Peak Analysis

Last updated: 2025-08-12

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Knit directory: DXR_continue/

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Peak 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,
    ...
  )
}

Peak Calling for Q=0.01 and Broad=0.1 with No Lambda

Data Initialization

peak_ct <- read_delim("data/peaks/peaks_cts.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 <- rbind(H3K27ac_peaks, H3K27me3_peaks, H3K36me3_peaks, H3K9me3_peaks)

all_peak <- all_peak %>%
  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 <- all_peak[(!all_peak$Treatment %in% "5FU"),]

Peak Visualization

all_peak %>% 
   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
3c5800a	infurnoheat	2025-07-22
1d53459	infurnoheat	2025-07-14

all_peak %>% 
  ggplot(., aes (x=Histone_Mark, y = Count, fill = Histone_Mark))+
  geom_boxplot()+
   ylab("Count")+
   theme_classic()+
  # facet_wrap(~histone)+
  ggtitle("Peak count across histones")

Version	Author	Date
3c5800a	infurnoheat	2025-07-22
1d53459	infurnoheat	2025-07-14

all_peak %>% 
  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
3c5800a	infurnoheat	2025-07-22
1d53459	infurnoheat	2025-07-14

all_peak %>% 
  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
3c5800a	infurnoheat	2025-07-22
1d53459	infurnoheat	2025-07-14

Peak Calling for Q=0.01 and Broad=0.1 with Lambda

Data Initialization

peak_ct <- read_delim("data/peaks/peaks_cts_lq1e2b1e1.txt", delim = "\t")
H3K27ac_peaks <- read_delim("data/peaks/H3K27ac_final_results.tsv",delim = "\t")
H3K27me3_peaks <- read_delim("data/peaks/H3K27me3_lq1e2b1e1_results.tsv",delim = "\t")
H3K36me3_peaks <- read_delim("data/peaks/H3K36me3_lq1e2b1e1_results.tsv",delim = "\t")
H3K9me3_peaks <- read_delim("data/peaks/H3K9me3_lq1e2b1e1_results.tsv",delim = "\t")

all_peak_var <- rbind(H3K27ac_peaks, H3K27me3_peaks, H3K36me3_peaks, H3K9me3_peaks)

all_peak_var <- all_peak_var %>%
  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_var <- all_peak_var[(!all_peak_var$Treatment %in% "5FU"),]
all_peak_var <- all_peak_var[(!all_peak_var$Histone_Mark %in% "H3K27ac"),]

Peak Visualization

all_peak_var %>% 
   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
3c5800a	infurnoheat	2025-07-22

all_peak_var %>% 
  ggplot(., aes (x=Histone_Mark, y = Count, fill = Histone_Mark))+
  geom_boxplot()+
   ylab("Count")+
   theme_classic()+
  # facet_wrap(~histone)+
  ggtitle("Peak count across histones")

Version	Author	Date
3c5800a	infurnoheat	2025-07-22

all_peak_var %>% 
  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
3c5800a	infurnoheat	2025-07-22

all_peak_var %>% 
  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
3c5800a	infurnoheat	2025-07-22

Peak Calling for Q=0.01 and Broad=0.5 with No Lambda

Data Initialization

peak_ct <- read_delim("data/peaks/peaks_cts_nlq1e2b5e1.txt", delim = "\t")
H3K27ac_peaks <- read_delim("data/peaks/H3K27ac_final_results.tsv",delim = "\t")
H3K27me3_peaks <- read_delim("data/peaks/H3K27me3_nlq1e2b5e1_results.tsv",delim = "\t")
H3K36me3_peaks <- read_delim("data/peaks/H3K36me3_nlq1e2b5e1_results.tsv",delim = "\t")
H3K9me3_peaks <- read_delim("data/peaks/H3K9me3_nlq1e2b5e1_results.tsv",delim = "\t")

all_peak_var <- rbind(H3K27ac_peaks, H3K27me3_peaks, H3K36me3_peaks, H3K9me3_peaks)

all_peak_var <- all_peak_var %>%
  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_var <- all_peak[(!all_peak$Treatment %in% "5FU"),]
all_peak_var <- all_peak_var[(!all_peak_var$Histone_Mark %in% "H3K27ac"),]

Peak Visualization

all_peak_var %>% 
   ggplot(.,aes(x=Sample, y=Count,fill=Histone_Mark))+
   geom_col()+
   ylab("Counts")+
   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
3c5800a	infurnoheat	2025-07-22

all_peak_var %>% 
  ggplot(., aes (x=Histone_Mark, y = Count, fill = Histone_Mark))+
  geom_boxplot()+
   ylab("Count")+
   theme_classic()+
  # facet_wrap(~histone)+
  ggtitle("Peak count across histones")

Version	Author	Date
3c5800a	infurnoheat	2025-07-22

all_peak_var %>% 
  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
3c5800a	infurnoheat	2025-07-22

all_peak_var %>% 
  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
3c5800a	infurnoheat	2025-07-22

Peak Calling for Q=0.01 and Broad=0.5 with Lambda

Data Initialization

peak_ct_var <- read_delim("data/peaks/peaks_cts_lq1e2b5e1.txt", delim = "\t")
H3K27ac_peaks <- read_delim("data/peaks/H3K27ac_final_results.tsv",delim = "\t")
H3K27me3_peaks <- read_delim("data/peaks/H3K27me3_lq1e2b5e1_results.tsv",delim = "\t")
H3K36me3_peaks <- read_delim("data/peaks/H3K36me3_lq1e2b5e1_results.tsv",delim = "\t")
H3K9me3_peaks <- read_delim("data/peaks/H3K9me3_lq1e2b5e1_results.tsv",delim = "\t")

all_peak_var <- rbind(H3K27ac_peaks, H3K27me3_peaks, H3K36me3_peaks, H3K9me3_peaks)

all_peak_var <- all_peak_var %>%
  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_var <- all_peak_var[(!all_peak_var$Treatment %in% "5FU"),]
all_peak_var <- all_peak_var[(!all_peak_var$Histone_Mark %in% "H3K27ac"),]

Peak Visualization

all_peak_var %>% 
   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
3c5800a	infurnoheat	2025-07-22

all_peak_var %>% 
  ggplot(., aes (x=Histone_Mark, y = Count, fill = Histone_Mark))+
  geom_boxplot()+
   ylab("Count")+
   theme_classic()+
  # facet_wrap(~histone)+
  ggtitle("Peak count across histones")

Version	Author	Date
3c5800a	infurnoheat	2025-07-22

all_peak_var %>% 
  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
3c5800a	infurnoheat	2025-07-22

all_peak_var %>% 
  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
3c5800a	infurnoheat	2025-07-22

Peak Calling for Q=0.005 and Broad=0.01 with No Lambda

Data Initialization

peak_ct <- read_delim("data/peaks/peaks_cts_nlq5e3b1e2.txt", delim = "\t")
H3K27ac_peaks <- read_delim("data/peaks/H3K27ac_final_results.tsv",delim = "\t")
H3K27me3_peaks <- read_delim("data/peaks/H3K27me3_nlq5e3b1e2_results.tsv",delim = "\t")
H3K36me3_peaks <- read_delim("data/peaks/H3K36me3_nlq5e3b1e2_results.tsv",delim = "\t")
H3K9me3_peaks <- read_delim("data/peaks/H3K9me3_nlq5e3b1e2_results.tsv",delim = "\t")

all_peak_var <- rbind(H3K27ac_peaks, H3K27me3_peaks, H3K36me3_peaks, H3K9me3_peaks)

all_peak_var <- all_peak_var %>%
  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_var <- all_peak_var[(!all_peak_var$Treatment %in% "5FU"),]
all_peak_var <- all_peak_var[(!all_peak_var$Histone_Mark %in% "H3K27ac"),]

Peak Visualization

all_peak_var %>% 
   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
3c5800a	infurnoheat	2025-07-22

all_peak_var %>% 
  ggplot(., aes (x=Histone_Mark, y = Count, fill = Histone_Mark))+
  geom_boxplot()+
   ylab("Count")+
   theme_classic()+
  # facet_wrap(~histone)+
  ggtitle("Peak count across histones")

Version	Author	Date
3c5800a	infurnoheat	2025-07-22

all_peak_var %>% 
  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
3c5800a	infurnoheat	2025-07-22

all_peak_var %>% 
  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
3c5800a	infurnoheat	2025-07-22

Peak Calling for Q=0.005 and Broad=0.01 with Lambda

Data Initialization

peak_ct <- read_delim("data/peaks/peaks_cts_lq5e3b1e2.txt", delim = "\t")
H3K27ac_peaks <- read_delim("data/peaks/H3K27ac_final_results.tsv",delim = "\t")
H3K27me3_peaks <- read_delim("data/peaks/H3K27me3_lq5e3b1e2_results.tsv",delim = "\t")
H3K36me3_peaks <- read_delim("data/peaks/H3K36me3_lq5e3b1e2_results.tsv",delim = "\t")
H3K9me3_peaks <- read_delim("data/peaks/H3K9me3_lq5e3b1e2_results.tsv",delim = "\t")

all_peak_var <- rbind(H3K27ac_peaks, H3K27me3_peaks, H3K36me3_peaks, H3K9me3_peaks)

all_peak_var <- all_peak_var %>%
  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_var <- all_peak_var[(!all_peak_var$Treatment %in% "5FU"),]
all_peak_var <- all_peak_var[(!all_peak_var$Histone_Mark %in% "H3K27ac"),]

Peak Visualization

all_peak_var %>% 
   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
3c5800a	infurnoheat	2025-07-22

all_peak_var %>% 
  ggplot(., aes (x=Histone_Mark, y = Count, fill = Histone_Mark))+
  geom_boxplot()+
   ylab("Count")+
   theme_classic()+
  # facet_wrap(~histone)+
  ggtitle("Peak count across histones")

Version	Author	Date
3c5800a	infurnoheat	2025-07-22

all_peak_var %>% 
  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
3c5800a	infurnoheat	2025-07-22

all_peak_var %>% 
  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
3c5800a	infurnoheat	2025-07-22

Picard with Broad Peaks

peak_ct <- read_delim("data/peaks/peaks_cts_picard_broad.txt", delim = "\t")
H3K27ac_peaks <- read_delim("data/peaks/H3K27ac_picard_results.tsv",delim = "\t")
H3K27me3_peaks <- read_delim("data/peaks/H3K27me3_picard_broad_results.tsv",delim = "\t")
H3K36me3_peaks <- read_delim("data/peaks/H3K36me3_picard_broad_results.tsv",delim = "\t")
H3K9me3_peaks <- read_delim("data/peaks/H3K9me3_picard_broad_results.tsv",delim = "\t")

all_peak_pb <- rbind(H3K27ac_peaks, H3K27me3_peaks, H3K36me3_peaks, H3K9me3_peaks)

all_peak_pb <- all_peak_pb %>%
  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_pb <- all_peak_pb[(!all_peak_pb$Treatment %in% "5FU"),]

all_peak_pb %>% 
   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
c6e3989	infurnoheat	2025-07-29

all_peak_pb %>% 
  ggplot(., aes (x=Histone_Mark, y = Count, fill = Histone_Mark))+
  geom_boxplot()+
   ylab("Count")+
   theme_classic()+
  # facet_wrap(~histone)+
  ggtitle("Peak count across histones")

Version	Author	Date
c6e3989	infurnoheat	2025-07-29

all_peak_pb %>% 
  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
c6e3989	infurnoheat	2025-07-29

all_peak_pb %>% 
  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
c6e3989	infurnoheat	2025-07-29

Picard with Broad as Narrow Peaks

peak_ct <- read_delim("data/peaks/peaks_cts_picard_narrow.txt", delim = "\t")
H3K27ac_peaks <- read_delim("data/peaks/H3K27ac_picard_results.tsv",delim = "\t")
H3K27me3_peaks <- read_delim("data/peaks/H3K27me3_picard_narrow_results.tsv",delim = "\t")
H3K36me3_peaks <- read_delim("data/peaks/H3K36me3_picard_narrow_results.tsv",delim = "\t")
H3K9me3_peaks <- read_delim("data/peaks/H3K9me3_picard_narrow_results.tsv",delim = "\t")

all_peak_pn <- rbind(H3K27ac_peaks, H3K27me3_peaks, H3K36me3_peaks, H3K9me3_peaks)

all_peak_pn <- all_peak_pn %>%
  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_pn <- all_peak_pn[(!all_peak_pn$Treatment %in% "5FU"),]

all_peak_pn %>% 
   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
c6e3989	infurnoheat	2025-07-29

all_peak_pn %>% 
  ggplot(., aes (x=Histone_Mark, y = Count, fill = Histone_Mark))+
  geom_boxplot()+
   ylab("Count")+
   theme_classic()+
  # facet_wrap(~histone)+
  ggtitle("Peak count across histones")

Version	Author	Date
c6e3989	infurnoheat	2025-07-29

all_peak_pn %>% 
  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
c6e3989	infurnoheat	2025-07-29

all_peak_pn %>% 
  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
c6e3989	infurnoheat	2025-07-29

Picard with Broad as Stringent Narrow Peaks

peak_ct <- read_delim("data/peaks/peaks_cts_1e3_narrow.txt", delim = "\t")
H3K27ac_peaks <- read_delim("data/peaks/H3K27ac_picard_results.tsv",delim = "\t")
H3K27me3_peaks <- read_delim("data/peaks/H3K27me3_1e3_narrow_results.tsv",delim = "\t")
H3K36me3_peaks <- read_delim("data/peaks/H3K36me3_1e3_narrow_results.tsv",delim = "\t")
H3K9me3_peaks <- read_delim("data/peaks/H3K9me3_1e3_narrow_results.tsv",delim = "\t")

all_peak_sn <- rbind(H3K27ac_peaks, H3K27me3_peaks, H3K36me3_peaks, H3K9me3_peaks)

all_peak_sn <- all_peak_sn %>%
  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_sn <- all_peak_sn[(!all_peak_sn$Treatment %in% "5FU"),]

all_peak_sn %>% 
   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
c6e3989	infurnoheat	2025-07-29

all_peak_sn %>% 
  ggplot(., aes (x=Histone_Mark, y = Count, fill = Histone_Mark))+
  geom_boxplot()+
   ylab("Count")+
   theme_classic()+
  # facet_wrap(~histone)+
  ggtitle("Peak count across histones")

Version	Author	Date
c6e3989	infurnoheat	2025-07-29

all_peak_sn %>% 
  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
c6e3989	infurnoheat	2025-07-29

all_peak_sn %>% 
  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
c6e3989	infurnoheat	2025-07-29

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] smplot2_0.2.5               cowplot_1.2.0              
 [3] ggrastr_1.0.2               Rsubread_2.20.0            
 [5] gcplyr_1.12.0               ggpmisc_0.6.2              
 [7] ggpp_0.5.9                  corrplot_0.95              
 [9] ggpubr_0.6.1                DESeq2_1.46.0              
[11] SummarizedExperiment_1.36.0 Biobase_2.66.0             
[13] MatrixGenerics_1.18.1       matrixStats_1.5.0          
[15] chromVAR_1.28.0             GenomicRanges_1.58.0       
[17] GenomeInfoDb_1.42.3         IRanges_2.40.1             
[19] S4Vectors_0.44.0            BiocGenerics_0.52.0        
[21] genomation_1.38.0           kableExtra_1.4.0           
[23] DT_0.33                     viridis_0.6.5              
[25] viridisLite_0.4.2           data.table_1.17.8          
[27] ComplexHeatmap_2.22.0       edgeR_4.4.2                
[29] limma_3.62.2                lubridate_1.9.4            
[31] forcats_1.0.0               stringr_1.5.1              
[33] dplyr_1.1.4                 purrr_1.1.0                
[35] readr_2.1.5                 tidyr_1.3.1                
[37] tibble_3.3.0                ggplot2_3.5.2              
[39] tidyverse_2.0.0             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           labeling_0.4.3             
 [19] sass_0.4.10                 Rsamtools_2.22.0           
 [21] systemfonts_1.2.3           foreign_0.8-90             
 [23] svglite_2.2.1               R.utils_2.13.0             
 [25] dichromat_2.0-0.1           plotrix_3.8-4              
 [27] BSgenome_1.74.0             pwr_1.3-0                  
 [29] rstudioapi_0.17.1           impute_1.80.0              
 [31] RSQLite_2.4.2               generics_0.1.4             
 [33] shape_1.4.6.1               BiocIO_1.16.0              
 [35] vroom_1.6.5                 gtools_3.9.5               
 [37] car_3.1-3                   GO.db_3.20.0               
 [39] Matrix_1.7-3                ggbeeswarm_0.7.2           
 [41] abind_1.4-8                 R.methodsS3_1.8.2          
 [43] lifecycle_1.0.4             whisker_0.4.1              
 [45] yaml_2.3.10                 carData_3.0-5              
 [47] SparseArray_1.6.2           blob_1.2.4                 
 [49] promises_1.3.3              crayon_1.5.3               
 [51] pwalign_1.2.0               miniUI_0.1.2               
 [53] lattice_0.22-7              annotate_1.84.0            
 [55] KEGGREST_1.46.0             pillar_1.11.0              
 [57] knitr_1.50                  rjson_0.2.23               
 [59] codetools_0.2-20            glue_1.8.0                 
 [61] getPass_0.2-4               vctrs_0.6.5                
 [63] png_0.1-8                   gtable_0.3.6               
 [65] poweRlaw_1.0.0              cachem_1.1.0               
 [67] xfun_0.52                   S4Arrays_1.6.0             
 [69] mime_0.13                   survival_3.8-3             
 [71] iterators_1.0.14            statmod_1.5.0              
 [73] bit64_4.6.0-1               rprojroot_2.1.0            
 [75] bslib_0.9.0                 vipor_0.4.7                
 [77] KernSmooth_2.23-26          rpart_4.1.24               
 [79] colorspace_2.1-1            seqLogo_1.72.0             
 [81] DBI_1.2.3                   Hmisc_5.2-3                
 [83] seqPattern_1.38.0           nnet_7.3-20                
 [85] tidyselect_1.2.1            processx_3.8.6             
 [87] bit_4.6.0                   compiler_4.4.2             
 [89] curl_6.4.0                  git2r_0.36.2               
 [91] htmlTable_2.4.3             SparseM_1.84-2             
 [93] xml2_1.3.8                  DelayedArray_0.32.0        
 [95] plotly_4.11.0               rtracklayer_1.66.0         
 [97] checkmate_2.3.2             scales_1.4.0               
 [99] caTools_1.18.3              callr_3.7.6                
[101] digest_0.6.37               rmarkdown_2.29             
[103] XVector_0.46.0              htmltools_0.5.8.1          
[105] pkgconfig_2.0.3             base64enc_0.1-3            
[107] fastmap_1.2.0               rlang_1.1.6                
[109] GlobalOptions_0.1.2         htmlwidgets_1.6.4          
[111] UCSC.utils_1.2.0            shiny_1.11.1               
[113] farver_2.1.2                jquerylib_0.1.4            
[115] zoo_1.8-14                  jsonlite_2.0.0             
[117] BiocParallel_1.40.2         R.oo_1.27.1                
[119] RCurl_1.98-1.17             magrittr_2.0.3             
[121] polynom_1.4-1               Formula_1.2-5              
[123] GenomeInfoDbData_1.2.13     patchwork_1.3.1            
[125] Rcpp_1.1.0                  stringi_1.8.7              
[127] zlibbioc_1.52.0             MASS_7.3-65                
[129] plyr_1.8.9                  parallel_4.4.2             
[131] CNEr_1.42.0                 Biostrings_2.74.1          
[133] splines_4.4.2               hms_1.1.3                  
[135] circlize_0.4.16             locfit_1.5-9.12            
[137] ps_1.9.1                    ggsignif_0.6.4             
[139] reshape2_1.4.4              TFMPvalue_0.0.9            
[141] XML_3.99-0.18               evaluate_1.0.4             
[143] tzdb_0.5.0                  foreach_1.5.2              
[145] httpuv_1.6.16               MatrixModels_0.5-4         
[147] clue_0.3-66                 gridBase_0.4-7             
[149] broom_1.0.9                 xtable_1.8-4               
[151] restfulr_0.0.16             rstatix_0.7.2              
[153] later_1.4.2                 memoise_2.0.1              
[155] beeswarm_0.4.0              AnnotationDbi_1.68.0       
[157] GenomicAlignments_1.42.0    cluster_2.1.8.1            
[159] timechange_0.3.0

Peak Analysis

Steven Yu

2025-07-14

Peak Anaylsis

Loading Packages

Data Initialization

Functions

Peak Calling for Q=0.01 and Broad=0.1 with No Lambda

Data Initialization

Peak Visualization

Peak Calling for Q=0.01 and Broad=0.1 with Lambda

Data Initialization

Peak Visualization

Peak Calling for Q=0.01 and Broad=0.5 with No Lambda

Data Initialization

Peak Visualization

Peak Calling for Q=0.01 and Broad=0.5 with Lambda

Data Initialization

Peak Visualization

Peak Calling for Q=0.005 and Broad=0.01 with No Lambda

Data Initialization

Peak Visualization

Peak Calling for Q=0.005 and Broad=0.01 with Lambda

Data Initialization

Peak Visualization

Picard with Broad Peaks

Picard with Broad as Narrow Peaks

Picard with Broad as Stringent Narrow Peaks