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compartment_analysis.r
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compartment_analysis.r
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############## RNA-genome interaction project compartment analysis
options(scipen=999)
chrSize = read.table("/dataOS/rcalandrelli/phase_separation/4DNFI823LSII.hg38.mainonly.chrom.sizes")
hg38_chromosomes = as.character(chrSize$V1)
hg38_lengths = as.numeric(chrSize$V2)
names(hg38_lengths) = hg38_chromosomes
### Make gene density files
annotation <- read.table('/dataOS/rcalandrelli/MARGI/Homo_sapiens.GRCh38.84.chr.gtf_to_geneTable.tsv', stringsAsFactors = F)
colnames(annotation)=annotation[1,]
annotation=annotation[-1,]
annotation$start = as.numeric(annotation$start)
annotation$end = as.numeric(annotation$end)
Gr_annotation <- GRanges(
seqnames = Rle(annotation[,1]),
ranges = IRanges(as.numeric(annotation[,2]), end = as.numeric(annotation[,3]), names = c(1:nrow(annotation))),
strand = Rle(strand(annotation[,4])),
gene_id = annotation[,5],
gene_name = annotation[,6],
gene_biotype = annotation[,7],
transcript_id = annotation[,8])
Gr_hg38 <- GRanges(
seqnames = Rle(names(hg38_lengths)),
ranges = IRanges(rep(1,24), end = as.numeric(hg38_lengths), names = c(1:length(hg38_lengths))),
strand = Rle(strand('*')))
seqlengths(Gr_hg38) <- hg38_lengths[names(seqlengths(Gr_hg38))]
make_gene_density_file <- function(annotation_gr, genome_gr, window_size) {
genome_window <- tileGenome(seqinfo(genome_gr), tilewidth = as.numeric(window_size), cut.last.tile.in.chrom = T)
overlaps = countOverlaps(genome_window,Gr_annotation)
mcols(genome_window)['overlap'] = overlaps
df = data.frame(genome_window)
df$start = df$start - 1
df = df[,c(1,2,3,6)]
write.table(df,paste0("/dataOS/rcalandrelli/phase_separation/HiC/compartments/gene_density_annotation/gene_density_",window_size,".txt"), row.names = F, col.names = F, sep = "\t", quote = F)
}
make_gene_density_file(Gr_annotation, Gr_hg38, "5000000")
make_gene_density_file(Gr_annotation, Gr_hg38, "2500000")
make_gene_density_file(Gr_annotation, Gr_hg38, "1000000")
make_gene_density_file(Gr_annotation, Gr_hg38, "500000")
### Make single file for all eigen value across all genome
make_eigen_all_chromosomes <- function(directory,
sample,
resolution # string
){
gene_density = read.table(paste0("/dataOS/rcalandrelli/phase_separation/HiC/compartments/gene_density_annotation/gene_density_",resolution,".txt"), stringsAsFactors = F)
eigen_all = data.frame()
for (i in hg38_chromosomes){
gene_density_chr = gene_density[which(gene_density$V1 == i),]
gene_density_norm = gene_density_chr$V4 - mean(gene_density_chr$V4)
temp_eigen = read.table(paste0(directory,"compartments/",sample,"/",resolution,"/eigen_",i,".txt"), stringsAsFactors = F)
temp_eigen[which(is.na(temp_eigen)),1] = 0
#print(cor(temp_eigen,gene_density_norm))
if (cor(temp_eigen,gene_density_norm)<0){
temp_eigen = temp_eigen*(-1)
}
chr = i
coord = seq(0, nrow(temp_eigen)*as.numeric(resolution) - as.numeric(resolution), by = as.numeric(resolution))
eigen = temp_eigen
temp_eigen_mat = data.frame(chr, coord, eigen)
#temp_eigen = read.table(paste0(directory,"compartments/H1_",sample,"_merged/",resolution,"/eigen_",i,".txt"), stringsAsFactors = F)
#temp_eigen1 = read.table(paste0("/mnt/extraids/OceanStor-SysCmn-5/wenxingzhao/project/8_phase_separation/3_compartment/1_juicer_eigenvec/HiC_H1_",sample,"/",resolution,"/",i,"_KR_eigen_",resolution,".txt"), stringsAsFactors = F)
eigen_all = rbind(eigen_all, temp_eigen_mat)
#eigen_all1 = c(eigen_all1, t(temp_eigen1))
}
colnames(eigen_all)[3] = "eigen"
write.table(eigen_all, paste0("/dataOS/rcalandrelli/phase_separation/HiC/compartments/",gsub("_merged","",sample),"_",resolution,".txt"), row.names = F, col.names = T, quote = F, sep = "\t")
return(eigen_all)
}
####### 2.5mb
eigen_control_2500000 = make_eigen_all_chromosomes("/dataOS/rcalandrelli/phase_separation/HiC/",
"H1_control_merged",
"2500000")
eigen_NH4OAc_2500000 = make_eigen_all_chromosomes("/dataOS/rcalandrelli/phase_separation/HiC/",
"H1_NH4OAc_merged",
"2500000")
eigen_FL_2500000 = make_eigen_all_chromosomes("/dataOS/rcalandrelli/phase_separation/HiC/",
"H1_FL_merged",
"2500000")
eigen_RNase_2500000 = make_eigen_all_chromosomes("/dataOS/rcalandrelli/phase_separation/HiC/",
"H1_RNase_merged",
"2500000")
####### 1mb
eigen_control_1000000 = make_eigen_all_chromosomes("/dataOS/rcalandrelli/phase_separation/HiC/",
"H1_control_merged",
"1000000")
eigen_NH4OAc_1000000 = make_eigen_all_chromosomes("/dataOS/rcalandrelli/phase_separation/HiC/",
"H1_NH4OAc_merged",
"1000000")
eigen_FL_1000000 = make_eigen_all_chromosomes("/dataOS/rcalandrelli/phase_separation/HiC/",
"H1_FL_merged",
"1000000")
eigen_RNase_1000000 = make_eigen_all_chromosomes("/dataOS/rcalandrelli/phase_separation/HiC/",
"H1_RNase_merged",
"1000000")
eigen_RNaseCtrl_1000000 = make_eigen_all_chromosomes("/dataOS/rcalandrelli/phase_separation/HiC/",
"H1_RNaseCtrl_merged",
"1000000")
eigen_HFF_1000000 = make_eigen_all_chromosomes("/dataOS/rcalandrelli/phase_separation/HiC/",
"HFF",
"1000000")
eigen_K562_1000000 = make_eigen_all_chromosomes("/dataOS/rcalandrelli/phase_separation/HiC/",
"K562",
"1000000")
####### 500kb
eigen_control_500000 = make_eigen_all_chromosomes("/dataOS/rcalandrelli/phase_separation/HiC/",
"H1_control_merged",
"500000")
eigen_NH4OAc_500000 = make_eigen_all_chromosomes("/dataOS/rcalandrelli/phase_separation/HiC/",
"H1_NH4OAc_merged",
"500000")
eigen_FL_500000 = make_eigen_all_chromosomes("/dataOS/rcalandrelli/phase_separation/HiC/",
"H1_FL_merged",
"500000")
eigen_RNase_500000 = make_eigen_all_chromosomes("/dataOS/rcalandrelli/phase_separation/HiC/",
"H1_RNase_merged",
"500000")
eigen_RNaseCtrl_500000 = make_eigen_all_chromosomes("/dataOS/rcalandrelli/phase_separation/HiC/",
"H1_RNaseCtrl_merged",
"500000")
eigen_HFF_500000 = make_eigen_all_chromosomes("/dataOS/rcalandrelli/phase_separation/HiC/",
"HFF",
"500000")
eigen_K562_500000 = make_eigen_all_chromosomes("/dataOS/rcalandrelli/phase_separation/HiC/",
"K562",
"500000")
###################### Plot OE/PCC heatmaps and eigen tracks to make sure that there was not any wrong flipping due to a correlation with gene density track around 0 (but in a sample positve, in another negative).
# These false sign flippings are clearly noticeable if the eigen track is completely flipped over across different samples
library(reshape2)
library(ggplot2)
library(gridExtra)
library(grid)
require(dplyr)
library(igraph)
edge_to_matrix <- function(df,attrname="V3"){
# df is source target wrights
g=graph.data.frame(df,directed = FALSE)
matrix <- as_adjacency_matrix(g,type = "both", names = TRUE, sparse = FALSE, attr = attrname)
return(matrix)
}
make_heatmap_eigen_plot <- function(directory="/dataOS/rcalandrelli/phase_separation/HiC/",
plot_type, # "pcc" or "oe"
sample,
resolution,
chr,
fig.title="Title",
force_manual_switch=F){
# draw a combine plot
# hicmap = matrix(c(1,NA,NA,NA,NA,1,5,6,3,5,1,7,4,6,7,1),nrow = 4, ncol=4, byrow = T)
# hicmap[is.na(hicmap)] = 0
# hicmap_pcc <- cor(hicmap, method = "pearson")
### Heatmap
hicmap <- read.table(paste0(directory,"HiC_contact_matrices/oe_KR/",sample,"/",resolution,"/",chr,"_",chr,"_",resolution,".txt")) %>% edge_to_matrix()
# Even though hicmap is a dense matrix there are still row and col indexes brought from the sparse matrix. This means that there is a gap in
# rownames and colnames over the centromere that is just plotted as "empty space" or "no data space", which is not NA.
if (plot_type == "pcc"){
hicmap[is.na(hicmap)] = 0
hicmap_pcc <- cor(hicmap, method = "pearson") # pearson correlation matrix of the OE
#entropy = entropySpecificity(hicmap_pcc)
m <- melt(hicmap_pcc)
temp1 = m[which(is.na(m$value)),]
m$Var2 = as.numeric(rownames(hicmap)[nrow(hicmap)]) - m$Var2 # to make the (0,0) vertex on the upper-left corner and have a classic heatmap visualization
hm <- ggplot(data = m, aes(x=Var1, y=Var2, fill=value)) +
geom_tile() +
scale_fill_gradient2(low = "blue", high = "red", limits=c(-1,1), na.value = "#969696") + #na.value = "#969696"
theme(axis.title.y = element_blank(), axis.text.y = element_blank(),
axis.ticks.y = element_blank(), axis.title.x = element_blank(),
axis.text.x = element_blank(), axis.ticks.x = element_blank(),
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
legend.position="none")
}
else if (plot_type == "oe"){
#entropy = entropySpecificity(hicmap)
m <- melt(hicmap)
#m[which(m$Var1==11000000 & m$Var2==41000000),]
m$Var2 = as.numeric(rownames(hicmap)[nrow(hicmap)]) - m$Var2 # to make the (0,0) vertex on the upper-left corner and have a classic heatmap visualization
#m[which(m$value==1),"value"] = 0
m$value = log(m$value)
#m[which(is.infinite(m$value)),"value"] = 0
hm <- ggplot(data = m, aes(x=Var1, y=Var2, fill=value)) +
geom_tile() +
#scale_fill_distiller(name = "Legend title", palette = "Reds", direction = 1, na.value = "transparent") +
scale_fill_gradient2(low = "blue", high = "red", na.value = "#969696") +
theme(axis.title.y = element_blank(), axis.text.y = element_blank(),
axis.ticks.y = element_blank(), axis.title.x = element_blank(),
axis.text.x = element_blank(), axis.ticks.x = element_blank(),
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
legend.position="none")
}
### Eigenvector
gene_density = read.table(paste0("/dataOS/rcalandrelli/phase_separation/HiC/compartments/gene_density_annotation/gene_density_",resolution,".txt"), stringsAsFactors = F)
gene_density_chr = gene_density[which(gene_density$V1 == chr),]
gene_density_norm = gene_density_chr$V4 - mean(gene_density_chr$V4)
ev <- read.table(paste0(directory,"compartments/",sample,"/",resolution,"/eigen_",chr,".txt"), header = F)
temp_ev = ev
temp_ev[which(is.na(temp_ev)),1] = 0
if (cor(temp_ev,gene_density_norm)<0){
ev = ev*(-1)
}
if (force_manual_switch){
ev = -1*ev
}
ev %>% as.data.frame()
colnames(ev) <- "V1"
ev$x <- seq(1,length(ev$V1))
bar_ev <- ggplot()+
geom_bar(data=ev,aes(x=x,y=V1,color=V1>0,fill=V1>0),stat = "identity")+
scale_fill_manual(name="compartment",labels=c("A","B"),values = c("#6b6665","#a31500"))+
scale_color_manual(name="compartment",labels=c("A","B"),values = c("#6b6665","#a31500"))+
xlab("Bins") +
theme_bw() +
ylab("EV1") +
#ylim(-1,1) +
theme(axis.text.x = element_text(size = 12),
axis.title.x = element_text(size = 12, margin = margin(5,0,0,0)),
panel.border = element_blank(), panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),panel.background = element_blank(),
axis.title.y = element_blank(),
axis.text.y = element_blank(),
axis.ticks.y = element_blank(),
legend.position="none")
### Entropy track
# ev$V2 = NaN
# ev[ev[which(!is.na(ev$V1)),"x"],"V2"] = entropy
# bar_entropy <- ggplot()+
# geom_bar(data=ev,aes(x=x,y=V2), color="blue", fill = "blue", stat="identity") +
# # scale_fill_manual(name="compartment",labels=c("A","B"),values = c("#6b6665","#a31500"))+
# # scale_color_manual(name="compartment",labels=c("A","B"),values = c("#6b6665","#a31500"))+
# xlab("Bins") +
# theme_bw() +
# ylab("Entropy") +
# #ylim(-1,1) +
# theme(axis.text.x = element_text(size = 12),
# axis.title.x = element_text(size = 12, margin = margin(5,0,0,0)),
# panel.border = element_blank(), panel.grid.major = element_blank(),
# panel.grid.minor = element_blank(),panel.background = element_blank(),
# axis.title.y = element_text(size = 12, margin = margin(5,0,0,0)),
# axis.text.y = element_blank(),
# axis.ticks.y = element_blank(),
# legend.position="none")
# grob
grob.title <- textGrob(fig.title, hjust = 0.5, vjust = 0.5, gp = gpar(fontsize = 20))
p<-grid.arrange(hm, bar_ev,
nrow = 2, ncol =1,
heights = c(30, 10), top = grob.title)
#return(list(p,entropy))
return(p)
}
### Make plots
for (my_chr in hg38_chromosomes){
my_plot_type = "pcc"
#my_resolution = "2500000"
#my_resolution = "1000000"
my_resolution = "500000"
g1 <- make_heatmap_eigen_plot(directory = "/dataOS/rcalandrelli/phase_separation/HiC/",
plot_type = my_plot_type,
sample = "H1_control_merged",
resolution = my_resolution,
chr = my_chr,
fig.title = paste0("Control_",my_chr))
g2 <- make_heatmap_eigen_plot(directory = "/dataOS/rcalandrelli/phase_separation/HiC/",
plot_type = my_plot_type,
sample = "H1_NH4OAc_merged",
resolution = my_resolution,
chr = my_chr,
fig.title = paste0("NH4OAc_",my_chr))
g3 <- make_heatmap_eigen_plot(directory = "/dataOS/rcalandrelli/phase_separation/HiC/",
plot_type = my_plot_type,
sample = "H1_FL_merged",
resolution = my_resolution,
chr = my_chr,
fig.title = paste0("FL_",my_chr))
g4 <- make_heatmap_eigen_plot(directory = "/dataOS/rcalandrelli/phase_separation/HiC/",
plot_type = my_plot_type,
sample = "H1_RNase_merged",
resolution = my_resolution,
chr = my_chr,
fig.title = paste0("Rnase_",my_chr))
g5 <- make_heatmap_eigen_plot(directory = "/dataOS/rcalandrelli/phase_separation/HiC/",
plot_type = my_plot_type,
sample = "H1_RNaseCtrl_merged",
resolution = my_resolution,
chr = my_chr,
fig.title = paste0("RnaseCtrl_",my_chr))
png(paste0("/dataOS/rcalandrelli/phase_separation/HiC/compartments/result/",my_chr,"_",my_plot_type,"_heatmap_eigen_",my_resolution,".png"), width = 17.5, height = 5, units = "in", res = 200)
grid.arrange(g1,g2,g3,g4,g5,nrow=1)
dev.off()
}
### Calculate entropy data
for (my_plot_type in c("oe","pcc")){
for (my_resolution in c("500000","1000000")){
entropy_list = list()
for (my_chr in hg38_chromosomes){
g1 <- make_heatmap_eigen_plot(directory = "/dataOS/rcalandrelli/phase_separation/",
plot_type = my_plot_type,
sample = "H1_control_merged",
resolution = my_resolution,
chr = my_chr,
fig.title = paste0("Control_",my_chr))
g2 <- make_heatmap_eigen_plot(directory = "/dataOS/rcalandrelli/phase_separation/",
plot_type = my_plot_type,
sample = "H1_NH4OAc_merged",
resolution = my_resolution,
chr = my_chr,
fig.title = paste0("NH4OAc_",my_chr))
g3 <- make_heatmap_eigen_plot(directory = "/dataOS/rcalandrelli/phase_separation/",
plot_type = my_plot_type,
sample = "H1_FL_merged",
resolution = my_resolution,
chr = my_chr,
fig.title = paste0("FL_",my_chr))
g4 <- make_heatmap_eigen_plot(directory = "/dataOS/rcalandrelli/phase_separation/",
plot_type = my_plot_type,
sample = "H1_RNaseTreat_merged",
resolution = my_resolution,
chr = my_chr,
fig.title = paste0("Rnase_",my_chr))
# E1 = mean(g1[[2]][which(!is.na(g1[[2]]))])
# E2 = mean(g2[[2]][which(!is.na(g2[[2]]))])
# E3 = mean(g3[[2]][which(!is.na(g3[[2]]))])
# E4 = mean(g4[[2]][which(!is.na(g4[[2]]))])
# entropy_list[[my_chr]] = c(E1,E2,E3,E4)
temp_list = list(g1[[2]], g2[[2]], g3[[2]], g4[[2]])
temp_out = do.call("cbind", lapply(temp_list, function(x) x[match(names(temp_list[[1]]), names(x))]))
temp_out = cbind(my_chr, rownames(temp_out), temp_out)
colnames(temp_out) = c("chr","coord","Control","NH4OAc","FL", "RNase")
entropy_list[[my_chr]] = temp_out
}
out = do.call("rbind",entropy_list)
#colnames(out) = c("chr","coord","Control","NH4OAc","FL", "RNase")
write.table(out, paste0("/dataOS/rcalandrelli/phase_separation/compartments/result/entropy/entropy_",my_plot_type,"_",my_resolution,"_all_rows.txt"), row.names = F, col.names = T, sep = "\t", quote = F)
}
}
### Entropy data analysis
entropy_oe_500000 = read.table("/dataOS/rcalandrelli/phase_separation/compartments/result/entropy/entropy_oe_500000_all_rows.txt", header = T)
entropy_oe_1000000 = read.table("/dataOS/rcalandrelli/phase_separation/compartments/result/entropy/entropy_oe_1000000_all_rows.txt", header = T)
# entropy_pcc_500000 = read.table("/dataOS/rcalandrelli/phase_separation/compartments/result/entropy/entropy_pcc_500000_all_rows.txt", header = T)
# entropy_pcc_1000000 = read.table("/dataOS/rcalandrelli/phase_separation/compartments/result/entropy/entropy_pcc_1000000_all_rows.txt", header = T)
entropy_oe_500000 = entropy_oe_500000[which(!is.na(rowSums(entropy_oe_500000[,3:6]))),]
entropy_oe_1000000 = entropy_oe_1000000[which(!is.na(rowSums(entropy_oe_1000000[,3:6]))),]
# entropy_pcc_500000 = entropy_pcc_500000[which(!is.na(rowSums(entropy_pcc_500000[,3:6]))),]
# entropy_pcc_1000000 = entropy_pcc_1000000[which(!is.na(rowSums(entropy_pcc_1000000[,3:6]))),]
entropy_oe = rbind(cbind(melt(entropy_oe_500000, id.vars = c("chr","coord")),resolution="500000"),
cbind(melt(entropy_oe_1000000, id.vars = c("chr","coord")),resolution="1000000"))
# entropy_pcc = rbind(cbind(melt(entropy_pcc_500000, id.vars = c("chr","coord")),resolution="500000"),
# cbind(melt(entropy_pcc_1000000, id.vars = c("chr","coord")),resolution="1000000"))
png("/dataOS/rcalandrelli/phase_separation/compartments/result/entropy_boxplot.png", width = 6, height = 6, units = "in", res = 200)
ggplot(entropy_oe, aes(x=variable, y=value, fill=resolution)) +
geom_boxplot() +
labs(x="", y="Entropy") +
theme(legend.title = element_blank(),
panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
panel.background = element_blank(), axis.line = element_line(colour = "black"),
text = element_text(size=20),
axis.text.x = element_text(size = 20, angle = 45, hjust = 1),
axis.text.y = element_text(size = 20),
axis.ticks.length = unit(0.2, "cm")) +
ggtitle("OE contact matrix")
dev.off()
# p2<-ggplot(entropy_pcc, aes(x=variable, y=value, fill=resolution)) +
# geom_boxplot() +
# labs(x="", y="Entropy") +
# #ylim(0,500) +
# theme(legend.title = element_blank(),
# panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
# panel.background = element_blank(), axis.line = element_line(colour = "black"),
# text = element_text(size=20),
# axis.text.x = element_text(size = 20, angle = 45, hjust = 1),
# axis.text.y = element_text(size = 20),
# axis.ticks.length = unit(0.2, "cm")) +
# ggtitle("Pearson correlation matrix")
# png("/dataOS/rcalandrelli/phase_separation/compartments/result/entropy_boxplots.png", width = 12, height = 6, units = "in", res = 200)
# plot_grid(p1,p2)
# dev.off()
wilcox.test(entropy_oe_500000$Control, entropy_oe_500000$NH4OAc)
wilcox.test(entropy_oe_500000$Control, entropy_oe_500000$FL)
wilcox.test(entropy_oe_500000$Control, entropy_oe_500000$RNase)
wilcox.test(entropy_oe_1000000$Control, entropy_oe_1000000$NH4OAc)
wilcox.test(entropy_oe_1000000$Control, entropy_oe_1000000$FL)
wilcox.test(entropy_oe_1000000$Control, entropy_oe_1000000$RNase)
wilcox.test(entropy_pcc_500000$Control, entropy_pcc_500000$NH4OAc)
wilcox.test(entropy_pcc_500000$Control, entropy_pcc_500000$FL)
wilcox.test(entropy_pcc_500000$Control, entropy_pcc_500000$RNase)
wilcox.test(entropy_pcc_1000000$Control, entropy_pcc_1000000$NH4OAc)
wilcox.test(entropy_pcc_1000000$Control, entropy_pcc_1000000$FL)
wilcox.test(entropy_pcc_1000000$Control, entropy_pcc_1000000$RNase)
### NB!!!!! Change sign again for eigen of chromosome/s and sample/s where the sign has been wrongly flipped
# 1Mb
eigen_RNase_1000000[which(eigen_RNase_1000000$chr == "chr19"),"eigen"] = -1*eigen_RNase_1000000[which(eigen_RNase_1000000$chr == "chr19"),"eigen"]
eigen_RNase_1000000[which(eigen_RNase_1000000$chr == "chr21"),"eigen"] = -1*eigen_RNase_1000000[which(eigen_RNase_1000000$chr == "chr21"),"eigen"]
eigen_RNaseCtrl_1000000[which(eigen_RNaseCtrl_1000000$chr == "chr19"),"eigen"] = -1*eigen_RNaseCtrl_1000000[which(eigen_RNaseCtrl_1000000$chr == "chr19"),"eigen"]
write.table(eigen_control_1000000,"/dataOS/rcalandrelli/phase_separation/HiC/compartments/H1_control_1000000.txt",sep="\t",row.names = F, col.names = T, quote = F)
write.table(eigen_NH4OAc_1000000,"/dataOS/rcalandrelli/phase_separation/HiC/compartments/H1_NH4OAc_1000000.txt",sep="\t",row.names = F, col.names = T, quote = F)
write.table(eigen_FL_1000000,"/dataOS/rcalandrelli/phase_separation/HiC/compartments/H1_FL_1000000.txt",sep="\t",row.names = F, col.names = T, quote = F)
write.table(eigen_RNase_1000000,"/dataOS/rcalandrelli/phase_separation/HiC/compartments/H1_RNase_1000000.txt",sep="\t",row.names = F, col.names = T, quote = F)
write.table(eigen_RNaseCtrl_1000000,"/dataOS/rcalandrelli/phase_separation/HiC/compartments/H1_RNaseCtrl_1000000.txt",sep="\t",row.names = F, col.names = T, quote = F)
# 500kb
eigen_RNase_500000[which(eigen_RNase_500000$chr == "chr21"),"eigen"] = -1*eigen_RNase_500000[which(eigen_RNase_500000$chr == "chr21"),"eigen"]
write.table(eigen_control_500000,"/dataOS/rcalandrelli/phase_separation/HiC/compartments/H1_control_500000.txt",sep="\t",row.names = F, col.names = T, quote = F)
write.table(eigen_NH4OAc_500000,"/dataOS/rcalandrelli/phase_separation/HiC/compartments/H1_NH4OAc_500000.txt",sep="\t",row.names = F, col.names = T, quote = F)
write.table(eigen_FL_500000,"/dataOS/rcalandrelli/phase_separation/HiC/compartments/H1_FL_500000.txt",sep="\t",row.names = F, col.names = T, quote = F)
write.table(eigen_RNase_500000,"/dataOS/rcalandrelli/phase_separation/HiC/compartments/H1_RNase_500000.txt",sep="\t",row.names = F, col.names = T, quote = F)
write.table(eigen_RNaseCtrl_500000,"/dataOS/rcalandrelli/phase_separation/HiC/compartments/H1_RNaseCtrl_500000.txt",sep="\t",row.names = F, col.names = T, quote = F)
################ Summary of A/B compartments
resolution_vector = c(rep("1000000",5), rep("500000",5))#, rep("500000",4))
sample_vector = rep(c("Control","NH4OAc","FL","RNase","RNaseCtrl"),2)
A_bins = c(#length(which(eigen_control_5000000$eigen>0)), length(which(eigen_NH4OAc_5000000$eigen>0)), length(which(eigen_FL_5000000$eigen>0)), length(which(eigen_RNaseTreat_5000000$eigen>0)),
length(which(eigen_control_1000000$eigen>0)), length(which(eigen_NH4OAc_1000000$eigen>0)), length(which(eigen_FL_1000000$eigen>0)), length(which(eigen_RNase_1000000$eigen>0)),length(which(eigen_RNaseCtrl_1000000$eigen>0)),
length(which(eigen_control_500000$eigen>0)), length(which(eigen_NH4OAc_500000$eigen>0)), length(which(eigen_FL_500000$eigen>0)), length(which(eigen_RNase_500000$eigen>0)),length(which(eigen_RNaseCtrl_500000$eigen>0)))
B_bins = c(#length(which(eigen_control_5000000$eigen<0)), length(which(eigen_NH4OAc_5000000$eigen<0)), length(which(eigen_FL_5000000$eigen<0)), length(which(eigen_RNaseTreat_5000000$eigen<0)),
length(which(eigen_control_1000000$eigen<0)), length(which(eigen_NH4OAc_1000000$eigen<0)), length(which(eigen_FL_1000000$eigen<0)), length(which(eigen_RNase_1000000$eigen<0)),length(which(eigen_RNaseCtrl_1000000$eigen<0)),
length(which(eigen_control_500000$eigen<0)), length(which(eigen_NH4OAc_500000$eigen<0)), length(which(eigen_FL_500000$eigen<0)), length(which(eigen_RNase_500000$eigen<0)),length(which(eigen_RNaseCtrl_500000$eigen<0)))
df_summary_eigen = data.frame(resolution_vector,sample_vector,A_bins,B_bins)
colnames(df_summary_eigen)[1:2] = c("resolution","sample")
write.table(df_summary_eigen,"/dataOS/rcalandrelli/phase_separation/HiC/compartments/result/summary_eigen.txt", row.names = F, col.names = T, sep = '\t', quote = F)
# Summary numbers
sum(df_summary_eigen[which(df_summary_eigen$resolution=="1000000"),"A_bins"])
sum(df_summary_eigen[which(df_summary_eigen$resolution=="1000000"),"B_bins"])
sum(df_summary_eigen[which(df_summary_eigen$resolution=="500000"),"A_bins"])
sum(df_summary_eigen[which(df_summary_eigen$resolution=="500000"),"B_bins"])
##### Summary of A/B compartment switch between control and treated
switch_stats <- function(x){
if (x[1]==0 | x[2]==0){
out = NA
} else {
if (x[1]>0 & x[2]>0){
out = "AA"
} else if (x[1]<0 & x[2]<0){
out = "BB"
} else if (x[1]>0 & x[2]<0){
out = "AB"
} else if (x[1]<0 & x[2]>0){
out = "BA"
}
}
return(out)
}
# 5mb
# full_eigen_data_5000000 = cbind(eigen_control_5000000,eigen_NH4OAc_5000000,eigen_FL_5000000,eigen_RNaseTreat_5000000)
# full_eigen_data_5000000 = full_eigen_data_5000000[,c(-4,-5,-7,-8,-10,-11)]
# colnames(full_eigen_data_5000000)[3:6] = c("eigen_control","eigen_NH4OAc","eigen_FL","eigen_RNase")
#
# full_eigen_data_5000000$switch_NH4OAc <- apply(full_eigen_data_5000000[,c(3,4)],1,switch_stats)
# full_eigen_data_5000000$switch_FL <- apply(full_eigen_data_5000000[,c(3,5)],1,switch_stats)
# full_eigen_data_5000000$switch_RNaseTreat <- apply(full_eigen_data_5000000[,c(3,6)],1,switch_stats)
#
# label_summary_5000000 = c(nrow(full_eigen_data_5000000[which(full_eigen_data_5000000$eigen_control>0),]),nrow(full_eigen_data_5000000[which(full_eigen_data_5000000$eigen_control<0),]),
# table(as.factor(full_eigen_data_5000000$switch_NH4OAc))["AB"],table(as.factor(full_eigen_data_5000000$switch_NH4OAc))["BA"],table(as.factor(full_eigen_data_5000000$switch_NH4OAc))["AA"],table(as.factor(full_eigen_data_5000000$switch_NH4OAc))["BB"],
# table(as.factor(full_eigen_data_5000000$switch_FL))["AB"],table(as.factor(full_eigen_data_5000000$switch_FL))["BA"],table(as.factor(full_eigen_data_5000000$switch_FL))["AA"],table(as.factor(full_eigen_data_5000000$switch_FL))["BB"],
# table(as.factor(full_eigen_data_5000000$switch_RNaseTreat))["AB"],table(as.factor(full_eigen_data_5000000$switch_RNaseTreat))["BA"],table(as.factor(full_eigen_data_5000000$switch_RNaseTreat))["AA"],table(as.factor(full_eigen_data_5000000$switch_RNaseTreat))["BB"])
# 1mb
full_eigen_data_1000000 = cbind(eigen_control_1000000,eigen_NH4OAc_1000000,eigen_FL_1000000,eigen_RNase_1000000,eigen_RNaseCtrl_1000000)
full_eigen_data_1000000 = full_eigen_data_1000000[,c(-4,-5,-7,-8,-10,-11,-13,-14)]
colnames(full_eigen_data_1000000)[3:7] = c("eigen_control","eigen_NH4OAc","eigen_FL","eigen_RNase","eigen_RNaseCtrl")
full_eigen_data_1000000$switch_NH4OAc <- apply(full_eigen_data_1000000[,c(3,4)],1,switch_stats)
full_eigen_data_1000000$switch_FL <- apply(full_eigen_data_1000000[,c(3,5)],1,switch_stats)
full_eigen_data_1000000$switch_RNase <- apply(full_eigen_data_1000000[,c(3,6)],1,switch_stats)
full_eigen_data_1000000$switch_RNaseCtrl <- apply(full_eigen_data_1000000[,c(3,7)],1,switch_stats)
label_summary_1000000 = c(nrow(full_eigen_data_1000000[which(full_eigen_data_1000000$eigen_control>0),]),nrow(full_eigen_data_1000000[which(full_eigen_data_1000000$eigen_control<0),]),
table(as.factor(full_eigen_data_1000000$switch_NH4OAc))["AB"],table(as.factor(full_eigen_data_1000000$switch_NH4OAc))["BA"],table(as.factor(full_eigen_data_1000000$switch_NH4OAc))["AA"],table(as.factor(full_eigen_data_1000000$switch_NH4OAc))["BB"],
table(as.factor(full_eigen_data_1000000$switch_FL))["AB"],table(as.factor(full_eigen_data_1000000$switch_FL))["BA"],table(as.factor(full_eigen_data_1000000$switch_FL))["AA"],table(as.factor(full_eigen_data_1000000$switch_FL))["BB"],
table(as.factor(full_eigen_data_1000000$switch_RNase))["AB"],table(as.factor(full_eigen_data_1000000$switch_RNase))["BA"],table(as.factor(full_eigen_data_1000000$switch_RNase))["AA"],table(as.factor(full_eigen_data_1000000$switch_RNase))["BB"],
table(as.factor(full_eigen_data_1000000$switch_RNaseCtrl))["AB"],table(as.factor(full_eigen_data_1000000$switch_RNaseCtrl))["BA"],table(as.factor(full_eigen_data_1000000$switch_RNaseCtrl))["AA"],table(as.factor(full_eigen_data_1000000$switch_RNaseCtrl))["BB"])
# 500kb
full_eigen_data_500000 = cbind(eigen_control_500000,eigen_NH4OAc_500000,eigen_FL_500000,eigen_RNase_500000,eigen_RNaseCtrl_500000)
full_eigen_data_500000 = full_eigen_data_500000[,c(-4,-5,-7,-8,-10,-11,-13,-14)]
colnames(full_eigen_data_500000)[3:7] = c("eigen_control","eigen_NH4OAc","eigen_FL","eigen_RNase","eigen_RNaseCtrl")
full_eigen_data_500000$switch_NH4OAc <- apply(full_eigen_data_500000[,c(3,4)],1,switch_stats)
full_eigen_data_500000$switch_FL <- apply(full_eigen_data_500000[,c(3,5)],1,switch_stats)
full_eigen_data_500000$switch_RNase <- apply(full_eigen_data_500000[,c(3,6)],1,switch_stats)
full_eigen_data_500000$switch_RNaseCtrl <- apply(full_eigen_data_500000[,c(3,7)],1,switch_stats)
label_summary_500000 = c(nrow(full_eigen_data_500000[which(full_eigen_data_500000$eigen_control>0),]),nrow(full_eigen_data_500000[which(full_eigen_data_500000$eigen_control<0),]),
table(as.factor(full_eigen_data_500000$switch_NH4OAc))["AB"],table(as.factor(full_eigen_data_500000$switch_NH4OAc))["BA"],table(as.factor(full_eigen_data_500000$switch_NH4OAc))["AA"],table(as.factor(full_eigen_data_500000$switch_NH4OAc))["BB"],
table(as.factor(full_eigen_data_500000$switch_FL))["AB"],table(as.factor(full_eigen_data_500000$switch_FL))["BA"],table(as.factor(full_eigen_data_500000$switch_FL))["AA"],table(as.factor(full_eigen_data_500000$switch_FL))["BB"],
table(as.factor(full_eigen_data_500000$switch_RNase))["AB"],table(as.factor(full_eigen_data_500000$switch_RNase))["BA"],table(as.factor(full_eigen_data_500000$switch_RNase))["AA"],table(as.factor(full_eigen_data_500000$switch_RNase))["BB"],
table(as.factor(full_eigen_data_500000$switch_RNaseCtrl))["AB"],table(as.factor(full_eigen_data_500000$switch_RNaseCtrl))["BA"],table(as.factor(full_eigen_data_500000$switch_RNaseCtrl))["AA"],table(as.factor(full_eigen_data_500000$switch_RNaseCtrl))["BB"])
### Make summary dataframe
resolution_vector = c(rep("1000000",18), rep("500000",18))
sample_vector = rep(c(rep("Control",2), rep("NH4OAc",4),rep("FL",4),rep("RNase",4),rep("RNaseCtrl",4)),2)
switch_label = rep(c("AA","BB",rep(c("AB","BA","AA","BB"),4)),2)
switch_vector = as.numeric(c(label_summary_1000000,label_summary_500000))
df_switch_summary = data.frame(resolution_vector, sample_vector, switch_label, switch_vector)
df_switch_summary$switch_label = factor(df_switch_summary$switch_label, levels = c("AB","BA","AA","BB"))
df_switch_summary$sample_vector = factor(df_switch_summary$sample_vector, levels = c("Control","NH4OAc","FL","RNase","RNaseCtrl"))
### Save data in a table
table_switch_summary = matrix(nrow=0,ncol=6)
for (i in c("1000000","500000")){
temp_mat = df_switch_summary[which(df_switch_summary$resolution_vector == i),]
temp_mat_1 = cbind(temp_mat[which(temp_mat$sample_vector=="NH4OAc"),"switch_vector"],
temp_mat[which(temp_mat$sample_vector=="FL"),"switch_vector"],
temp_mat[which(temp_mat$sample_vector=="RNase"),"switch_vector"],
temp_mat[which(temp_mat$sample_vector=="RNaseCtrl"),"switch_vector"])
temp_mat_1 = cbind(i,c("AB","BA","AA","BB"),temp_mat_1)
table_switch_summary = rbind(table_switch_summary,temp_mat_1)
}
colnames(table_switch_summary) = c("resolution","compartment_switch","NH4OAc","FL","RNase","RNaseCtrl")
write.table(table_switch_summary,"/dataOS/rcalandrelli/phase_separation/HiC/compartments/result/summary_eigen_switch.txt", row.names = F, col.names = T, sep = '\t', quote = F)
######## Distribution of length of consecutive switches: we want to see how many are actually "meaningful" switches that comprise more than one bin
calculate_length_consecutive_switches <- function(eigen_data,
sample,
switch_type){
i = 1
out_list = list()
while (i <= nrow(eigen_data)){
if(!is.na(eigen_data[i,paste0("switch_", sample)]) & eigen_data[i,paste0("switch_", sample)] == switch_type){
counter = 1
k = i + 1
while(k <= nrow(eigen_data)){
if (!is.na(eigen_data[k,paste0("switch_", sample)]) & eigen_data[k,paste0("switch_", sample)] == switch_type){
counter = counter + 1
k = k + 1
} else {
out = c(as.character(eigen_data[i,1]), eigen_data[i,2], counter, switch_type)
out_list[[i]] = out
i = k
break
}
}
} else {
i = i + 1
}
}
out_df = do.call("rbind", out_list)
colnames(out_df) = c("chr", "coord", "consecutive_switches", "switch_label")
return(out_df)
}
##### 1 Mb
switch_length_distribution_1000000_list = list()
switch_length_distribution_1000000_list[["NH4OAc"]] = list()
switch_length_distribution_1000000_list[["FL"]] = list()
switch_length_distribution_1000000_list[["RNase"]] = list()
switch_length_distribution_1000000_list[["RNaseCtrl"]] = list()
for (x in c("NH4OAc","FL","RNase","RNaseCtrl")){
for (y in c("AB", "BA")){
switch_length_distribution_1000000_list[[x]][[y]] = calculate_length_consecutive_switches(eigen_data = full_eigen_data_1000000, sample = x, switch_type = y)
}
}
summary(as.numeric(switch_length_distribution_1000000_list[["RNase"]][["AB"]][,"consecutive_switches"]))
quantile(as.numeric(switch_length_distribution_1000000_list[["RNase"]][["AB"]][,"consecutive_switches"]),0.95)
sum(as.numeric(switch_length_distribution_1000000_list[["RNase"]][["AB"]][,"consecutive_switches"]) >= 3)
switch_length_distribution_1000000_list[["RNase"]][["AB"]][which(switch_length_distribution_1000000_list[["RNase"]][["AB"]][,"consecutive_switches"] >= 3),]
summary(as.numeric(switch_length_distribution_1000000_list[["RNase"]][["BA"]][,"consecutive_switches"]))
quantile(as.numeric(switch_length_distribution_1000000_list[["RNase"]][["BA"]][,"consecutive_switches"]),0.95)
sum(as.numeric(switch_length_distribution_1000000_list[["RNase"]][["BA"]][,"consecutive_switches"]) >= 3)
switch_length_distribution_1000000_list[["RNase"]][["BA"]][which(switch_length_distribution_1000000_list[["RNase"]][["BA"]][,"consecutive_switches"] >= 3),]
### 500 kb
switch_length_distribution_500000_list = list()
switch_length_distribution_500000_list[["NH4OAc"]] = list()
switch_length_distribution_500000_list[["FL"]] = list()
switch_length_distribution_500000_list[["RNase"]] = list()
switch_length_distribution_500000_list[["RNaseCtrl"]] = list()
for (x in c("NH4OAc","FL","RNase","RNaseCtrl")){
for (y in c("AB", "BA")){
switch_length_distribution_500000_list[[x]][[y]] = calculate_length_consecutive_switches(eigen_data = full_eigen_data_500000, sample = x, switch_type = y)
}
}
summary(as.numeric(switch_length_distribution_500000_list[["RNase"]][["AB"]][,"consecutive_switches"]))
quantile(as.numeric(switch_length_distribution_500000_list[["RNase"]][["AB"]][,"consecutive_switches"]),0.95)
sum(as.numeric(switch_length_distribution_500000_list[["RNase"]][["BA"]][,"consecutive_switches"]) > 4)
summary(as.numeric(switch_length_distribution_500000_list[["RNase"]][["BA"]][,"consecutive_switches"]))
quantile(as.numeric(switch_length_distribution_500000_list[["RNase"]][["BA"]][,"consecutive_switches"]),0.95)
sum(as.numeric(switch_length_distribution_500000_list[["RNase"]][["BA"]][,"consecutive_switches"]) > 2)
##################################### 1 MB
switch_length_distribution_1000000_summary = matrix(data = 0, nrow=7, ncol=9)
switch_length_distribution_1000000_summary[,1] = seq(1,7)
colnames(switch_length_distribution_1000000_summary) = c("x",paste0(c("NH4OAc","FL","RNase","RNaseCtrl"),"_AB"),paste0(c("NH4OAc","FL","RNase","RNaseCtrl"),"_BA"))
for (i in c("NH4OAc","FL","RNase","RNaseCtrl")){
for (j in c("AB", "BA")){
temp = table(as.factor(as.numeric(switch_length_distribution_1000000_list[[i]][[j]][,"consecutive_switches"])))
switch_length_distribution_1000000_summary[as.numeric(names(temp)),paste0(i,"_",j)] = as.numeric(temp)
}
}
switch_length_distribution_1000000_summary = data.frame(switch_length_distribution_1000000_summary)
switch_length_distribution_1000000_summary[1,] / colSums(switch_length_distribution_1000000_summary)
df_AB_1000000 = melt(switch_length_distribution_1000000_summary[,1:5], id.vars = "x")
df_AB_1000000 = df_AB_1000000[which(df_AB_1000000$x %in% seq(1,max(df_AB_1000000[which(df_AB_1000000$value != 0),"x"]))),]
df_BA_1000000 = melt(switch_length_distribution_1000000_summary[,c(1,6:9)], id.vars = "x")
df_BA_1000000 = df_BA_1000000[which(df_BA_1000000$x %in% seq(1,max(df_BA_1000000[which(df_BA_1000000$value != 0),"x"]))),]
p1<-ggplot(data = df_AB_1000000, aes(x=x, y=value, color=variable)) +
geom_point(size = 0.8) +
geom_line() +
xlab("Number of consecutive switches") +
ylab("Frequency") +
scale_x_continuous(labels = as.character(seq(1,max(df_AB_1000000$x))), breaks = seq(1,max(df_AB_1000000$x))) +
ylim(c(0,80)) +
scale_color_manual(labels = c("NH4OAc", "FL", "RNase", "RNaseCtrl"), values = c("red", "green","blue", "orange")) +
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
panel.background = element_blank(), axis.line = element_line(colour = "black"),
text = element_text(size=8),
axis.text.x = element_text(size=8),
axis.text.y = element_text(size=8),
plot.title = element_text(size=8),
legend.position = "none") +
geom_vline(xintercept = 3, linetype = "dashed") +
ggtitle("AB switches - 1 Mb")
p2<-ggplot(data = df_BA_1000000, aes(x=x, y=value, color=variable)) +
geom_point(size = 0.8) +
geom_line() +
xlab("Number of consecutive switches") +
ylab("Frequency") +
scale_x_continuous(labels = as.character(seq(1,max(df_BA_1000000$x))), breaks = seq(1,max(df_BA_1000000$x))) +
ylim(c(0,80)) +
scale_color_manual(labels = c("NH4OAc", "FL", "RNase","RNaseCtrl"), values = c("red", "green","blue","orange")) +
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
panel.background = element_blank(), axis.line = element_line(colour = "black"),
text = element_text(size=8),
axis.text.x = element_text(size=8),
axis.text.y = element_text(size=8),
plot.title = element_text(size=8),
legend.position = "none") +
geom_vline(xintercept = 3, linetype = "dashed") +
ggtitle("BA switches - 1 Mb")
png("/dataOS/rcalandrelli/phase_separation/HiC/compartments/result/consecutive_switches_histogram_1000000.png", width = 5, height = 2.5, res = 300, units = "in")
plot_grid(p1,p2)
dev.off()
##################################### 500 kb
switch_length_distribution_500000_summary = matrix(data = 0, nrow=16, ncol=9)
switch_length_distribution_500000_summary[,1] = seq(1,16)
colnames(switch_length_distribution_500000_summary) = c("x",paste0(c("NH4OAc","FL","RNase","RNaseCtrl"),"_AB"),paste0(c("NH4OAc","FL","RNase","RNaseCtrl"),"_BA"))
for (i in c("NH4OAc","FL","RNase","RNaseCtrl")){
for (j in c("AB", "BA")){
temp = table(as.factor(as.numeric(switch_length_distribution_500000_list[[i]][[j]][,"consecutive_switches"])))
switch_length_distribution_500000_summary[as.numeric(names(temp)),paste0(i,"_",j)] = as.numeric(temp)
}
}
switch_length_distribution_500000_summary = data.frame(switch_length_distribution_500000_summary)
switch_length_distribution_500000_summary[1,] / colSums(switch_length_distribution_500000_summary)
df_AB_500000 = melt(switch_length_distribution_500000_summary[,1:5], id.vars = "x")
df_AB_500000 = df_AB_500000[which(df_AB_500000$x %in% seq(1,max(df_AB_500000[which(df_AB_500000$value != 0),"x"]))),]
df_BA_500000 = melt(switch_length_distribution_500000_summary[,c(1,6:9)], id.vars = "x")
df_BA_500000 = df_BA_500000[which(df_BA_500000$x %in% seq(1,max(df_BA_500000[which(df_BA_500000$value != 0),"x"]))),]
p1<-ggplot(data = df_AB_500000, aes(x=x, y=value, color=variable)) +
geom_point(size = 0.8) +
geom_line() +
xlab("Number of consecutive switches") +
ylab("Frequency") +
scale_x_continuous(labels = as.character(seq(1,max(df_AB_500000$x), by=2)), breaks = seq(1,max(df_AB_500000$x),by=2)) +
ylim(c(0,135)) +
scale_color_manual(labels = c("NH4OAc", "FL", "RNase","RNaseCtrl"), values = c("red", "green","blue","orange")) +
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
panel.background = element_blank(), axis.line = element_line(colour = "black"),
text = element_text(size=8),
axis.text.x = element_text(size=8),
axis.text.y = element_text(size=8),
plot.title = element_text(size = 8),
legend.position = "none") +
geom_vline(xintercept = 4, linetype = "dashed") +
ggtitle("AB switches - 500 kb")
p2<-ggplot(data = df_BA_500000, aes(x=x, y=value, color=variable)) +
geom_point(size = 0.8) +
geom_line() +
xlab("Number of consecutive switches") +
ylab("Frequency") +
scale_x_continuous(labels = as.character(seq(1,max(df_BA_500000$x),by = 2)), breaks = seq(1,max(df_BA_500000$x),by=2)) +
ylim(c(0,135)) +
scale_color_manual(labels = c("NH4OAc", "FL", "RNase","RNaseCtrl"), values = c("red", "green","blue","orange")) +
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
panel.background = element_blank(), axis.line = element_line(colour = "black"),
text = element_text(size=8),
axis.text.x = element_text(size=8),
axis.text.y = element_text(size=8),
plot.title = element_text(size = 8),
legend.position = "none") +
geom_vline(xintercept = 3, linetype = "dashed") +
ggtitle("BA switches - 500 kb")
png("/dataOS/rcalandrelli/phase_separation/HiC/compartments/result/consecutive_switches_histogram_500000.png", width = 5, height = 2.5, res = 300, units = "in")
plot_grid(p1,p2)
dev.off()
####### Switch stats
df.table_switch_summary = data.frame(table_switch_summary[which(table_switch_summary[,"resolution"]=="500000"),])
df.table_switch_summary$NH4OAc = as.numeric(as.character(df.table_switch_summary$NH4OAc))
df.table_switch_summary$FL = as.numeric(as.character(df.table_switch_summary$FL))
df.table_switch_summary$RNase = as.numeric(as.character(df.table_switch_summary$RNase))
average_perc_switch_sample <- function(sample){
return(sum(df.table_switch_summary[which(df.table_switch_summary$compartment_switch %in% c("AB","BA")),sample])/sum(df.table_switch_summary[,sample]))
}
average_perc_switch_sample("NH4OAc")
average_perc_switch_sample("FL")
average_perc_switch_sample("RNase")
switch_ratio_all <- function(switch_type){
out = c()
for (i in c("500000")){
for (j in c("NH4OAc","FL","RNase")){
switch_ratio = df.table_switch_summary[which(df.table_switch_summary$resolution == i &
df.table_switch_summary$compartment_switch == switch_type),j]/sum(df.table_switch_summary[which(df.table_switch_summary$resolution == i),j])
out = c(out,switch_ratio)
}
}
return(out)
}
AB_all_ratios = switch_ratio_all("AB")
BA_all_ratios = switch_ratio_all("BA")
t.test(BA_all_ratios-AB_all_ratios)
######### Chi-square tests
# to test the association between AB switches and treatment
temp = rbind(colSums(df.table_switch_summary[which(df.table_switch_summary$compartment_switch=="AB"),c("RNase","FL","NH4OAc")]),
colSums(df.table_switch_summary[which(df.table_switch_summary$compartment_switch!="AB"),c("RNase","FL","NH4OAc")]))
chisq.test(temp)
# to test the association between BA switches and treatment
temp = rbind(colSums(df.table_switch_summary[which(df.table_switch_summary$compartment_switch=="BA"),c("RNase","FL","NH4OAc")]),
colSums(df.table_switch_summary[which(df.table_switch_summary$compartment_switch!="BA"),c("RNase","FL","NH4OAc")]))
chisq.test(temp)
# to test the association between switches and treatment
temp = rbind(colSums(df.table_switch_summary[which(df.table_switch_summary$compartment_switch %in% c("AB","BA")),c("RNase","FL","NH4OAc")]),
colSums(df.table_switch_summary[which(df.table_switch_summary$compartment_switch %in% c("AA","BB")),c("RNase","FL","NH4OAc")]))
chisq.test(temp)
### Table update to test association between RNase and NonRNase
chisquare_sample <- function(sample,
switch_type){
df.table_switch_summary_mod = data.frame(cbind(as.character(df.table_switch_summary[,"compartment_switch"]),
rowSums(df.table_switch_summary[,setdiff(c("FL","NH4OAc","RNase"), sample)]),
df.table_switch_summary[,sample]))
colnames(df.table_switch_summary_mod) = c("compartment_switch",paste0("Non",sample), sample)
df.table_switch_summary_mod[,2] = as.numeric(as.character(df.table_switch_summary_mod[,2]))
df.table_switch_summary_mod[,3] = as.numeric(as.character(df.table_switch_summary_mod[,3]))
# to test the association between AB switches and RNase treatment
if (switch_type == "AB"){
temp = rbind(colSums(df.table_switch_summary_mod[which(df.table_switch_summary_mod$compartment_switch=="AB"),3:2]),
colSums(df.table_switch_summary_mod[which(df.table_switch_summary_mod$compartment_switch!="AB"),3:2]))
out = chisq.test(temp)
or = temp[1,1]*temp[2,2]/(temp[1,2]*temp[2,1])
}
else if (switch_type == "BA"){
# to test the association between BA switches and RNase treatment
temp = rbind(colSums(df.table_switch_summary_mod[which(df.table_switch_summary_mod$compartment_switch=="BA"),3:2]),
colSums(df.table_switch_summary_mod[which(df.table_switch_summary_mod$compartment_switch!="BA"),3:2]))
out = chisq.test(temp)
or = temp[1,1]*temp[2,2]/(temp[1,2]*temp[2,1])
}
else if (switch_type == "both") {
# to test the association between switches and RNase treatment
temp = rbind(colSums(df.table_switch_summary_mod[which(df.table_switch_summary_mod$compartment_switch %in% c("AB","BA")),3:2]),
colSums(df.table_switch_summary_mod[which(df.table_switch_summary_mod$compartment_switch %in% c("AA","BB")),3:2]))
out = chisq.test(temp)
or = temp[1,1]*temp[2,2]/(temp[1,2]*temp[2,1])
}
return(list(out,or))
}
chisquare_sample("NH4OAc","both")
chisquare_sample("FL","both")
chisquare_sample("RNase","both")
chisquare_sample("RNase","AB")
chisquare_sample("RNase","BA")
### Plot data
make_barplot_switch <- function(df_switch_summary,resolution,label_size=10){
df_switch_summary_res = df_switch_summary[which(df_switch_summary$resolution_vector == resolution),]
p <- ggplot(df_switch_summary_res, aes(y=switch_vector, x=sample_vector, fill=switch_label)) +
geom_bar(stat="identity") +
scale_fill_manual(values=c('#b2bc9b','#6ca7d2','#a81900','#716c6b')) +
labs(x = "", y = "Number of cases") +
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=label_size),
axis.text.x = element_text(size = label_size, color = "black", angle = 45, hjust = 1),
axis.text.y = element_text(size = label_size, color = "black"),
axis.ticks.length = unit(0.2, "cm"),
legend.title = element_blank()) +
ggtitle(paste0("Resolution: ",resolution))
return(p)
}
make_scatter_plot_switch <- function(full_eigen_data,
sample,
resolution,
manual_switch = T, # set to F to plot data without manual switch of false eigen signs to show where these bins would be
label_size=8
){
if (manual_switch == T){
full_eigen_data_temp = full_eigen_data[,c("eigen_control",paste0("eigen_",sample),paste0("switch_",sample))]
colnames(full_eigen_data_temp) = c("x","y","switch")
full_eigen_data_temp = full_eigen_data_temp[which(!is.na(full_eigen_data_temp$switch)),] # to do not consider NA compartments
full_eigen_data_temp$switch = factor(full_eigen_data_temp$switch, levels = c("AB","BA","AA","BB"))
max_axis_lim = max(full_eigen_data_temp$x,full_eigen_data_temp$y)
min_axis_lim = min(full_eigen_data_temp$x,full_eigen_data_temp$y)
AB_num = as.character(table(as.factor(full_eigen_data_temp$switch))["AB"])
BA_num = as.character(table(as.factor(full_eigen_data_temp$switch))["BA"])
AB_perc = paste0(round(table(as.factor(full_eigen_data_temp$switch))["AB"] / nrow(full_eigen_data_temp) * 100,2),"%")
BA_perc = paste0(round(table(as.factor(full_eigen_data_temp$switch))["BA"] / nrow(full_eigen_data_temp) * 100,2),"%")
scc = round(cor(full_eigen_data_temp$x,full_eigen_data_temp$y,method = "spearman"), digits = 2)
p <- ggplot(full_eigen_data_temp) +
geom_point(aes(y=y, x=x), size = 0.2) +
xlim(min_axis_lim, max_axis_lim) +
ylim(min_axis_lim, max_axis_lim) +
#geom_text(aes(x=max_axis_lim*0.6,y=min_axis_lim*0.8),label=paste0("SCC = ",scc), size = 2) +
# geom_text(aes(x=max_axis_lim*0.6,y=min_axis_lim*0.8),label=paste0("A to B:\n",AB_num,"(",AB_perc,")")) +
# geom_text(aes(x=min_axis_lim*0.6,y=max_axis_lim*0.8),label=paste0("B to A:\n",BA_num,"(",BA_perc,")")) +
#scale_color_manual(values=c('#b2bc9b','#6ca7d2','#a81900','#716c6b')) +
scale_color_manual(values=c('#808080','#808080','black','black')) +
labs(x = "Control", y = sample) +
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
panel.background = element_blank(), axis.line = element_line(colour = "black"),
text = element_text(size=label_size),
axis.text.x = element_text(size = label_size, color = "black"),
axis.text.y = element_text(size = label_size, color = "black"),
#axis.ticks.length = unit(0.2, "cm"),
legend.title = element_blank(),
legend.position = "none")
#ggtitle(sample)
} else {
full_eigen_data_temp = full_eigen_data[,c("chr","eigen_control",paste0("eigen_",sample),paste0("switch_",sample))]
full_eigen_data_temp$shape = "round"
if (resolution == "1000000"){
full_eigen_data_temp[which(full_eigen_data_temp$chr %in% c("chr19","chr21")),"shape"] = "cross"
} else if (resolution == "500000"){
full_eigen_data_temp[which(full_eigen_data_temp$chr == "chr21"),"shape"] = "cross"
}
full_eigen_data_temp = full_eigen_data_temp[,c(2,3,4,5)]
colnames(full_eigen_data_temp) = c("x","y","switch","shape")
full_eigen_data_temp = full_eigen_data_temp[which(!is.na(full_eigen_data_temp$switch)),] # to do not consider NA compartments
full_eigen_data_temp$switch = factor(full_eigen_data_temp$switch, levels = c("AB","BA","AA","BB"))
max_axis_lim = max(full_eigen_data_temp$x,full_eigen_data_temp$y)
min_axis_lim = min(full_eigen_data_temp$x,full_eigen_data_temp$y)
AB_num = as.character(table(as.factor(full_eigen_data_temp$switch))["AB"])
BA_num = as.character(table(as.factor(full_eigen_data_temp$switch))["BA"])
AB_perc = paste0(round(table(as.factor(full_eigen_data_temp$switch))["AB"] / nrow(full_eigen_data_temp) * 100,2),"%")
BA_perc = paste0(round(table(as.factor(full_eigen_data_temp$switch))["BA"] / nrow(full_eigen_data_temp) * 100,2),"%")
p <- ggplot(full_eigen_data_temp) +
geom_point(aes(y=y, x=x, color=switch, shape=shape)) +
xlim(min_axis_lim, max_axis_lim) +
ylim(min_axis_lim, max_axis_lim) +
geom_text(aes(x=max_axis_lim*0.6,y=min_axis_lim*0.8),label=paste0("A to B:\n",AB_num,"(",AB_perc,")")) +
geom_text(aes(x=min_axis_lim*0.6,y=max_axis_lim*0.8),label=paste0("B to A:\n",BA_num,"(",BA_perc,")")) +
scale_color_manual(values=c('#b2bc9b','#6ca7d2','#a81900','#716c6b')) +
scale_shape_manual(values=c(4,19), guide = F) +
labs(x = "eigen_control", y = paste0("eigen_",sample)) +
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=label_size),
axis.text.x = element_text(size = label_size, color = "black"),
axis.text.y = element_text(size = label_size, color = "black"),
axis.ticks.length = unit(0.2, "cm"),
legend.title = element_blank()) +
ggtitle(sample)
}
return(p)
}
p5 <- make_barplot_switch(df_switch_summary,"1000000")
p6 <- make_scatter_plot_switch(full_eigen_data_1000000,"NH4OAc","1000000")
p7 <- make_scatter_plot_switch(full_eigen_data_1000000,"FL","1000000")
p8 <- make_scatter_plot_switch(full_eigen_data_1000000,"RNase","1000000")
p9 <- make_barplot_switch(df_switch_summary,"500000")
p10 <- make_scatter_plot_switch(full_eigen_data_500000,"NH4OAc","500000")
p11 <- make_scatter_plot_switch(full_eigen_data_500000,"FL","500000")
p12 <- make_scatter_plot_switch(full_eigen_data_500000,"RNase","500000")
png("/dataOS/rcalandrelli/phase_separation/HiC/compartments/result/eigen_switch_summary_plot_1000000.png", width = 13, height = 3, units = "in", res = 200)
plot_grid(p5,p7,p6,p8, nrow = 1)