# Usage: python create_edges0.py parameter_for_net.txt
#
# Purpose: tissue specific
#
# Quickly create edges using all samples in TPM.txt (with the same
# tissue). tfs and targets are from target_tf.txt. Results will be
# written to
# ../Data/history/edges/many_targets/edges.txt.simple.correlation.tissue.date
# target_tf.txt is produced by make_target_tf.py.
#
#
# 26 JAN 2017, hui, slcu
# Last modified 13 June 2017, hui, slcu
# Last modified 8 Aug 2019, hui, zjnu
import sys, os, operator, itertools, glob
from datetime import datetime
from configure import UPDATE_NETWORK_LOG_FILE
from geneid2name import make_gene_name_AGI_map_dict, get_gene_name
from param4net import make_global_param_dict
TARGET_FILE = '../Data/temp/all_targets.txt'
TF_FILE = '../Data/temp/all_tfs.txt'
R_SCRIPT_FILE = 'correlation_per_tissue.R'
TISSUE_INFO_FILE = '../Data/information/experiment.and.tissue.txt' # make sure this file is the same as TISSUE.FILE in R_SCRIPT_FILE
HISTORY_DIR = '../Data/history/edges/many_targets' # edges.txt.* files are here
def get_value(s, delimit):
lst = s.split(delimit)
return lst[1].strip()
def get_gene_list(fname):
result = []
f = open(fname)
for line in f:
line = line.strip()
lst = line.split()
result.append(lst[0])
f.close()
return result
def make_tf_dict(fname):
d = {}
f = open(fname)
for line in f:
line = line.strip()
lst = line.split('\t')
target = lst[0]
tf = lst[1]
cond = lst[2].split()
if not target in d:
d[target] = {tf:cond}
else:
d[target][tf] = cond
f.close()
return d
def get_targets_and_tfs(fname):
f = open(fname)
target_lst = []
tf_lst = []
for line in f:
line = line.strip()
lst = line.split('\t')
target = lst[0]
tf = lst[1]
target_lst.append(target)
tf_lst.append(tf)
f.close()
return sorted(list(set(target_lst))), sorted(list(set(tf_lst)))
def write_lst_to_file(lst, fname):
f = open(fname, 'w')
for x in lst:
f.write(x + '\n')
f.close()
def establish_edges(corr_fname, target_tf_fname, result_fname, agi2name_dict, tissue_dict, loglikhood_dict):
big_tf_dict = make_tf_dict(target_tf_fname)
f = open(corr_fname)
lines = f.readlines()
f.close()
result = ''
for line in lines:
line = line.strip()
lst = line.split('\t')
target = lst[0]
tf = lst[1]
score = '%4.2f' % (float(lst[2]))
tissue = lst[3]
num_rnaseq_id = lst[4]
loglike = '-9999.0'
if tissue in loglikhood_dict:
loglik = loglikhood_dict[tissue]
if target in big_tf_dict and tf in big_tf_dict[target]:
target_str = target + ' ' + get_gene_name(target, agi2name_dict)
tf_str = tf + ' ' + get_gene_name(tf, agi2name_dict)
score_str = score
cond_str = ' '.join(big_tf_dict[target][tf])
curr_date = datetime.now().strftime('%Y%m%d')
rnaseq_subset = '.'
if tissue in tissue_dict:
rnaseq_subset = ' '.join(list(set(tissue_dict[tissue])))
s = '\t'.join([target_str, tf_str, score_str, 'all', num_rnaseq_id, cond_str, loglik, curr_date, score_str, tissue])
result += s + '\n'
f = open(result_fname, 'w')
f.write(result)
f.close()
def get_tissue_from_filename(s, d):
for k in d:
if k in s:
return k, d[k]
return 'unknown', '-9999.0'
def make_tissue_dict(fname):
f = open(fname)
lines = f.readlines()
f.close()
d = {}
for line in lines[1:]:
line = line.strip()
if line != '':
lst = line.split('\t')
k = lst[0] # run.id
v = lst[4]
d[k] = v
k2 = v.split('.')[0] # broad tissue category, ignore subcategories, for example, flower.anther, only keep flower.
if not k2 in d:
d[k2] = [k]
else:
d[k2].append(k)
return d
def target_tf_file_compare_same(fname1, fname2):
if not os.path.exists(fname1):
return False
if not os.path.exists(fname2):
return False
f1 = open(fname1)
s1 = f1.read()
f1.close()
f2 = open(fname2)
s2 = f2.read()
f2.close()
return s1 == s2
########## main ##################################################
param_file = sys.argv[1] # a single prameter file
glb_param_dict = make_global_param_dict(param_file)
agi2name_dict = make_gene_name_AGI_map_dict(glb_param_dict['GENE_ID_AND_GENE_NAME'])
target_tf_fname = '../Data/information/target_tf.txt'
if not os.path.exists(target_tf_fname):
write_log_file('[create_edges0B.py] Critical file %s does not exists.' % (target_tf_fname), UPDATE_NETWORK_LOG_FILE)
sys.exit()
all_targets, all_tfs = get_targets_and_tfs(target_tf_fname)
write_lst_to_file(all_targets, TARGET_FILE)
write_lst_to_file(all_tfs, TF_FILE)
if os.path.exists(R_SCRIPT_FILE):
cmd = 'Rscript %s' % (R_SCRIPT_FILE)
os.system(cmd)
else:
sys.exit()
loglikhood_dict = {
'seedling':'-999.0',
'meristem':'-998.0',
'root':'-997.0',
'leaf':'-996.0',
'flower':'-995.0',
'shoot':'-994.0',
'seed':'-993.0',
'stem':'-992.0',
'aerial':'-990.0'
}
if not os.path.isdir(HISTORY_DIR):
os.makedirs(HISTORY_DIR)
file_lst = glob.glob('../Data/temp/edges.txt.simple.correlation.tissue.*.txt')
curr_time = datetime.now().strftime('%Y%m%d_%H%M%S')
if os.path.exists(TISSUE_INFO_FILE):
tissue_dict = make_tissue_dict(TISSUE_INFO_FILE) # assign each rnaseq a tissue, and each tissue a list of rnaseq
else:
sys.exit()
for fname in file_lst:
tissue, loglik_placeholder = get_tissue_from_filename(fname, loglikhood_dict)
if tissue != 'unknown':
print(fname)
result_fname = os.path.join(HISTORY_DIR, 'edges.txt.simple.correlation.%s.%s' % (tissue, curr_time))
RESULT_FILE = fname
establish_edges(RESULT_FILE, target_tf_fname, result_fname, agi2name_dict, tissue_dict, loglikhood_dict) # change
cmd = 'rm -f %s %s' % (TARGET_FILE, TF_FILE)
os.system(cmd)
cmd = 'rm -f ../Data/temp/edges.txt.simple.correlation.tissue.*.txt'
os.system(cmd)
#print('Done.')