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# Usage: python create_edges0.py parameter_for_net.txt
#
# Make it faster by spawning subprocesses.
#
# Quickly create edges using all samples in TPM.txt. TF and targets
# are from target_tf.txt. Results will be written to
# ../Data/history/edge_pool/edges.txt.simple.correlation.all.conditions.date
# target_tf.txt is produced by make_target_tf.py.
#
#
# 26 JAN 2017, hui, slcu
# Last modified 5 APR 2017, hui, slcu
import sys, os, operator, itertools
from datetime import datetime
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'
RESULT_FILE = '../Data/temp/corr_all.txt'
R_SCRIPT_FILE = '../Data/temp/compute_simple_correlation.r'
HISTORY_DIR = '../Data/history/edge_pool' # 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 make_r_script(fname, result_file, data_file, target_file, tf_file, r_tau=0.75):
head = 'OUTPUT_FILE <- \'%s\'\n DATA_FILE <- \'%s\'\n TARGET_FILE <- \'%s\'\n TF_FILE <- \'%s\'\n tau <- %0.2f\n' % (result_file, data_file, target_file, tf_file, r_tau)
body = '''
targets <- read.table(TARGET_FILE, header=FALSE)
tfs <- read.table(TF_FILE, header=FALSE)
X <- read.table(DATA_FILE, header=TRUE, check.names=FALSE)
targets <- as.vector(targets$V1)
tfs <- as.vector(tfs$V1)
all_genes <- rownames(X)
X <- as.matrix(X)
sd.1 <- apply(X, 1, sd) # sd of each row
s0 <- apply(X, 1, function(c) sum(c==0)) # number of zeros in each row
sd.tau <- (quantile(sd.1,na.rm=TRUE)[1] + quantile(sd.1,na.rm=TRUE)[2]) / 2.0 # min SD
good <- sd.1 > max(sd.tau, 0.05)
tf_good <- which( good & (all_genes %in% tfs) == T )
target_good <- which( good & (all_genes %in% targets) == T )
# compute correlation coefficient
X <- log(X + 1)
X[X<0.01] <- NA
if (length(tf_good) < 2) {
c <- cor(t(X[target_good,]), t(X[c(tf_good, tf_good),]), use='pairwise.complete.obs')
} else {
c <- cor(t(X[target_good,]), t(X[tf_good,]), use='pairwise.complete.obs')
}
index <- !is.na(c) & abs(c) >= tau & abs(c) <= 0.99
row_names <- rownames(c)
col_names <- colnames(c)
result <- data.frame(row = row_names[row(c)[index]], col = col_names[col(c)[index]], r = c[index])
# write results
write.table(result, OUTPUT_FILE, col.names=F, row.names=F, sep='\\t', quote=F)
'''
f = open(fname, 'w')
content = head + body
lst = [x.strip() for x in content.split('\n')]
f.write('\n'.join(lst))
f.close()
def edit_headline(fname):
''' Remove gene_id from first line. For easier R matrix reading. '''
new_fname = fname + '.copy'
f = open(fname)
lines = f.readlines()
f.close()
f = open(new_fname, 'w')
head = lines[0].strip()
head_lst = head.split('\t')[1:]
num_rnaseq = len(head.split('\t')) - 1
f.write('\t'.join(head_lst) + '\n')
for line in lines[1:]:
f.write(line)
f.close()
return new_fname, num_rnaseq
def establish_edges(corr_fname, target_tf_fname, result_fname, agi2name_dict, num_rnaseq, glb_param_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]))
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')
method_or_tissue = 'all' if glb_param_dict['EXPRESSION_MATRIX_DESCRIPTION'].strip() == '' else glb_param_dict['EXPRESSION_MATRIX_DESCRIPTION']
s = '\t'.join([target_str, tf_str, score_str, 'all', str(num_rnaseq), cond_str, '.', curr_date, score_str, method_or_tissue])
result += s + '\n'
f = open(result_fname, 'w')
f.write(result)
f.close()
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):
print('create_edges0: file %s does not exist. Produce this file use make_target_tf.py.' % (target_tf_fname))
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)
data_file, num_rnaseq = edit_headline(glb_param_dict['EXPRESSION_MATRIX'])
make_r_script(R_SCRIPT_FILE, RESULT_FILE, data_file, TARGET_FILE, TF_FILE, 0.60)
cmd = 'Rscript %s' % (R_SCRIPT_FILE)
os.system(cmd)
if not os.path.isdir(HISTORY_DIR):
os.makedirs(HISTORY_DIR)
curr_time = datetime.now().strftime('%Y%m%d_%H%M%S')
result_fname = os.path.join(HISTORY_DIR, 'edges.txt.simple.correlation.all.conditions.' + curr_time)
establish_edges(RESULT_FILE, target_tf_fname, result_fname, agi2name_dict, num_rnaseq, glb_param_dict) # change
cmd = 'rm -f %s %s %s %s %s' % (data_file, TARGET_FILE, TF_FILE, R_SCRIPT_FILE, RESULT_FILE)
os.system(cmd)
print('Done. Check %s.' % (result_fname))
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