_donors, pancan_bg_gene,
pancan_other, pancan_cooc, expected, p_smaller, p_bigger))
if write_to_file: outf.close()
#print myfisher(total_donors*4, total_gene_1*4, total_other*4, total_cooc*4)
cursor.close()
db.close()
#########################################
if __name__ == '__main__':
main()