Final Analysis

Last updated: 2025-08-21

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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(df,
           col_var = NULL,
           shape_var = NULL,
           text_var = NULL,
           title = "") {
    ggplot(df, aes_string(x = "PC1", y = "PC2")) +
      geom_point(aes_string(color = col_var, shape = shape_var), size = 5) +
      labs(title = title, x = "PC 1", y = "PC 2") +
      ggrepel::geom_text_repel(label = text_var, vjust = -.5, max.overlaps = 30) +
      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"),]
# 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 < 0.02),]
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_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_cor)>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_cor)>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_cor)>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_cor)>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             14729            6921                17
NotSig          190397          201986            216822
Up               11715            7934                 2

plotSA(efit2, main="Mean-Variance trend for final model for H3K27ac")

Version	Author	Date
fda033f	reneeisnowhere	2025-08-12
f4826e1	infurnoheat	2025-08-01

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))

Version	Author	Date
fda033f	reneeisnowhere	2025-08-12
f4826e1	infurnoheat	2025-08-01

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               5                 0
NotSig          160535          160600            160611
Up                  56               6                 0

plotSA(efit2, main="Mean-Variance trend for final model for H3K27me3")

Version	Author	Date
fda033f	reneeisnowhere	2025-08-12
f4826e1	infurnoheat	2025-08-01

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))

Version	Author	Date
fda033f	reneeisnowhere	2025-08-12
f4826e1	infurnoheat	2025-08-01

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                 0

plotSA(efit2, main="Mean-Variance trend for final model for H3K36me3")

Version	Author	Date
fda033f	reneeisnowhere	2025-08-12
f4826e1	infurnoheat	2025-08-01

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
fda033f	reneeisnowhere	2025-08-12
f4826e1	infurnoheat	2025-08-01

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                 0

plotSA(efit2, main="Mean-Variance trend for final model for H3K9me3")

Version	Author	Date
fda033f	reneeisnowhere	2025-08-12
f4826e1	infurnoheat	2025-08-01

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
fda033f	reneeisnowhere	2025-08-12
f4826e1	infurnoheat	2025-08-01

PCA Plots

H3K27ac

pca_H3K27ac <- calc_pca(t(H3K27ac_merged_lcpm))
pca_var_plot(pca_H3K27ac)

Version	Author	Date
6c6c7f4	infurnoheat	2025-08-04

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")

Version	Author	Date
fda033f	reneeisnowhere	2025-08-12
6c6c7f4	infurnoheat	2025-08-04

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)

Version	Author	Date
6c6c7f4	infurnoheat	2025-08-04

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")

Version	Author	Date
6c6c7f4	infurnoheat	2025-08-04

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

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

Version	Author	Date
6c6c7f4	infurnoheat	2025-08-04

pca_H3K36me3 <- pca_H3K36me3$x %>% cbind(., H3K36me3_annomat)
pca_plot(pca_H3K36me3, col_var = "time", shape_var = "tx", text_var = pca_H3K36me3$ind, title = "H3K36me3 lcpm PCA")

Version	Author	Date
fda033f	reneeisnowhere	2025-08-12
6c6c7f4	infurnoheat	2025-08-04

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")

#### H3K9me3

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

Version	Author	Date
6c6c7f4	infurnoheat	2025-08-04

pca_H3K9me3 <- pca_H3K9me3$x %>% cbind(., H3K9me3_annomat)
pca_plot(pca_H3K9me3, col_var = "time", shape_var = "tx", text_var = pca_H3K9me3$ind, title = "H3K9me3 lcpm PCA")

Version	Author	Date
fda033f	reneeisnowhere	2025-08-12
6c6c7f4	infurnoheat	2025-08-04

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")

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))

Version	Author	Date
fda033f	reneeisnowhere	2025-08-12
6c6c7f4	infurnoheat	2025-08-04

H3K27me3 Venn Diagrams

ggVennDiagram(list("24T regions"=genes_H3K27me3_24T,"24R regions"=genes_H3K27me3_24R, "144R regions"=genes_H3K27me3_144R))

Version	Author	Date
fda033f	reneeisnowhere	2025-08-12
6c6c7f4	infurnoheat	2025-08-04

H3K36me3 Venn Diagrams

ggVennDiagram(list("24T regions"=genes_H3K36me3_24T,"24R regions"=genes_H3K36me3_24R, "144R regions"=genes_H3K36me3_144R))

Version	Author	Date
fda033f	reneeisnowhere	2025-08-12
6c6c7f4	infurnoheat	2025-08-04

H3K9me3 Venn Diagrams

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

Version	Author	Date
fda033f	reneeisnowhere	2025-08-12
6c6c7f4	infurnoheat	2025-08-04

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             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

Final Analysis

Steven Yu, Renee Matthews edits

2025-08-01

Final Anaylsis

Loading Packages

Data Initialization

Functions

Final Analysis

Tagging Questionable Libraries by FRiP

Feature Counts

H3K27ac Count Analysis

H3K27me3 Count Analysis

H3K36me3 Count Analysis

H3K9me3 Count Analysis

Fragment Analysis

Differential Analysis

Filtering Sex Chromosomes

Setting up Matrix

Volcano Plots

H3K27ac

H3K27me3

H3K36me3

H3K9me3

PCA Plots

H3K27ac

Venn Diagrams

Venn Set Up

H3K27ac Venn Diagrams

H3K27me3 Venn Diagrams

H3K36me3 Venn Diagrams

H3K9me3 Venn Diagrams