DER_analysis
Renee Matthews
2025-08-12
Last updated: 2025-08-22
Checks: 7 0
Knit directory: DXR_continue/
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library(tidyverse)
library(readr)
library(edgeR)
library(ComplexHeatmap)
library(data.table)
library(dplyr)
library(stringr)
library(ggplot2)
library(viridis)
library(DT)
library(kableExtra)
library(genomation)
library(GenomicRanges)
library(ggpubr) ## For customizing figures
library(corrplot) ## For correlation plot
library(ggpmisc)
library(gcplyr)
library(Rsubread)
library(limma)
library(ggrastr)
library(cowplot)
library(smplot2)
library(ggVennDiagram)
library(ggsignif)Differential analysis data frames:
H3K27ac_toplist <- readRDS( "data/DER_data/H3K27ac_toplist.RDS")
H3K27me3_toplist <- readRDS("data/DER_data/H3K27me3_toplist.RDS")
H3K36me3_toplist <- readRDS("data/DER_data/H3K36me3_toplist.RDS")
H3K9me3_toplist <- readRDS( "data/DER_data/H3K9me3_toplist.RDS")
sampleinfo <- read_delim("data/sample_info.tsv", delim = "\t")
cell_line_info <- data.frame(Individual = c("Ind1","Ind2","Ind3","Ind4","Ind5"),Cell_line = c ("78-1","75-1","17-3","90-1","84-1"), sex = c("F","F","M","M","M"))
# write_delim(cell_line_info,"data/cell_line_info_.tsv",delim = "\t")
H3K27ac_toptable_list <- bind_rows(H3K27ac_toplist, .id = "group")
H3K27me3_toptable_list <- bind_rows(H3K27me3_toplist, .id = "group")
H3K36me3_toptable_list <- bind_rows(H3K36me3_toplist, .id = "group")
H3K9me3_toptable_list <- bind_rows(H3K9me3_toplist, .id = "group")counts data frames:
H3K27ac_merged <- read_delim("data/peaks/H3K27ac_FINAL_counts.txt",
delim = "\t", escape_double = FALSE,
trim_ws = TRUE, skip = 1)
H3K27me3_merged <- read_delim("data/peaks/H3K27me3_FINAL_counts.txt",
delim = "\t", escape_double = FALSE,
trim_ws = TRUE, skip = 1)
H3K36me3_merged <- read_delim("data/peaks/H3K36me3_FINAL_counts.txt",
delim = "\t", escape_double = FALSE,
trim_ws = TRUE, skip = 1)
H3K9me3_merged <- read_delim("data/peaks/H3K9me3_FINAL_counts.txt",
delim = "\t", escape_double = FALSE,
trim_ws = TRUE, skip = 1)
rename_list <- sampleinfo %>%
mutate(stem= "_nobl.bam") %>%
mutate(prefix=paste0("/scratch/10819/styu/MW_multiQC/peaks/",Histone_Mark,"/",Treatment,"/",Timepoint,"/")) %>%
mutate(oldname=paste0(prefix,`Library ID`,"/",`Library ID`,stem)) %>%
mutate(newname=paste0(Individual,"_",Treatment,"_",Timepoint)) %>%
dplyr::select(oldname,newname)
rename_vec <- setNames(rename_list$newname, rename_list$oldname)
names(H3K27ac_merged)[names(H3K27ac_merged) %in% names(rename_vec)] <- rename_vec[names(H3K27ac_merged)[names(H3K27ac_merged) %in% names(rename_vec)]]
names(H3K27me3_merged)[names(H3K27me3_merged) %in% names(rename_vec)] <- rename_vec[names(H3K27me3_merged)[names(H3K27me3_merged) %in% names(rename_vec)]]
names(H3K36me3_merged)[names(H3K36me3_merged) %in% names(rename_vec)] <- rename_vec[names(H3K36me3_merged)[names(H3K36me3_merged) %in% names(rename_vec)]]
names(H3K9me3_merged)[names(H3K9me3_merged) %in% names(rename_vec)] <- rename_vec[names(H3K9me3_merged)[names(H3K9me3_merged) %in% names(rename_vec)]]Removing chrX and chrY
H3K27ac_merged_raw <- H3K27ac_merged %>%
dplyr::select(Geneid,contains("Ind")) %>%
column_to_rownames("Geneid") %>%
as.matrix()
H3K27ac_merged_lcpm <- H3K27ac_merged %>%
dplyr::select(Geneid,contains("Ind")) %>%
column_to_rownames("Geneid") %>%
cpm(., log = TRUE)
H3K27ac_merged_cor <- H3K27ac_merged_lcpm %>%
cor()
H3K27me3_merged_raw <- H3K27me3_merged %>%
dplyr::select(Geneid,contains("Ind")) %>%
column_to_rownames("Geneid") %>%
as.matrix()
H3K27me3_merged_lcpm <- H3K27me3_merged %>%
dplyr::select(Geneid,contains("Ind")) %>%
column_to_rownames("Geneid") %>%
cpm(., log = TRUE)
H3K27me3_merged_cor <- H3K27me3_merged_lcpm %>%
cor()
H3K36me3_merged_raw <- H3K36me3_merged %>%
dplyr::select(Geneid,contains("Ind")) %>%
column_to_rownames("Geneid") %>%
as.matrix()
H3K36me3_merged_lcpm <- H3K36me3_merged %>%
dplyr::select(Geneid,contains("Ind")) %>%
column_to_rownames("Geneid") %>%
cpm(., log = TRUE)
H3K36me3_merged_cor <- H3K36me3_merged_lcpm %>%
cor()
H3K9me3_merged_raw <- H3K9me3_merged %>%
dplyr::select(Geneid,contains("Ind")) %>%
column_to_rownames("Geneid") %>%
as.matrix()
H3K9me3_merged_lcpm <- H3K9me3_merged %>%
dplyr::select(Geneid,contains("Ind")) %>%
column_to_rownames("Geneid") %>%
cpm(., log = TRUE)
H3K9me3_merged_cor <- H3K9me3_merged_lcpm %>%
cor()H3K27ac_merged_raw <- H3K27ac_merged_raw[rowMeans(H3K27ac_merged_lcpm)>0,]
H3K27ac_merged_raw <- H3K27ac_merged_raw[!grepl("chrY",rownames(H3K27ac_merged_raw)),]
H3K27ac_merged_raw <- H3K27ac_merged_raw[!grepl("chrX",rownames(H3K27ac_merged_raw)),]
H3K27ac_merged_filt_lcpm <- H3K27ac_merged_raw %>% cpm(., log = TRUE)
H3K27me3_merged_raw <- H3K27me3_merged_raw[rowMeans(H3K27me3_merged_lcpm)>0,]
H3K27me3_merged_raw <- H3K27me3_merged_raw[!grepl("chrY",rownames(H3K27me3_merged_raw)),]
H3K27me3_merged_raw <- H3K27me3_merged_raw[!grepl("chrX",rownames(H3K27me3_merged_raw)),]
H3K27me3_merged_filt_lcpm <- H3K27me3_merged_raw %>% cpm(., log = TRUE)
H3K36me3_merged_raw <- H3K36me3_merged_raw[rowMeans(H3K36me3_merged_lcpm)>0,]
H3K36me3_merged_raw <- H3K36me3_merged_raw[!grepl("chrY",rownames(H3K36me3_merged_raw)),]
H3K36me3_merged_raw <- H3K36me3_merged_raw[!grepl("chrX",rownames(H3K36me3_merged_raw)),]
H3K36me3_merged_filt_lcpm <- H3K36me3_merged_raw %>% cpm(., log = TRUE)
H3K9me3_merged_raw <- H3K9me3_merged_raw[rowMeans(H3K9me3_merged_lcpm)>0,]
H3K9me3_merged_raw <- H3K9me3_merged_raw[!grepl("chrY",rownames(H3K9me3_merged_raw)),]
H3K9me3_merged_raw <- H3K9me3_merged_raw[!grepl("chrX",rownames(H3K9me3_merged_raw)),]
H3K9me3_merged_filt_lcpm <- H3K9me3_merged_raw %>% cpm(., log = TRUE)H3K27ac LFC correlation
H3K27ac_cor_mat <- H3K27ac_toptable_list %>%
dplyr::select(group,genes,logFC) %>%
pivot_wider(id_cols = genes, values_from = logFC, names_from = group) %>%
column_to_rownames("genes") %>%
as.matrix() %>%
cor()
ComplexHeatmap::Heatmap(H3K27ac_cor_mat,
column_title = "H3K27ac correlation of LFC",
cell_fun = function(j, i, x, y, width, height, fill) {
grid.text(sprintf("%.2f", H3K27ac_cor_mat[i, j]), x, y, gp = gpar(fontsize = 8, col = "black"))
})
LFC and abs LFC by trt time
H3K27ac_toptable_list %>%
dplyr::select(group,genes,logFC) %>%
mutate(group=factor(group, levels = c("H3K27ac_24T","H3K27ac_24R","H3K27ac_144R"))) %>%
ggplot(., aes(x=group, y=logFC))+
geom_boxplot(aes(fill=group))+
geom_signif(comparisons = list(c("H3K27ac_24T", "H3K27ac_144R"),
c("H3K27ac_24T","H3K27ac_24R"),
c("H3K27ac_24R", "H3K27ac_144R")),
step_increase = 0.1,
map_signif_level = FALSE,
test = "wilcox.test")+
theme_bw()+
ggtitle("LFC across time for H3K27ac")
H3K27ac_toptable_list %>%
dplyr::select(group,genes,logFC) %>%
mutate(group=factor(group, levels = c("H3K27ac_24T","H3K27ac_24R","H3K27ac_144R")),
logFC=abs(logFC)) %>%
ggplot(., aes(x=group, y=logFC))+
geom_boxplot(aes(fill=group))+
geom_signif(comparisons = list(c("H3K27ac_24T", "H3K27ac_144R"),
c("H3K27ac_24T","H3K27ac_24R"),
c("H3K27ac_24R", "H3K27ac_144R")),
step_increase = 0.1,
map_signif_level = FALSE,
test = "wilcox.test")+
theme_bw()+
ggtitle("Absolute LFC across time for H3K27ac")
| Version | Author | Date |
|---|---|---|
| c3469d3 | reneeisnowhere | 2025-08-18 |
Example log2cpm top
### getting the Top3 DERs by 24T, 24R, or 144R (Time point)
Top_H3K27ac <- H3K27ac_toptable_list %>%
dplyr::filter(adj.P.Val<0.05) %>%
dplyr::select(genes, group, adj.P.Val) %>%
separate_wider_delim(., cols=group, delim="_",names=c("histone","cond"),cols_remove=FALSE) %>%
group_by(cond) %>%
slice_head(n=3)
### filtering the lcpm matrix by Top3 DERs for TP
H3K27ac_merged_filt_lcpm %>%
as.data.frame() %>%
dplyr::filter(row.names(H3K27ac_merged_filt_lcpm) %in% Top_H3K27ac$genes) %>%
###pivot and add additional information from Top3 dataframe
rownames_to_column("Peakid") %>%
pivot_longer(., cols = !Peakid, names_to = "sample", values_to = "lcpm" ) %>%
separate_wider_delim(., cols=sample, delim="_", names=c("ind","tx","time")) %>%
left_join(., Top_H3K27ac, by = c("Peakid"="genes")) %>%
mutate(ind=factor(ind, levels=c("Ind1", "Ind2", "Ind3", "Ind4","Ind5")),
tx=factor(tx,levels = c("DOX","VEH")),
time=factor(time, levels=c("24T","24R","144R")),
group_graph= paste0(tx, "_", time),
group_graph = factor(group_graph, levels = c(
"DOX_24T", "VEH_24T",
"DOX_24R", "VEH_24R",
"DOX_144R", "VEH_144R"))) %>%
ggplot(., aes(x=group_graph, y=lcpm)) +
geom_boxplot()+
facet_wrap(group~Peakid, scales="free")+
theme_bw()+
theme(axis.text.x=element_text(angle = 90))
| Version | Author | Date |
|---|---|---|
| 3b2a52b | reneeisnowhere | 2025-08-13 |
Proportion of H3K27ac DERs by time
H3K27ac_toptable_list %>%
dplyr::select(group,genes,logFC,adj.P.Val) %>%
mutate(sig_val=if_else(adj.P.Val<0.05,"sig","not_sig")) %>%
mutate(
sig_val = factor(sig_val, levels = c("not_sig", "sig")),
group = factor(group, levels = c("H3K27ac_24T","H3K27ac_24R","H3K27ac_144R"))) %>%
ggplot(aes(x = group, fill = sig_val)) +
geom_bar(position = "fill") +
theme_bw() +
ggtitle("H3K27ac") +
ylab("Proportion") +
scale_y_continuous(labels = scales::percent)
| Version | Author | Date |
|---|---|---|
| c3469d3 | reneeisnowhere | 2025-08-18 |
H3K27me3 LFC correlation
H3K27me3_cor_mat <- H3K27me3_toptable_list %>%
dplyr::select(group,genes,logFC) %>%
pivot_wider(id_cols = genes, values_from = logFC, names_from = group) %>%
column_to_rownames("genes") %>%
as.matrix() %>%
cor()
ComplexHeatmap::Heatmap(H3K27me3_cor_mat,
column_title = "H3K27me3 correlation of LFC",
cell_fun = function(j, i, x, y, width, height, fill) {
grid.text(sprintf("%.2f", H3K27me3_cor_mat[i, j]), x, y, gp = gpar(fontsize = 8, col = "black"))
})
LFC and abs LFC by trt time
H3K27me3_toptable_list %>%
dplyr::select(group,genes,logFC) %>%
mutate(group=factor(group, levels = c("H3K27me3_24T","H3K27me3_24R","H3K27me3_144R"))) %>%
ggplot(., aes(x=group, y=logFC))+
geom_boxplot(aes(fill=group))+
geom_signif(comparisons = list(c("H3K27me3_24T", "H3K27me3_144R"),
c("H3K27me3_24T","H3K27me3_24R"),
c("H3K27me3_24R", "H3K27me3_144R")),
step_increase = 0.1,
map_signif_level = FALSE,
test = "wilcox.test")+
theme_bw()+
ggtitle("LFC across time for H3K27me3")
H3K27me3_toptable_list %>%
dplyr::select(group,genes,logFC) %>%
mutate(group=factor(group, levels = c("H3K27me3_24T","H3K27me3_24R","H3K27me3_144R")),
logFC=abs(logFC)) %>%
ggplot(., aes(x=group, y=logFC))+
geom_boxplot(aes(fill=group))+
geom_signif(comparisons = list(c("H3K27me3_24T", "H3K27me3_144R"),
c("H3K27me3_24T","H3K27me3_24R"),
c("H3K27me3_24R", "H3K27me3_144R")),
step_increase = 0.1,
map_signif_level = FALSE,
test = "wilcox.test")+
theme_bw()+
ggtitle("Absolute LFC across time for H3K27me3")
| Version | Author | Date |
|---|---|---|
| c3469d3 | reneeisnowhere | 2025-08-18 |
Example log2cpm top
### getting the Top3 DERs by 24T, 24R, or 144R (Time point)
Top_H3K27me3 <- H3K27me3_toptable_list %>%
dplyr::filter(adj.P.Val<0.05) %>%
dplyr::select(genes, group, adj.P.Val) %>%
separate_wider_delim(., cols=group, delim="_",names=c("histone","cond"),cols_remove=FALSE) %>%
group_by(cond) %>%
slice_head(n=3)
### filtering the lcpm matrix by Top3 DERs for TP
H3K27me3_merged_filt_lcpm %>%
as.data.frame() %>%
dplyr::filter(row.names(H3K27me3_merged_filt_lcpm) %in% Top_H3K27me3$genes) %>%
###pivot and add additional information from Top3 dataframe
rownames_to_column("Peakid") %>%
pivot_longer(., cols = !Peakid, names_to = "sample", values_to = "lcpm" ) %>%
separate_wider_delim(., cols=sample, delim="_", names=c("ind","tx","time")) %>%
left_join(., Top_H3K27me3, by = c("Peakid"="genes")) %>%
mutate(ind=factor(ind, levels=c("Ind1", "Ind2", "Ind3", "Ind4","Ind5")),
tx=factor(tx,levels = c("DOX","VEH")),
time=factor(time, levels=c("24T","24R","144R")),
group_graph= paste0(tx, "_", time),
group_graph = factor(group_graph, levels = c(
"DOX_24T", "VEH_24T",
"DOX_24R", "VEH_24R",
"DOX_144R", "VEH_144R"))) %>%
ggplot(., aes(x=group_graph, y=lcpm)) +
geom_boxplot(aes(fill=tx))+
facet_wrap(group~Peakid, scales="free")+
theme_bw()+
theme(axis.text.x=element_text(angle = 90))
| Version | Author | Date |
|---|---|---|
| 3b2a52b | reneeisnowhere | 2025-08-13 |
Proportion of H3K27me3 DERs by time
H3K27me3_toptable_list %>%
dplyr::select(group,genes,logFC,adj.P.Val) %>%
mutate(sig_val=if_else(adj.P.Val<0.05,"sig","not_sig")) %>%
mutate(
sig_val = factor(sig_val, levels = c("not_sig", "sig")),
group = factor(group, levels = c("H3K27me3_24T","H3K27me3_24R","H3K27me3_144R"))) %>%
ggplot(aes(x = group, fill = sig_val)) +
geom_bar(position = "fill") +
theme_bw() +
ggtitle("H3K27me3") +
ylab("Proportion") +
scale_y_continuous(labels = scales::percent)
| Version | Author | Date |
|---|---|---|
| c3469d3 | reneeisnowhere | 2025-08-18 |
H3K36me3 LFC correlation
H3K36me3_cor_mat <- H3K36me3_toptable_list %>%
dplyr::select(group,genes,logFC) %>%
pivot_wider(id_cols = genes, values_from = logFC, names_from = group) %>%
column_to_rownames("genes") %>%
as.matrix() %>%
cor()
ComplexHeatmap::Heatmap(H3K36me3_cor_mat,
column_title = "H3K36me3 correlation of LFC",
cell_fun = function(j, i, x, y, width, height, fill) {
grid.text(sprintf("%.2f", H3K36me3_cor_mat[i, j]), x, y, gp = gpar(fontsize = 8, col = "black"))
})
LFC and abs LFC by trt time
H3K36me3_toptable_list %>%
dplyr::select(group,genes,logFC) %>%
mutate(group=factor(group, levels = c("H3K36me3_24T","H3K36me3_24R","H3K36me3_144R"))) %>%
ggplot(., aes(x=group, y=logFC))+
geom_boxplot(aes(fill=group))+
geom_signif(comparisons = list(c("H3K36me3_24T", "H3K36me3_144R"),
c("H3K36me3_24T","H3K36me3_24R"),
c("H3K36me3_24R", "H3K36me3_144R")),
step_increase = 0.1,
map_signif_level = FALSE,
test = "wilcox.test")+
theme_bw()+
ggtitle("LFC across time for H3K36me3")
H3K36me3_toptable_list %>%
dplyr::select(group,genes,logFC) %>%
mutate(group=factor(group, levels = c("H3K36me3_24T","H3K36me3_24R","H3K36me3_144R")),
logFC=abs(logFC)) %>%
ggplot(., aes(x=group, y=logFC))+
geom_boxplot(aes(fill=group))+
geom_signif(comparisons = list(c("H3K36me3_24T", "H3K36me3_144R"),
c("H3K36me3_24T","H3K36me3_24R"),
c("H3K36me3_24R", "H3K36me3_144R")),
step_increase = 0.1,
map_signif_level = FALSE,
test = "wilcox.test")+
theme_bw()+
ggtitle("Absolute LFC across time for H3K36me3")
| Version | Author | Date |
|---|---|---|
| c3469d3 | reneeisnowhere | 2025-08-18 |
Example log2cpm top
### getting the Top3 DERs by 24T, 24R, or 144R (Time point)
Top_H3K36me3 <- H3K36me3_toptable_list %>%
dplyr::filter(adj.P.Val<0.05) %>%
dplyr::select(genes, group, adj.P.Val) %>%
separate_wider_delim(., cols=group, delim="_",names=c("histone","cond"),cols_remove=FALSE) %>%
group_by(cond) %>%
slice_head(n=3)
### filtering the lcpm matrix by Top3 DERs for TP
H3K36me3_merged_filt_lcpm %>%
as.data.frame() %>%
dplyr::filter(row.names(H3K36me3_merged_filt_lcpm) %in% Top_H3K36me3$genes) %>%
###pivot and add additional information from Top3 dataframe
rownames_to_column("Peakid") %>%
pivot_longer(., cols = !Peakid, names_to = "sample", values_to = "lcpm" ) %>%
separate_wider_delim(., cols=sample, delim="_", names=c("ind","tx","time")) %>%
left_join(., Top_H3K36me3, by = c("Peakid"="genes")) %>%
mutate(ind=factor(ind, levels=c("Ind1", "Ind2", "Ind3", "Ind4","Ind5")),
tx=factor(tx,levels = c("DOX","VEH")),
time=factor(time, levels=c("24T","24R","144R")),
group_graph= paste0(tx, "_", time),
group_graph = factor(group_graph, levels = c(
"DOX_24T", "VEH_24T",
"DOX_24R", "VEH_24R",
"DOX_144R", "VEH_144R"))) %>%
ggplot(., aes(x=group_graph, y=lcpm)) +
geom_boxplot(aes(fill=tx))+
facet_wrap(group~Peakid, scales="free")+
theme_bw()+
theme(axis.text.x=element_text(angle = 90))
| Version | Author | Date |
|---|---|---|
| 3b2a52b | reneeisnowhere | 2025-08-13 |
Proportion of H3K36me3 DERs by time
H3K36me3_toptable_list %>%
dplyr::select(group,genes,logFC,adj.P.Val) %>%
mutate(sig_val=if_else(adj.P.Val<0.05,"sig","not_sig")) %>%
mutate(
sig_val = factor(sig_val, levels = c("not_sig", "sig")),
group = factor(group, levels = c("H3K36me3_24T","H3K36me3_24R","H3K36me3_144R"))) %>%
ggplot(aes(x = group, fill = sig_val)) +
geom_bar(position = "fill") +
theme_bw() +
ggtitle("H3K36me3") +
ylab("Proportion") +
scale_y_continuous(labels = scales::percent)
| Version | Author | Date |
|---|---|---|
| c3469d3 | reneeisnowhere | 2025-08-18 |
H3K9me3 LFC correlation
H3K9me3_cor_mat <- H3K9me3_toptable_list %>%
dplyr::select(group,genes,logFC) %>%
pivot_wider(id_cols = genes, values_from = logFC, names_from = group) %>%
column_to_rownames("genes") %>%
as.matrix() %>%
cor()
ComplexHeatmap::Heatmap(H3K9me3_cor_mat,
column_title = "H3K9me3 correlation of LFC",
cell_fun = function(j, i, x, y, width, height, fill) {
grid.text(sprintf("%.2f", H3K9me3_cor_mat[i, j]), x, y, gp = gpar(fontsize = 8, col = "black"))
})
LFC and abs LFC by trt time
H3K9me3_toptable_list %>%
dplyr::select(group,genes,logFC) %>%
mutate(group=factor(group, levels = c("H3K9me3_24T","H3K9me3_24R","H3K9me3_144R"))) %>%
ggplot(., aes(x=group, y=logFC))+
geom_boxplot(aes(fill=group))+
geom_signif(comparisons = list(c("H3K9me3_24T", "H3K9me3_144R"),
c("H3K9me3_24T","H3K9me3_24R"),
c("H3K9me3_24R", "H3K9me3_144R")),
step_increase = 0.1,
map_signif_level = FALSE,
test = "wilcox.test")+
theme_bw()+
ggtitle("LFC across time for H3K9me3")
H3K9me3_toptable_list %>%
dplyr::select(group,genes,logFC) %>%
mutate(group=factor(group, levels = c("H3K9me3_24T","H3K9me3_24R","H3K9me3_144R")),
logFC=abs(logFC)) %>%
ggplot(., aes(x=group, y=logFC))+
geom_boxplot(aes(fill=group))+
geom_signif(comparisons = list(c("H3K9me3_24T", "H3K9me3_144R"),
c("H3K9me3_24T","H3K9me3_24R"),
c("H3K9me3_24R", "H3K9me3_144R")),
step_increase = 0.1,
map_signif_level = FALSE,
test = "wilcox.test")+
theme_bw()+
ggtitle("Absolute LFC across time for H3K9me3")
| Version | Author | Date |
|---|---|---|
| c3469d3 | reneeisnowhere | 2025-08-18 |
Example log2cpm top
### getting the Top3 DERs by 24T, 24R, or 144R (Time point)
Top_H3K9me3 <- H3K9me3_toptable_list %>%
dplyr::filter(adj.P.Val<0.05) %>%
dplyr::select(genes, group, adj.P.Val) %>%
separate_wider_delim(., cols=group, delim="_",names=c("histone","cond"),cols_remove=FALSE) %>%
group_by(cond) %>%
slice_head(n=3)
### filtering the lcpm matrix by Top3 DERs for TP
H3K9me3_merged_filt_lcpm %>%
as.data.frame() %>%
dplyr::filter(row.names(H3K9me3_merged_filt_lcpm) %in% Top_H3K9me3$genes) %>%
###pivot and add additional information from Top3 dataframe
rownames_to_column("Peakid") %>%
pivot_longer(., cols = !Peakid, names_to = "sample", values_to = "lcpm" ) %>%
separate_wider_delim(., cols=sample, delim="_", names=c("ind","tx","time")) %>%
left_join(., Top_H3K9me3, by = c("Peakid"="genes")) %>%
mutate(ind=factor(ind, levels=c("Ind1", "Ind2", "Ind3", "Ind4","Ind5")),
tx=factor(tx,levels = c("DOX","VEH")),
time=factor(time, levels=c("24T","24R","144R")),
group_graph= paste0(tx, "_", time),
group_graph = factor(group_graph, levels = c(
"DOX_24T", "VEH_24T",
"DOX_24R", "VEH_24R",
"DOX_144R", "VEH_144R"))) %>%
ggplot(., aes(x=group_graph, y=lcpm)) +
geom_boxplot(aes(fill=tx))+
facet_wrap(group~Peakid, scales="free")+
theme_bw()+
theme(axis.text.x=element_text(angle = 90))
| Version | Author | Date |
|---|---|---|
| 3b2a52b | reneeisnowhere | 2025-08-13 |
Proportion of H3K9me3 DERs by time
H3K9me3_toptable_list %>%
dplyr::select(group,genes,logFC,adj.P.Val) %>%
mutate(sig_val=if_else(adj.P.Val<0.05,"sig","not_sig")) %>%
mutate(
sig_val = factor(sig_val, levels = c("not_sig", "sig")),
group = factor(group, levels = c("H3K9me3_24T","H3K9me3_24R","H3K9me3_144R"))) %>%
ggplot(aes(x = group, fill = sig_val)) +
geom_bar(position = "fill") +
theme_bw() +
ggtitle("H3K9me3") +
ylab("Proportion") +
scale_y_continuous(labels = scales::percent)
| Version | Author | Date |
|---|---|---|
| c3469d3 | reneeisnowhere | 2025-08-18 |
sessionInfo()R version 4.4.2 (2024-10-31 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 26100)
Matrix products: default
locale:
[1] LC_COLLATE=English_United States.utf8
[2] LC_CTYPE=English_United States.utf8
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C
[5] LC_TIME=English_United States.utf8
time zone: America/Chicago
tzcode source: internal
attached base packages:
[1] stats4 grid stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] ggsignif_0.6.4 ggVennDiagram_1.5.4 smplot2_0.2.5
[4] cowplot_1.2.0 ggrastr_1.0.2 Rsubread_2.20.0
[7] gcplyr_1.12.0 ggpmisc_0.6.2 ggpp_0.5.9
[10] corrplot_0.95 ggpubr_0.6.1 GenomicRanges_1.58.0
[13] GenomeInfoDb_1.42.3 IRanges_2.40.1 S4Vectors_0.44.0
[16] BiocGenerics_0.52.0 genomation_1.38.0 kableExtra_1.4.0
[19] DT_0.33 viridis_0.6.5 viridisLite_0.4.2
[22] data.table_1.17.8 ComplexHeatmap_2.22.0 edgeR_4.4.2
[25] limma_3.62.2 lubridate_1.9.4 forcats_1.0.0
[28] stringr_1.5.1 dplyr_1.1.4 purrr_1.1.0
[31] readr_2.1.5 tidyr_1.3.1 tibble_3.3.0
[34] ggplot2_3.5.2 tidyverse_2.0.0 workflowr_1.7.1
loaded via a namespace (and not attached):
[1] splines_4.4.2 later_1.4.2
[3] BiocIO_1.16.0 bitops_1.0-9
[5] rpart_4.1.24 XML_3.99-0.18
[7] lifecycle_1.0.4 rstatix_0.7.2
[9] doParallel_1.0.17 rprojroot_2.1.0
[11] vroom_1.6.5 processx_3.8.6
[13] lattice_0.22-7 MASS_7.3-65
[15] backports_1.5.0 magrittr_2.0.3
[17] Hmisc_5.2-3 sass_0.4.10
[19] rmarkdown_2.29 jquerylib_0.1.4
[21] yaml_2.3.10 plotrix_3.8-4
[23] httpuv_1.6.16 RColorBrewer_1.1-3
[25] abind_1.4-8 zlibbioc_1.52.0
[27] RCurl_1.98-1.17 nnet_7.3-20
[29] git2r_0.36.2 circlize_0.4.16
[31] GenomeInfoDbData_1.2.13 MatrixModels_0.5-4
[33] svglite_2.2.1 codetools_0.2-20
[35] DelayedArray_0.32.0 xml2_1.4.0
[37] tidyselect_1.2.1 shape_1.4.6.1
[39] UCSC.utils_1.2.0 farver_2.1.2
[41] matrixStats_1.5.0 base64enc_0.1-3
[43] GenomicAlignments_1.42.0 jsonlite_2.0.0
[45] GetoptLong_1.0.5 Formula_1.2-5
[47] survival_3.8-3 iterators_1.0.14
[49] systemfonts_1.2.3 foreach_1.5.2
[51] tools_4.4.2 Rcpp_1.1.0
[53] glue_1.8.0 gridExtra_2.3
[55] SparseArray_1.6.2 xfun_0.52
[57] MatrixGenerics_1.18.1 withr_3.0.2
[59] fastmap_1.2.0 SparseM_1.84-2
[61] callr_3.7.6 digest_0.6.37
[63] timechange_0.3.0 R6_2.6.1
[65] seqPattern_1.38.0 textshaping_1.0.1
[67] colorspace_2.1-1 Cairo_1.6-5
[69] dichromat_2.0-0.1 generics_0.1.4
[71] rtracklayer_1.66.0 httr_1.4.7
[73] htmlwidgets_1.6.4 S4Arrays_1.6.0
[75] whisker_0.4.1 pkgconfig_2.0.3
[77] gtable_0.3.6 impute_1.80.0
[79] XVector_0.46.0 htmltools_0.5.8.1
[81] carData_3.0-5 pwr_1.3-0
[83] clue_0.3-66 scales_1.4.0
[85] Biobase_2.66.0 png_0.1-8
[87] knitr_1.50 rstudioapi_0.17.1
[89] tzdb_0.5.0 reshape2_1.4.4
[91] rjson_0.2.23 checkmate_2.3.3
[93] curl_7.0.0 zoo_1.8-14
[95] cachem_1.1.0 GlobalOptions_0.1.2
[97] KernSmooth_2.23-26 parallel_4.4.2
[99] vipor_0.4.7 foreign_0.8-90
[101] restfulr_0.0.16 pillar_1.11.0
[103] vctrs_0.6.5 promises_1.3.3
[105] car_3.1-3 cluster_2.1.8.1
[107] htmlTable_2.4.3 beeswarm_0.4.0
[109] evaluate_1.0.4 magick_2.8.7
[111] cli_3.6.5 locfit_1.5-9.12
[113] compiler_4.4.2 Rsamtools_2.22.0
[115] rlang_1.1.6 crayon_1.5.3
[117] labeling_0.4.3 ps_1.9.1
[119] getPass_0.2-4 plyr_1.8.9
[121] fs_1.6.6 ggbeeswarm_0.7.2
[123] stringi_1.8.7 gridBase_0.4-7
[125] BiocParallel_1.40.2 Biostrings_2.74.1
[127] quantreg_6.1 Matrix_1.7-3
[129] BSgenome_1.74.0 patchwork_1.3.1
[131] hms_1.1.3 bit64_4.6.0-1
[133] statmod_1.5.0 SummarizedExperiment_1.36.0
[135] broom_1.0.9 bslib_0.9.0
[137] bit_4.6.0 polynom_1.4-1