small_bonferroni.py

import argparse
import sys
import os
import re
import time
import multiprocessing
import logging
import subprocess
from Bio import SeqIO
import Bio.SeqIO.FastaIO as bio
import numpy as np
from collections import defaultdict
from scipy import stats
import pyBigWig
from statsmodels.sandbox.stats.multicomp import multipletests

def main():

	read_file = "test.txt"
	motifs_array = []
	p_values_array = []

	with open(read_file) as r_file:
		for r_line in r_file:
			r_line_array = re.split(r'\s', r_line.rstrip('\n'))
			if float(r_line_array[1]) > 9.7e-90:
				motifs_array.append(r_line_array[0])
				p_values_array.append(float(r_line_array[1]))
			
	r_file.close()

	#apply bonferroni correction
	p_adjusted = multipletests(p_values_array, method = 'bonferroni')[1]

	dict_motifs_pvalues = {}

	for i in range(len(motifs_array)):
		motif = motifs_array[i]
		dict_motifs_pvalues[motif] = dict_motifs_pvalues.get(motif, {})
		dict_motifs_pvalues[motif] = {'fisher_p_value': p_values_array[i], 'adjusted_p_value': p_adjusted[i]}

	#now sort the data after adjusted p_value
	sorted_dict = sorted(dict_motifs_pvalues.items(), key = lambda x : (x[1]['adjusted_p_value']), reverse = False)

	for i in range(35):
		print(sorted_dict[i])

	
if __name__ == "__main__":
	main()