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# Usage: python exclude_edges.py edges.txt
#
# Purpose: Exclude the edge whose TF is in exclude_tf_list, since we want to hide unpublished data. Don't distinguish +/- edges.
# For a TF-Target pair, use the edge that has largest value of metric. If correlation is negative, use its absolute value.
#
# Created by Hui on 5 Jan 2018
import os, sys
def remove_minus(s):
''' Remove the minus sign in s '''
index = s.find('-')
return s[index+1:]
def neg2pos(s):
s = s.strip()
lst = s.split('\t')
x = float(lst[2])
if x < 0:
lst[2] = remove_minus(lst[2])
return '\t'.join(lst)
def make_edge_dict(fname, exclude_lst):
d = {}
f = open(fname)
for line in f:
line = line.strip()
lst = line.split('\t')
tf_id = lst[1].split()[0]
if not tf_id in exclude_lst:
target_id = lst[0].split()[0]
metric = float(lst[8])
k = tf_id + '.' + target_id
if not k in d:
d[k] = {}
d[k]['metric'] = metric
d[k]['line'] = neg2pos(line) # make the third field (correlation) positive if it is negative. Indicate influence, not activation/repression.
else:
if d[k]['metric'] < metric:
d[k]['metric'] = metric
d[k]['line'] = neg2pos(line)
f.close()
return d
# main
exclude_tf_list = ['AT4G26840', 'AT3G18550']
edge_file_name = sys.argv[1]
d = make_edge_dict(edge_file_name, exclude_tf_list)
f = open(edge_file_name, 'w') # this will make edge.txt empty
for k in sorted(d.keys()):
f.write('%s\n' % d[k]['line'])
f.close()
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