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# Make tissue specific networks
import os, sys
from geneid2name import make_gene_name_AGI_map_dict
def get_tfs(fname_lst):
d = {}
for fname in fname_lst:
f = open(fname)
for line in f:
line = line.strip()
lst = line.split('\t')
tf = lst[1].split()[0]
if not tf in d:
d[tf] = 1
else:
d[tf] += 1
f.close()
return d
def get_tissue_from_fname(fname):
tissue_lst = [
'seedling',
'meristem',
'flower',
'aerial',
'shoot',
'seed',
'leaf',
'root',
'stem']
for x in tissue_lst:
if x in fname:
return x
return 'unknown'
def get_edges_consisting_of_tfs(fname_lst, tf_dict):
d = {}
for fname in fname_lst:
kt = get_tissue_from_fname(fname)
d[kt] = {}
f = open(fname)
for line in f:
line = line.strip()
lst = line.split('\t')
target = lst[0].split()[0].strip()
tf = lst[1].split()[0].strip()
k = target + '_' + tf
score = float(lst[2])
if tf in tf_dict and target in tf_dict:
if not k in d[kt]:
d[kt][k] = [(lst[0], lst[1], score)]
else:
d[kt][k].append((lst[0], lst[1], score))
f.close()
return d
def get_degree(fname_lst, tf_dict):
d_out = {}
d_in = {}
d_all = {}
for fname in fname_lst:
kt = get_tissue_from_fname(fname)
d_out[kt] = {}
d_in[kt] = {}
d_all[kt] = {}
f = open(fname)
for line in f:
line = line.strip()
lst = line.split('\t')
target = lst[0].split()[0].strip()
tf = lst[1].split()[0].strip()
if True or tf in tf_dict and target in tf_dict:
if not tf in d_out[kt]:
d_out[kt][tf] = 1
else:
d_out[kt][tf] += 1
if not target in d_in[kt]:
d_in[kt][target] = 1
else:
d_in[kt][target] += 1
if not target in d_all[kt]:
d_all[kt][target] = 1
else:
d_all[kt][target] += 1
if not tf in d_all[kt]:
d_all[kt][tf] = 1
else:
d_all[kt][tf] += 1
f.close()
return d_all, d_out, d_in
def simplify(s):
result = ''
lst = s.split('\t')
a = (lst[0].split()[1]).split(';')[0]
if a == '.':
a = lst[0].split()[0]
else:
a = lst[0].split()[0] + '_' + (lst[0].split()[1]).split(';')[0]
b = (lst[1].split()[1]).split(';')[0]
if b == '.':
b = lst[1].split()[0]
else:
b = lst[1].split()[0] + '_' + (lst[1].split()[1]).split(';')[0]
return '%s\t%s\t%s' % (a, b, lst[2])
# main
GENE_ID_TO_GENE_NAME = '../Data/information/AGI-to-gene-names_v2.txt'
agi2name_dict = make_gene_name_AGI_map_dict(GENE_ID_TO_GENE_NAME)
edge_file_lst = [
'/home/hui/network/v03/Data/history/edges/many_targets/edges.txt.simple.correlation.seedling.txt.20170629_203729',
'/home/hui/network/v03/Data/history/edges/many_targets/edges.txt.simple.correlation.meristem.txt.20170629_203729',
'/home/hui/network/v03/Data/history/edges/many_targets/edges.txt.simple.correlation.flower.txt.20170629_203729',
'/home/hui/network/v03/Data/history/edges/many_targets/edges.txt.simple.correlation.aerial.txt.20170629_203729',
'/home/hui/network/v03/Data/history/edges/many_targets/edges.txt.simple.correlation.shoot.txt.20170629_203729',
'/home/hui/network/v03/Data/history/edges/many_targets/edges.txt.simple.correlation.seed.txt.20170629_203729',
'/home/hui/network/v03/Data/history/edges/many_targets/edges.txt.simple.correlation.leaf.txt.20170629_203729',
'/home/hui/network/v03/Data/history/edges/many_targets/edges.txt.simple.correlation.root.txt.20170629_203729',
'/home/hui/network/v03/Data/history/edges/many_targets/edges.txt.simple.correlation.stem.txt.20170629_203729'
]
tf_dict = get_tfs(edge_file_lst)
f = open('result.skeleton.txt', 'w')
print('Total number of TFs: %d' % (len(tf_dict)))
d0 = get_edges_consisting_of_tfs(edge_file_lst, tf_dict)
for kt in d0: # kt is tissue
f.write('##TF skeleton size in %s: %d.\n' % (kt, len(d0[kt])))
d = d0[kt]
for k in d:
lst = d[k]
for x in lst: # {'shoot':{'target_tf':[], }, 'flower':{} }
max_score = -9
s = ''
if abs(x[2]) > max_score:
s = '%s\t%s\t%4.2f' % (x[0], x[1], x[2])
max_score = x[2]
f.write(simplify(s) + '\n')
f.close()
# for each TF, get its out-degree and in-degree in each tissue
dd_all, dd_out, dd_in = get_degree(edge_file_lst, tf_dict)
f = open('result.out.txt', 'w')
head_lst = ['TF']
for k in dd_out:
head_lst.append(k)
f.write('%s\n' %('\t'.join(head_lst)))
for tf in tf_dict:
s = tf
name = '.'
if tf in agi2name_dict and agi2name_dict[tf] != tf:
name = agi2name_dict[tf]
s += ' ' + name
for k in dd_out:
if tf in dd_out[k]:
s += '\t%d' % (dd_out[k][tf])
else:
s += '\t0'
f.write(s + '\n')
f.close()
f = open('result.in.txt', 'w')
head_lst = ['TF']
for k in dd_in:
head_lst.append(k)
f.write('%s\n' %('\t'.join(head_lst)))
for tf in tf_dict:
s = tf
name = '.'
if tf in agi2name_dict and agi2name_dict[tf] != tf:
name = agi2name_dict[tf]
s += ' ' + name
for k in dd_in:
if tf in dd_in[k]:
s += '\t%d' % (dd_in[k][tf])
else:
s += '\t0'
f.write(s + '\n')
f.close()
f = open('result.all.txt', 'w')
head_lst = ['TF']
for k in dd_all:
head_lst.append(k)
f.write('%s\n' %('\t'.join(head_lst)))
for tf in tf_dict:
s = tf
name = '.'
if tf in agi2name_dict and agi2name_dict[tf] != tf:
name = agi2name_dict[tf]
s += ' ' + name
for k in dd_all:
if tf in dd_all[k]:
s += '\t%d' % (dd_all[k][tf])
else:
s += '\t0'
f.write(s + '\n')
f.close()
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