Final Analysis
Steven Yu, Renee Matthews edits
2025-08-01
Last updated: 2025-08-25
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Knit directory: DXR_continue/
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Final Anaylsis
Loading Packages
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(chromVAR) ## For FRiP analysis and differential analysis
library(DESeq2) ## For differential analysis section
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)Data Initialization
sampleinfo <- read_delim("data/sample_info.tsv", delim = "\t")Functions
drug_pal <- c("#8B006D","#DF707E","#F1B72B", "#3386DD","#707031","#41B333")
pca_plot <- function(pca_obj, df,
col_var = NULL,
shape_var = NULL,
text_var = NULL,
title = "") {
# variance explained
a <- prop_var_percent(pca_obj)
ggplot(df, aes_string(x = "PC1", y = "PC2")) +
geom_point(aes_string(color = col_var, shape = shape_var), size = 5) +
ggrepel::geom_text_repel(aes_string(label = text_var),
vjust = -.5,
max.overlaps = 30) +
labs(
title = title,
x = paste0("PC1 (", round(a[1], 1), "%)"),
y = paste0("PC2 (", round(a[2], 1), "%)")
) +
scale_color_manual(values = c(
"#8B006D", "#DF707E", "#F1B72B",
"#3386DD", "#707031", "#41B333"
))
}
pca_var_plot <- function(pca) {
# x: class == prcomp
pca.var <- pca$sdev ^ 2
pca.prop <- pca.var / sum(pca.var)
var.plot <-
qplot(PC, prop, data = data.frame(PC = 1:length(pca.prop),
prop = pca.prop)) +
labs(title = 'Variance contributed by each PC',
x = 'PC', y = 'Proportion of variance')
plot(var.plot)
}
calc_pca <- function(x) {
# Performs principal components analysis with prcomp
# x: a sample-by-gene numeric matrix
prcomp(x, scale. = TRUE, retx = TRUE)
}
get_regr_pval <- function(mod) {
# Returns the p-value for the Fstatistic of a linear model
# mod: class lm
stopifnot(class(mod) == "lm")
fstat <- summary(mod)$fstatistic
pval <- 1 - pf(fstat[1], fstat[2], fstat[3])
return(pval)
}
prop_var_percent <- function(pca_result){
# Ensure the input is a PCA result object
if (!inherits(pca_result, "prcomp")) {
stop("Input must be a result from prcomp()")
}
# Get the standard deviations from the PCA result
sdev <- pca_result$sdev
# Calculate the proportion of variance
proportion_variance <- (sdev^2) / sum(sdev^2)*100
return(proportion_variance)
}
plot_versus_pc <- function(df, pc_num, fac) {
# df: data.frame
# pc_num: numeric, specific PC for plotting
# fac: column name of df for plotting against PC
pc_char <- paste0("PC", pc_num)
# Calculate F-statistic p-value for linear model
pval <- get_regr_pval(lm(df[, pc_char] ~ df[, fac]))
if (is.numeric(df[, f])) {
ggplot(df, aes_string(x = f, y = pc_char)) + geom_point() +
geom_smooth(method = "lm") + labs(title = sprintf("p-val: %.2f", pval))
} else {
ggplot(df, aes_string(x = f, y = pc_char)) + geom_boxplot() +
labs(title = sprintf("p-val: %.2f", pval))
}
}
x_axis_labels = function(labels, every_nth = 1, ...) {
axis(side = 1,
at = seq_along(labels),
labels = F)
text(
x = (seq_along(labels))[seq_len(every_nth) == 1],
y = par("usr")[3] - 0.075 * (par("usr")[4] - par("usr")[3]),
labels = labels[seq_len(every_nth) == 1],
xpd = TRUE,
...
)
}
volcanosig <- function(df, psig.lvl) {
df <- df %>%
mutate(threshold = ifelse(adj.P.Val > psig.lvl, "A", ifelse(adj.P.Val <= psig.lvl & logFC<=0,"B","C")))
# ifelse(adj.P.Val <= psig.lvl & logFC >= 0,"B", "C")))
##This is where I could add labels, but I have taken out
# df <- df %>% mutate(genelabels = "")
# df$genelabels[1:topg] <- df$rownames[1:topg]
ggplot(df, aes(x=logFC, y=-log10(P.Value))) +
ggrastr::geom_point_rast(aes(color=threshold))+
# geom_text_repel(aes(label = genelabels), segment.curvature = -1e-20,force = 1,size=2.5,
# arrow = arrow(length = unit(0.015, "npc")), max.overlaps = Inf) +
#geom_hline(yintercept = -log10(psig.lvl))+
xlab(expression("Log"[2]*" FC"))+
ylab(expression("-log"[10]*"P Value"))+
scale_color_manual(values = c("black", "red","blue"))+
theme_cowplot()+
ylim(0,25)+
xlim(-6,6)+
theme(legend.position = "none",
plot.title = element_text(size = rel(1.5), hjust = 0.5),
axis.title = element_text(size = rel(0.8)))
}Final Analysis
peak_ct <- read_delim("data/peaks/peaks_cts_FINAL.txt", delim = "\t")
H3K27ac_peaks <- read_delim("data/peaks/H3K27ac_FINAl_results.tsv",delim = "\t")
H3K27me3_peaks <- read_delim("data/peaks/H3K27me3_FINAL_results.tsv",delim = "\t")
H3K36me3_peaks <- read_delim("data/peaks/H3K36me3_FINAL_results.tsv",delim = "\t")
H3K9me3_peaks <- read_delim("data/peaks/H3K9me3_FINAL_results.tsv",delim = "\t")
all_peak_final <- rbind(H3K27ac_peaks, H3K27me3_peaks, H3K36me3_peaks, H3K9me3_peaks)
all_peak_final <- all_peak_final %>%
dplyr::select(Sample, Total_Reads, Fragments, Reads_in_Peaks, FRiP) %>%
left_join(.,sampleinfo, by=c("Sample"="Library ID")) %>%
left_join(.,peak_ct, by=c("Sample"="Sample"))
all_peak_final <- all_peak_final[(!all_peak_final$Treatment %in% "5FU"),]
# write_delim(all_peak_final,"data/all_peak_final_dataframe.txt",delim = "\t")all_peak_final %>%
ggplot(.,aes(x=Sample, y=Count,fill=Histone_Mark))+
geom_col()+
ylab("Count")+
theme_classic()+
# facet_wrap(~histone)+
ggtitle("Peak number for all samples")+
theme(axis.text.x=element_text(vjust = .2,angle=90))+
scale_y_continuous( expand = expansion(mult = c(0, .1)))
| Version | Author | Date |
|---|---|---|
| f4826e1 | infurnoheat | 2025-08-01 |
all_peak_final %>%
ggplot(., aes (x=Treatment, y = Count, fill = Histone_Mark))+
geom_boxplot()+
ylab("Count")+
theme_classic()+
# facet_wrap(~histone)+
ggtitle("Peak count across histones")
| Version | Author | Date |
|---|---|---|
| f4826e1 | infurnoheat | 2025-08-01 |
all_peak_final %>%
ggplot(., aes (x=Timepoint, y = Count, fill = Histone_Mark))+
geom_boxplot()+
ylab("Count")+
theme_classic()+
# facet_wrap(~histone)+
ggtitle("Peak count across histones")
| Version | Author | Date |
|---|---|---|
| f4826e1 | infurnoheat | 2025-08-01 |
Tagging Questionable Libraries by FRiP
questionable_frip = all_peak_final[(all_peak_final$FRiP < 0.2),]
questionable_frip# A tibble: 28 × 10
Sample Total_Reads Fragments Reads_in_Peaks FRiP Histone_Mark Individual
<chr> <dbl> <dbl> <dbl> <dbl> <chr> <chr>
1 MCW_CT_R… 8333500 8333500 1412823 0.170 H3K36me3 Ind1
2 MCW_CT_R… 6705202 6705202 1250936 0.186 H3K36me3 Ind4
3 MCW_CT_R… 951069 951069 130753 0.137 H3K36me3 Ind1
4 MCW_CT_R… 8360401 8360401 1063627 0.127 H3K36me3 Ind3
5 MCW_CT_R… 7240342 7240342 1032016 0.142 H3K36me3 Ind1
6 MCW_CT_R… 13447374 13447374 2214575 0.165 H3K36me3 Ind2
7 MCW_CT_R… 5545665 5545665 644296 0.116 H3K36me3 Ind4
8 MCW_CT_R… 8211417 8211417 672532 0.0819 H3K36me3 Ind2
9 MCW_CT_R… 9520493 9520493 1332644 0.140 H3K36me3 Ind4
10 MCW_CT_R… 23130869 23130869 3401081 0.147 H3K9me3 Ind1
# ℹ 18 more rows
# ℹ 3 more variables: Treatment <chr>, Timepoint <chr>, Count <dbl>Feature Counts
H3K27ac_merged <- read_delim("data/peaks/H3K27ac_FINAL_counts.txt",
delim = "\t", escape_double = FALSE,
trim_ws = TRUE, skip = 1)
H3K27me3_merged <- read_delim("data/peaks/H3K27me3_FINAL_counts.txt",
delim = "\t", escape_double = FALSE,
trim_ws = TRUE, skip = 1)
H3K36me3_merged <- read_delim("data/peaks/H3K36me3_FINAL_counts.txt",
delim = "\t", escape_double = FALSE,
trim_ws = TRUE, skip = 1)
H3K9me3_merged <- read_delim("data/peaks/H3K9me3_FINAL_counts.txt",
delim = "\t", escape_double = FALSE,
trim_ws = TRUE, skip = 1)
rename_list <- sampleinfo %>%
mutate(stem= "_nobl.bam") %>%
mutate(prefix=paste0("/scratch/10819/styu/MW_multiQC/peaks/",Histone_Mark,"/",Treatment,"/",Timepoint,"/")) %>%
mutate(oldname=paste0(prefix,`Library ID`,"/",`Library ID`,stem)) %>%
mutate(newname=paste0(Individual,"_",Treatment,"_",Timepoint)) %>%
dplyr::select(oldname,newname)
rename_vec <- setNames(rename_list$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)]]H3K27ac Count Analysis
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()
annomat <- data.frame(sample=colnames(H3K27ac_merged_cor)) %>%
separate_wider_delim(sample,delim="_",names=c("Ind","Treatment","Timepoint"),cols_remove = FALSE) %>%
mutate(Treatment=factor(Treatment, levels = c("VEH","5FU","DOX")),
Timepoint=factor(Timepoint, levels =c("24T","24R","144R"))) %>%
column_to_rownames("sample")
heatmap_first <- ComplexHeatmap::HeatmapAnnotation(df = annomat)
Heatmap(H3K27ac_merged_cor,
top_annotation = heatmap_first,
column_title="Unfiltered log2cpm H3K27ac with Standard Merging")
| Version | Author | Date |
|---|---|---|
| f4826e1 | infurnoheat | 2025-08-01 |
H3K27me3 Count Analysis
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()
annomat <- data.frame(sample=colnames(H3K27me3_merged_cor)) %>%
separate_wider_delim(sample,delim="_",names=c("Ind","Treatment","Timepoint"),cols_remove = FALSE) %>%
mutate(Treatment=factor(Treatment, levels = c("VEH","5FU","DOX")),
Timepoint=factor(Timepoint, levels =c("24T","24R","144R"))) %>%
column_to_rownames("sample")
heatmap_first <- ComplexHeatmap::HeatmapAnnotation(df = annomat)
Heatmap(H3K27me3_merged_cor,
top_annotation = heatmap_first,
column_title="Unfiltered log2cpm H3K27me3 with Standard Merging")
| Version | Author | Date |
|---|---|---|
| f4826e1 | infurnoheat | 2025-08-01 |
H3K36me3 Count Analysis
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()
annomat <- data.frame(sample=colnames(H3K36me3_merged_cor)) %>%
separate_wider_delim(sample,delim="_",names=c("Ind","Treatment","Timepoint"),cols_remove = FALSE) %>%
mutate(Treatment=factor(Treatment, levels = c("VEH","5FU","DOX")),
Timepoint=factor(Timepoint, levels =c("24T","24R","144R"))) %>%
column_to_rownames("sample")
heatmap_first <- ComplexHeatmap::HeatmapAnnotation(df = annomat)
Heatmap(H3K36me3_merged_cor,
top_annotation = heatmap_first,
column_title="Unfiltered log2cpm H3K36me3 with Standard Merging")
| Version | Author | Date |
|---|---|---|
| f4826e1 | infurnoheat | 2025-08-01 |
H3K9me3 Count Analysis
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()
annomat <- data.frame(sample=colnames(H3K9me3_merged_cor)) %>%
separate_wider_delim(sample,delim="_",names=c("Ind","Treatment","Timepoint"),cols_remove = FALSE) %>%
mutate(Treatment=factor(Treatment, levels = c("VEH","5FU","DOX")),
Timepoint=factor(Timepoint, levels =c("24T","24R","144R"))) %>%
column_to_rownames("sample")
heatmap_first <- ComplexHeatmap::HeatmapAnnotation(df = annomat)
Heatmap(H3K9me3_merged_cor,
top_annotation = heatmap_first,
column_title="Unfiltered log2cpm H3K9me3 with Standard Merging")
| Version | Author | Date |
|---|---|---|
| f4826e1 | infurnoheat | 2025-08-01 |
Fragment Analysis
all_peak_final %>%
mutate(Fragments=Fragments/1000000) %>%
ggplot(., aes(x=interaction(Individual,Treatment,Timepoint), y=Fragments, fill=Treatment, group = Treatment))+
geom_col()+
geom_text(aes(y = 0,label = Sample), vjust = 0.2, size = 3, angle = 90)+
theme_classic()+
facet_wrap(~Histone_Mark)+
ggtitle("Fragment count by histone and sample")+
ylab("Count of Fragments * 10^6")+
xlab("Samples")+
theme(axis.text.x=element_text(vjust = .2,angle=90))+
scale_y_continuous( expand = expansion(mult = c(0, .1)))
| Version | Author | Date |
|---|---|---|
| f4826e1 | infurnoheat | 2025-08-01 |
all_peak_final %>%
mutate(FRiP=FRiP * 100) %>%
ggplot(., aes(x=interaction(Individual,Treatment,Timepoint), y=FRiP, fill=Treatment, group = Treatment))+
geom_col()+
geom_text(aes(y = 0,label = Sample), vjust = 0.2, size = 3, angle = 90)+
theme_classic()+
facet_wrap(~Histone_Mark)+
ggtitle("Frip Percent by histone and sample")+
ylab("Frip %")+
xlab("Samples")+
theme(axis.text.x=element_text(vjust = .2,angle=90))+
scale_y_continuous( expand = expansion(mult = c(0, .1)))
| Version | Author | Date |
|---|---|---|
| f4826e1 | infurnoheat | 2025-08-01 |
Differential Analysis
Filtering Sex Chromosomes
Removing chrX and chrY
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)),]
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)),]
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)),]
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)),]Setting up Matrix
H3K27ac_annomat <- data.frame(timeset=colnames(H3K27ac_merged_raw)) %>%
mutate(sample=timeset) %>%
separate(timeset, into = c("ind","tx","time")) %>%
mutate(tx=factor(tx, levels = c("VEH", "DOX")),
time=factor(time, levels =c("24T","24R","144R"))) %>%
mutate(ind = gsub("Ind", "", ind)) %>%
mutate(txtime = paste0(tx, "_", time)) %>%
mutate(group = txtime)
H3K27ac_annomat$group <- H3K27ac_annomat$group %>%
gsub("DOX_24T", "1", .) %>%
gsub("DOX_24R", "2", .) %>%
gsub("DOX_144R", "3", .) %>%
gsub("VEH_24T", "4", .) %>%
gsub("VEH_24R", "5", .) %>%
gsub("VEH_144R", "6", .)
H3K27me3_annomat <- data.frame(timeset=colnames(H3K27me3_merged_raw)) %>%
mutate(sample=timeset) %>%
separate(timeset, into = c("ind","tx","time")) %>%
mutate(tx=factor(tx, levels = c("VEH", "DOX")),
time=factor(time, levels =c("24T","24R","144R"))) %>%
mutate(ind = gsub("Ind", "", ind)) %>%
mutate(txtime = paste0(tx, "_", time)) %>%
mutate(group = txtime)
H3K27me3_annomat$group <- H3K27me3_annomat$group %>%
gsub("DOX_24T", "1", .) %>%
gsub("DOX_24R", "2", .) %>%
gsub("DOX_144R", "3", .) %>%
gsub("VEH_24T", "4", .) %>%
gsub("VEH_24R", "5", .) %>%
gsub("VEH_144R", "6", .)
H3K36me3_annomat <- data.frame(timeset=colnames(H3K36me3_merged_raw)) %>%
mutate(sample=timeset) %>%
separate(timeset, into = c("ind","tx","time")) %>%
mutate(tx=factor(tx, levels = c("VEH", "DOX")),
time=factor(time, levels =c("24T","24R","144R"))) %>%
mutate(ind = gsub("Ind", "", ind)) %>%
mutate(txtime = paste0(tx, "_", time)) %>%
mutate(group = txtime)
H3K36me3_annomat$group <- H3K36me3_annomat$group %>%
gsub("DOX_24T", "1", .) %>%
gsub("DOX_24R", "2", .) %>%
gsub("DOX_144R", "3", .) %>%
gsub("VEH_24T", "4", .) %>%
gsub("VEH_24R", "5", .) %>%
gsub("VEH_144R", "6", .)
H3K9me3_annomat <- data.frame(timeset=colnames(H3K9me3_merged_raw)) %>%
mutate(sample=timeset) %>%
separate(timeset, into = c("ind","tx","time")) %>%
mutate(tx=factor(tx, levels = c("VEH", "DOX")),
time=factor(time, levels =c("24T","24R","144R"))) %>%
mutate(ind = gsub("Ind", "", ind)) %>%
mutate(txtime = paste0(tx, "_", time)) %>%
mutate(group = txtime)
H3K9me3_annomat$group <- H3K9me3_annomat$group %>%
gsub("DOX_24T", "1", .) %>%
gsub("DOX_24R", "2", .) %>%
gsub("DOX_144R", "3", .) %>%
gsub("VEH_24T", "4", .) %>%
gsub("VEH_24R", "5", .) %>%
gsub("VEH_144R", "6", .)
dge_H3K27ac <- edgeR::DGEList(counts = H3K27ac_merged_raw, group = H3K27ac_annomat$group, genes = row.names(H3K27ac_merged_raw))
dge_H3K27me3 <- edgeR::DGEList(counts = H3K27me3_merged_raw, group = H3K27me3_annomat$group, genes = row.names(H3K27me3_merged_raw))
dge_H3K36me3 <- edgeR::DGEList(counts = H3K36me3_merged_raw, group = H3K36me3_annomat$group, genes = row.names(H3K36me3_merged_raw))
dge_H3K9me3 <- edgeR::DGEList(counts = H3K9me3_merged_raw, group = H3K9me3_annomat$group, genes = row.names(H3K9me3_merged_raw))
dge_H3K27ac <- edgeR::calcNormFactors(dge_H3K27ac)
dge_H3K27me3 <- edgeR::calcNormFactors(dge_H3K27me3)
dge_H3K36me3 <- edgeR::calcNormFactors(dge_H3K36me3)
dge_H3K9me3 <- edgeR::calcNormFactors(dge_H3K9me3)
mm_H3K27ac <- model.matrix(~0 + H3K27ac_annomat$txtime)
colnames(mm_H3K27ac) <- H3K27ac_annomat$txtime %>% unique()
mm_H3K27me3 <- model.matrix(~0 + H3K27me3_annomat$txtime)
colnames(mm_H3K27me3) <- H3K27me3_annomat$txtime %>% unique()
mm_H3K36me3 <- model.matrix(~0 + H3K36me3_annomat$txtime)
colnames(mm_H3K36me3) <- H3K36me3_annomat$txtime %>% unique()
mm_H3K9me3 <- model.matrix(~0 + H3K9me3_annomat$txtime)
colnames(mm_H3K9me3) <- H3K9me3_annomat$txtime %>% unique()Volcano Plots
H3K27ac
y <- voom(dge_H3K27ac, mm_H3K27ac, plot = FALSE)
corfit <- duplicateCorrelation(y, mm_H3K27ac, block = H3K27ac_annomat$ind)
v <- voom(dge_H3K27ac, mm_H3K27ac, block = H3K27ac_annomat$ind, correlation = corfit$consensus.correlation)
fit <- lmFit(v, mm_H3K27ac, block = H3K27ac_annomat$ind, correlation = corfit$consensus.correlation)
cm <- makeContrasts(
DOX_24T.VEH_24T = DOX_24T-VEH_24T,
DOX_24R.VEH_24R = DOX_24R-VEH_24R,
DOX_144R.VEH_144R = DOX_144R-VEH_144R,
levels = mm_H3K27ac)
fit2<- contrasts.fit(fit, contrasts=cm)
efit2 <- eBayes(fit2)
results = decideTests(efit2)
summary(results) DOX_24T.VEH_24T DOX_24R.VEH_24R DOX_144R.VEH_144R
Down 13935 8070 22
NotSig 124546 133714 150045
Up 11590 8287 4plotSA(efit2, main="Mean-Variance trend for final model for H3K27ac")
V.24T.top= topTable(efit2, coef=1, adjust.method="BH", number=Inf, sort.by="p")
V.24R.top= topTable(efit2, coef=2, adjust.method="BH", number=Inf, sort.by="p")
V.144R.top= topTable(efit2, coef=3, adjust.method="BH", number=Inf, sort.by="p")
H3K27ac_24T <- volcanosig(V.24T.top, 0.05)+ ggtitle("DOX 24T")
H3K27ac_24R <- volcanosig(V.24R.top, 0.05)+ ggtitle("DOX 24R")+ylab("")
H3K27ac_144R <- volcanosig(V.144R.top, 0.05)+ ggtitle("DOX 144R")+ylab("")
plot_grid(H3K27ac_24T, H3K27ac_24R, H3K27ac_144R, rel_widths =c(1,1,1))
H3K27me3
y <- voom(dge_H3K27me3, mm_H3K27me3, plot = FALSE)
corfit <- duplicateCorrelation(y, mm_H3K27me3, block = H3K27me3_annomat$ind)
v <- voom(dge_H3K27me3, mm_H3K27me3, block = H3K27me3_annomat$ind, correlation = corfit$consensus.correlation)
fit <- lmFit(v, mm_H3K27me3, block = H3K27me3_annomat$ind, correlation = corfit$consensus.correlation)
cm <- makeContrasts(
DOX_24T.VEH_24T = DOX_24T-VEH_24T,
DOX_24R.VEH_24R = DOX_24R-VEH_24R,
DOX_144R.VEH_144R = DOX_144R-VEH_144R,
levels = mm_H3K27me3)
fit2<- contrasts.fit(fit, contrasts=cm)
efit2 <- eBayes(fit2)
results = decideTests(efit2)
summary(results) DOX_24T.VEH_24T DOX_24R.VEH_24R DOX_144R.VEH_144R
Down 8 4 0
NotSig 150449 150457 150464
Up 7 3 0plotSA(efit2, main="Mean-Variance trend for final model for H3K27me3")
V.24T.top= topTable(efit2, coef=1, adjust.method="BH", number=Inf, sort.by="p")
V.24R.top= topTable(efit2, coef=2, adjust.method="BH", number=Inf, sort.by="p")
V.144R.top= topTable(efit2, coef=3, adjust.method="BH", number=Inf, sort.by="p")
H3K27me3_24T <- volcanosig(V.24T.top, 0.05)+ ggtitle("DOX 24T")
H3K27me3_24R <- volcanosig(V.24R.top, 0.05)+ ggtitle("DOX 24R")+ylab("")
H3K27me3_144R <- volcanosig(V.144R.top, 0.05)+ ggtitle("DOX 144R")+ylab("")
plot_grid(H3K27me3_24T, H3K27me3_24R, H3K27me3_144R, rel_widths =c(1,1,1))
H3K36me3
y <- voom(dge_H3K36me3, mm_H3K36me3, plot = FALSE)
corfit <- duplicateCorrelation(y, mm_H3K36me3, block = H3K36me3_annomat$ind)
v <- voom(dge_H3K36me3, mm_H3K36me3, block = H3K36me3_annomat$ind, correlation = corfit$consensus.correlation)
fit <- lmFit(v, mm_H3K36me3, block = H3K36me3_annomat$ind, correlation = corfit$consensus.correlation)
cm <- makeContrasts(
DOX_24T.VEH_24T = DOX_24T-VEH_24T,
DOX_24R.VEH_24R = DOX_24R-VEH_24R,
DOX_144R.VEH_144R = DOX_144R-VEH_144R,
levels = mm_H3K36me3)
fit2<- contrasts.fit(fit, contrasts=cm)
efit2 <- eBayes(fit2)
results = decideTests(efit2)
summary(results) DOX_24T.VEH_24T DOX_24R.VEH_24R DOX_144R.VEH_144R
Down 1455 186 0
NotSig 184217 186246 186724
Up 1052 292 0plotSA(efit2, main="Mean-Variance trend for final model for H3K36me3")
V.24T.top= topTable(efit2, coef=1, adjust.method="BH", number=Inf, sort.by="p")
V.24R.top= topTable(efit2, coef=2, adjust.method="BH", number=Inf, sort.by="p")
V.144R.top= topTable(efit2, coef=3, adjust.method="BH", number=Inf, sort.by="p")
H3K36me3_24T <- volcanosig(V.24T.top, 0.05)+ ggtitle("DOX 24T")
H3K36me3_24R <- volcanosig(V.24R.top, 0.05)+ ggtitle("DOX 24R")+ylab("")
H3K36me3_144R <- volcanosig(V.144R.top, 0.05)+ ggtitle("DOX 144R")+ylab("")
plot_grid(H3K36me3_24T, H3K36me3_24R, H3K36me3_144R, rel_widths =c(1,1,1))
H3K9me3
y <- voom(dge_H3K9me3, mm_H3K9me3, plot = FALSE)
corfit <- duplicateCorrelation(y, mm_H3K9me3, block = H3K9me3_annomat$ind)
v <- voom(dge_H3K9me3, mm_H3K9me3, block = H3K9me3_annomat$ind, correlation = corfit$consensus.correlation)
fit <- lmFit(v, mm_H3K9me3, block = H3K9me3_annomat$ind, correlation = corfit$consensus.correlation)
cm <- makeContrasts(
DOX_24T.VEH_24T = DOX_24T-VEH_24T,
DOX_24R.VEH_24R = DOX_24R-VEH_24R,
DOX_144R.VEH_144R = DOX_144R-VEH_144R,
levels = mm_H3K9me3)
fit2<- contrasts.fit(fit, contrasts=cm)
efit2 <- eBayes(fit2)
results = decideTests(efit2)
summary(results) DOX_24T.VEH_24T DOX_24R.VEH_24R DOX_144R.VEH_144R
Down 793 1 0
NotSig 208703 218530 218647
Up 9151 116 0plotSA(efit2, main="Mean-Variance trend for final model for H3K9me3")
V.24T.top= topTable(efit2, coef=1, adjust.method="BH", number=Inf, sort.by="p")
V.24R.top= topTable(efit2, coef=2, adjust.method="BH", number=Inf, sort.by="p")
V.144R.top= topTable(efit2, coef=3, adjust.method="BH", number=Inf, sort.by="p")
H3K9me3_24T <- volcanosig(V.24T.top, 0.05)+ ggtitle("DOX 24T")
H3K9me3_24R <- volcanosig(V.24R.top, 0.05)+ ggtitle("DOX 24R")+ylab("")
H3K9me3_144R <- volcanosig(V.144R.top, 0.05)+ ggtitle("DOX 144R")+ylab("")
plot_grid(H3K9me3_24T, H3K9me3_24R, H3K9me3_144R, rel_widths =c(1,1,1))
PCA Plots
H3K27ac
H3K27ac_merged_filt_lcpm <- H3K27ac_merged_raw %>% cpm(., log = TRUE)
pca_H3K27ac <- calc_pca(t(H3K27ac_merged_filt_lcpm))
pca_var_plot(pca_H3K27ac)
pca_H3K27ac_df <- data.frame(pca_H3K27ac$x , H3K27ac_annomat)
pca_plot(
pca_H3K27ac,
pca_H3K27ac_df,
col_var = "time",
shape_var = "tx",
text_var = "ind", # <-- string, not vector
title = "H3K27ac filtered lcpm PCA"
) 
H3K27ac_merged_raw_lcpm <- H3K27ac_merged_raw %>%
cpm(., log = TRUE)
H3K27ac_merged_filt_cor <- H3K27ac_merged_raw_lcpm %>%
cor()
annomat <- data.frame(sample=colnames(H3K27ac_merged_filt_cor)) %>%
separate_wider_delim(sample,delim="_",names=c("Ind","Treatment","Timepoint"),cols_remove = FALSE) %>%
mutate(Treatment=factor(Treatment, levels = c("VEH","5FU","DOX")),
Timepoint=factor(Timepoint, levels =c("24T","24R","144R"))) %>%
column_to_rownames("sample")
heatmap_second <- ComplexHeatmap::HeatmapAnnotation(df = annomat)
Heatmap(H3K27ac_merged_filt_cor,
top_annotation = heatmap_second,
column_title="Filtered log2cpm H3K27ac with Standard Merging")
H3K27me3
H3K27me3_merged_filt_lcpm <- H3K27me3_merged_raw %>% cpm(., log = TRUE)
pca_H3K27me3 <- calc_pca(t(H3K27me3_merged_filt_lcpm))
pca_var_plot(pca_H3K27me3)
pca_H3K27me3_df <- data.frame(pca_H3K27me3$x , H3K27me3_annomat)
pca_plot(
pca_H3K27me3,
pca_H3K27me3_df,
col_var = "time",
shape_var = "tx",
text_var = "ind", # <-- string, not vector
title = "H3K27me3 filtered lcpm PCA"
) 
H3K27me3_merged_raw_lcpm <- H3K27me3_merged_raw %>%
cpm(., log = TRUE)
H3K27me3_merged_filt_cor <- H3K27me3_merged_raw_lcpm %>%
cor()
annomat <- data.frame(sample=colnames(H3K27me3_merged_filt_cor)) %>%
separate_wider_delim(sample,delim="_",names=c("Ind","Treatment","Timepoint"),cols_remove = FALSE) %>%
mutate(Treatment=factor(Treatment, levels = c("VEH","5FU","DOX")),
Timepoint=factor(Timepoint, levels =c("24T","24R","144R"))) %>%
column_to_rownames("sample")
heatmap_second <- ComplexHeatmap::HeatmapAnnotation(df = annomat)
Heatmap(H3K27me3_merged_filt_cor,
top_annotation = heatmap_second,
column_title="Filtered log2cpm H3K27me3 with Standard Merging")
H3K36me3
H3K36me3_merged_filt_lcpm <- H3K36me3_merged_raw %>% cpm(., log = TRUE)
pca_H3K36me3 <- calc_pca(t(H3K36me3_merged_filt_lcpm))
pca_var_plot(pca_H3K36me3)
pca_H3K36me3_df <- data.frame(pca_H3K36me3$x , H3K36me3_annomat)
pca_plot(
pca_H3K36me3,
pca_H3K36me3_df,
col_var = "time",
shape_var = "tx",
text_var = "ind", # <-- string, not vector
title = "H3K36me3 filtered lcpm PCA"
) 
H3K36me3_merged_raw_lcpm <- H3K36me3_merged_raw %>%
cpm(., log = TRUE)
H3K36me3_merged_filt_cor <- H3K36me3_merged_raw_lcpm %>%
cor()
annomat <- data.frame(sample=colnames(H3K36me3_merged_filt_cor)) %>%
separate_wider_delim(sample,delim="_",names=c("Ind","Treatment","Timepoint"),cols_remove = FALSE) %>%
mutate(Treatment=factor(Treatment, levels = c("VEH","5FU","DOX")),
Timepoint=factor(Timepoint, levels =c("24T","24R","144R"))) %>%
column_to_rownames("sample")
heatmap_second <- ComplexHeatmap::HeatmapAnnotation(df = annomat)
Heatmap(H3K36me3_merged_filt_cor,
top_annotation = heatmap_second,
column_title="Filtered log2cpm H3K36me3")
| Version | Author | Date |
|---|---|---|
| ac6eb8d | reneeisnowhere | 2025-08-21 |
H3K9me3
H3K9me3_merged_filt_lcpm <- H3K9me3_merged_raw %>% cpm(., log = TRUE)
pca_H3K9me3 <- calc_pca(t(H3K9me3_merged_filt_lcpm))
pca_var_plot(pca_H3K9me3)
pca_H3K9me3_df <- data.frame(pca_H3K9me3$x , H3K9me3_annomat)
pca_plot(
pca_H3K9me3,
pca_H3K9me3_df,
col_var = "time",
shape_var = "tx",
text_var = "ind", # <-- string, not vector
title = "H3K9me3 filtered lcpm PCA"
) 
H3K9me3_merged_raw_lcpm <- H3K9me3_merged_raw %>%
cpm(., log = TRUE)
H3K9me3_merged_filt_cor <- H3K9me3_merged_raw_lcpm %>%
cor()
annomat <- data.frame(sample=colnames(H3K9me3_merged_filt_cor)) %>%
separate_wider_delim(sample,delim="_",names=c("Ind","Treatment","Timepoint"),cols_remove = FALSE) %>%
mutate(Treatment=factor(Treatment, levels = c("VEH","5FU","DOX")),
Timepoint=factor(Timepoint, levels =c("24T","24R","144R"))) %>%
column_to_rownames("sample")
heatmap_second <- ComplexHeatmap::HeatmapAnnotation(df = annomat)
Heatmap(H3K9me3_merged_filt_cor,
top_annotation = heatmap_second,
column_title="Filtered log2cpm H3K9me3 with Standard Merging")
| Version | Author | Date |
|---|---|---|
| ac6eb8d | reneeisnowhere | 2025-08-21 |
Venn Diagrams
Venn Set Up
genes_H3K27ac_24T <- H3K27ac_24T$data$genes[(H3K27ac_24T$data$adj.P.Val < 0.05)]
genes_H3K27ac_24R <- H3K27ac_24R$data$genes[(H3K27ac_24R$data$adj.P.Val < 0.05)]
genes_H3K27ac_144R <- H3K27ac_144R$data$genes[(H3K27ac_144R$data$adj.P.Val < 0.05)]
genes_H3K27me3_24T <- H3K27me3_24T$data$genes[(H3K27me3_24T$data$adj.P.Val < 0.05)]
genes_H3K27me3_24R <- H3K27me3_24R$data$genes[(H3K27me3_24R$data$adj.P.Val < 0.05)]
genes_H3K27me3_144R <- H3K27me3_144R$data$genes[(H3K27me3_144R$data$adj.P.Val < 0.05)]
genes_H3K36me3_24T <- H3K36me3_24T$data$genes[(H3K36me3_24T$data$adj.P.Val < 0.05)]
genes_H3K36me3_24R <- H3K36me3_24R$data$genes[(H3K36me3_24R$data$adj.P.Val < 0.05)]
genes_H3K36me3_144R <- H3K36me3_144R$data$genes[(H3K36me3_144R$data$adj.P.Val < 0.05)]
genes_H3K9me3_24T <- H3K9me3_24T$data$genes[(H3K9me3_24T$data$adj.P.Val < 0.05)]
genes_H3K9me3_24R <- H3K9me3_24R$data$genes[(H3K9me3_24R$data$adj.P.Val < 0.05)]
genes_H3K9me3_144R <- H3K9me3_144R$data$genes[(H3K9me3_144R$data$adj.P.Val < 0.05)]H3K27ac Venn Diagrams
ggVennDiagram(list("24T regions"=genes_H3K27ac_24T,"24R regions"=genes_H3K27ac_24R, "144R regions"=genes_H3K27ac_144R))
H3K27me3 Venn Diagrams
ggVennDiagram(list("24T regions"=genes_H3K27me3_24T,"24R regions"=genes_H3K27me3_24R, "144R regions"=genes_H3K27me3_144R))
H3K36me3 Venn Diagrams
ggVennDiagram(list("24T regions"=genes_H3K36me3_24T,"24R regions"=genes_H3K36me3_24R, "144R regions"=genes_H3K36me3_144R))
H3K9me3 Venn Diagrams
ggVennDiagram(list("24T regions"=genes_H3K9me3_24T,"24R regions"=genes_H3K9me3_24R, "144R regions"=genes_H3K9me3_144R))
H3K27ac_toplist <- list("H3K27ac_24T"=H3K27ac_24T$data,"H3K27ac_24R"= H3K27ac_24R$data, "H3K27ac_144R"= H3K27ac_144R$data)
saveRDS(H3K27ac_toplist, "data/DER_data/H3K27ac_toplist.RDS")
H3K27me3_toplist <- list("H3K27me3_24T"=H3K27me3_24T$data,"H3K27me3_24R"= H3K27me3_24R$data, "H3K27me3_144R"= H3K27me3_144R$data)
saveRDS(H3K27me3_toplist,"data/DER_data/H3K27me3_toplist.RDS")
H3K36me3_toplist <- list("H3K36me3_24T"=H3K36me3_24T$data,"H3K36me3_24R"= H3K36me3_24R$data, "H3K36me3_144R"= H3K36me3_144R$data)
saveRDS(H3K36me3_toplist,"data/DER_data/H3K36me3_toplist.RDS")
H3K9me3_toplist <- list("H3K9me3_24T"=H3K9me3_24T$data,"H3K9me3_24R"= H3K9me3_24R$data, "H3K9me3_144R"= H3K9me3_144R$data)
saveRDS(H3K9me3_toplist, "data/DER_data/H3K9me3_toplist.RDS")
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] ggVennDiagram_1.5.4 smplot2_0.2.5
[3] cowplot_1.2.0 ggrastr_1.0.2
[5] Rsubread_2.20.0 gcplyr_1.12.0
[7] ggpmisc_0.6.2 ggpp_0.5.9
[9] corrplot_0.95 ggpubr_0.6.1
[11] DESeq2_1.46.0 SummarizedExperiment_1.36.0
[13] Biobase_2.66.0 MatrixGenerics_1.18.1
[15] matrixStats_1.5.0 chromVAR_1.28.0
[17] GenomicRanges_1.58.0 GenomeInfoDb_1.42.3
[19] IRanges_2.40.1 S4Vectors_0.44.0
[21] BiocGenerics_0.52.0 genomation_1.38.0
[23] kableExtra_1.4.0 DT_0.33
[25] viridis_0.6.5 viridisLite_0.4.2
[27] data.table_1.17.8 ComplexHeatmap_2.22.0
[29] edgeR_4.4.2 limma_3.62.2
[31] lubridate_1.9.4 forcats_1.0.0
[33] stringr_1.5.1 dplyr_1.1.4
[35] purrr_1.1.0 readr_2.1.5
[37] tidyr_1.3.1 tibble_3.3.0
[39] ggplot2_3.5.2 tidyverse_2.0.0
[41] workflowr_1.7.1
loaded via a namespace (and not attached):
[1] fs_1.6.6 bitops_1.0-9
[3] DirichletMultinomial_1.48.0 TFBSTools_1.44.0
[5] httr_1.4.7 RColorBrewer_1.1-3
[7] doParallel_1.0.17 tools_4.4.2
[9] backports_1.5.0 utf8_1.2.6
[11] R6_2.6.1 lazyeval_0.2.2
[13] GetoptLong_1.0.5 withr_3.0.2
[15] gridExtra_2.3 quantreg_6.1
[17] cli_3.6.5 textshaping_1.0.1
[19] Cairo_1.6-5 labeling_0.4.3
[21] sass_0.4.10 Rsamtools_2.22.0
[23] systemfonts_1.2.3 foreign_0.8-90
[25] svglite_2.2.1 R.utils_2.13.0
[27] dichromat_2.0-0.1 plotrix_3.8-4
[29] BSgenome_1.74.0 pwr_1.3-0
[31] rstudioapi_0.17.1 impute_1.80.0
[33] RSQLite_2.4.3 generics_0.1.4
[35] shape_1.4.6.1 BiocIO_1.16.0
[37] vroom_1.6.5 gtools_3.9.5
[39] car_3.1-3 GO.db_3.20.0
[41] Matrix_1.7-3 ggbeeswarm_0.7.2
[43] abind_1.4-8 R.methodsS3_1.8.2
[45] lifecycle_1.0.4 whisker_0.4.1
[47] yaml_2.3.10 carData_3.0-5
[49] SparseArray_1.6.2 blob_1.2.4
[51] promises_1.3.3 crayon_1.5.3
[53] pwalign_1.2.0 miniUI_0.1.2
[55] lattice_0.22-7 annotate_1.84.0
[57] KEGGREST_1.46.0 magick_2.8.7
[59] pillar_1.11.0 knitr_1.50
[61] rjson_0.2.23 codetools_0.2-20
[63] glue_1.8.0 getPass_0.2-4
[65] vctrs_0.6.5 png_0.1-8
[67] gtable_0.3.6 poweRlaw_1.0.0
[69] cachem_1.1.0 xfun_0.52
[71] S4Arrays_1.6.0 mime_0.13
[73] survival_3.8-3 iterators_1.0.14
[75] statmod_1.5.0 bit64_4.6.0-1
[77] rprojroot_2.1.0 bslib_0.9.0
[79] vipor_0.4.7 KernSmooth_2.23-26
[81] rpart_4.1.24 colorspace_2.1-1
[83] seqLogo_1.72.0 DBI_1.2.3
[85] Hmisc_5.2-3 seqPattern_1.38.0
[87] nnet_7.3-20 tidyselect_1.2.1
[89] processx_3.8.6 bit_4.6.0
[91] compiler_4.4.2 curl_7.0.0
[93] git2r_0.36.2 htmlTable_2.4.3
[95] SparseM_1.84-2 xml2_1.4.0
[97] DelayedArray_0.32.0 plotly_4.11.0
[99] rtracklayer_1.66.0 checkmate_2.3.3
[101] scales_1.4.0 caTools_1.18.3
[103] callr_3.7.6 digest_0.6.37
[105] rmarkdown_2.29 XVector_0.46.0
[107] htmltools_0.5.8.1 pkgconfig_2.0.3
[109] base64enc_0.1-3 fastmap_1.2.0
[111] rlang_1.1.6 GlobalOptions_0.1.2
[113] htmlwidgets_1.6.4 UCSC.utils_1.2.0
[115] shiny_1.11.1 farver_2.1.2
[117] jquerylib_0.1.4 zoo_1.8-14
[119] jsonlite_2.0.0 BiocParallel_1.40.2
[121] R.oo_1.27.1 RCurl_1.98-1.17
[123] magrittr_2.0.3 polynom_1.4-1
[125] Formula_1.2-5 GenomeInfoDbData_1.2.13
[127] patchwork_1.3.1 Rcpp_1.1.0
[129] stringi_1.8.7 zlibbioc_1.52.0
[131] MASS_7.3-65 plyr_1.8.9
[133] ggrepel_0.9.6 parallel_4.4.2
[135] CNEr_1.42.0 Biostrings_2.74.1
[137] splines_4.4.2 hms_1.1.3
[139] circlize_0.4.16 locfit_1.5-9.12
[141] ps_1.9.1 ggsignif_0.6.4
[143] reshape2_1.4.4 TFMPvalue_0.0.9
[145] XML_3.99-0.18 evaluate_1.0.4
[147] tzdb_0.5.0 foreach_1.5.2
[149] httpuv_1.6.16 MatrixModels_0.5-4
[151] clue_0.3-66 gridBase_0.4-7
[153] broom_1.0.9 xtable_1.8-4
[155] restfulr_0.0.16 rstatix_0.7.2
[157] later_1.4.2 memoise_2.0.1
[159] beeswarm_0.4.0 AnnotationDbi_1.68.0
[161] GenomicAlignments_1.42.0 cluster_2.1.8.1
[163] timechange_0.3.0