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loop_analysis.r
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library(ggtern)
library(plotly)
library(readr)
library(dplyr)
library(tidyr)
###### Loop analysis RNA-genome interaction project
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
loops_control = read.table("/dataOS/rcalandrelli/phase_separation/HiC/loops/hiccups_H1_control_merged/merged_loops.bedpe")
loops_NH4OAc = read.table("/dataOS/rcalandrelli/phase_separation/HiC/loops/hiccups_H1_NH4OAc_merged/merged_loops.bedpe")
loops_FL = read.table("/dataOS/rcalandrelli/phase_separation/HiC/loops/hiccups_H1_FL_merged/merged_loops.bedpe")
loops_RNase = read.table("/dataOS/rcalandrelli/phase_separation/HiC/loops/hiccups_H1_RNase_merged/merged_loops.bedpe")
############### Statistical difference loop numbers
require(dplyr)
tab_loop_control = loops_control %>% count(V1)
tab_loop_NH4OAc = loops_NH4OAc %>% count(V1)
tab_loop_FL = loops_FL %>% count(V1)
tab_loop_RNase = loops_RNase %>% count(V1)
t.test(tab_loop_control[which(tab_loop_control$V1 %in% tab_loop_NH4OAc$V1),"n"], tab_loop_NH4OAc$n, paired = T)
t.test(tab_loop_control[which(tab_loop_control$V1 %in% tab_loop_FL$V1),"n"], tab_loop_FL[which(tab_loop_FL$V1 != "Y"),"n"], paired = T)
t.test(tab_loop_control[which(tab_loop_control$V1 %in% tab_loop_RNase$V1),"n"], tab_loop_RNase[which(tab_loop_RNase$V1 != "Y"),"n"], paired = T)
############### Summary of distance between loop anchor points
distance_anchor_control = loops_control$V5 - loops_control$V2
distance_anchor_FL = loops_FL$V5 - loops_FL$V2
distance_anchor_NH4OAc = loops_NH4OAc$V5 - loops_NH4OAc$V2
distance_anchor_RNase = loops_RNase$V5 - loops_RNase$V2
sample = c(rep("Control",length(distance_anchor_control)),
rep("FL",length(distance_anchor_FL)),
rep("NH4OAc",length(distance_anchor_NH4OAc)),
rep("RNase",length(distance_anchor_RNase)))
distances = c(distance_anchor_control,
distance_anchor_FL,
distance_anchor_NH4OAc,
distance_anchor_RNase)
df = data.frame(sample, distances)
df$sample = factor(df$sample, levels = c("Control","NH4OAc", "FL","RNase"))
png("/dataOS/rcalandrelli/phase_separation/HiC/loops/result/anchor_points_distance_boxplots_log10.png", width = 6, height = 6, units = "in", res = 200)
ggplot(df, aes(x=sample, y=log10(distances))) +
geom_boxplot() +
labs(x="", y="Log10 (Anchor points distance)") +
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=16),
axis.text.x = element_text(size = 16, angle = 45, hjust = 1),
axis.text.y = element_text(size = 16),
axis.ticks.length = unit(0.2, "cm"))
dev.off()
wilcox.test(distance_anchor_control,distance_anchor_FL)
wilcox.test(distance_anchor_control,distance_anchor_NH4OAc)
wilcox.test(distance_anchor_control,distance_anchor_RNase)
mean(distance_anchor_control)
mean(distance_anchor_FL)
mean(distance_anchor_NH4OAc)
mean(distance_anchor_RNase)
hist(distance_anchor_control)
hist(distance_anchor_FL)
hist(distance_anchor_NH4OAc)
hist(distance_anchor_RNase)
############### Aggregate peak analysis (APA)
## Summary statistics
make_summary_apa <- function(directory="/dataOS/rcalandrelli/phase_separation/HiC/loops/APA/",
samples=c("control","NH4OAc","FL","RNase"),
resolution){
sample_summary = list()
for (i in samples){
sample_summary[[i]] = read.table(paste0(directory,"H1_",i,"_merged/",resolution,"/gw/measures.txt"))
}
df_sample_summary = do.call("cbind", sample_summary)
df_sample_summary = df_sample_summary[,c(1,2,4,6,8)]
colnames(df_sample_summary) = c("Index","Control","NH4OAc","FL","RNase")
df_sample_summary[,2:5] = round(df_sample_summary[,2:5],2)
write.table(df_sample_summary,paste0(directory,"APA_statistics_summary_",resolution,".txt"), row.names = F, col.names = T, sep = "\t", quote = F)
return(df_sample_summary)
}
summary_apa_5000 = melt(make_summary_apa(resolution="5000"))
summary_apa_10000 = melt(make_summary_apa(resolution="10000"))
df = summary_apa_5000[which(summary_apa_5000$Index == "P2LL"),]
df$Index = 5000
df = rbind(df, summary_apa_10000[which(summary_apa_10000$Index == "P2LL"),])
df[which(df$Index=="P2LL"),"Index"] = 10000
df$Index = factor(df$Index, c(5000,10000))
colnames(df)[1] = "Resolution"
df$variable = factor(df$variable, levels = c("Control", "FL", "NH4OAc", "RNase"))
p1<-ggplot(df, aes(fill=Resolution, y=value, x=variable)) +
geom_bar(stat="identity", position=position_dodge()) +
labs(x = "", y = "P2LL") +
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
panel.background = element_blank(), axis.line = element_line(colour = "black"),
text = element_text(size=24),
axis.text.x = element_text(size = 24, color = "black", angle = 45, hjust = 1),
axis.text.y = element_text(size = 24, color = "black"),
axis.ticks.length = unit(0.2, "cm"))
df = summary_apa_5000[which(summary_apa_5000$Index == "ZscoreLL"),]
df$Index = 5000
df = rbind(df, summary_apa_10000[which(summary_apa_10000$Index == "ZscoreLL"),])
df[which(df$Index=="ZscoreLL"),"Index"] = 10000
df$Index = factor(df$Index, c(5000,10000))
colnames(df)[1] = "Resolution"
df$variable = factor(df$variable, levels = c("Control", "FL", "NH4OAc", "RNase"))
p2<-ggplot(df, aes(fill=Resolution, y=value, x=variable)) +
geom_bar(stat="identity", position=position_dodge()) +
labs(x = "", y = "ZscoreLL") +
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", angle = 45, hjust = 1),
axis.text.y = element_text(size = 24, color = "black"),
axis.ticks.length = unit(0.2, "cm"))
png("/dataOS/rcalandrelli/phase_separation/HiC/loops/result/barplot_P2LL_ZscoreLL.png", width = 16, height = 8, units = "in", res = 200)
plot_grid(p1,p2)
dev.off()
## Plotting figures derived from Juicer apa
library(png)
library(grid)
library(gridExtra)
directory = "/dataOS/rcalandrelli/phase_separation/HiC/loops/APA/"
samples = paste0("H1_",c("control","FL", "NH4OAc", "RNase"),"_merged")
resolutions = c("5000","10000")
figures_list = list()
k = 1
for (i in samples){
for (j in resolutions){
figures_list[[k]] = readPNG(paste0(directory,i,"/",j,"/gw/APA.png"))
k = k + 1
}
}
png("/dataOS/rcalandrelli/phase_separation/HiC/loops/result/APA_global_plots.png", width = 5, height = 7, units = "in", res = 500)
grid.arrange(rasterGrob(figures_list[[1]]),
rasterGrob(figures_list[[2]]),
rasterGrob(figures_list[[3]]),
rasterGrob(figures_list[[4]]),
rasterGrob(figures_list[[5]]),
rasterGrob(figures_list[[6]]),
rasterGrob(figures_list[[7]]),
rasterGrob(figures_list[[8]]), ncol=2)
# rasterGrob(figures_list[[9]]),
# rasterGrob(figures_list[[10]]),
# rasterGrob(figures_list[[11]]),
# rasterGrob(figures_list[[12]]), ncol=3)
dev.off()
### Plot P2LL scores across all the chromosomes, for each sample, at the 3 resolutions
make_P2LL_ZscoreLL_df_plot <- function(directory="/dataOS/rcalandrelli/phase_separation/HiC/loops/APA/",
samples = c("control","FL","NH4OAc","RNase"),
resolution){
p2ll_vector = c()
Zscore_vector = c()
for (i in samples){
for (j in gsub("chr","",hg38_chromosomes)[1:23]){
infile = paste0(directory,"H1_",i,"_merged/",resolution,"/",j,"v",j,"/measures.txt")
if (file.exists(infile)){
p2ll_temp = read.table(infile)[4,2]
Zscore_temp = read.table(infile)[6,2]
} else {
p2ll_temp = NA
Zscore_temp = NA
}
p2ll_vector = c(p2ll_vector, p2ll_temp)
Zscore_vector = c(Zscore_vector, Zscore_temp)
}
}
sample = c(rep("Control",23),
rep("NH4OAc",23),
rep("FL",23),
rep("RNase",23))
#chromosomes = rep(hg38_chromosomes[1:23],4)
chromosomes = rep(c(1:23),4)
# P2LL data and plot
df.p2ll = data.frame(sample,chromosomes,p2ll_vector)
df.p2ll$p2ll_vector[which(is.na(df.p2ll$p2ll_vector))] = 0
df.p2ll$sample = factor(df.p2ll$sample, c("Control","FL","NH4OAc","RNase"))
p<-ggplot(df.p2ll, aes(x=chromosomes, y=p2ll_vector, color=sample)) +
geom_point() + geom_line() +
labs(x = "", y = "P2LL") +
scale_x_discrete(breaks=c(1:23), limits = c(1:23), labels=hg38_chromosomes[1:23]) +
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
panel.background = element_blank(), axis.line = element_line(colour = "black"),
text = element_text(size=20),
axis.text.x = element_text(size = 14, color = "black", angle = 45, hjust = 1),
axis.text.y = element_text(size = 20, color = "black"),
axis.ticks.length = unit(0.2, "cm"),
legend.title = element_blank()) +
ggtitle(paste0("Resolution: ", resolution))
# ZscoreLL data and plot
df.Zscore = data.frame(sample,chromosomes,Zscore_vector)
df.Zscore$Zscore_vector[which(is.na(df.Zscore$Zscore_vector))] = 0
df.Zscore$sample = factor(df.Zscore$sample, c("Control","FL","NH4OAc","RNase"))
p1<-ggplot(df.Zscore, aes(x=chromosomes, y=Zscore_vector, color=sample)) +
geom_point() + geom_line() +
labs(x = "", y = "ZscoreLL") +
scale_x_discrete(breaks=c(1:23), limits = c(1:23), labels=hg38_chromosomes[1:23]) +
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
panel.background = element_blank(), axis.line = element_line(colour = "black"),
text = element_text(size=20),
axis.text.x = element_text(size = 14, color = "black", angle = 45, hjust = 1),
axis.text.y = element_text(size = 20, color = "black"),
axis.ticks.length = unit(0.2, "cm"),
legend.title = element_blank()) +
ggtitle(paste0("Resolution: ", resolution))
return(list(df.p2ll,p,df.Zscore,p1))
}
P2LL_Zscore_list_5000 = make_P2LL_ZscoreLL_df_plot(resolution = "5000")
P2LL_Zscore_list_10000 = make_P2LL_ZscoreLL_df_plot(resolution = "10000")
png("/dataOS/rcalandrelli/phase_separation/HiC/loops/result/P2LL_ZscoreLL_tracks.png", width = 8, height = 14, units = "in", res = 200)
plot_grid(P2LL_Zscore_list_5000[[2]],P2LL_Zscore_list_5000[[4]],
P2LL_Zscore_list_10000[[2]],P2LL_Zscore_list_10000[[4]],
nrow=4)
dev.off()
# Wilcoxon test
P2LL_5000_df = P2LL_Zscore_list_5000[[1]]
wilcox.test(P2LL_5000_df[which(P2LL_5000_df$sample=="Control"),"p2ll_vector"][c(1:21,23)],P2LL_5000_df[which(P2LL_5000_df$sample=="NH4OAc"),"p2ll_vector"][c(1:21,23)])
wilcox.test(P2LL_5000_df[which(P2LL_5000_df$sample=="Control"),"p2ll_vector"][c(1:21,23)],P2LL_5000_df[which(P2LL_5000_df$sample=="FL"),"p2ll_vector"][c(1:21,23)])
wilcox.test(P2LL_5000_df[which(P2LL_5000_df$sample=="Control"),"p2ll_vector"][c(1:21,23)],P2LL_5000_df[which(P2LL_5000_df$sample=="RNase"),"p2ll_vector"][c(1:21,23)])
Zscore_5000_df = P2LL_Zscore_list_5000[[3]]
wilcox.test(Zscore_5000_df[which(Zscore_5000_df$sample=="Control"),"Zscore_vector"][c(1:21,23)],Zscore_5000_df[which(Zscore_5000_df$sample=="NH4OAc"),"Zscore_vector"][c(1:21,23)])
wilcox.test(Zscore_5000_df[which(Zscore_5000_df$sample=="Control"),"Zscore_vector"][c(1:21,23)],Zscore_5000_df[which(Zscore_5000_df$sample=="FL"),"Zscore_vector"][c(1:21,23)])
wilcox.test(Zscore_5000_df[which(Zscore_5000_df$sample=="Control"),"Zscore_vector"][c(1:21,23)],Zscore_5000_df[which(Zscore_5000_df$sample=="RNase"),"Zscore_vector"][c(1:21,23)])
P2LL_10000_df = P2LL_Zscore_list_10000[[1]]
wilcox.test(P2LL_10000_df[which(P2LL_10000_df$sample=="Control"),"p2ll_vector"][c(1:20,23)],P2LL_10000_df[which(P2LL_10000_df$sample=="NH4OAc"),"p2ll_vector"][c(1:20,23)])
wilcox.test(P2LL_10000_df[which(P2LL_10000_df$sample=="Control"),"p2ll_vector"][c(1:20,23)],P2LL_10000_df[which(P2LL_10000_df$sample=="FL"),"p2ll_vector"][c(1:20,23)])
wilcox.test(P2LL_10000_df[which(P2LL_10000_df$sample=="Control"),"p2ll_vector"][c(1:20,23)],P2LL_10000_df[which(P2LL_10000_df$sample=="RNase"),"p2ll_vector"][c(1:20,23)])
Zscore_10000_df = P2LL_Zscore_list_10000[[3]]
wilcox.test(Zscore_10000_df[which(Zscore_10000_df$sample=="Control"),"Zscore_vector"][c(1:20,23)],Zscore_10000_df[which(Zscore_10000_df$sample=="NH4OAc"),"Zscore_vector"][c(1:20,23)])
wilcox.test(Zscore_10000_df[which(Zscore_10000_df$sample=="Control"),"Zscore_vector"][c(1:20,23)],Zscore_10000_df[which(Zscore_10000_df$sample=="FL"),"Zscore_vector"][c(1:20,23)])
wilcox.test(Zscore_10000_df[which(Zscore_10000_df$sample=="Control"),"Zscore_vector"][c(1:20,23)],Zscore_10000_df[which(Zscore_10000_df$sample=="RNase"),"Zscore_vector"][c(1:20,23)])
##### P2M values for each loop: the ratio of the central pixel to the mean of the remaining pixels
p2m_control_5000 = read.table("/dataOS/rcalandrelli/phase_separation/HiC/loops/APA/H1_control_merged/5000/gw/enhancement.txt")
p2m_NH4OAc_5000 = read.table("/dataOS/rcalandrelli/phase_separation/HiC/loops/APA/H1_NH4OAc_merged/5000/gw/enhancement.txt")
p2m_FL_5000 = read.table("/dataOS/rcalandrelli/phase_separation/HiC/loops/APA/H1_FL_merged/5000/gw/enhancement.txt")
p2m_RNase_5000 = read.table("/dataOS/rcalandrelli/phase_separation/HiC/loops/APA/H1_RNase_merged/5000/gw/enhancement.txt")
wilcox.test(as.numeric(p2m_control_5000),as.numeric(p2m_NH4OAc_5000))
wilcox.test(as.numeric(p2m_control_5000),as.numeric(p2m_FL_5000))
wilcox.test(as.numeric(p2m_control_5000),as.numeric(p2m_RNase_5000))
p2m_control_10000 = read.table("/dataOS/rcalandrelli/phase_separation/HiC/loops/APA/H1_control_merged/10000/gw/enhancement.txt")
p2m_NH4OAc_10000 = read.table("/dataOS/rcalandrelli/phase_separation/HiC/loops/APA/H1_NH4OAc_merged/10000/gw/enhancement.txt")
p2m_FL_10000 = read.table("/dataOS/rcalandrelli/phase_separation/HiC/loops/APA/H1_FL_merged/10000/gw/enhancement.txt")
p2m_RNase_10000 = read.table("/dataOS/rcalandrelli/phase_separation/HiC/loops/APA/H1_RNase_merged/10000/gw/enhancement.txt")
wilcox.test(as.numeric(p2m_control_10000),as.numeric(p2m_NH4OAc_10000))
wilcox.test(as.numeric(p2m_control_10000),as.numeric(p2m_FL_10000))
wilcox.test(as.numeric(p2m_control_10000),as.numeric(p2m_RNase_10000))
# Distribution plots
df_p2m_5000 = data.frame(rbind(
cbind("Control",t(p2m_control_5000)),
cbind("NH4OAc",t(p2m_NH4OAc_5000)),
cbind("FL",t(p2m_FL_5000)),
cbind("RNase",t(p2m_RNase_5000))
))
df_p2m_5000$X1 = factor(df_p2m_5000$X1, levels = c("Control","FL","NH4OAc","RNase"))
df_p2m_5000$X2 = as.numeric(as.character(df_p2m_5000$X2))
colnames(df_p2m_5000) = c("sample","P2M")
df_p2m_10000 = data.frame(rbind(
cbind("Control",t(p2m_control_10000)),
cbind("NH4OAc",t(p2m_NH4OAc_10000)),
cbind("FL",t(p2m_FL_10000)),
cbind("RNase",t(p2m_RNase_10000))
))
df_p2m_10000$X1 = factor(df_p2m_10000$X1, levels = c("Control","FL","NH4OAc","RNase"))
df_p2m_10000$X2 = as.numeric(as.character(df_p2m_10000$X2))
colnames(df_p2m_10000) = c("sample","P2M")
df_p2m_10000$sample = factor(df_p2m_10000$sample, levels = c("Control","NH4OAc", "FL","RNase"))
png("/dataOS/rcalandrelli/phase_separation/HiC/loops/result/P2M_boxplots.png", width = 12, height = 12, units = "in", res = 200)
p1<-ggplot(df_p2m_5000, aes(x=sample, y=P2M)) +
geom_boxplot() +
labs(x="", y="P2M") +
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=24),
axis.text.x = element_text(size = 24, angle = 45, hjust = 1),
axis.text.y = element_text(size = 24),
axis.ticks.length = unit(0.2, "cm")) +
ggtitle("Resolution: 5000")
p2<-ggplot(df_p2m_10000, aes(x=sample, y=P2M)) +
geom_boxplot() +
labs(x="", y="P2M") +
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=24),
axis.text.x = element_text(size = 24, angle = 45, hjust = 1),
axis.text.y = element_text(size = 24),
axis.ticks.length = unit(0.2, "cm")) +
ggtitle("Resolution: 10000")
p3<-ggplot(df_p2m_5000, aes(x=sample, y=P2M)) +
geom_boxplot() +
labs(x="", y="P2M") +
ylim(0,20) +
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=24),
axis.text.x = element_text(size = 24, angle = 45, hjust = 1),
axis.text.y = element_text(size = 24),
axis.ticks.length = unit(0.2, "cm")) +
ggtitle("Resolution: 5000")
p4<-ggplot(df_p2m_10000, aes(x=sample, y=P2M)) +
geom_boxplot() +
labs(x="", y="P2M") +
ylim(0,20) +
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=24),
axis.text.x = element_text(size = 24, angle = 45, hjust = 1),
axis.text.y = element_text(size = 24),
axis.ticks.length = unit(0.2, "cm")) +
ggtitle("Resolution: 10000")
plot_grid(p1,p2,p3,p4,ncol = 2)
dev.off()
################################ Union of loops analysis
loops_control = read.table("/dataOS/rcalandrelli/phase_separation/HiC/loops/hiccups_H1_control_merged/merged_loops.bedpe")
loops_NH4OAc = read.table("/dataOS/rcalandrelli/phase_separation/HiC/loops/hiccups_H1_NH4OAc_merged/merged_loops.bedpe")
loops_FL = read.table("/dataOS/rcalandrelli/phase_separation/HiC/loops/hiccups_H1_FL_merged/merged_loops.bedpe")
loops_RNase = read.table("/dataOS/rcalandrelli/phase_separation/HiC/loops/hiccups_H1_RNase_merged/merged_loops.bedpe")
table_loops = as.data.frame(rbind(table(as.factor(loops_control$V3-loops_control$V2)),
table(as.factor(loops_NH4OAc$V3-loops_NH4OAc$V2)),
table(as.factor(loops_FL$V3-loops_FL$V2)),
table(as.factor(loops_RNase$V3-loops_RNase$V2))))
table_loops$sample = c("Control","NH4OAc","FL","RNase")
df = melt(table_loops)
#df$sample = factor(df$sample, levels = c("Control","NH4OAc","FL","RNase"))
colnames(df)[2] = "Resolution"
png("/dataOS/rcalandrelli/phase_separation/HiC/loops/result/barplot_loops.png", width = 8, height = 8, units = "in", res = 200)
ggplot(df, aes(fill=Resolution, y=value, x=sample)) +
geom_bar(stat="identity", position=position_dodge()) +
labs(x = "", y = "Number of loops") +
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", angle = 45, hjust = 1),
axis.text.y = element_text(size = 24, color = "black"),
axis.ticks.length = unit(0.2, "cm"))
dev.off()
### Union of loops
temp = rbind(loops_control,loops_NH4OAc,loops_FL,loops_RNase)
loops_union = temp[!duplicated(temp[,c(1,2,3,4,5,6)]),]
loops_union$V1 = as.character(loops_union$V1)
loops_union$V4 = as.character(loops_union$V4)
# Header to this file same as "merged_loops.bedpe" is manually added after saving
write.table(loops_union,"/dataOS/rcalandrelli/phase_separation/HiC/loops/union_of_loops.bedpe", row.names = F, col.names = F, sep = "\t", quote = F)
##### Specific loop in Figure LOOPD B
loops_FL[which(loops_FL$V1 == "12" &
loops_FL$V2 == 53030000 &
loops_FL$V6 == 53480000),]
loops_RNase[which(loops_RNase$V1 == "12" &
loops_RNase$V2 == 53020000 &
loops_RNase$V6 == 53480000),]
temp = loops_union[which(loops_union$V1 == "12" &
loops_union$V2 %in% c(53020000,53030000) &
loops_union$V6 == 53480000),]
write.table(temp[,1:6],"/dataOS/rcalandrelli/phase_separation/HiC/loops/loops_LOOPD_B.bedpe", row.names = F, col.names = F, sep = "\t", quote = F)
out_list = list()
for (i in 1:nrow(loops_union)){
out_list[[i]] = c(nrow(loops_control[do.call(paste, loops_control[1:6]) %in% paste(loops_union[i,1:6], collapse = " "), ]),
nrow(loops_NH4OAc[do.call(paste, loops_NH4OAc[1:6]) %in% paste(loops_union[i,1:6], collapse = " "), ]),
nrow(loops_FL[do.call(paste, loops_FL[1:6]) %in% paste(loops_union[i,1:6], collapse = " "), ]),
nrow(loops_RNase[do.call(paste, loops_RNase[1:6]) %in% paste(loops_union[i,1:6], collapse = " "), ]))
}
### Upset plot of union of loops
df_loops_union_upset = do.call("rbind", out_list)
colnames(df_loops_union_upset) = c("Control","NH4OAc","FL","RNase")
df_loops_union_upset = data.frame(df_loops_union_upset)
png("/dataOS/rcalandrelli/phase_separation/HiC/loops/result/union_loops_upset_plot.png", width = 9, height = 5, units = "in", res = 200)
upset(df_loops_union_upset,
sets = c("RNase","NH4OAc","FL","Control"),
sets.x.label = "Number of loops",
order.by = "freq",
keep.order = T,
text.scale = c(2, 2, 1.5, 1.5, 2, 1.5),
point.size = 3, line.size = 1)
dev.off()
### APA summaries
summary_apa_5000_union = melt(make_summary_apa(directory="/dataOS/rcalandrelli/phase_separation/HiC/loops/APA_union_loops/",
samples=c("control","NH4OAc","FL","RNase"),
resolution="5000"))
summary_apa_10000_union = melt(make_summary_apa(directory="/dataOS/rcalandrelli/phase_separation/HiC/loops/APA_union_loops/",
samples=c("control","NH4OAc","FL","RNase"),
resolution="10000"))
df = summary_apa_5000_union[which(summary_apa_5000_union$Index == "P2LL"),]
df$Index = 5000
df = rbind(df, summary_apa_10000_union[which(summary_apa_10000_union$Index == "P2LL"),])
df[which(df$Index=="P2LL"),"Index"] = 10000
df$Index = factor(df$Index, c(5000,10000))
colnames(df)[1] = "Resolution"
df$variable = factor(df$variable, levels = c("Control", "FL", "NH4OAc", "RNase"))
p1<-ggplot(df, aes(fill=Resolution, y=value, x=variable)) +
geom_bar(stat="identity", position=position_dodge()) +
labs(x = "", y = "P2LL") +
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
panel.background = element_blank(), axis.line = element_line(colour = "black"),
text = element_text(size=24),
axis.text.x = element_text(size = 24, color = "black", angle = 45, hjust = 1),
axis.text.y = element_text(size = 24, color = "black"),
axis.ticks.length = unit(0.2, "cm"))
df = summary_apa_5000_union[which(summary_apa_5000_union$Index == "ZscoreLL"),]
df$Index = 5000
df = rbind(df, summary_apa_10000_union[which(summary_apa_10000_union$Index == "ZscoreLL"),])
df[which(df$Index=="ZscoreLL"),"Index"] = 10000
df$Index = factor(df$Index, c(5000,10000))
colnames(df)[1] = "Resolution"
df$variable = factor(df$variable, levels = c("Control", "FL", "NH4OAc", "RNase"))
p2<-ggplot(df, aes(fill=Resolution, y=value, x=variable)) +
geom_bar(stat="identity", position=position_dodge()) +
labs(x = "", y = "ZscoreLL") +
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", angle = 45, hjust = 1),
axis.text.y = element_text(size = 24, color = "black"),
axis.ticks.length = unit(0.2, "cm"))
png("/dataOS/rcalandrelli/phase_separation/HiC/loops/result/barplot_P2LL_ZscoreLL_union.png", width = 16, height = 8, units = "in", res = 200)
plot_grid(p1,p2)
dev.off()
## Plotting figures derived from Juicer apa
library(png)
library(grid)
library(gridExtra)
directory = "/dataOS/rcalandrelli/phase_separation/HiC/loops/APA_union_loops/"
samples = paste0("H1_",c("control","FL", "NH4OAc", "RNase"),"_merged")
resolutions = c("5000","10000")
figures_list = list()
k = 1
for (i in samples){
for (j in resolutions){
figures_list[[k]] = readPNG(paste0(directory,i,"/",j,"/gw/APA.png"))
k = k + 1
}
}
png("/dataOS/rcalandrelli/phase_separation/HiC/loops/result/APA_union_global_plots.png", width = 5, height = 7, units = "in", res = 500)
grid.arrange(rasterGrob(figures_list[[1]]),
rasterGrob(figures_list[[2]]),
rasterGrob(figures_list[[3]]),
rasterGrob(figures_list[[4]]),
rasterGrob(figures_list[[5]]),
rasterGrob(figures_list[[6]]),
rasterGrob(figures_list[[7]]),
rasterGrob(figures_list[[8]]), ncol=2)
dev.off()
### P2LL and ZscoreLL analysis
P2LL_Zscore_union_list_5000 = make_P2LL_ZscoreLL_df_plot(directory="/dataOS/rcalandrelli/phase_separation/HiC/loops/APA_union_loops/",
samples = c("control","NH4OAc","FL","RNase"),
resolution="5000")
P2LL_Zscore_union_list_10000 = make_P2LL_ZscoreLL_df_plot(directory="/dataOS/rcalandrelli/phase_separation/HiC/loops/APA_union_loops/",
samples = c("control","NH4OAc","FL","RNase"),
resolution="10000")
png("/dataOS/rcalandrelli/phase_separation/HiC/loops/result/P2LL_union_ZscoreLL_tracks.png", width = 8, height = 14, units = "in", res = 200)
plot_grid(P2LL_Zscore_union_list_5000[[2]],P2LL_Zscore_union_list_5000[[4]],
P2LL_Zscore_union_list_10000[[2]],P2LL_Zscore_union_list_10000[[4]],
nrow=4)
dev.off()
# Wilcoxon test
P2LL_union_5000_df = P2LL_Zscore_union_list_5000[[1]]
wilcox.test(P2LL_union_5000_df[which(P2LL_union_5000_df$sample=="Control"),"p2ll_vector"][c(1:21,23)],P2LL_union_5000_df[which(P2LL_union_5000_df$sample=="NH4OAc"),"p2ll_vector"][c(1:21,23)])
wilcox.test(P2LL_union_5000_df[which(P2LL_union_5000_df$sample=="Control"),"p2ll_vector"][c(1:21,23)],P2LL_union_5000_df[which(P2LL_union_5000_df$sample=="FL"),"p2ll_vector"][c(1:21,23)])
wilcox.test(P2LL_union_5000_df[which(P2LL_union_5000_df$sample=="Control"),"p2ll_vector"][c(1:21,23)],P2LL_union_5000_df[which(P2LL_union_5000_df$sample=="RNase"),"p2ll_vector"][c(1:21,23)])
Zscore_union_5000_df = P2LL_Zscore_union_list_5000[[3]]
wilcox.test(Zscore_union_5000_df[which(Zscore_union_5000_df$sample=="Control"),"Zscore_vector"][c(1:21,23)],Zscore_union_5000_df[which(Zscore_union_5000_df$sample=="NH4OAc"),"Zscore_vector"][c(1:21,23)])
wilcox.test(Zscore_union_5000_df[which(Zscore_union_5000_df$sample=="Control"),"Zscore_vector"][c(1:21,23)],Zscore_union_5000_df[which(Zscore_union_5000_df$sample=="FL"),"Zscore_vector"][c(1:21,23)])
wilcox.test(Zscore_union_5000_df[which(Zscore_union_5000_df$sample=="Control"),"Zscore_vector"][c(1:21,23)],Zscore_union_5000_df[which(Zscore_union_5000_df$sample=="RNase"),"Zscore_vector"][c(1:21,23)])
P2LL_union_10000_df = P2LL_Zscore_union_list_10000[[1]]
wilcox.test(P2LL_union_10000_df[which(P2LL_union_10000_df$sample=="Control"),"p2ll_vector"][c(1:20,23)],P2LL_union_10000_df[which(P2LL_union_10000_df$sample=="NH4OAc"),"p2ll_vector"][c(1:20,23)])
wilcox.test(P2LL_union_10000_df[which(P2LL_union_10000_df$sample=="Control"),"p2ll_vector"][c(1:20,23)],P2LL_union_10000_df[which(P2LL_union_10000_df$sample=="FL"),"p2ll_vector"][c(1:20,23)])
wilcox.test(P2LL_union_10000_df[which(P2LL_union_10000_df$sample=="Control"),"p2ll_vector"][c(1:20,23)],P2LL_union_10000_df[which(P2LL_union_10000_df$sample=="RNase"),"p2ll_vector"][c(1:20,23)])
Zscore_union_10000_df = P2LL_Zscore_union_list_10000[[3]]
wilcox.test(Zscore_union_10000_df[which(Zscore_union_10000_df$sample=="Control"),"Zscore_vector"][c(1:20,23)],Zscore_union_10000_df[which(Zscore_union_10000_df$sample=="NH4OAc"),"Zscore_vector"][c(1:20,23)])
wilcox.test(Zscore_union_10000_df[which(Zscore_union_10000_df$sample=="Control"),"Zscore_vector"][c(1:20,23)],Zscore_union_10000_df[which(Zscore_union_10000_df$sample=="FL"),"Zscore_vector"][c(1:20,23)])
wilcox.test(Zscore_union_10000_df[which(Zscore_union_10000_df$sample=="Control"),"Zscore_vector"][c(1:20,23)],Zscore_union_10000_df[which(Zscore_union_10000_df$sample=="RNase"),"Zscore_vector"][c(1:20,23)])
##### P2M values for each loop: the ratio of the central pixel to the mean of the remaining pixels
### 5 kb
p2m_union_control_5000 = read.table("/dataOS/rcalandrelli/phase_separation/HiC/loops/APA_union_loops/H1_control_merged/5000/gw/enhancement.txt")
p2m_union_NH4OAc_5000 = read.table("/dataOS/rcalandrelli/phase_separation/HiC/loops/APA_union_loops/H1_NH4OAc_merged/5000/gw/enhancement.txt")
p2m_union_FL_5000 = read.table("/dataOS/rcalandrelli/phase_separation/HiC/loops/APA_union_loops/H1_FL_merged/5000/gw/enhancement.txt")
p2m_union_RNase_5000 = read.table("/dataOS/rcalandrelli/phase_separation/HiC/loops/APA_union_loops/H1_RNase_merged/5000/gw/enhancement.txt")
# Remove NA entries
na.entries = unique(c(which(is.na(p2m_union_control_5000[1,])),
which(is.na(p2m_union_NH4OAc_5000[1,])),
which(is.na(p2m_union_FL_5000[1,])),
which(is.na(p2m_union_RNase_5000[1,]))))
p2m_union_control_5000 = p2m_union_control_5000[1,-na.entries]
p2m_union_NH4OAc_5000 = p2m_union_NH4OAc_5000[1,-na.entries]
p2m_union_FL_5000 = p2m_union_FL_5000[1,-na.entries]
p2m_union_RNase_5000 = p2m_union_RNase_5000[1,-na.entries]
wilcox.test(as.numeric(p2m_union_control_5000),as.numeric(p2m_union_NH4OAc_5000))
wilcox.test(as.numeric(p2m_union_control_5000),as.numeric(p2m_union_FL_5000))
wilcox.test(as.numeric(p2m_union_control_5000),as.numeric(p2m_union_RNase_5000))
### 10 kb
p2m_union_control_10000 = read.table("/dataOS/rcalandrelli/phase_separation/HiC/loops/APA_union_loops/H1_control_merged/10000/gw/enhancement.txt")
p2m_union_NH4OAc_10000 = read.table("/dataOS/rcalandrelli/phase_separation/HiC/loops/APA_union_loops/H1_NH4OAc_merged/10000/gw/enhancement.txt")
p2m_union_FL_10000 = read.table("/dataOS/rcalandrelli/phase_separation/HiC/loops/APA_union_loops/H1_FL_merged/10000/gw/enhancement.txt")
p2m_union_RNase_10000 = read.table("/dataOS/rcalandrelli/phase_separation/HiC/loops/APA_union_loops/H1_RNase_merged/10000/gw/enhancement.txt")
p2m_union_control_10000 = read.table("/dataOS/rcalandrelli/phase_separation/HiC/loops/APA_union_loops/ALL/H1_control_merged/10000/gw/enhancement.txt")
p2m_union_NH4OAc_10000 = read.table("/dataOS/rcalandrelli/phase_separation/HiC/loops/APA_union_loops/ALL/H1_NH4OAc_merged/10000/gw/enhancement.txt")
p2m_union_FL_10000 = read.table("/dataOS/rcalandrelli/phase_separation/HiC/loops/APA_union_loops/ALL/H1_FL_merged/10000/gw/enhancement.txt")
p2m_union_RNase_10000 = read.table("/dataOS/rcalandrelli/phase_separation/HiC/loops/APA_union_loops/ALL/H1_RNase_merged/10000/gw/enhancement.txt")
# Remove NA entries
na.entries = unique(c(which(is.na(p2m_union_control_10000[1,])),
which(is.na(p2m_union_NH4OAc_10000[1,])),
which(is.na(p2m_union_FL_10000[1,])),
which(is.na(p2m_union_RNase_10000[1,]))))
p2m_union_control_10000 = p2m_union_control_10000[1,-na.entries]
p2m_union_NH4OAc_10000 = p2m_union_NH4OAc_10000[1,-na.entries]
p2m_union_FL_10000 = p2m_union_FL_10000[1,-na.entries]
p2m_union_RNase_10000 = p2m_union_RNase_10000[1,-na.entries]
wilcox.test(as.numeric(p2m_union_control_10000),as.numeric(p2m_union_NH4OAc_10000))
wilcox.test(as.numeric(p2m_union_control_10000),as.numeric(p2m_union_FL_10000))
wilcox.test(as.numeric(p2m_union_control_10000),as.numeric(p2m_union_RNase_10000))
# Distribution plots
df_p2m_union_5000 = data.frame(Control = t(p2m_union_control_5000),
FL = t(p2m_union_FL_5000),
NH4OAc = t(p2m_union_NH4OAc_5000),
RNase = t(p2m_union_RNase_5000))
colnames(df_p2m_union_5000) = c("Control","FL","NH4OAc","RNase")
df_p2m_union_10000 = data.frame(Control = t(p2m_union_control_10000),
FL = t(p2m_union_FL_10000),
NH4OAc = t(p2m_union_NH4OAc_10000),
RNase = t(p2m_union_RNase_10000))
colnames(df_p2m_union_10000) = c("Control","FL","NH4OAc","RNase")
df_10000 = melt(df_p2m_union_10000)
df_10000$variable = factor(df_10000$variable, levels = c("Control", "NH4OAc", "FL", "RNase"))
png("/dataOS/rcalandrelli/phase_separation/HiC/loops/result/P2M_union_boxplots.png", width = 12, height = 12, units = "in", res = 200)
p1<-ggplot(melt(df_p2m_union_5000), aes(x=variable, y=value)) +
geom_boxplot() +
labs(x="", y="P2M") +
theme(legend.title = element_blank(),
panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
panel.background = element_blank(), axis.line = element_line(colour = "black"),
text = element_text(size=24),
axis.text.x = element_text(size = 24, angle = 45, hjust = 1),
axis.text.y = element_text(size = 24),
axis.ticks.length = unit(0.2, "cm")) +
ggtitle("Resolution: 5000")
p2<-ggplot(melt(df_p2m_union_10000), aes(x=variable, y=value)) +
geom_boxplot() +
labs(x="", y="P2M") +
theme(legend.title = element_blank(),
panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
panel.background = element_blank(), axis.line = element_line(colour = "black"),
text = element_text(size=24),
axis.text.x = element_text(size = 24, angle = 45, hjust = 1),
axis.text.y = element_text(size = 24),
axis.ticks.length = unit(0.2, "cm")) +
ggtitle("Resolution: 10000")
p3<-ggplot(melt(df_p2m_union_5000), aes(x=variable, y=value)) +
geom_boxplot() +
labs(x="", y="P2M") +
ylim(0,20) +
theme(legend.title = element_blank(),
panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
panel.background = element_blank(), axis.line = element_line(colour = "black"),
text = element_text(size=24),
axis.text.x = element_text(size = 24, angle = 45, hjust = 1),
axis.text.y = element_text(size = 24),
axis.ticks.length = unit(0.2, "cm")) +
ggtitle("Resolution: 5000")
my_comparisons <- list(c("Control","NH4OAc"), c("Control","FL"), c("Control","RNase"))
p4<-ggplot(df_10000[which(df_10000$value <= 20),], aes(x=variable, y=value)) +
geom_boxplot() +
stat_compare_means(comparisons = my_comparisons, label = "p.signif", label.x = c(2,3,4), label.y = c(20,22,24), size = 6) +
labs(x="", y="P2M") +
ylim(0,25) +
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=24),
axis.text.x = element_text(size = 24, angle = 45, hjust = 1),
axis.text.y = element_text(size = 24),
axis.ticks.length = unit(0.2, "cm")) +
ggtitle("Resolution: 10000")
plot_grid(p1,p2,p3,p4,ncol=2)
dev.off()
#### Scatter plots of the P2M values for each samples vs control
make_scatter_P2M <- function(df_p2m_union,
sample1="Control",
sample2,
resolution,
log_log=F){
df_p2m_union = melt(df_p2m_union[,c("Control","NH4OAc","FL","RNase")])
x = df_p2m_union[which(df_p2m_union$variable==sample1),"value"]
y = df_p2m_union[which(df_p2m_union$variable==sample2),"value"]
if (log_log==T){
x = log(x+1)
y = log(y+1)
}
#print(cor(x,y))
df = data.frame(x,y)
linearMod <- lm(y ~ x, data = df)
regression_label = paste0("y=",round(linearMod$coefficients[2],2),"x+",round(linearMod$coefficients[1],3))
annotations <- data.frame(
xpos = c(Inf,Inf),
ypos = c(-Inf,-Inf),
annotateText = c(paste0("r=",round(cor(x,y),2)), regression_label),
hjustvar = c(1.5, 1) ,
vjustvar = c(-1, -3))
p<-ggplot(df, aes(y=y, x=x)) +
geom_point() +
geom_text(data=annotations,aes(x=xpos,y=ypos,hjust=hjustvar,vjust=vjustvar,label=annotateText), size=5) +
labs(x = ifelse(log_log==F,paste0("P2M_",sample1),paste0("log(P2M+1)_",sample1)),
y = ifelse(log_log==F,paste0("P2M_",sample2),paste0("log(P2M+1)_",sample2))) +
geom_smooth(method=lm) +
# xlim(0,20) +
# ylim(0,20) +
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=20),
axis.text.x = element_text(size = 20, color = "black"),
axis.text.y = element_text(size = 20, color = "black"),
axis.ticks.length = unit(0.2, "cm"),
legend.title = element_blank()) +
ggtitle(paste0(sample2,"_",resolution))
return(p)
}
p1 <- make_scatter_P2M(df_p2m_union_5000,
sample1="Control",
sample2="NH4OAc",
resolution="5000")
p2 <- make_scatter_P2M(df_p2m_union_5000,
sample1="Control",
sample2="FL",
resolution="5000")
p3 <- make_scatter_P2M(df_p2m_union_5000,
sample1="Control",
sample2="RNase",
resolution="5000")
p4 <- make_scatter_P2M(df_p2m_union_10000,
sample1="Control",
sample2="NH4OAc",
resolution="10000")
p5 <- make_scatter_P2M(df_p2m_union_10000,
sample1="Control",
sample2="FL",
resolution="10000")
p6 <- make_scatter_P2M(df_p2m_union_10000,
sample1="Control",
sample2="RNase",
resolution="10000")
png("/dataOS/rcalandrelli/phase_separation/HiC/loops/result/P2M_union_scatter_plots.png", width = 12, height = 8, units = "in", res = 200)
plot_grid(p2,p1,p3,p5,p4,p6, ncol = 3)
dev.off()
p1 <- make_scatter_P2M(df_p2m_union_5000,
sample1="Control",
sample2="NH4OAc",
resolution="5000",
log_log=T)
p2 <- make_scatter_P2M(df_p2m_union_5000,
sample1="Control",
sample2="FL",
resolution="5000",
log_log=T)
p3 <- make_scatter_P2M(df_p2m_union_5000,
sample1="Control",
sample2="RNase",
resolution="5000",
log_log=T)
p4 <- make_scatter_P2M(df_p2m_union_10000,
sample1="Control",
sample2="NH4OAc",
resolution="10000",
log_log=T)
p5 <- make_scatter_P2M(df_p2m_union_10000,
sample1="Control",
sample2="FL",
resolution="10000",
log_log=T)
p6 <- make_scatter_P2M(df_p2m_union_10000,
sample1="Control",
sample2="RNase",
resolution="10000",
log_log=T)
png("/dataOS/rcalandrelli/phase_separation/HiC/loops/result/P2M_union_scatter_plots_log_log.png", width = 12, height = 8, units = "in", res = 200)
plot_grid(p2,p1,p3,p5,p4,p6, ncol = 3)
dev.off()
### Scatter plot P2M with outliers that could be identified as differential loops
make_scatter_P2M_outliers <- function(df_p2m_union,
sample1="Control",
sample2,
resolution,
sd_factor=2,
multiple_factor = 3){
df_p2m_union_melt = melt(df_p2m_union[,c("Control","NH4OAc","FL","RNase")])
x = df_p2m_union_melt[which(df_p2m_union_melt$variable==sample1),"value"]
y = df_p2m_union_melt[which(df_p2m_union_melt$variable==sample2),"value"]
#print(cor(x,y))
df = data.frame(x,y)
# df$diff = df$y - df$x
# my_sd = sd(abs(df$diff))
# df$position = "within"
# df[which(abs(df$diff) > sd_factor*my_sd &
# df$diff > 0),"position"] = "above"
# df[which(abs(df$diff) > sd_factor*my_sd &
# df$diff < 0),"position"] = "below"
df$position = "within"
df[which(df$y > multiple_factor*df$x),"position"] = "above"
df[which(df$x > multiple_factor*df$y),"position"] = "below"
above_num = as.character(table(as.factor(df$position))["above"])
below_num = as.character(table(as.factor(df$position))["below"])
above_perc = paste0(round(table(as.factor(df$position))["above"] / nrow(df) * 100,2),"%")
below_perc = paste0(round(table(as.factor(df$position))["below"] / nrow(df) * 100,2),"%")
annotations <- data.frame(
xpos = c(Inf,-Inf),
ypos = c(-Inf,Inf),
annotateText = c(paste0(below_num," (",below_perc,")"),
paste0(above_num," (",above_perc,")")),
hjustvar = c(1.2,-0.2),
vjustvar = c(-2,2))
p <- ggplot(df) +
geom_point(aes(y=y, x=x, color=position), size = 2, alpha = 0.5) +
geom_line(aes(x=x, y=x)) +
geom_line(aes(x=x, y=x*multiple_factor), linetype = "dashed") +
geom_line(aes(x=x, y=x/multiple_factor), linetype = "dashed") +
geom_text(data=annotations,aes(x=xpos,y=ypos,hjust=hjustvar,vjust=vjustvar,label=annotateText), size=5) +
labs(x = paste0("P2M_",sample1), y = paste0("P2M_",sample2)) +
# xlim(0,20) +
ylim(0,max(df$y)) +
scale_color_manual(values=c('blue','red','gray')) +
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
panel.background = element_blank(), axis.line = element_line(colour = "black"),
text = element_text(size=16),
axis.text.x = element_text(size = 16, color = "black"),
axis.text.y = element_text(size = 16, color = "black"),
axis.ticks.length = unit(0.2, "cm"),
legend.position = "none") +
ggtitle(paste0(sample2,"_",resolution))
return(p)
}
p1 <- make_scatter_P2M_outliers(df_p2m_union_5000,
sample1="Control",
sample2="NH4OAc",
resolution="5000")
p2 <- make_scatter_P2M_outliers(df_p2m_union_5000,
sample1="Control",
sample2="FL",
resolution="5000")
p3 <- make_scatter_P2M_outliers(df_p2m_union_5000,
sample1="Control",
sample2="RNase",
resolution="5000")
p4 <- make_scatter_P2M_outliers(df_p2m_union_10000,
sample1="Control",
sample2="NH4OAc",
resolution="10000")
p5 <- make_scatter_P2M_outliers(df_p2m_union_10000,
sample1="Control",
sample2="FL",
resolution="10000")
p6 <- make_scatter_P2M_outliers(df_p2m_union_10000,
sample1="Control",
sample2="RNase",
resolution="10000")
png("/dataOS/rcalandrelli/phase_separation/HiC/loops/result/P2M_union_scatter_plots_outliers.png", width = 12, height = 8, units = "in", res = 200)
plot_grid(p2,p1,p3,p5,p4,p6, ncol = 3)
dev.off()
### Add columns to label "differential peaks" based on P2M values
multiple_factor = 3
df_p2m_union_5000$NH4OAc_diff = ifelse(df_p2m_union_5000$NH4OAc > multiple_factor*df_p2m_union_5000$Control, "Up", ifelse(df_p2m_union_5000$NH4OAc < 1/multiple_factor*df_p2m_union_5000$Control,"Down","Not"))
df_p2m_union_5000$FL_diff = ifelse(df_p2m_union_5000$FL > multiple_factor*df_p2m_union_5000$Control, "Up", ifelse(df_p2m_union_5000$FL < 1/multiple_factor*df_p2m_union_5000$Control,"Down","Not"))
df_p2m_union_5000$RNase_diff = ifelse(df_p2m_union_5000$RNase > multiple_factor*df_p2m_union_5000$Control, "Up", ifelse(df_p2m_union_5000$RNase < 1/multiple_factor*df_p2m_union_5000$Control,"Down","Not"))
df_p2m_union_10000$NH4OAc_diff = ifelse(df_p2m_union_10000$NH4OAc > multiple_factor*df_p2m_union_10000$Control, "Up", ifelse(df_p2m_union_10000$NH4OAc < 1/multiple_factor*df_p2m_union_10000$Control,"Down","Not"))
df_p2m_union_10000$FL_diff = ifelse(df_p2m_union_10000$FL > multiple_factor*df_p2m_union_10000$Control, "Up", ifelse(df_p2m_union_10000$FL < 1/multiple_factor*df_p2m_union_10000$Control,"Down","Not"))
df_p2m_union_10000$RNase_diff = ifelse(df_p2m_union_10000$RNase > multiple_factor*df_p2m_union_10000$Control, "Up", ifelse(df_p2m_union_10000$RNase < 1/multiple_factor*df_p2m_union_10000$Control,"Down","Not"))
library(VennDiagram)
make_venn_diagram <- function(df_p2m_union,
resolution,
diff_type){
NH4OAc <- which(df_p2m_union$NH4OAc_diff == diff_type)
FL <- which(df_p2m_union$FL_diff == diff_type)
RNase <- which(df_p2m_union$RNase_diff == diff_type)
library(RColorBrewer)
myCol <- brewer.pal(3, "Pastel2")
# Chart
venn.diagram(
x = list(NH4OAc, FL, RNase),
category.names = c(paste0("NH4OAc (",length(NH4OAc),")"),
paste0("FL (",length(FL),")"),
paste0("RNase (",length(RNase),")")),
filename = paste0('/dataOS/rcalandrelli/phase_separation/loops/result/venn_diagrams/venn_diagram_',diff_type,"_",resolution,'.png'),
output=T,
# Main title
main = diff_type,
main.fontfamily = "sans",
main.fontface = "bold",
main.cex = 1,
# Output features
imagetype="png" ,
height = 650 ,
width = 650 ,
resolution = 300,
compression = "lzw",
# Circles
lwd = 2,
lty = 'blank',
fill = myCol,
# 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(0.055, 0.055, 0.085),
cat.fontfamily = "sans",
rotation = 1
)
}
make_venn_diagram(df_p2m_union_5000,"5000","Up")
make_venn_diagram(df_p2m_union_5000,"5000","Down")
make_venn_diagram(df_p2m_union_5000,"5000","Not")
make_venn_diagram(df_p2m_union_10000,"10000","Up")
make_venn_diagram(df_p2m_union_10000,"10000","Down")
make_venn_diagram(df_p2m_union_10000,"10000","Not")
diff_types = c("Up","Down","Not")
resolutions = c("5000","10000")
figures_list = list()
k = 1
for (i in resolutions){
for (j in diff_types){
figures_list[[k]] = readPNG(paste0("/dataOS/rcalandrelli/phase_separation/loops/result/venn_diagrams/venn_diagram_",j,"_",i,".png"))
k = k + 1
}
}
png("/dataOS/rcalandrelli/phase_separation/loops/result/venn_diagrams_5000.png", width = 8, height = 2.2, units = "in", res = 200)
grid.arrange(rasterGrob(figures_list[[1]]),
rasterGrob(figures_list[[2]]),
rasterGrob(figures_list[[3]]), ncol=3, left = "Res: 5000")
dev.off()
png("/dataOS/rcalandrelli/phase_separation/loops/result/venn_diagrams_10000.png", width = 8, height = 2.2, units = "in", res = 200)
grid.arrange(rasterGrob(figures_list[[4]]),
rasterGrob(figures_list[[5]]),
rasterGrob(figures_list[[6]]), ncol=3, left = "Res: 10000")
dev.off()
### 3D scatter plots relative P2M versus control
df_p2m_union_5000_rel = df_p2m_union_5000
df_p2m_union_5000_rel$NH4OAc = df_p2m_union_5000_rel$NH4OAc / df_p2m_union_5000_rel$Control
df_p2m_union_5000_rel$FL = df_p2m_union_5000_rel$FL / df_p2m_union_5000_rel$Control
df_p2m_union_5000_rel$RNase = df_p2m_union_5000_rel$RNase / df_p2m_union_5000_rel$Control
df_p2m_union_10000_rel = df_p2m_union_10000
df_p2m_union_10000_rel$NH4OAc = df_p2m_union_10000_rel$NH4OAc / df_p2m_union_10000_rel$Control
df_p2m_union_10000_rel$FL = df_p2m_union_10000_rel$FL / df_p2m_union_10000_rel$Control
df_p2m_union_10000_rel$RNase = df_p2m_union_10000_rel$RNase / df_p2m_union_10000_rel$Control
library(scatterplot3d)
png("/dataOS/rcalandrelli/phase_separation/HiC/loops/result/scatter3D_P2M_5000.png", width = 5, height = 5, units = "in", res = 200)
scatterplot3d(df_p2m_union_5000_rel[,2:4], main = "Resolution: 5000", cex.axis = 1, cex.lab = 1,
xlab = "P2M_NH4OAc / P2M_control", ylab = "P2M_FL / P2M_control", zlab = "P2M_RNase / P2M_control")
dev.off()
png("/dataOS/rcalandrelli/phase_separation/HiC/loops/result/scatter3D_P2M_10000.png", width = 5, height = 5, units = "in", res = 200)
scatterplot3d(df_p2m_union_10000_rel[,2:4], main = "Resolution: 10000", cex.axis = 1, cex.lab = 1,
xlab = "P2M_NH4OAc / P2M_control", ylab = "P2M_FL / P2M_control", zlab = "P2M_RNase / P2M_control")
dev.off()
figures_list = list()