Last updated: 2025-08-01

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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)

Data Initialization

sampleinfo <- read_delim("data/sample_info.tsv", delim = "\t")

Functions

drug_pal <- c("#8B006D","#DF707E","#F1B72B", "#3386DD","#707031","#41B333")
pca_plot <-
  function(df,
           col_var = NULL,
           shape_var = NULL,
           title = "") {
    ggplot(df) + geom_point(aes_string(
      x = "PC1",
      y = "PC2",
      color = col_var,
      shape = shape_var
    ),
    size = 5) +
      labs(title = title, x = "PC 1", y = "PC 2") +
      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)
}

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"),]
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)))

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")

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")

Tagging Questionable Libraries by FRiP

questionable_frip = all_peak_final[(all_peak_final$FRiP < 0.02),]
questionable_frip
# A tibble: 0 × 10
# ℹ 10 variables: Sample <chr>, Total_Reads <dbl>, Fragments <dbl>,
#   Reads_in_Peaks <dbl>, FRiP <dbl>, Histone_Mark <chr>, Individual <chr>,
#   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_cor <- H3K27ac_merged %>% 
  dplyr::select(Geneid,contains("Ind")) %>% 
  column_to_rownames("Geneid") %>% 
  cpm(., log = TRUE) %>% 
  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")

H3K27me3 Count Analysis

H3K27me3_merged_raw <- H3K27me3_merged %>% 
  dplyr::select(Geneid,contains("Ind")) %>% 
  column_to_rownames("Geneid") %>% 
  as.matrix()

H3K27me3_merged_cor <- H3K27me3_merged %>% 
  dplyr::select(Geneid,contains("Ind")) %>% 
  column_to_rownames("Geneid") %>% 
  cpm(., log = TRUE) %>% 
  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")

H3K36me3 Count Analysis

H3K36me3_merged_raw <- H3K36me3_merged %>% 
  dplyr::select(Geneid,contains("Ind")) %>% 
  column_to_rownames("Geneid") %>% 
  as.matrix()

H3K36me3_merged_cor <- H3K36me3_merged %>% 
  dplyr::select(Geneid,contains("Ind")) %>% 
  column_to_rownames("Geneid") %>% 
  cpm(., log = TRUE) %>% 
  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")

H3K9me3 Count Analysis

H3K9me3_merged_raw <- H3K9me3_merged %>% 
  dplyr::select(Geneid,contains("Ind")) %>% 
  column_to_rownames("Geneid") %>% 
  as.matrix()

H3K9me3_merged_cor <- H3K9me3_merged %>% 
  dplyr::select(Geneid,contains("Ind")) %>% 
  column_to_rownames("Geneid") %>% 
  cpm(., log = TRUE) %>% 
  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")

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)))

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)))

Differential Analysis

Filtering Sex Chromosome

H3K27ac_merged_raw <- H3K27ac_merged_raw[rowMeans(H3K27ac_merged_cor)>0,]
H3K27ac_merged_raw <- H3K27ac_merged_raw[!grepl("chrY",rownames(H3K27ac_merged_raw)),]

H3K27me3_merged_raw <- H3K27me3_merged_raw[rowMeans(H3K27me3_merged_cor)>0,]
H3K27me3_merged_raw <- H3K27me3_merged_raw[!grepl("chrY",rownames(H3K27me3_merged_raw)),]

H3K36me3_merged_raw <- H3K36me3_merged_raw[rowMeans(H3K36me3_merged_cor)>0,]
H3K36me3_merged_raw <- H3K36me3_merged_raw[!grepl("chrY",rownames(H3K36me3_merged_raw)),]

H3K9me3_merged_raw <- H3K9me3_merged_raw[rowMeans(H3K9me3_merged_cor)>0,]
H3K9me3_merged_raw <- H3K9me3_merged_raw[!grepl("chrY",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             14891            7175                 9
NotSig          194933          206740            221757
Up               11944            7853                 2
plotSA(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")

v1 <- volcanosig(V.24T.top, 0.05)+ ggtitle("DOX 24T")
v2 <- volcanosig(V.24R.top, 0.05)+ ggtitle("DOX 24R")+ylab("")
v3 <- volcanosig(V.144R.top, 0.05)+ ggtitle("DOX 144R")+ylab("")
plot_grid(v1,v2, v3, 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                20               6                 1
NotSig          163181          163273            163284
Up                  84               6                 0
plotSA(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")

v1 <- volcanosig(V.24T.top, 0.05)+ ggtitle("DOX 24T")
v2 <- volcanosig(V.24R.top, 0.05)+ ggtitle("DOX 24R")+ylab("")
v3 <- volcanosig(V.144R.top, 0.05)+ ggtitle("DOX 144R")+ylab("")
plot_grid(v1,v2, v3, 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              1461             189                 0
NotSig          187538          189577            190076
Up                1077             310                 0
plotSA(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")

v1 <- volcanosig(V.24T.top, 0.05)+ ggtitle("DOX 24T")
v2 <- volcanosig(V.24R.top, 0.05)+ ggtitle("DOX 24R")+ylab("")
v3 <- volcanosig(V.144R.top, 0.05)+ ggtitle("DOX 144R")+ylab("")
plot_grid(v1,v2, v3, 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               833               1                 0
NotSig          215326          225399            225547
Up                9388             147                 0
plotSA(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")

v1 <- volcanosig(V.24T.top, 0.05)+ ggtitle("DOX 24T")
v2 <- volcanosig(V.24R.top, 0.05)+ ggtitle("DOX 24R")+ylab("")
v3 <- volcanosig(V.144R.top, 0.05)+ ggtitle("DOX 144R")+ylab("")
plot_grid(v1,v2, v3, rel_widths =c(1,1,1))

PCA Plots

H3K27ac

pca_H3K27ac <- calc_pca(H3K27ac_merged_raw)
#pca_H3K27ac <- pca_H3K27ac$x %>% cbind(.,H3K27ac_annomat)
#pca_var_plot(pca_H3K27ac, col_var = tx, shape_var = ind)
#pca_plot(pca_H3K27ac, col_var = )

H3K27me3

#pca_H3K27me3 <- calc_pca(H3K27me3_merged_raw)
#pca_var_plot(pca_H3K27me3)
#pca_plot(pca_H3K27me3)

H3K36me3

#pca_H3K36me3 <- calc_pca(H3K36me3_merged_raw)
#pca_var_plot(pca_H3K36me3)
#pca_plot(pca_H3K36me3)

H3K9me3

#pca_H3K9me3 <- calc_pca(H3K9me3_merged_raw)
#pca_var_plot(pca_H3K9me3)
#pca_plot(pca_H3K9me3)

Venn Diagrams


sessionInfo()
R version 4.5.1 (2025-06-13 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 26100)

Matrix products: default
  LAPACK version 3.12.1

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] smplot2_0.2.5               cowplot_1.2.0              
 [3] ggrastr_1.0.2               Rsubread_2.22.1            
 [5] gcplyr_1.12.0               ggpmisc_0.6.2              
 [7] ggpp_0.5.9                  corrplot_0.95              
 [9] ggpubr_0.6.1                DESeq2_1.48.1              
[11] SummarizedExperiment_1.38.1 Biobase_2.68.0             
[13] MatrixGenerics_1.20.0       matrixStats_1.5.0          
[15] chromVAR_1.30.1             GenomicRanges_1.60.0       
[17] GenomeInfoDb_1.44.1         IRanges_2.42.0             
[19] S4Vectors_0.46.0            BiocGenerics_0.54.0        
[21] generics_0.1.4              genomation_1.40.1          
[23] kableExtra_1.4.0            DT_0.33                    
[25] viridis_0.6.5               viridisLite_0.4.2          
[27] data.table_1.17.8           ComplexHeatmap_2.24.1      
[29] edgeR_4.6.3                 limma_3.64.1               
[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] splines_4.5.1               later_1.4.2                
  [3] BiocIO_1.18.0               bitops_1.0-9               
  [5] rpart_4.1.24                XML_3.99-0.18              
  [7] DirichletMultinomial_1.50.0 lifecycle_1.0.4            
  [9] pwalign_1.4.0               rstatix_0.7.2              
 [11] doParallel_1.0.17           rprojroot_2.1.0            
 [13] vroom_1.6.5                 MASS_7.3-65                
 [15] processx_3.8.6              lattice_0.22-7             
 [17] backports_1.5.0             magrittr_2.0.3             
 [19] Hmisc_5.2-3                 plotly_4.11.0              
 [21] sass_0.4.10                 rmarkdown_2.29             
 [23] jquerylib_0.1.4             yaml_2.3.10                
 [25] plotrix_3.8-4               httpuv_1.6.16              
 [27] DBI_1.2.3                   RColorBrewer_1.1-3         
 [29] abind_1.4-8                 RCurl_1.98-1.17            
 [31] nnet_7.3-20                 git2r_0.36.2               
 [33] circlize_0.4.16             GenomeInfoDbData_1.2.14    
 [35] seqLogo_1.74.0              MatrixModels_0.5-4         
 [37] svglite_2.2.1               codetools_0.2-20           
 [39] DelayedArray_0.34.1         xml2_1.3.8                 
 [41] tidyselect_1.2.1            shape_1.4.6.1              
 [43] UCSC.utils_1.4.0            farver_2.1.2               
 [45] base64enc_0.1-3             GenomicAlignments_1.44.0   
 [47] jsonlite_2.0.0              GetoptLong_1.0.5           
 [49] Formula_1.2-5               survival_3.8-3             
 [51] iterators_1.0.14            systemfonts_1.2.3          
 [53] foreach_1.5.2               tools_4.5.1                
 [55] TFMPvalue_0.0.9             Rcpp_1.1.0                 
 [57] glue_1.8.0                  gridExtra_2.3              
 [59] SparseArray_1.8.1           xfun_0.52                  
 [61] withr_3.0.2                 fastmap_1.2.0              
 [63] SparseM_1.84-2              callr_3.7.6                
 [65] caTools_1.18.3              digest_0.6.37              
 [67] timechange_0.3.0            R6_2.6.1                   
 [69] mime_0.13                   seqPattern_1.40.0          
 [71] textshaping_1.0.1           colorspace_2.1-1           
 [73] Cairo_1.6-2                 gtools_3.9.5               
 [75] RSQLite_2.4.2               rtracklayer_1.68.0         
 [77] httr_1.4.7                  htmlwidgets_1.6.4          
 [79] S4Arrays_1.8.1              TFBSTools_1.46.0           
 [81] whisker_0.4.1               pkgconfig_2.0.3            
 [83] gtable_0.3.6                blob_1.2.4                 
 [85] impute_1.82.0               XVector_0.48.0             
 [87] htmltools_0.5.8.1           carData_3.0-5              
 [89] pwr_1.3-0                   clue_0.3-66                
 [91] scales_1.4.0                png_0.1-8                  
 [93] knitr_1.50                  rstudioapi_0.17.1          
 [95] tzdb_0.5.0                  reshape2_1.4.4             
 [97] rjson_0.2.23                checkmate_2.3.2            
 [99] curl_6.4.0                  zoo_1.8-14                 
[101] cachem_1.1.0                GlobalOptions_0.1.2        
[103] KernSmooth_2.23-26          vipor_0.4.7                
[105] parallel_4.5.1              miniUI_0.1.2               
[107] foreign_0.8-90              restfulr_0.0.16            
[109] pillar_1.11.0               vctrs_0.6.5                
[111] promises_1.3.3              car_3.1-3                  
[113] xtable_1.8-4                cluster_2.1.8.1            
[115] htmlTable_2.4.3             beeswarm_0.4.0             
[117] evaluate_1.0.4              cli_3.6.5                  
[119] locfit_1.5-9.12             compiler_4.5.1             
[121] Rsamtools_2.24.0            rlang_1.1.6                
[123] crayon_1.5.3                ggsignif_0.6.4             
[125] labeling_0.4.3              ps_1.9.1                   
[127] ggbeeswarm_0.7.2            getPass_0.2-4              
[129] plyr_1.8.9                  fs_1.6.6                   
[131] stringi_1.8.7               gridBase_0.4-7             
[133] BiocParallel_1.42.1         Biostrings_2.76.0          
[135] lazyeval_0.2.2              quantreg_6.1               
[137] Matrix_1.7-3                BSgenome_1.76.0            
[139] patchwork_1.3.1             hms_1.1.3                  
[141] bit64_4.6.0-1               statmod_1.5.0              
[143] shiny_1.11.1                broom_1.0.9                
[145] memoise_2.0.1               bslib_0.9.0                
[147] bit_4.6.0                   polynom_1.4-1