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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))) 
}

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)]]

Removing outliers and reanalyzing

This is removing outliers in H3K36me3: Ind1_VEH_144R and in H3K9me3: Ind1_VEH_24T, Ind3_DOX_24T, Ind5_DOX_144R

H3K36me3_merged <- H3K36me3_merged %>% 
  dplyr::select(!Ind1_VEH_144R)

H3K9me3_merged<- H3K9me3_merged %>%
  dplyr::select(!Ind1_VEH_24T) %>% 
  dplyr::select(!Ind3_DOX_24T) %>%
  dplyr::select(!Ind5_DOX_144R)

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, no removal")

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 no removal")

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 removal of 1 sample")

Version Author Date
cafdfac reneeisnowhere 2025-08-22
ac6eb8d reneeisnowhere 2025-08-21

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 Outlier removal")

Version Author Date
cafdfac reneeisnowhere 2025-08-22
ac6eb8d reneeisnowhere 2025-08-21

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)),]
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 with removal of 1 sample")

Version Author Date
cafdfac reneeisnowhere 2025-08-22
ac6eb8d reneeisnowhere 2025-08-21 
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 removal of 3 samples")

Version Author Date
cafdfac reneeisnowhere 2025-08-22
ac6eb8d reneeisnowhere 2025-08-21

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()

PCA Plots

H3K27ac

# pca_H3K27ac <- calc_pca(t(H3K27ac_merged_lcpm))
# pca_var_plot(pca_H3K27ac)
# pca_H3K27ac <- pca_H3K27ac$x %>% cbind(., H3K27ac_annomat)
# pca_plot(pca_H3K27ac, col_var = "time", shape_var = "tx", text_var = pca_H3K27ac$ind, title = "H3K27ac 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

pca_H3K27me3 <- calc_pca(t(H3K27me3_merged_lcpm))
pca_var_plot(pca_H3K27me3)
pca_H3K27me3 <- pca_H3K27me3$x %>% cbind(., H3K27me3_annomat)
pca_plot(pca_H3K27me3, col_var = "time", shape_var = "tx", text_var = pca_H3K27me3$ind, title = "H3K27me3 lcpm PCA")

H3K36me3

pca_H3K36me3 <- calc_pca(t(H3K36me3_merged_lcpm))
pca_var_plot(pca_H3K36me3)

Version Author Date
ac6eb8d reneeisnowhere 2025-08-21 
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 = "H3K36me3c filtered lcpm PCA"
)

Version Author Date
cafdfac reneeisnowhere 2025-08-22
ac6eb8d reneeisnowhere 2025-08-21

H3K9me3

pca_H3K9me3 <- calc_pca(t(H3K9me3_merged_lcpm))
pca_var_plot(pca_H3K9me3)

Version Author Date
ac6eb8d reneeisnowhere 2025-08-21 
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"
)

Version Author Date
cafdfac reneeisnowhere 2025-08-22
ac6eb8d reneeisnowhere 2025-08-21

Differential Enrichment and 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)
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")

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)
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")

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              1541             209                 2
NotSig          184086          186161            186722
Up                1097             354                 0
plotSA(efit2, main="Mean-Variance trend for final model for H3K36me3")

Version Author Date
ac6eb8d reneeisnowhere 2025-08-21 
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))

Version Author Date
ac6eb8d reneeisnowhere 2025-08-21

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              2530              10                 0
NotSig          198298          218099            218647
Up               17819             538                 0
plotSA(efit2, main="Mean-Variance trend for final model for H3K9me3")

Version Author Date
ac6eb8d reneeisnowhere 2025-08-21 
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))

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))+
  ggtitle("H3K36me3")

Version Author Date
cafdfac reneeisnowhere 2025-08-22
ac6eb8d reneeisnowhere 2025-08-21

H3K9me3 Venn Diagrams

ggVennDiagram(list("24T regions"=genes_H3K9me3_24T,"24R regions"=genes_H3K9me3_24R, "144R regions"=genes_H3K9me3_144R))+ ggtitle("H3K9me3")

Version Author Date
cafdfac reneeisnowhere 2025-08-22
ac6eb8d reneeisnowhere 2025-08-21 
# 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_nooutlier.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_nooutlier.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_nooutlier.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_nooutlier.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             R6_2.6.1                   
 [11] lazyeval_0.2.2              GetoptLong_1.0.5           
 [13] withr_3.0.2                 gridExtra_2.3              
 [15] quantreg_6.1                cli_3.6.5                  
 [17] textshaping_1.0.1           Cairo_1.6-5                
 [19] labeling_0.4.3              sass_0.4.10                
 [21] Rsamtools_2.22.0            systemfonts_1.2.3          
 [23] foreign_0.8-90              svglite_2.2.1              
 [25] R.utils_2.13.0              dichromat_2.0-0.1          
 [27] plotrix_3.8-4               BSgenome_1.74.0            
 [29] pwr_1.3-0                   rstudioapi_0.17.1          
 [31] impute_1.80.0               RSQLite_2.4.3              
 [33] generics_0.1.4              shape_1.4.6.1              
 [35] BiocIO_1.16.0               vroom_1.6.5                
 [37] gtools_3.9.5                car_3.1-3                  
 [39] GO.db_3.20.0                Matrix_1.7-3               
 [41] ggbeeswarm_0.7.2            abind_1.4-8                
 [43] R.methodsS3_1.8.2           lifecycle_1.0.4            
 [45] whisker_0.4.1               yaml_2.3.10                
 [47] carData_3.0-5               SparseArray_1.6.2          
 [49] blob_1.2.4                  promises_1.3.3             
 [51] crayon_1.5.3                pwalign_1.2.0              
 [53] miniUI_0.1.2                lattice_0.22-7             
 [55] annotate_1.84.0             KEGGREST_1.46.0            
 [57] magick_2.8.7                pillar_1.11.0              
 [59] knitr_1.50                  rjson_0.2.23               
 [61] codetools_0.2-20            glue_1.8.0                 
 [63] getPass_0.2-4               vctrs_0.6.5                
 [65] png_0.1-8                   gtable_0.3.6               
 [67] poweRlaw_1.0.0              cachem_1.1.0               
 [69] xfun_0.52                   S4Arrays_1.6.0             
 [71] mime_0.13                   survival_3.8-3             
 [73] iterators_1.0.14            statmod_1.5.0              
 [75] bit64_4.6.0-1               rprojroot_2.1.0            
 [77] bslib_0.9.0                 vipor_0.4.7                
 [79] KernSmooth_2.23-26          rpart_4.1.24               
 [81] colorspace_2.1-1            seqLogo_1.72.0             
 [83] DBI_1.2.3                   Hmisc_5.2-3                
 [85] seqPattern_1.38.0           nnet_7.3-20                
 [87] tidyselect_1.2.1            processx_3.8.6             
 [89] bit_4.6.0                   compiler_4.4.2             
 [91] curl_7.0.0                  git2r_0.36.2               
 [93] htmlTable_2.4.3             SparseM_1.84-2             
 [95] xml2_1.4.0                  DelayedArray_0.32.0        
 [97] plotly_4.11.0               rtracklayer_1.66.0         
 [99] checkmate_2.3.3             scales_1.4.0               
[101] caTools_1.18.3              callr_3.7.6                
[103] digest_0.6.37               rmarkdown_2.29             
[105] XVector_0.46.0              htmltools_0.5.8.1          
[107] pkgconfig_2.0.3             base64enc_0.1-3            
[109] fastmap_1.2.0               rlang_1.1.6                
[111] GlobalOptions_0.1.2         htmlwidgets_1.6.4          
[113] UCSC.utils_1.2.0            shiny_1.11.1               
[115] farver_2.1.2                jquerylib_0.1.4            
[117] zoo_1.8-14                  jsonlite_2.0.0             
[119] BiocParallel_1.40.2         R.oo_1.27.1                
[121] RCurl_1.98-1.17             magrittr_2.0.3             
[123] polynom_1.4-1               Formula_1.2-5              
[125] GenomeInfoDbData_1.2.13     patchwork_1.3.1            
[127] Rcpp_1.1.0                  stringi_1.8.7              
[129] zlibbioc_1.52.0             MASS_7.3-65                
[131] plyr_1.8.9                  ggrepel_0.9.6              
[133] parallel_4.4.2              CNEr_1.42.0                
[135] Biostrings_2.74.1           splines_4.4.2              
[137] hms_1.1.3                   circlize_0.4.16            
[139] locfit_1.5-9.12             ps_1.9.1                   
[141] ggsignif_0.6.4              reshape2_1.4.4             
[143] TFMPvalue_0.0.9             XML_3.99-0.18              
[145] evaluate_1.0.4              tzdb_0.5.0                 
[147] foreach_1.5.2               httpuv_1.6.16              
[149] MatrixModels_0.5-4          clue_0.3-66                
[151] gridBase_0.4-7              broom_1.0.9                
[153] xtable_1.8-4                restfulr_0.0.16            
[155] rstatix_0.7.2               later_1.4.2                
[157] memoise_2.0.1               beeswarm_0.4.0             
[159] AnnotationDbi_1.68.0        GenomicAlignments_1.42.0   
[161] cluster_2.1.8.1             timechange_0.3.0