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ESscore_TE_TF_neural_teleng_control.py
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ESscore_TE_TF_neural_teleng_control.py
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#!/usr/bin/env python
# coding: utf-8
# In[214]:
import csv
from re import S
import numpy as np
from scipy.sparse import csr_matrix, csc_matrix, coo_matrix, lil_matrix
import scipy.io
import collections
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('-tf','--tf',type=str,default='N',help='tfname')
parser.add_argument('-pm','--pm',type=str,default='N',help='p:peak,m:,motif')
parser.add_argument('-pre','--pre',type=str,default='N',help='control prepare')
args=parser.parse_args()
tf=args.tf
pm=args.pm
pre=args.pre
with open('data/data_3_bigdata_mm9_onlyTE.bed') as f:
reader = csv.reader(f, delimiter='\t')
TE_peak = [row for row in reader]
# In[151]:
tename=[]
for i in range(len(TE_peak)):
tename.append(TE_peak[i][3])
telabel=list(set(tename))
f = open('data/te_tf_mat_TElabel_cortex.txt')
data3 = f.read()
f.close()
tename= data3.split('\n')
# In[119]:
#tename=np.delete(tename,-1)
tename=list(tename)
TEmat=[[0 for i in range(3)] for j in range(len(tename))]
for i in range(len(TE_peak)):
TEmat[tename.index(TE_peak[i][3])][0]+=float(((int(TE_peak[i][2])-int(TE_peak[i][1]))))
# In[1872]:
for i in range(len(TEmat)):
TEmat[i][2]=float(TEmat[i][0])
leng=[]
s3=[]
s4=[]
for i in range(len(TEmat)):
s3.append(TEmat[i][2])
s4.append(2654895218)
leng.append(TEmat[i][2]/(2654895218))
with open('out/background_peaks_kmers_500bp.csv') as f:
reader = csv.reader(f, delimiter=' ')
bg = [row for row in reader]
cp=[]
for i in range(1,len(bg)):
cp.append(bg[i][1:])
bg=cp
bg=np.array(bg,dtype='float')
bg=np.array(bg,dtype='int')
if pm=='m' and pre=='Y':
f = open('out/merged_cortex_500bp.txt')
data3 = f.read()
f.close()
peakid= data3.split('\n')
peakid=peakid[:-1]
peak=[]
for i in range(len(peakid)):
peak.append(peakid[i].split('_'))
with open('out/'+str(tf)+'_motif_pos_ATAC_merge.bed') as f:
reader = csv.reader(f, delimiter='\t')
motifATAC = [row for row in reader]
for i in range(len(bg[0])):
cp=[]
for j in range(len(motifATAC)):
ind_p=peakid.index('_'.join(motifATAC[j][-3:]))
ind=bg[ind_p,i]-1
cp.append([peak[ind][0],int(peak[ind][1])+int(motifATAC[j][1])-int(motifATAC[j][-2]),int(peak[ind][1])+int(motifATAC[j][2])-int(motifATAC[j][-2]),motifATAC[j][3],motifATAC[j][4],peak[ind][0],peak[ind][1],peak[ind][2]])
motifATAC=cp
with open('out/'+str(tf)+'_motif_pos_ATAC_merge_control_'+str(i+1)+'.bed','w') as file:
writer = csv.writer(file,delimiter='\t')
writer.writerows(motifATAC)
elif pm=='m':
for k in range(len(bg[0])):
with open('out/'+str(tf)+'_motif_pos_TEsubfamily_ATA_control_'+str(k+1)+'.txt') as f:
reader = csv.reader(f, delimiter='\t')
motifTE = [row for row in reader]
with open('out/'+str(tf)+'_motif_pos_ATAC_merge_control_'+str(k+1)+'.bed') as f:
reader = csv.reader(f, delimiter='\t')
motifATAC = [row for row in reader]
motifind=[]
ATACind=[]
for i in range(len(motifATAC)):
motifind.append([motifATAC[i][0],motifATAC[i][1],motifATAC[i][2]])
ATACind.append([motifATAC[i][-3],motifATAC[i][-2],motifATAC[i][-1]])
a=[0]*len(tename)
for j in range(len(tename)):
count=[]
for i in range(len(motifTE)):
if motifTE[i][-2]==tename[j]:
ind=motifind.index([motifTE[i][0],motifTE[i][1],motifTE[i][2]])
count.append([motifATAC[ind][-3],motifATAC[ind][-2],motifATAC[ind][-1]])
a[j]+=len(list(map(list, set(map(tuple, count)))))
out=[]
s1=[]
s2=[]
for i in range(len(a)):
out.append(a[i])
a_2=len(list(map(list, set(map(tuple, ATACind)))))
for i in range(len(a)):
s1.append(a[i])
s2.append(a_2)
a[i]=float(a[i])/float(a_2)
import math
ans=[]
for i in range(len(a)):
if leng[i]==0.0 or a[i]==0.0:
ans.append('None')
else:
ans.append(math.log(a[i]/leng[i],2))
s=[s1,s2,s3,s4]
with open('out/'+str(tf)+'_TE_log2fold_count_motif_control_'+str(k+1)+'_detail.bed', 'w') as file:
writer = csv.writer(file,delimiter='\t')
writer.writerows(s)
f = open('out/'+str(tf)+'_TE_log2fold_count_motif_control_'+str(k+1)+'.txt', 'w')
for x in ans:
f.write(str(x) + "\n")
f.close()
f = open('out/'+str(tf)+'_TE_log2fold_count_motif_outTE_control_'+str(k+1)+'.txt', 'w')
for x in out:
f.write(str(x) + "\n")
f.close()