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margi_loops.r
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############## MARGI analysis related to Hi-C loops
library(reshape2)
library(data.table)
library(GenomicRanges)
library(GenomicAlignments)
library(pracma)
library(VennDiagram)
options(scipen=999)
############### Detect pattern 1 (high density regions) systematically using an "iMARGI insulation score" (NOT USED, see below)
calculate_insulation_score_and_HDR_margi <- function(contact_matrix_file,
chrSize_file="/dataOS/rcalandrelli/phase_separation/4DNFI823LSII.hg38.mainonly.chrom.sizes",
chromosome,
sample,
bin_size,
template_dim,
n_remove_diag=0,
non_zero_cutoff_score,
bins_cutoff_HDR){
print(paste0(sample,"_",chromosome))
out_dir_score = paste0("/dataOS/rcalandrelli/phase_separation/MARGI/insulation_score/", sample, "_", bin_size)
if (!dir.exists(out_dir_score)){
dir.create(out_dir_score)
}
out_dir_HDR = paste0("/dataOS/rcalandrelli/phase_separation/MARGI/HDR/", sample, "_", bin_size)
if (!dir.exists(out_dir_HDR)){
dir.create(out_dir_HDR)
}
chrSize = read.table(chrSize_file)
Gr_genome <- GRanges(
seqnames = Rle(as.character(chrSize$V1)),
ranges = IRanges(rep(1,nrow(chrSize)), end = as.numeric(chrSize$V2), names = c(1:nrow(chrSize))),
strand = Rle(strand('*')))
seqlengths(Gr_genome) <- as.numeric(chrSize$V2)
########## Tile genome and make genomic windows for selected chromosomes
genome_window <- tileGenome(seqinfo(Gr_genome), tilewidth = bin_size, cut.last.tile.in.chrom = T)
chr_genome_window = genome_window[seqnames(genome_window)==chromosome]
########## Load contact matrix
temp = data.frame(fread(contact_matrix_file))
contact_matrix = matrix(0, nrow = length(chr_genome_window), ncol = length(chr_genome_window))
contact_matrix[as.matrix(temp[,c("RNA_end_bins","DNA_end_bins")])] = temp$x
chr = rep(chromosome, nrow(contact_matrix))
bin = seq(1,nrow(contact_matrix))
insulation_score_data = data.frame(chr, bin)
insulation_score_data$insulation_score = 0
########## Calculate insulation score for each bin
for (bin in 1:nrow(contact_matrix)){
#print(paste0("Bin ", bin))
shift = template_dim/2
shift_bin = shift / bin_size
upstream_bin = bin - shift_bin
downstream_bin = bin + shift_bin - 1
if (upstream_bin > 0 & downstream_bin <= chrSize[which(chrSize$V1 == chromosome),"V2"] %/% bin_size){
L_matrix = contact_matrix[upstream_bin:bin, upstream_bin:bin]
R_matrix = contact_matrix[bin:downstream_bin, bin:downstream_bin]
X_up_matrix = contact_matrix[upstream_bin:bin, bin:downstream_bin]
X_down_matrix = contact_matrix[bin:downstream_bin, upstream_bin:bin]
non_zero_L_ratio = sum(L_matrix != 0) / shift_bin^2
non_zero_R_ratio = sum(R_matrix != 0) / shift_bin^2
### Check that either L_matrix or R_matrix have a percentage of non-zero bins above non_zero_cutoff_score
if (non_zero_L_ratio > non_zero_cutoff_score | non_zero_R_ratio > non_zero_cutoff_score){
if (n_remove_diag == -1){
mean_L = sum(L_matrix) / shift_bin^2
mean_R = sum(R_matrix) / shift_bin^2
mean_X_up = sum(X_up_matrix) / shift_bin^2
mean_X_down = sum(X_down_matrix) / shift_bin^2
} else {
### Removing elements on the diagonals
num_L = sum(L_matrix)
num_R = sum(R_matrix)
denom = shift_bin^2
for (i in 0:n_remove_diag){
num_L = num_L - sum(Diag(L_matrix,i))
num_R = num_R - sum(Diag(R_matrix,i))
denom = denom - length(Diag(L_matrix,i))
}
mean_L = num_L / denom
mean_R = num_R / denom
mean_X_up = sum(X_up_matrix) / shift_bin^2
mean_X_down = sum(X_down_matrix) / shift_bin^2
}
### Insulation score calculation
# if (non_zero_L_ratio > non_zero_cutoff & non_zero_R_ratio > non_zero_cutoff){
# insulation_score = (max(mean_L, mean_R)) / (max(mean_X_up, mean_X_down))
# }
# else if (non_zero_L_ratio > non_zero_cutoff & non_zero_R_ratio < non_zero_cutoff){
# insulation_score = mean_L / (max(mean_X_up, mean_X_down))
# }
# else if (non_zero_L_ratio < non_zero_cutoff & non_zero_R_ratio > non_zero_cutoff){
# insulation_score = mean_R / (max(mean_X_up, mean_X_down))
# }
insulation_score = (max(mean_L, mean_R)) / (max(mean_X_up, mean_X_down))
} else {
insulation_score = 0
}
} else {
insulation_score = 0
}
insulation_score_data[bin,"insulation_score"] = insulation_score
}
write.table(insulation_score_data, paste0(out_dir_score, "/", chromosome, "_template_", template_dim, "_CS_", gsub("\\.","",as.character(non_zero_cutoff_score)), ".txt"), row.names = F, col.names = T, quote = F, sep = "\t")
################## Extract high density regions (HDR)
peaks = findpeaks(insulation_score_data$insulation_score, nup = 1, minpeakdistance = 5, sortstr = F)
insulation_score_data$peak = 0
insulation_score_data[peaks[,2],"peak"] = 1
##### Plotting insulation score track with peaks
df = insulation_score_data[325:375,]
ggplot(df) +
geom_point(aes(x=bin, y=insulation_score, color=peak), size = 3) +
#scale_size(range=c(0,1.5)) +
geom_line(aes(x=bin, y=insulation_score)) +
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
panel.background = element_blank(), axis.line = element_line(colour = "black"),
text = element_text(size=24),
axis.text.x = element_text(size = 24, color = "black"),
axis.text.y = element_text(size = 24, color = "black"),
axis.ticks.length = unit(0.2, "cm"),
legend.position="none")
##### Calculation of high density regions (HDR)
peak_bins = unique(peaks[,2])
peak_bins = peak_bins[order(peak_bins)]
HDR_list = list()
n = length(peak_bins)-1
for (i in 1:n){
start_bin = peak_bins[i]
end_bin = peak_bins[i+1]
temp_matrix = contact_matrix[start_bin:end_bin, start_bin:end_bin]
k = sum(temp_matrix > 1) / dim(temp_matrix)[1]^2 # ratio of bins with > 1 interactions
if (k > bins_cutoff_HDR){
start = (start_bin * bin_size) - bin_size
end = end_bin * bin_size
out = c(chromosome, start, end, chromosome, start, end)
HDR_list[[as.character(i)]] = out
}
}
HDR = do.call("rbind", HDR_list)
write.table(HDR, paste0(out_dir_HDR, "/", chromosome, "_template_", template_dim, "_CS_",gsub("\\.","",as.character(non_zero_cutoff_score)) , "_CHDR_" ,gsub("\\.","",as.character(bins_cutoff_HDR)) ,".bedpe"), row.names = F, col.names = F, quote = F, sep = "\t")
}
# my_file = "/dataOS/rcalandrelli/phase_separation/MARGI/contact_data/iMARGI_H1_control/10000_filter1k/chr1_chr1.txt"
# calculate_insulation_score_and_HDR_margi(contact_matrix_file=my_file,
# chrSize_file="/dataOS/chenweizhong/Project____InterChrom/Data/4DN_files_reference/4DNFI823LSII.hg38.mainonly.chrom.sizes",
# chromosome="chr1",
# sample="control",
# bin_size=10000,
# template_dim=500000,
# n_remove_diag = 1,
# non_zero_cutoff_score = 0.5,
# bins_cutoff_HDR = 0.8)
for (my_sample in c("H1_control","H1_NH4OAc","H1_FL","H1_RNase","K562_control","HFF_control")){
for (my_chr in hg38_chromosomes){
my_file = paste0("/dataOS/rcalandrelli/phase_separation/MARGI/contact_data/iMARGI_",my_sample,"/10000_filter1k/", my_chr, "_", my_chr, ".txt")
calculate_insulation_score_and_HDR_margi(contact_matrix_file=my_file,
chrSize_file="/dataOS/rcalandrelli/phase_separation/4DNFI823LSII.hg38.mainonly.chrom.sizes",
chromosome=my_chr,
sample=my_sample,
bin_size=10000,
template_dim=300000,
n_remove_diag = 1,
non_zero_cutoff_score = 0.5,
bins_cutoff_HDR = 0.7)
}
}
###### Merge HDR together from all the chromosomes
HDR_list = list()
for (my_chr in hg38_chromosomes){
temp = read.table(paste0("/dataOS/rcalandrelli/phase_separation/MARGI/HDR/H1_control_10000/", my_chr, "_template_300000_CS_05_CHDR_07.bedpe"))
HDR_list[[my_chr]] = temp
}
HDR_all_chromosomes = do.call("rbind", HDR_list)
write.table(HDR_all_chromosomes, "/dataOS/rcalandrelli/phase_separation/MARGI/HDR/H1_control_10000/all_chr_template_300000_CS_05_CHDR_07.bedpe", row.names = F, col.names = F, sep = "\t", quote = F)
Gr_HDR_all_chromosomes = GRanges(
seqnames = Rle(HDR_all_chromosomes$V1),
ranges = IRanges(HDR_all_chromosomes$V2+1, end = HDR_all_chromosomes$V3, names = c(1:nrow(HDR_all_chromosomes))),
strand = Rle(strand('*')))
Gr_loop_control = GRanges(
seqnames = Rle(paste0("chr",loops_control$V1)),
ranges = IRanges(loops_control$V2+1, end = loops_control$V6, names = c(1:nrow(loops_control))),
strand = Rle(strand('*')))
### Overlaps of HDR with loop regions
overlaps = countOverlaps(Gr_loop_control, Gr_HDR_all_chromosomes)
Gr_loop_control_HDR = Gr_loop_control[overlaps > 0]
Gr_loop_control_HDR = sort(Gr_loop_control_HDR)
names(Gr_loop_control_HDR) = seq(1:length(Gr_loop_control_HDR))
loop_control_HDR = data.frame(Gr_loop_control_HDR)
loop_control_HDR$seqnames = as.character(loop_control_HDR$seqnames)
add_ep_label_HDR_loops <- function(x, loop_ep_data)
{
temp = loop_ep_data[which(loop_ep_data$V1 == gsub("chr","",as.character(x[1])) &
loop_ep_data$V2 == as.numeric(x[2]) - 1 &
loop_ep_data$V6 == as.numeric(x[3])),]
if(nrow(temp)>0){
return(temp$V25)
} else {
return("NA")
}
}
loop_control_HDR$EP = apply(loop_control_HDR, 1, add_ep_label_HDR_loops, loop_ep_data = loop_control_EP)
table(as.factor(loop_control_HDR$EP))
loop_control_HDR[which(loop_control_HDR$seqnames=="chr1" & loop_control_HDR$EP != "NA"),]
loop_control_HDR[which(loop_control_HDR$seqnames=="chr20" &
loop_control_HDR$start >= 37150000 &
loop_control_HDR$end <= 37650000),]
HDR_all_chromosomes[which(HDR_all_chromosomes$V1=="chr20" &
HDR_all_chromosomes$V2 >= 37150000 &
HDR_all_chromosomes$V3 <= 37650000),]
############### Detect pattern 1 (high density regions within loops) using a "in vs out" approach starting from loop coordinates (USE THIS!!!)
calculate_HDR_loop_margi <- function(loop_file,
chrSize_file="/dataOS/rcalandrelli/phase_separation/4DNFI823LSII.hg38.mainonly.chrom.sizes",
chromosomes,
sample,
bin_size,
out_template_factor,
shuffle_loops = F,
out_dir_HDR=""
)
{
if (out_dir_HDR == ""){
out_dir_HDR = paste0("/dataOS/rcalandrelli/phase_separation/MARGI/loops/iMARGI_", sample)
}
chrSize = read.table(chrSize_file)
Gr_genome <- GRanges(
seqnames = Rle(as.character(chrSize$V1)),
ranges = IRanges(rep(1,nrow(chrSize)), end = as.numeric(chrSize$V2), names = c(1:nrow(chrSize))),
strand = Rle(strand('*')))
seqlengths(Gr_genome) <- as.numeric(chrSize$V2)
########## Tile genome and make genomic windows for selected chromosomes
genome_window <- tileGenome(seqinfo(Gr_genome), tilewidth = bin_size, cut.last.tile.in.chrom = T)
loop_chr_list = list()
for (my_chr in chromosomes){
print(my_chr)
chr_genome_window = genome_window[seqnames(genome_window)==my_chr]
########## Load contact matrix
temp = data.frame(fread(paste0("/dataOS/rcalandrelli/phase_separation/MARGI/contact_data/iMARGI_",sample,"/10000_filter1k/", my_chr, "_", my_chr, ".txt")))
contact_matrix = matrix(0, nrow = length(chr_genome_window), ncol = length(chr_genome_window))
contact_matrix[as.matrix(temp[,c("RNA_end_bins","DNA_end_bins")])] = temp$x
########## Load loop data
temp = read.table(loop_file)
loops = temp[which(temp$V1 == gsub("chr","",my_chr)),]
if (shuffle_loops == T){
if (nrow(loops) > 0){
for (l in 1:nrow(loops)){
l_res = loops[l,3] - loops[l,2] # loop resolution
l_size = loops[l,6] - loops[l,2] # loop_size
random_pos = runif(1, min=0, max=(chrSize[which(chrSize$V1==my_chr),2] - l_size)) # to avoid that the random loop goes beyond chromosome size
loops[l,2] = random_pos
loops[l,3] = random_pos + l_res
loops[l,5] = random_pos + l_size - l_res
loops[l,6] = random_pos + l_size
}
}
}
########## Calculate density ratio for each bin
if (nrow(loops) > 0){
ratio_loops = list()
for (i in 1:nrow(loops)){
#print(paste0("Loop ", i))
start_bin_loop = floor(loops[i,"V2"] / bin_size) + 1
end_bin_loop = floor(loops[i,"V5"] / bin_size) + 1
shift_bin = round((end_bin_loop - start_bin_loop + 1) * (out_template_factor - 1) / 2)
start_bin_out_template = start_bin_loop - shift_bin
end_bin_out_template = end_bin_loop + shift_bin
if (start_bin_out_template > 0 & end_bin_out_template <= chrSize[which(chrSize$V1 == my_chr),"V2"] %/% bin_size){
### Contacts within the loop region
X_loop = contact_matrix[start_bin_loop:end_bin_loop, start_bin_loop:end_bin_loop]
X_loop_mean = mean(X_loop)
### Contacts spanning outside of the loop region
X_out_1 = contact_matrix[start_bin_out_template:(start_bin_loop - 1), start_bin_loop:end_bin_loop]
X_out_2 = contact_matrix[start_bin_loop:end_bin_loop, (end_bin_loop + 1):end_bin_out_template]
X_out_3 = contact_matrix[(end_bin_loop + 1):end_bin_out_template, start_bin_loop:end_bin_loop]
X_out_4 = contact_matrix[start_bin_loop:end_bin_loop, start_bin_out_template:(start_bin_loop - 1)]
X_out_mean = mean(c(X_out_1,X_out_2,X_out_3,X_out_4))
in_out_ratio = ifelse(!is.na(X_loop_mean / X_out_mean), X_loop_mean / X_out_mean, 0)
ratio_loops[[i]] = in_out_ratio
} else {
ratio_loops[[i]] = NaN
}
}
loops$density_ratio = do.call("rbind", ratio_loops)
loop_chr_list[[my_chr]] = loops
}
}
loops_out = do.call("rbind", loop_chr_list)
if (shuffle_loops == F){
write.table(loops_out, paste0(out_dir_HDR, "/loops_EP_ratio_template_factor_", out_template_factor,".bedpe"), row.names = F, col.names = F, sep= "\t", quote = F)
} else {
write.table(loops_out, paste0(out_dir_HDR, "/loops_EP_ratio_template_factor_", out_template_factor,"_shuffle.bedpe"), row.names = F, col.names = F, sep= "\t", quote = F)
}
return(loops_out)
}
loops_control_EP_ratio = calculate_HDR_loop_margi(loop_file="/dataOS/rcalandrelli/phase_separation/HiC/loops/enhancer_promoter_loops/H1_control_EP.bedpe",
chrSize_file="/dataOS/rcalandrelli/phase_separation/4DNFI823LSII.hg38.mainonly.chrom.sizes",
chromosomes=hg38_chromosomes,
sample="H1_control",
bin_size=10000,
out_template_factor=2)
loops_NH4OAc_EP_ratio = calculate_HDR_loop_margi(loop_file="/dataOS/rcalandrelli/phase_separation/HiC/loops/enhancer_promoter_loops/H1_NH4OAc_EP.bedpe",
chrSize_file="/dataOS/rcalandrelli/phase_separation/4DNFI823LSII.hg38.mainonly.chrom.sizes",
chromosomes=hg38_chromosomes,
sample="H1_NH4OAc",
bin_size=10000,
out_template_factor=2)
loops_FL_EP_ratio = calculate_HDR_loop_margi(loop_file="/dataOS/rcalandrelli/phase_separation/HiC/loops/enhancer_promoter_loops/H1_FL_EP.bedpe",
chrSize_file="/dataOS/rcalandrelli/phase_separation/4DNFI823LSII.hg38.mainonly.chrom.sizes",
chromosomes=hg38_chromosomes,
sample="H1_FL",
bin_size=10000,
out_template_factor=2)
loops_RNase_EP_ratio = calculate_HDR_loop_margi(loop_file="/dataOS/rcalandrelli/phase_separation/HiC/loops/enhancer_promoter_loops/H1_RNase_EP.bedpe",
chrSize_file="/dataOS/rcalandrelli/phase_separation/4DNFI823LSII.hg38.mainonly.chrom.sizes",
chromosomes=hg38_chromosomes,
sample="H1_RNase",
bin_size=10000,
out_template_factor=2)
loops_control_EP_ratio[which(loops_control_EP_ratio$density_ratio > 3 &
loops_control_EP_ratio$V1 == 10 & loops_control_EP_ratio$V2 > 69600000 & loops_control_EP_ratio$V6 < 70100000),]
loops_control_EP_ratio[which(loops_control_EP_ratio$density_ratio > 2.819 &
loops_control_EP_ratio$V1 == 1 & loops_control_EP_ratio$V2 > 3250000 & loops_control_EP_ratio$V6 < 3750000),]
loops_control_EP_ratio_shuffle = calculate_HDR_loop_margi(loop_file="/dataOS/rcalandrelli/phase_separation/HiC/loops/enhancer_promoter_loops/H1_control_EP.bedpe",
chrSize_file="/dataOS/rcalandrelli/phase_separation/4DNFI823LSII.hg38.mainonly.chrom.sizes",
chromosomes=hg38_chromosomes[1:23],
sample="control",
bin_size=10000,
out_template_factor=2,
shuffle_loops = T)
loops_control_EP_ratio$density_ratio_shuffle = loops_control_EP_ratio_shuffle$density_ratio
wilcox.test(loops_control_EP_ratio$density_ratio, loops_control_EP_ratio$density_ratio_shuffle, alternative = "two.sided")
###### MicroC validation analysis
loops_MicroC_ratio = calculate_HDR_loop_margi(loop_file="/dataOS/rcalandrelli/phase_separation/HiC/loops/hiccups_H1_ES_MicroC/merged_loops.bedpe",
chrSize_file="/dataOS/rcalandrelli/phase_separation/4DNFI823LSII.hg38.mainonly.chrom.sizes",
chromosomes=hg38_chromosomes,
sample="H1_control",
bin_size=10000,
out_template_factor=2,
out_dir_HDR="/dataOS/rcalandrelli/phase_separation/MARGI/loops/H1_MicroC")
loops_MicroC_ratio_shuffle = calculate_HDR_loop_margi(loop_file="/dataOS/rcalandrelli/phase_separation/HiC/loops/hiccups_H1_ES_MicroC/merged_loops.bedpe",
chrSize_file="/dataOS/rcalandrelli/phase_separation/4DNFI823LSII.hg38.mainonly.chrom.sizes",
chromosomes=hg38_chromosomes,
sample="H1_control",
bin_size=10000,
out_template_factor=2,
shuffle_loops = T,
out_dir_HDR="/dataOS/rcalandrelli/phase_separation/MARGI/loops/H1_MicroC")
wilcox.test(loops_MicroC_ratio$density_ratio, loops_MicroC_ratio_shuffle$density_ratio, alternative = "two.sided")
### Distribution of density ratio
quantile(loops_control_EP_ratio$density_ratio, 0.85)
nrow(loops_control_EP_ratio[which(loops_control_EP_ratio$density_ratio > 3.7 &
!is.infinite(loops_control_EP_ratio$density_ratio)),])
nrow(loops_control_EP_ratio[which(loops_control_EP_ratio$density_ratio > 3.7 &
!is.infinite(loops_control_EP_ratio$density_ratio) &
!is.na(loops_control_EP_ratio$V25)),])
nrow(loops_control_EP_ratio[which(loops_control_EP_ratio$density_ratio < 3.7 &
!is.infinite(loops_control_EP_ratio$density_ratio) &
!is.na(loops_control_EP_ratio$V25)),])
temp = as.table(matrix(c(71,239-71,491,1571-491), nrow=2, ncol=2, byrow = T))
chisq.test(temp)
nrow(loops_control_EP_ratio[which(loops_control_EP_ratio$density_ratio_shuffle > 3.7),])
p1<-ggplot(loops_control_EP_ratio, aes(x=density_ratio)) +
geom_histogram(color="black", fill="blue", bins=100) +
labs(x="MARGI density ratio", y="Number of loops") +
scale_x_continuous(name="MARGI density ratio", breaks=c(0,5,10,15,20,25), labels=c("0","5","10","15","20","25")) +
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
panel.background = element_blank(), axis.line = element_line(colour = "black"),
text = element_text(size=16),
axis.text.x = element_text(size = 16, color = "black"),
axis.text.y = element_text(size = 16, color = "black"),
axis.ticks.length = unit(0.2, "cm"),
legend.position = "none") +
geom_vline(aes(xintercept=3.7),
color="red", size=0.8)
##### Violin plots
temp_melt = reshape2::melt(loops_control_EP_ratio[,c("density_ratio", "density_ratio_shuffle")])
temp_melt$value = as.numeric(temp_melt$value)
temp_melt$variable = gsub("density_ratio_shuffle","Shuffle data",temp_melt$variable)
temp_melt$variable = gsub("density_ratio","Real data",temp_melt$variable)
p2<-ggplot(temp_melt, aes(x=variable, y=value, fill=variable)) +
geom_violin() +
geom_boxplot(width=0.1) +
stat_compare_means(method = "wilcox", size = 6, label.y = 35) +
labs(x="", y=expression("MARGI density ratio")) +
ylim(c(0,36)) +
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
panel.background = element_blank(), axis.line = element_line(colour = "black"),
text = element_text(size=16),
axis.text.x = element_text(size = 16, color = "black"),
axis.text.y = element_text(size = 16, color = "black"),
axis.ticks.length = unit(0.2, "cm"),
legend.position = "none")
png("/dataOS/rcalandrelli/phase_separation/MARGI/loops/iMARGI_H1_control/MARGI_density_ratio_hist_box.png", width = 6, height = 8, res = 200, units = "in")
plot_grid(p1,p2,nrow=2, labels = c("E","F"), label_size = 18)
dev.off()
make_loop_EP_ratio_file_higlass <- function(loop_file_EP_ratio_path,
threshold_ratio,
out_template_factor){
loop_file_EP_ratio = read.table(paste0(loop_file_EP_ratio_path, "/loops_EP_ratio_template_factor_",out_template_factor,".bedpe"))
HDR = loop_file_EP_ratio[which(loop_file_EP_ratio$density_ratio > threshold_ratio),]
HDR = HDR[,1:6]
HDR[,1] = paste0("chr",HDR[,1])
HDR[,4] = paste0("chr",HDR[,4])
HDR[,3] = HDR[,6]
HDR[,5] = HDR[,2]
write.table(HDR, paste0(loop_file_EP_ratio_path, "/HDR_template_factor_",out_template_factor, "_thres_",threshold_ratio,".bedpe"), row.names = F, col.names = F, quote = F, sep="\t")
}
make_loop_EP_ratio_file_higlass(loop_file_EP_ratio_path="/dataOS/rcalandrelli/phase_separation/MARGI/loops/iMARGI_H1_control",
threshold_ratio=3,
out_template_factor=2)
######### Merge the loop associated to pattern 1 in iMARGI with loop domains
make_loop_full <- function(loops_EP_ratio,
loop_domain,
loop_density_ratio_hic){
temp_loop_margi = loops_EP_ratio[,c(1,2,3,4,5,6,25,26,27)]
colnames(temp_loop_margi) = c("chr_1","start_1","end_1","chr_2","start_2","end_2","index","EP","density_ratio")
temp_loop_hic = loop_domain[,c(1,2,3,4,5,6,25)]
colnames(temp_loop_hic) = c("chr_1","start_1","end_1","chr_2","start_2","end_2","loop_domain")
temp_loop_hic_2 = loop_density_ratio_hic[,c(1:6,26)]
colnames(temp_loop_hic_2) = c("chr_1","start_1","end_1","chr_2","start_2","end_2","density_ratio_hic")
loops_full = merge(temp_loop_margi, temp_loop_hic, by = c("chr_1","start_1","end_1","chr_2","start_2","end_2"))
loops_full = merge(loops_full, temp_loop_hic_2, by = c("chr_1","start_1","end_1","chr_2","start_2","end_2"))
return(loops_full)
}
loops_control_full = make_loop_full(loops_control_EP_ratio, loop_domain_control, loop_control_density_ratio_hic)
loops_control_full$density_ratio = as.numeric(loops_control_full$density_ratio)
loops_control_full$density_ratio_hic = as.numeric(loops_control_full$density_ratio_hic)
loops_NH4OAc_full = make_loop_full(loops_NH4OAc_EP_ratio, loop_domain_NH4OAc, loop_NH4OAc_density_ratio_hic)
loops_NH4OAc_full$density_ratio = as.numeric(loops_NH4OAc_full$density_ratio)
loops_NH4OAc_full$density_ratio_hic = as.numeric(loops_NH4OAc_full$density_ratio_hic)
loops_FL_full = make_loop_full(loops_FL_EP_ratio, loop_domain_FL, loop_FL_density_ratio_hic)
loops_FL_full$density_ratio = as.numeric(loops_FL_full$density_ratio)
loops_FL_full$density_ratio_hic = as.numeric(loops_FL_full$density_ratio_hic)
loops_RNase_full = make_loop_full(loops_RNase_EP_ratio, loop_domain_RNase, loop_RNase_density_ratio_hic)
loops_RNase_full$density_ratio = as.numeric(loops_RNase_full$density_ratio)
loops_RNase_full$density_ratio_hic = as.numeric(loops_RNase_full$density_ratio_hic)
loops_control_full = loops_control_full[which(!is.infinite(loops_control_full$density_ratio)),]
wilcox.test(loops_control_full$density_ratio_hic, loops_control_full$density_ratio, paired = T)
##### Scatter plot
thres_density_ratio = 3.7
thres_hic_density_ratio = 3.7
labelsize = 12
p1<-ggplot(loops_control_full, aes(x=density_ratio_hic, y=density_ratio)) +
geom_point(aes(color=loop_domain)) +
geom_smooth(method='lm', se = F, color="black") +
geom_text(x=25, y=5, label="r = 0.21", size = 5) +
scale_color_manual(labels = c("Loop domain", "Loop"), values = c("red", "blue")) +
labs(x="HiC density ratio", y="MARGI density ratio") +
xlim(0,30) + ylim(0,30) +
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
panel.background = element_blank(), axis.line = element_line(colour = "black"),
text = element_text(size=labelsize),
axis.text.x = element_text(size = labelsize, color = "black"),
axis.text.y = element_text(size = labelsize, color = "black"),
axis.ticks.length = unit(0.2, "cm"),
legend.title = element_blank(),
legend.position = "top")
x = as.numeric(loops_control_full$density_ratio)
y = as.numeric(loops_control_full$density_ratio_hic)
cor(x,y, method = "pearson")
table(as.factor(loops_control_full[which(loops_control_full$density_ratio>3),"loop_domain"]))
hist(loops_control_full[which(loops_control_full$loop_domain=="loop_domain"),"density_ratio"])
##### Violin plots to compare HiC and MARGI density ratios
temp_melt = reshape2::melt(loops_control_full[,c("density_ratio", "density_ratio_hic")])
temp_melt$value = as.numeric(temp_melt$value)
temp_melt$variable = gsub("density_ratio_hic","HiC",temp_melt$variable)
temp_melt$variable = gsub("density_ratio","MARGI",temp_melt$variable)
p2<-ggplot(temp_melt, aes(x=variable, y=value, fill=variable)) +
geom_violin() +
geom_boxplot(width=0.1) +
stat_compare_means(method = "wilcox", size = 6, label.y = 22, label = "p.signif") +
ylim(c(0,23)) +
labs(x="", y=expression("Density ratio")) +
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
panel.background = element_blank(), axis.line = element_line(colour = "black"),
text = element_text(size=labelsize),
axis.text.x = element_text(size = labelsize, color = "black"),
axis.text.y = element_text(size = labelsize, color = "black"),
axis.ticks.length = unit(0.2, "cm"),
legend.position = "none")
# Find p3 in script loop_analysis.r
png("/dataOS/rcalandrelli/phase_separation/MARGI/loops/iMARGI_H1_control/density_ratio_violin_HiC_MARGI.png", width = 10, height = 4, res = 200, units = "in")
plot_grid(p1,p2,p3, nrow=1, labels = c("A","B","C"), label_size = 18)
dev.off()
##### Venn plots
temp1 = rownames(loops_control_full[which(loops_control_full$density_ratio>thres_density_ratio),]) # Loops with high MARGI density ratio
temp2 = rownames(loops_control_full[which(loops_control_full$density_ratio_hic>thres_hic_density_ratio),]) # Loops with high HiC density ratio
temp3 = rownames(loops_control_full[which(loops_control_full$loop_domain=="loop_domain"),]) # Loop domains
### All together
venn.diagram(
x = list(temp1, temp2, temp3),
category.names = c("MARGI_HDR","HiC_HDR","LD"),
filename = "/dataOS/rcalandrelli/phase_separation/MARGI/loops/iMARGI_H1_control/venn_diagram_3.png",
output=T,
# Main title
main = "",
main.fontfamily = "sans",
main.fontface = "bold",
main.cex = .8,
# Output features
imagetype="png" ,
height = 600 ,
width = 600 ,
resolution = 300,
compression = "lzw",
# Circles
lwd = 2,
lty = 'blank',
fill = c("red", "blue", "darkgreen"),
# Numbers
cex = .6,
fontface = "bold",
fontfamily = "sans",
# Set names
cat.cex = 0.6,
cat.fontface = "bold",
cat.default.pos = "outer",
cat.pos = c(-27, 27, 135),
cat.dist = c(0.06, 0.06, 0.06),
cat.fontfamily = "sans"
#rotation = 1
)
make_venn <- function(x1, x2, categories, colors, cat_dist){
venn.diagram(
x = list(x1, x2),
category.names = rev(categories),
filename = paste0("/dataOS/rcalandrelli/phase_separation/MARGI/loops/iMARGI_H1_control/", categories[1], "_", categories[2], "_venn.png"),
output=T,
# Main title
main = "",
main.fontfamily = "sans",
main.fontface = "bold",
main.cex = .8,
# Output features
imagetype="png" ,
height = 500 ,
width = 600 ,
resolution = 300,
compression = "lzw",
# Circles
lwd = 2,
lty = 'blank',
fill = colors,
# Numbers
cex = .6,
fontface = "bold",
fontfamily = "sans",
# Set names
cat.cex = 0.6,
cat.fontface = "bold",
cat.default.pos = "outer",
cat.pos = c(130, -130),
cat.dist = cat_dist,
cat.fontfamily = "sans"
#rotation = 1
)
}
make_venn(temp1, temp2, c("MARGI_HDR","HiC_HDR"), c("red","blue"), c(0.22, 0.1))
make_venn(temp1, temp3, c("MARGI_HDR","LD"), c("red","darkgreen"), c(0.23, 0.08))
make_venn(temp2, temp3, c("HiC_HDR","LD"), c("blue","darkgreen"), c(0.16, 0.1))
figures_list = list()
figures_list[[1]] = readPNG("/dataOS/rcalandrelli/phase_separation/MARGI/loops/iMARGI_H1_control/MARGI_HDR_HiC_HDR_venn.png")
figures_list[[2]] = readPNG("/dataOS/rcalandrelli/phase_separation/MARGI/loops/iMARGI_H1_control/MARGI_HDR_LD_venn.png")
figures_list[[3]] = readPNG("/dataOS/rcalandrelli/phase_separation/MARGI/loops/iMARGI_H1_control/HiC_HDR_LD_venn.png")
figures_list[[4]] = readPNG("/dataOS/rcalandrelli/phase_separation/MARGI/loops/iMARGI_H1_control/venn_diagram_3.png")
pdf("/dataOS/rcalandrelli/phase_separation/MARGI/loops/iMARGI_H1_control/venn_diagrams_separated.pdf", width = 10, height = 4)
plot_grid(rasterGrob(figures_list[[4]]),
rasterGrob(figures_list[[1]]),
rasterGrob(figures_list[[2]]), ncol=3, labels = c("D","E","F"), label_size = 18)
dev.off()
###### the high-HDR loops were associated with the high-MDR loops (odds ratio = xx, p-value = xx, Chi-square test)
nrow(loops_control_full[which(loops_control_full$density_ratio>3.7),])
nrow(loops_control_full[which(loops_control_full$density_ratio_hic>3.7),])
nrow(loops_control_full[which(loops_control_full$density_ratio_hic<3.7 & loops_control_full$density_ratio<3.7),])
nrow(loops_control_full[which(loops_control_full$density_ratio_hic>3.7 & loops_control_full$density_ratio>3.7),])
temp = as.table(matrix(c(100,342,139,1229), nrow=2, ncol=2, byrow = T))
chisq.test(temp)
###### the loop domains were associated with the high-MDR loops (odds ratio = xx, p-value = xx, Chi-square test)
nrow(loops_control_full[which(loops_control_full$density_ratio>3.7),])
nrow(loops_control_full[which(loops_control_full$loop_domain=="loop_domain"),])
nrow(loops_control_full[which(loops_control_full$density_ratio<3.7 & loops_control_full$loop_domain=="NA"),])
nrow(loops_control_full[which(loops_control_full$density_ratio>3.7 & loops_control_full$loop_domain=="loop_domain"),])
temp = as.table(matrix(c(106,514,133,1057), nrow=2, ncol=2, byrow = T))
chisq.test(temp)
################# Loop CTCF analysis
loops_ctcf_analysis <- function(loops_file,
ctcf_file_path){
### Loop data
# loops_file = read.table(loops_file_path)[,1:6]
# colnames(loops_file)[1] = "V1"
loops_file[,1] = paste0("chr", loops_file[,1])
loops_file[,4] = paste0("chr", loops_file[,4])
Gr_loop_upstream <- GRanges(
seqnames = Rle(loops_file$chr_1),
ranges = IRanges(as.numeric(loops_file$start_1), end = as.numeric(loops_file$end_1), names = c(1:nrow(loops_file))),
strand = Rle(strand('*')))
Gr_loop_downstream <- GRanges(
seqnames = Rle(loops_file$chr_2),
ranges = IRanges(as.numeric(loops_file$start_2), end = as.numeric(loops_file$end_2), names = c(1:nrow(loops_file))),
strand = Rle(strand('*')))
### CTCF data
ctcf_file = data.frame(fread(ctcf_file_path))
Gr_ctcf <- GRanges(
seqnames = Rle(as.character(ctcf_file[,1])),
ranges = IRanges(as.numeric(ctcf_file[,2]), end = as.numeric(ctcf_file[,3]), names = c(1:nrow(ctcf_file))),
strand = Rle(strand('*')))
### Calculating overlap
overlap_upstream <- countOverlaps(Gr_loop_upstream, Gr_ctcf)
overlap_downstream <- countOverlaps(Gr_loop_downstream, Gr_ctcf)
loops_file$ctcf_upstream <- overlap_upstream
loops_file$ctcf_downstream <- overlap_downstream
# total_loops = nrow(loops_file)
# loops_both_end_ctcf <- nrow(loops_file[which(loops_file$ctcf_upstream > 0 & loops_file$ctcf_downstream > 0),])
# loops_one_end_ctcf <- nrow(loops_file[which((loops_file$ctcf_upstream > 0 & loops_file$ctcf_downstream == 0) |
# (loops_file$ctcf_downstream > 0 & loops_file$ctcf_upstream == 0)),])
# loops_no_ctcf <- nrow(loops_file[which(loops_file$ctcf_upstream == 0 & loops_file$ctcf_downstream == 0),])
#
# out = c(total_loops, loops_both_end_ctcf, loops_one_end_ctcf, loops_no_ctcf)
return(loops_file)
}
GeomSplitViolin <- ggproto("GeomSplitViolin", GeomViolin,
draw_group = function(self, data, ..., draw_quantiles = NULL) {
data <- transform(data, xminv = x - violinwidth * (x - xmin), xmaxv = x + violinwidth * (xmax - x))
grp <- data[1, "group"]
newdata <- plyr::arrange(transform(data, x = if (grp %% 2 == 1) xminv else xmaxv), if (grp %% 2 == 1) y else -y)
newdata <- rbind(newdata[1, ], newdata, newdata[nrow(newdata), ], newdata[1, ])
newdata[c(1, nrow(newdata) - 1, nrow(newdata)), "x"] <- round(newdata[1, "x"])
if (length(draw_quantiles) > 0 & !scales::zero_range(range(data$y))) {
stopifnot(all(draw_quantiles >= 0), all(draw_quantiles <=
1))
quantiles <- ggplot2:::create_quantile_segment_frame(data, draw_quantiles)
aesthetics <- data[rep(1, nrow(quantiles)), setdiff(names(data), c("x", "y")), drop = FALSE]
aesthetics$alpha <- rep(1, nrow(quantiles))
both <- cbind(quantiles, aesthetics)
quantile_grob <- GeomPath$draw_panel(both, ...)
ggplot2:::ggname("geom_split_violin", grid::grobTree(GeomPolygon$draw_panel(newdata, ...), quantile_grob))
}
else {
ggplot2:::ggname("geom_split_violin", GeomPolygon$draw_panel(newdata, ...))
}
})
geom_split_violin <- function(mapping = NULL, data = NULL, stat = "ydensity", position = "identity", ...,
draw_quantiles = NULL, trim = TRUE, scale = "area", na.rm = FALSE,
show.legend = NA, inherit.aes = TRUE) {
layer(data = data, mapping = mapping, stat = stat, geom = GeomSplitViolin,
position = position, show.legend = show.legend, inherit.aes = inherit.aes,
params = list(trim = trim, scale = scale, draw_quantiles = draw_quantiles, na.rm = na.rm, ...))
}
ctcf_file_path = "/dataOS/rcalandrelli/phase_separation/MARGI/loops/H1____GSM822297_hg38_wgEncodeOpenChromChipH1hescCtcfPk.bed"
ctcf_file_path = "/dataOS/wenxingzhao/database/ENCODE/H1_chipseq/MACS2_narrow_peaks/CTCF_MACS2.peak"
loops_control_full = loops_ctcf_analysis(loops_control_full, ctcf_file_path)
loops_control_full$ctcf_both = loops_control_full$ctcf_upstream + loops_control_full$ctcf_downstream
loops_NH4OAc_full = loops_ctcf_analysis(loops_NH4OAc_full, ctcf_file_path)
loops_NH4OAc_full$ctcf_both = loops_NH4OAc_full$ctcf_upstream + loops_NH4OAc_full$ctcf_downstream
loops_FL_full = loops_ctcf_analysis(loops_FL_full, ctcf_file_path)
loops_FL_full$ctcf_both = loops_FL_full$ctcf_upstream + loops_FL_full$ctcf_downstream
loops_RNase_full = loops_ctcf_analysis(loops_RNase_full, ctcf_file_path)
loops_RNase_full$ctcf_both = loops_RNase_full$ctcf_upstream + loops_RNase_full$ctcf_downstream
### HiC HDR
df_hic_hdr = loops_control_full[,c("density_ratio_hic","ctcf_upstream","ctcf_downstream","ctcf_both")]
df_hic_hdr$density_ratio_hic = ifelse(df_hic_hdr$density_ratio_hic > 3.7, "HiC_HDR","Not HiC_HDR")
df_hic_hdr_melt = reshape2::melt(df_hic_hdr)
df_hic_hdr_melt$title = "HiC density ratio"
colnames(df_hic_hdr_melt)[1] = "type"
p1<-ggplot(df_hic_hdr_melt, aes(x=variable, y=value, fill = type)) +
geom_split_violin() +
stat_compare_means(method = "wilcox", size = 3, label.y = 15, label = "p.format") +
labs(x="", y=expression("CTCF enrichment")) +
scale_x_discrete(labels=c("ctcf_upstream" = "Up anchor", "ctcf_downstream" = "Down anchor", "ctcf_both" = "Both anchors")) +
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
panel.background = element_blank(), axis.line = element_line(colour = "black"),
text = element_text(size=10),
axis.text.x = element_text(size = 10, color = "black"),
axis.text.y = element_text(size = 10, color = "black"),
axis.ticks.length = unit(0.2, "cm"),
legend.title = element_blank(),
legend.position = "top")
### MARGI HDR
df_margi_hdr = loops_control_full[,c("density_ratio","ctcf_upstream","ctcf_downstream","ctcf_both")]
df_margi_hdr$density_ratio = ifelse(df_margi_hdr$density_ratio > 3.7, "MARGI_HDR","Not MARGI_HDR")
df_margi_hdr_melt = reshape2::melt(df_margi_hdr)
df_margi_hdr_melt$title = "MARGI density ratio"
colnames(df_margi_hdr_melt)[1] = "type"
p2<-ggplot(df_margi_hdr_melt, aes(x=variable, y=value, fill = type)) +
geom_split_violin() +
stat_compare_means(method = "wilcox", size = 3, label.y = 15, label = "p.format") +
labs(x="", y=expression("CTCF enrichment")) +
scale_x_discrete(labels=c("ctcf_upstream" = "Up anchor", "ctcf_downstream" = "Down anchor", "ctcf_both" = "Both anchors")) +
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
panel.background = element_blank(), axis.line = element_line(colour = "black"),
text = element_text(size=10),
axis.text.x = element_text(size = 10, color = "black"),
axis.text.y = element_text(size = 10, color = "black"),
axis.ticks.length = unit(0.2, "cm"),
legend.title = element_blank(),
legend.position = "top")
### Loop domains
df_LD = loops_control_full[,c("loop_domain","ctcf_upstream","ctcf_downstream","ctcf_both")]
df_LD$loop_domain = ifelse(df_LD$loop_domain =="loop_domain", "Loop domain","Not loop domain")
df_LD_melt = reshape2::melt(df_LD)
df_LD_melt$title = "Loop domains"
colnames(df_LD_melt)[1] = "type"
p3<-ggplot(df_LD_melt, aes(x=variable, y=value, fill = type)) +
#facet_grid(cols=vars(title)) +
geom_split_violin() +
stat_compare_means(method = "wilcox", size = 3, label.y = 15, label = "p.format") +
labs(x="", y=expression("CTCF enrichment")) +
scale_x_discrete(labels=c("ctcf_upstream" = "Up anchor", "ctcf_downstream" = "Down anchor", "ctcf_both" = "Both anchors")) +
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
panel.background = element_blank(), axis.line = element_line(colour = "black"),
text = element_text(size=10),
axis.text.x = element_text(size = 10, color = "black"),
axis.text.y = element_text(size = 10, color = "black"),
axis.ticks.length = unit(0.2, "cm"),
legend.title = element_blank(),
legend.position = "top")
png("/dataOS/rcalandrelli/phase_separation/MARGI/loops/iMARGI_H1_control/CTCF_violin_plots.png", width = 10, height = 4, res = 200, units = "in")
plot_grid(p1,p2,p3, ncol = 3, labels = c("G","",""), label_size = 18)
dev.off()
# loop_ctcf_summary_control = loops_ctcf_analysis(loops_file_path = "/dataOS/rcalandrelli/phase_separation/HiC/loops/hiccups_H1_control_merged/merged_loops.bedpe",
# ctcf_file_path)
# loop_ctcf_summary_NH4OAc = loops_ctcf_analysis(loops_file_path = "/dataOS/rcalandrelli/phase_separation/HiC/loops/hiccups_H1_NH4OAc_merged/merged_loops.bedpe",
# ctcf_file_path)
# loop_ctcf_summary_FL = loops_ctcf_analysis(loops_file_path = "/dataOS/rcalandrelli/phase_separation/HiC/loops/hiccups_H1_FL_merged/merged_loops.bedpe",
# ctcf_file_path)
# loop_ctcf_summary_RNase = loops_ctcf_analysis(loops_file_path = "/dataOS/rcalandrelli/phase_separation/HiC/loops/hiccups_H1_RNaseTreat_merged/merged_loops.bedpe",
# ctcf_file_path)
#
# df_loop_ctcf_summary = as.data.frame(rbind(loop_ctcf_summary_control,
# loop_ctcf_summary_NH4OAc,
# loop_ctcf_summary_FL,
# loop_ctcf_summary_RNase))
# rownames(df_loop_ctcf_summary) = c("Control","NH4OAc","FL","RNase")
# colnames(df_loop_ctcf_summary) = c("Total_loops", "CTCF_both_sides", "CTCF_one_side", "CTCF_none")
# df_loop_ctcf_summary$sample = c("Control","NH4OAc","FL","RNase")
# df_loop_ctcf_summary[,2:4] = round(df_loop_ctcf_summary[,2:4] / df_loop_ctcf_summary[,1], 3)
# df_loop_ctcf_summary = df_loop_ctcf_summary[,-1]
#
# df = melt(df_loop_ctcf_summary)
#
# ggplot(df, aes(x=sample, y=value, fill=variable)) +
# geom_bar(stat="identity", position=position_dodge())
#
#
# my_data = data.frame(
# y=c(rnorm(1000), rnorm(1000, 0.5), rnorm(1000, 1), rnorm(1000, 1.5)),
# x=c(rep('a', 2000), rep('b', 2000)),
# m=c(rep('i', 1000), rep('j', 2000), rep('i', 1000))
# )
################# Loops associated with enriched iMARGI regions (computation in margi_loops_run_fun.r)
loops_label = read.table("/dataOS/rcalandrelli/phase_separation/MARGI/loops/iMARGI_H1_control/loops_RNA_enrich_label.bedpe", header = T)
table(as.factor(loops_label$label))
loops_label$seqnames = as.character(loops_label$seqnames)
### Adding enhancer_promoter labeling
add_ep_label <- function(x,
loop_ep_data)
{
temp = loop_ep_data[which(loop_ep_data[,1] == gsub("chr","",as.character(x[1])) &
loop_ep_data[,2] == as.numeric(x[2]) &
loop_ep_data[,6] == as.numeric(x[3])),]
if(nrow(temp)>0){
return(temp$V25)
} else {
return("NA")
}
}
loops_label$EP = apply(loops_label, 1, add_ep_label, loop_ep_data = loop_control_EP)
enriched_loops = loops_label[which(loops_label$label=="enrichment"),]
table(as.factor(enriched_loops$EP))
# temp_list = list()
# for (i in 1:nrow(loops_label)){
# temp1 = loop_ep_data[which(loop_ep_data$V1 == gsub("chr","",as.character(loops_label[i,1])) &
# loop_ep_data$V2 == as.numeric(loops_label[i,2]) &
# loop_ep_data$V6 == as.numeric(loops_label[i,3])),]
#
# if (nrow(temp1)>0){
# temp_list[[i]] = as.character(temp1$V25)
# } else {
# temp_list[[i]] = "NA"
# }
# }
# loops_label$EP = do.call("rbind", temp_list)
enriched_loops[which(enriched_loops$EP=="both"),]
loops_label[which(loops_label$seqnames=="chr10" &
loops_label$start==73530000 &
loops_label$end == 73650000),]