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Distribution_of_classes_of_the_TE.py
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Distribution_of_classes_of_the_TE.py
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#!/usr/bin/env python
# coding: utf-8
# In[1]:
import csv
import numpy as np
import pandas as pd
import seaborn as sns
import itertools
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import scipy.stats as st
from pybedtools import BedTool
import pybedtools
with open('data/data_3_bigdata_mm9_onlyTE.bed') as f:
reader = csv.reader(f, delimiter='\t')
TE = [row for row in reader]
g_reg=[0,0,0,0,0]
j=0
refcl=['LINE','SINE','LTR','DNA','the_others']
cl=[[],[],[],[],[]]
ans=[0,0,0,0,0]
for i in range(len(ATAC)):
if refcl.count(ATAC[i][-1].split('/')[0])>0:
cl[refcl.index(ATAC[i][-1].split('/')[0])].append([ATAC[i][0],int(ATAC[i][1]),int(ATAC[i][2])])
else:
cl[4].append([ATAC[i][0],int(ATAC[i][1]),int(ATAC[i][2])])
for j in range(len(refcl)):
with open('out/TE_test_merge_'+str(refcl[j])+'.bed','w') as file:
writer = csv.writer(file,delimiter='\t')
writer.writerows(cl[j])
a = pybedtools.example_bedtool('out/TE_merge_'+str(cl[j])+'.bed')
b=a.sort()
c = b.merge()
reg=0
for i in range(len(c)):
reg+=c[i].end-c[i].start
g_reg[j]=reg
## ChIP-seq peak
with open('data/TE_contain_Neurod2_peak_neural_merge.bed') as f:
reader = csv.reader(f, delimiter='\t')
TEATAC = [row for row in reader]
refcl=['LINE','SINE','LTR','DNA','the_others']
cl=[[],[],[],[],[]]
ans=[0,0,0,0,0]
for i in range(len(TEATAC)):
if refcl.count(TEATAC[i][-4].split('/')[0])>0:
cl[refcl.index(TEATAC[i][-4].split('/')[0])].append([TEATAC[i][0],max(int(TEATAC[i][1]),int(TEATAC[i][6])),min(int(TEATAC[i][2]),int(TEATAC[i][7]))])
else:
cl[4].append([TEATAC[i][0],max(int(TEATAC[i][1]),int(TEATAC[i][6])),min(int(TEATAC[i][2]),int(TEATAC[i][7]))])
for j in range(len(refcl)):
with open('out/TF_chipatlas_Neurod2_merge_'+str(refcl[j])+'.bed','w') as file:
writer = csv.writer(file,delimiter='\t')
writer.writerows(cl[j])
a = pybedtools.example_bedtool('out/TF_chipatlas_Neurod2_merge_'+str(refcl[j])+'.bed')
b=a.sort()
c = b.merge()
reg=0
for i in range(len(c)):
reg+=c[i].end-c[i].start
ans[j]=reg
de1reg=ans
with open('data/TE_contain_Lhx2_peak_neural_merge.bed') as f:
reader = csv.reader(f, delimiter='\t')
TEATAC = [row for row in reader]
refcl=['LINE','SINE','LTR','DNA','the_others']
cl=[[],[],[],[],[]]
ans=[0,0,0,0,0]
for i in range(len(TEATAC)):
if refcl.count(TEATAC[i][-4].split('/')[0])>0:
cl[refcl.index(TEATAC[i][-4].split('/')[0])].append([TEATAC[i][0],max(int(TEATAC[i][1]),int(TEATAC[i][6])),min(int(TEATAC[i][2]),int(TEATAC[i][7]))])
else:
cl[4].append([TEATAC[i][0],max(int(TEATAC[i][1]),int(TEATAC[i][6])),min(int(TEATAC[i][2]),int(TEATAC[i][7]))])
for j in range(len(refcl)):
with open('out/TF_chipatlas_Lhx2_merge_'+str(refcl[j])+'.bed','w') as file:
writer = csv.writer(file,delimiter='\t')
writer.writerows(cl[j])
a = pybedtools.example_bedtool('out/TF_chipatlas_Lhx2_merge_'+str(refcl[j])+'.bed')
b=a.sort()
c = b.merge()
reg=0
for i in range(len(c)):
reg+=c[i].end-c[i].start
ans[j]=reg
de3reg=ans
plt.style.use('default')
plt.rcParams['savefig.dpi']=400
N = 3
A = np.array([g_reg[0]/sum(g_reg),de1reg[0]/sum(de1reg),de3reg[0]/sum(de3reg)])
B = np.array([g_reg[1]/sum(g_reg),de1reg[1]/sum(de1reg),de3reg[1]/sum(de3reg)])
C = np.array([g_reg[2]/sum(g_reg),de1reg[2]/sum(de1reg),de3reg[2]/sum(de3reg)])
D = np.array([g_reg[3]/sum(g_reg),de1reg[3]/sum(de1reg),de3reg[3]/sum(de3reg)])
E = np.array([g_reg[4]/sum(g_reg),de1reg[4]/sum(de1reg),de3reg[4]/sum(de3reg)])
ind = np.arange(N) # the x locations for the groups
width = 0.35 # the width of the bars: can also be len(x) sequence
ind_p = ind + width/2
ind_m = ind - width/2
ind_line = np.sort(np.concatenate([ind_p,ind_m]))
A_line = (np.insert(A, np.arange(3), A))
B_line = (np.insert(B, np.arange(3), B)) + A_line
C_line = (np.insert(C, np.arange(3), C)) + B_line
D_line = (np.insert(D, np.arange(3), D)) + C_line
E_line = (np.insert(E, np.arange(3), E)) + D_line
#plt.figure(figsize=(5,5),dpi=400)
fig, ax = plt.subplots(dpi=400)
p1 = ax.bar(ind, A, width,zorder=2,hatch='||')
p2 = ax.bar(ind, B, width,bottom=A,zorder=2,hatch='..')
p3 = ax.bar(ind, C, width,bottom=A+B,zorder=2,hatch='x')
p4 = ax.bar(ind, D, width,bottom=A+B+C,zorder=2,hatch='//')
p5 = ax.bar(ind, E, width,bottom=A+B+C+D,zorder=2)
ax.plot(ind_line,A_line,'--k',zorder=1)
ax.plot(ind_line,B_line,'--k',zorder=1)
ax.plot(ind_line,C_line,'--k',zorder=1)
ax.plot(ind_line,D_line,'--k',zorder=1)
ax.plot(ind_line,E_line,'--k',zorder=1)
plt.ylabel('Relative proportion of each TE class')
#plt.title('Scores by group')
#plt.xticks(ind, ('G1', 'G2', 'G3', 'G4'))
plt.xticks([0,1,2], ['genome','Neurod2','Lhx2'])
plt.legend((p1[0], p2[0],p3[0],p4[0],p5[0]), ("LINE", "SINE","LTR","DNA","the others"),
bbox_to_anchor=(1.02, 1), loc='upper left', borderaxespad=.1, fontsize=14)
#plt.savefig("ChIP_TE_rate.png",bbox_inches = 'tight', pad_inches = 0)
plt.show()
## ATAC-seq peak
with open('data/merged_cortex_500bp_TE.bed') as f:
reader = csv.reader(f, delimiter='\t')
TEATAC = [row for row in reader]
tereg=[[],[],[],[],[]]
for i in range(len(TEATAC)):
if TEATAC[i][-1].split('/')[0]=='LINE':
tereg[0].append(TEATAC[i][3])
elif TEATAC[i][-1].split('/')[0]=='SINE':
tereg[1].append(TEATAC[i][3])
elif TEATAC[i][-1].split('/')[0]=='LTR':
tereg[2].append(TEATAC[i][3])
elif TEATAC[i][-1].split('/')[0]=='DNA':
tereg[3].append(TEATAC[i][3])
else:
tereg[4].append(TEATAC[i][3])
leng=[0,0,0,0,0]
for i in range(len(tereg)):
leng[i]=len(list(set(tereg[i])))
tereg=leng
with open('out/denovo_1_motif_pos_TEsubfamily_ATAC.bed') as f:
reader = csv.reader(f, delimiter='\t')
motifTE = [row for row in reader]
refcl=['LINE','SINE','LTR','DNA','the others']
cl=[[],[],[],[],[]]
for i in range(len(motifTE)):
if refcl.count(motifTE[i][-1].split('/')[0])>0:
cl[refcl.index(motifTE[i][-1].split('/')[0])].append('_'.join(motifTE[i][5:8]))
else:
cl[4].append('_'.join(motifTE[i][5:8]))
leng=[]
for i in range(len(cl)):
leng.append(len(list(set(cl[i]))))
de1reg=leng
with open('out/denovo_2_motif_pos_TEsubfamily_ATAC.bed') as f:
reader = csv.reader(f, delimiter='\t')
motifTE = [row for row in reader]
refcl=['LINE','SINE','LTR','DNA','the others']
cl=[[],[],[],[],[]]
for i in range(len(motifTE)):
if refcl.count(motifTE[i][-1].split('/')[0])>0:
cl[refcl.index(motifTE[i][-1].split('/')[0])].append('_'.join(motifTE[i][5:8]))
else:
cl[4].append('_'.join(motifTE[i][5:8]))
leng=[]
for i in range(len(cl)):
leng.append(len(list(set(cl[i]))))
de2reg=leng
with open('out/denovo_3_motif_pos_TEsubfamily_ATAC.bed') as f:
reader = csv.reader(f, delimiter='\t')
motifTE = [row for row in reader]
refcl=['LINE','SINE','LTR','DNA','the others']
cl=[[],[],[],[],[]]
for i in range(len(motifTE)):
if refcl.count(motifTE[i][-1].split('/')[0])>0:
cl[refcl.index(motifTE[i][-1].split('/')[0])].append('_'.join(motifTE[i][5:8]))
else:
cl[4].append('_'.join(motifTE[i][5:8]))
leng=[]
for i in range(len(cl)):
leng.append(len(list(set(cl[i]))))
de3reg=leng
plt.style.use('default')
plt.rcParams['savefig.dpi']=400
N = 5
A = np.array([g_reg[0]/sum(g_reg),tereg[0]/sum(tereg),de1reg[0]/sum(de1reg),de2reg[0]/sum(de2reg),de3reg[0]/sum(de3reg)])
B = np.array([g_reg[1]/sum(g_reg),tereg[1]/sum(tereg),de1reg[1]/sum(de1reg),de2reg[1]/sum(de2reg),de3reg[1]/sum(de3reg)])
C = np.array([g_reg[2]/sum(g_reg),tereg[2]/sum(tereg),de1reg[2]/sum(de1reg),de2reg[2]/sum(de2reg),de3reg[2]/sum(de3reg)])
D = np.array([g_reg[3]/sum(g_reg),tereg[3]/sum(tereg),de1reg[3]/sum(de1reg),de2reg[3]/sum(de2reg),de3reg[3]/sum(de3reg)])
E = np.array([g_reg[4]/sum(g_reg),tereg[4]/sum(tereg),de1reg[4]/sum(de1reg),de2reg[4]/sum(de2reg),de3reg[4]/sum(de3reg)])
ind = np.arange(N) # the x locations for the groups
width = 0.35 # the width of the bars: can also be len(x) sequence
ind_p = ind + width/2
ind_m = ind - width/2
ind_line = np.sort(np.concatenate([ind_p,ind_m]))
A_line = (np.insert(A, np.arange(5), A))
B_line = (np.insert(B, np.arange(5), B)) + A_line
C_line = (np.insert(C, np.arange(5), C)) + B_line
D_line = (np.insert(D, np.arange(5), D)) + C_line
E_line = (np.insert(E, np.arange(5), E)) + D_line
#plt.figure(figsize=(5,5),dpi=400)
fig, ax = plt.subplots(dpi=400)
p1 = ax.bar(ind, A, width,zorder=2,hatch='||')
p2 = ax.bar(ind, B, width,bottom=A,zorder=2,hatch='..')
p3 = ax.bar(ind, C, width,bottom=A+B,zorder=2,hatch='x')
p4 = ax.bar(ind, D, width,bottom=A+B+C,zorder=2,hatch='//')
p5 = ax.bar(ind, E, width,bottom=A+B+C+D,zorder=2)
ax.plot(ind_line,A_line,'--k',zorder=1)
ax.plot(ind_line,B_line,'--k',zorder=1)
ax.plot(ind_line,C_line,'--k',zorder=1)
ax.plot(ind_line,D_line,'--k',zorder=1)
ax.plot(ind_line,E_line,'--k',zorder=1)
plt.ylabel('Relative proportion of each TE class')
#plt.title('Scores by group')
#plt.xticks(ind, ('G1', 'G2', 'G3', 'G4'))
plt.xticks([0,1, 2,3,4], ['genome','ATAC peak \n (n=63059)','ATAC peak \n with $\it{de}$ $\it{novo}$ 1 \n (n=4744)','ATAC peak \n with $\it{de}$ $\it{novo}$ 2 \n (n=4356)','ATAC peak \n with $\it{de}$ $\it{novo}$ 3 \n (n=930)'],rotation=30)
plt.legend((p1[0], p2[0],p3[0],p4[0],p5[0]), ("LINE", "SINE","LTR","DNA","the others"),
bbox_to_anchor=(1.02, 1), loc='upper left', borderaxespad=.1, fontsize=14)
#plt.savefig("ATAC_TE_rate.png",bbox_inches = 'tight', pad_inches = 0)
plt.show()