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Figure_2e_insulation_pileup_H1-hESC_HFFc6.py
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Figure_2e_insulation_pileup_H1-hESC_HFFc6.py
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from matplotlib.gridspec import GridSpec
import matplotlib.pyplot as plt
import matplotlib as mpl
#mpl.style.use('seaborn-white')
import multiprocess as mp
import numpy as np
import pandas as pd
import cooltools
import cooler
#import bbi
import bioframe
conditions = [
"H1-hESC_MicroC",
"H1-hESC_FA_DSG_DpnII_Hi-C",
"H1-hESC_PLAC-Seq",
"H1-hESC_ChIA_PET_PolII",
"H1-hESC_ChIA_PET_CTCF",
"H1-hESC_DNA_clusters_R1_R2_2-100",
"H1-hESC-GAM"]
path="mcool/"
out="insulation/"
path2="insulation/"
ins_files=[
"4DNFI9GMP2J8_H1-hESC_MicroC.25kb.cis.bed",
"4DNFI82R42AD_H1-hESC_FA_DSG_DpnII_Hi-C.25kb.cis.bed",
"4DNFICOGAKW2_H1-hESC_PLAC-Seq.25kb.cis.bed",
"4DNFIF1J6GC5_H1-hESC_ChIA_PET_PolII.25kb.cis.bed",
"4DNFIK3276U7_H1-hESC_ChIA_PET_CTCF.25kb.cis.bed",
"4DNFIV3PDS5F_4DNFIIY1TXUZ_H1-hESC_DNA_clusters_R1_R2_2-100.25kb.cis.bed",
"GAM_H1_new.IS.scores.at25Kb_formatted.bed"]
names = [
"MicroC",
"Hi-C",
"PLAC-Seq",
"ChIA-PET PolII",
"ChIA-PET CTCF",
"SPRITE",
"GAM"]
filename1="4DNFI9GMP2J8_H1-hESC_MicroC.mcool"
filename2="4DNFI82R42AD_H1-hESC_FA_DSG_DpnII_Hi-C.mcool"
filename3="4DNFICOGAKW2_H1-hESC_PLAC-Seq.mcool"
filename4="4DNFIF1J6GC5_H1-hESC_ChIA_PET_PolII.mcool"
filename5="4DNFIK3276U7_H1-hESC_ChIA_PET_CTCF.mcool"
filename6="H1-hESC_DNA_clusters_R1_R2_2-100.mcool"
filename7="H1hESC.GAM.NPMI.normalized.pairwise.curated.matrices.at25Kb.mcool"
cooler_paths = {
"H1-hESC_MicroC" : path+filename1+'::/resolutions/25000',
"H1-hESC_FA_DSG_DpnII_Hi-C": path+filename2+'::/resolutions/25000',
"H1-hESC_PLAC-Seq": path+filename3+'::/resolutions/25000',
"H1-hESC_ChIA_PET_PolII": path+filename4+'::/resolutions/25000',
"H1-hESC_ChIA_PET_CTCF": path+filename5+'::/resolutions/25000',
"H1-hESC_DNA_clusters_R1_R2_2-100": path+filename6+'::/resolutions/25000',
"H1-hESC-GAM": 'GAM_2024/'+filename7+'::/resolutions/25000'
}
long_names = {
"H1-hESC_MicroC" : "H1-hESC_MicroC",
"H1-hESC_FA_DSG_DpnII_Hi-C" : "H1-hESC_FA_DSG_DpnII_Hi-C",
"H1-hESC_PLAC-Seq": "H1-hESC_PLAC-Seq",
"H1-hESC_ChIA_PET_PolII": "H1-hESC_ChIA_PET_PolII",
"H1-hESC_ChIA_PET_CTCF": "H1-hESC_ChIA_PET_CTCF",
"H1-hESC_DNA_clusters_R1_R2_2-100" : "H1-hESC_DNA_clusters_R1_R2_2-100",
"H1-hESC-GAM" : "H1-hESC-GAM"
}
#exp_name_list=[file.split(".")[0]+"25000.cis.expected" for file in cool]
#ins_name_list=[file.split(".")[0]+".txt" for file in cool]
path_gam="GAM_2024/"
insulation_path={
"H1-hESC_MicroC" : path2+ins_files[0],
"H1-hESC_FA_DSG_DpnII_Hi-C": path2+ins_files[1],
"H1-hESC_PLAC-Seq": path2+ins_files[2],
"H1-hESC_ChIA_PET_PolII": path2+ins_files[3],
"H1-hESC_ChIA_PET_CTCF": path2+ins_files[4],
"H1-hESC_DNA_clusters_R1_R2_2-100": path2+ins_files[5],
"H1-hESC-GAM" : path2+ins_files[6]
}
all_m=[]
new_data=pd.DataFrame()
index_list_dict={}
for i,cond in enumerate(conditions):
insulations_new_all=pd.DataFrame()
insulations = pd.read_table(insulation_path[cond])
print(insulations.head(5))
insulations = insulations.dropna()
if insulations.shape[1]>4:
insulations.columns=["chrom","start","end","is_bad_bin","log2_insulation_score_100000","n_valid_pixels_100000","boundary_strength_100000"]
else:
insulations.columns=["chrom","start","end","log2_insulation_score_100000"]
strong_weak=insulations[insulations["log2_insulation_score_100000"] !=0]
print(strong_weak.shape)
m=np.mean(strong_weak["log2_insulation_score_100000"])
all_m.append(m)
#name=insulation_path[cond].split("/")[-1].split(".")[0]
#print(name)
x = np.log10(strong_weak['log2_insulation_score_100000'].values)
bins = np.linspace(x.min(), x.max(), num=100)
boundary_list=strong_weak[strong_weak["log2_insulation_score_100000"] <= -0.226]
if insulations.shape[1]>4:
insulations.columns=["chrom","start","end","is_bad_bin","log2_insulation_score_100000","n_valid_pixels_100000","boundary_strength_100000"]
else:
insulations.columns=["chrom","start","end","log2_insulation_score_100000"]
#boundary_list.to_csv("ESC_"+cond+"_strong_boundaries.bed",sep="\t",index=None)
for chrom in np.unique(insulations[["chrom"]]):
insulations_new_chr=pd.DataFrame()
boundary_list_chr=boundary_list[boundary_list["chrom"]==chrom]
mid_point_new=boundary_list_chr[["start","end"]].mean(axis=1)+1
insulations_chr=insulations[insulations["chrom"]==chrom]
for i in mid_point_new:
temp=insulations_chr[insulations_chr.eval('(start < {}) & ({} < end)'.format(i,i))]
insulations_new_chr=pd.concat([insulations_new_chr,pd.DataFrame(temp)],ignore_index=False)
insulations_new_all=pd.concat([insulations_new_all,insulations_new_chr])
print(insulations_new_all.shape)
index_list_dict[cond]=insulations_new_all.index
def pile_up(df,index_list):
start = df.iloc[index_list].index-10
end = df.iloc[index_list].index+11
#print(end)
return (np.nanmean(np.array(list(map(lambda x: df["log2_insulation_score_100000"].values[x[0]:x[1]], zip(start,end)))), axis=0))
i=0
ins_data=pd.DataFrame()
file_list=[]
for file_name in ins_files:
print(file_name)
file_list.append(file_name.split('.')[0])
file=pd.read_csv(path2+file_name,sep="\t")
#print(file.head(5))
ins=file["log2_insulation_score_100000"]
ins_data=pd.concat([ins_data,ins],axis=1)
hg38 = bioframe.fetch_chromsizes('hg38')
chromsizes = bioframe.fetch_chromsizes('hg38')
chromosomes = list(chromsizes.index)
print(filename1,filename2,filename3)
path="mcool/"
out="insulation/"
hg38 = bioframe.fetch_chromsizes('hg38')
chromsizes = bioframe.fetch_chromsizes('hg38')
chromosomes = list(chromsizes.index)
print(filename1,filename2,filename3,filename4,filename5,filename6,filename7)
cool = [filename1,filename2,filename3,filename4,filename5,filename6,filename7]
clrs = {
cond: cooler.Cooler(cooler_paths[cond]) for cond in conditions
}
gs = GridSpec(nrows=1, ncols=1)
plt.figure(figsize=(8,8))
k=0
names=conditions
names = [
"MicroC",
"Hi-C",
"PLAC-Seq",
"ChIA-PET PolII",
"ChIA-PET CTCF",
"SPRITE",
"GAM"]
c=['g','dodgerblue','orange','cyan','pink','r','k']
#adds=[0.04, 0, 0.08,0,0,0.12,0.13 ]
#adds=[0.04, 0, 0.08,0.07,0,-0.12,-0.03 ]
for i, cond in enumerate(conditions):
insulations = pd.read_table(insulation_path[cond])
#insulations.columns=["chrom","start","end","is_bad_bin","log2_insulation_score_100000","n_valid_pixels_100000","boundary_strength_100000"]
if insulations.shape[1]>4:
insulations.columns=["chrom","start","end","is_bad_bin","log2_insulation_score_100000","n_valid_pixels_100000","boundary_strength_100000"]
else:
insulations.columns=["chrom","start","end","log2_insulation_score_100000"]
ax = plt.subplot(gs[k])
print(cond)
#print(np.max(pile_up(insulations,index_list_dict[cond].values)))
sub=pile_up(insulations,index_list_dict[cond].values)[0]
#print(np.min(pile_up(insulations,index_list_dict[cond].values)))
ins=pile_up(insulations,index_list_dict[cond].values)
ins_new=ins-sub
img=ax.plot(ins_new,label=names[i],color=c[i])
ax.xaxis.tick_bottom()
ax.set_ylim(-0.65,0.4)
ax.legend()
#plt.legend(bbox_to_anchor=(1.1, 1.05))
plt.ylabel("log2 insulation score")
plt.tight_layout()
plt.savefig("Figure_2e_Hi_hESC_insulation_pileup_25kb.pdf")
##### ##################
path="dcic_mcool/mcool/"
out="insulation/"
path2="insulation/"
ins_files=[
"4DNFI9FVHJZQ_HFFc6_MicroC.25kb.cis.bed",
"4DNFIAVXXO55_HFFc6_FA_DSG_DpnII_Hi-C.25kb.cis.bed",
"4DNFI9REIU8H_HFFc6_PLAC-Seq.25kb.cis.bed",
"4DNFIOOSHTQV_HFFc6_ChIA_PET_PolII.25kb.cis.bed",
"4DNFIELOAD41_HFFc6_ChIA_PET_CTCF.25kb.cis.bed",
"4DNFIRXON7Z2_HFFc6_DNA_clusters_R1_R2_2-100.25kb.cis.bed"]
names = [
"MicroC",
"Hi-C",
"PLAC-Seq",
"ChIA-PET PolII",
"ChIA-PET CTCF",
"SPRITE"]
filename1="4DNFI9FVHJZQ_HFFc6_MicroC.mcool"
filename2="4DNFI9REIU8H_HFFc6_PLAC-Seq.mcool"
filename3="4DNFIAVXXO55_HFFc6_FA_DSG_DpnII_Hi-C.mcool"
filename4="4DNFIELOAD41_HFFc6_ChIA_PET_CTCF.mcool"
filename5="4DNFIOOSHTQV_HFFc6_ChIA_PET_PolII.mcool"
filename6="4DNFIRXON7Z2_HFFc6_DNA_clusters_R1_R2_2-100.mcool"
path="mcool/"
out="insulation/"
cooler_paths = {
"HFFc6_MicroC" : path+filename1+'::/resolutions/25000',
"HFFc6_FA_DSG_DpnII_Hi-C": path+filename2+'::/resolutions/25000',
"HFFc6_PLAC-Seq": path+filename3+'::/resolutions/25000',
"HFFc6_ChIA_PET_PolII": path+filename4+'::/resolutions/25000',
"HFFc6_ChIA_PET_CTCF": path+filename5+'::/resolutions/25000',
"HFFc6_DNA_clusters_R1_R2_2-100": path+filename6+'::/resolutions/25000'
}
long_names = {
"HFFc6_MicroC" : "HFFc6_MicroC",
"HFFc6_FA_DSG_DpnII_Hi-C" : "HFFc6_FA_DSG_DpnII_Hi-C",
"HFFc6_PLAC-Seq": "HFFc6_PLAC-Seq",
"HFFc6_ChIA_PET_PolII": "HFFc6_ChIA_PET_PolII",
"HFFc6_ChIA_PET_CTCF": "HFFc6_ChIA_PET_CTCF",
"HFFc6_DNA_clusters_R1_R2_2-100" : "HFFc6_DNA_clusters_R1_R2_2-100",
}
#exp_name_list=[file.split(".")[0]+"25000.cis.expected" for file in cool]
conditions = [
"HFFc6_MicroC",
"HFFc6_FA_DSG_DpnII_Hi-C",
"HFFc6_PLAC-Seq",
"HFFc6_ChIA_PET_PolII",
"HFFc6_ChIA_PET_CTCF",
"HFFc6_DNA_clusters_R1_R2_2-100"]
#ins_name_list=[file.split(".")[0]+".txt" for file in cool]
path_gam="GAM_2024/"
insulation_path={
"HFFc6_MicroC" : path2+ins_files[0],
"HFFc6_FA_DSG_DpnII_Hi-C": path2+ins_files[1],
"HFFc6_PLAC-Seq": path2+ins_files[2],
"HFFc6_ChIA_PET_PolII": path2+ins_files[3],
"HFFc6_ChIA_PET_CTCF": path2+ins_files[4],
"HFFc6_DNA_clusters_R1_R2_2-100": path2+ins_files[5]
}
all_m=[]
new_data=pd.DataFrame()
index_list_dict={}
for i,cond in enumerate(conditions):
insulations_new_all=pd.DataFrame()
insulations = pd.read_table(insulation_path[cond])
print(insulations.head(5))
insulations = insulations.dropna()
if insulations.shape[1]>4:
insulations.columns=["chrom","start","end","is_bad_bin","log2_insulation_score_100000","n_valid_pixels_100000","boundary_strength_100000"]
else:
insulations.columns=["chrom","start","end","log2_insulation_score_100000"]
strong_weak=insulations[insulations["log2_insulation_score_100000"] !=0]
print(strong_weak.shape)
m=np.mean(strong_weak["log2_insulation_score_100000"])
all_m.append(m)
#name=insulation_path[cond].split("/")[-1].split(".")[0]
#print(name)
x = np.log10(strong_weak['log2_insulation_score_100000'].values)
bins = np.linspace(x.min(), x.max(), num=100)
boundary_list=strong_weak[strong_weak["log2_insulation_score_100000"] <= -0.25]
if insulations.shape[1]>4:
insulations.columns=["chrom","start","end","is_bad_bin","log2_insulation_score_100000","n_valid_pixels_100000","boundary_strength_100000"]
else:
insulations.columns=["chrom","start","end","log2_insulation_score_100000"]
#boundary_list.to_csv("ESC_"+cond+"_strong_boundaries.bed",sep="\t",index=None)
for chrom in np.unique(insulations[["chrom"]]):
insulations_new_chr=pd.DataFrame()
boundary_list_chr=boundary_list[boundary_list["chrom"]==chrom]
mid_point_new=boundary_list_chr[["start","end"]].mean(axis=1)+1
insulations_chr=insulations[insulations["chrom"]==chrom]
for i in mid_point_new:
temp=insulations_chr[insulations_chr.eval('(start < {}) & ({} < end)'.format(i,i))]
insulations_new_chr=pd.concat([insulations_new_chr,pd.DataFrame(temp)],ignore_index=False)
insulations_new_all=pd.concat([insulations_new_all,insulations_new_chr])
print(insulations_new_all.shape)
index_list_dict[cond]=insulations_new_all.index
def pile_up(df,index_list):
start = df.iloc[index_list].index-10
end = df.iloc[index_list].index+11
#print(end)
return (np.nanmean(np.array(list(map(lambda x: df["log2_insulation_score_100000"].values[x[0]:x[1]], zip(start,end)))), axis=0))
c=['g','dodgerblue','orange','cyan','pink','r']
names = [
"MicroC",
"Hi-C",
"PLAC-Seq",
"ChIA-PET PolII",
"ChIA-PET CTCF",
"SPRITE"]
i=0
ins_data=pd.DataFrame()
file_list=[]
for file_name in ins_files:
print(file_name)
file_list.append(file_name.split('.')[0])
file=pd.read_csv(path2+file_name,sep="\t")
#print(file.head(5))
ins=file["log2_insulation_score_100000"]
ins_data=pd.concat([ins_data,ins],axis=1)
hg38 = bioframe.fetch_chromsizes('hg38')
chromsizes = bioframe.fetch_chromsizes('hg38')
chromosomes = list(chromsizes.index)
print(filename1,filename2,filename3)
hg38 = bioframe.fetch_chromsizes('hg38')
chromsizes = bioframe.fetch_chromsizes('hg38')
chromosomes = list(chromsizes.index)
print(filename1,filename2,filename3,filename4,filename5,filename6,filename7)
cool = [filename1,filename2,filename3,filename4,filename5,filename6,filename7]
clrs = {
cond: cooler.Cooler(cooler_paths[cond]) for cond in conditions
}
gs = GridSpec(nrows=1, ncols=1)
plt.figure(figsize=(8,8))
k=0
#adds=[0.04, 0, 0.08,0,0,0.12,0.13 ]
#adds=[0.04, 0, 0.08,0.07,0,-0.12,-0.03 ]
for i, cond in enumerate(conditions):
insulations = pd.read_table(insulation_path[cond])
#insulations.columns=["chrom","start","end","is_bad_bin","log2_insulation_score_100000","n_valid_pixels_100000","boundary_strength_100000"]
if insulations.shape[1]>4:
insulations.columns=["chrom","start","end","is_bad_bin","log2_insulation_score_100000","n_valid_pixels_100000","boundary_strength_100000"]
else:
insulations.columns=["chrom","start","end","log2_insulation_score_100000"]
ax = plt.subplot(gs[k])
print(cond)
#print(np.max(pile_up(insulations,index_list_dict[cond].values)))
sub=pile_up(insulations,index_list_dict[cond].values)[0]
#print(np.min(pile_up(insulations,index_list_dict[cond].values)))
ins=pile_up(insulations,index_list_dict[cond].values)
ins_new=ins-sub
img=ax.plot(ins_new,label=names[i],color=c[i])
ax.xaxis.tick_bottom()
ax.set_ylim(-0.65,0.4)
ax.legend()
#plt.legend(bbox_to_anchor=(1.1, 1.05))
plt.ylabel("log2 insulation score")
plt.tight_layout()
plt.savefig("Figure_2e_HFFc6_insulation_pileup_25kb.pdf")