Total Reads and Mapped Reads (TOP2B and P53)

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Last updated: 2025-08-19

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Knit directory: ChIPSeq_project/

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---

📌 Total Reads by Sample

Load Required Libraries

# Load necessary R packages
library(edgeR)

Warning: package 'edgeR' was built under R version 4.3.2

Loading required package: limma

Warning: package 'limma' was built under R version 4.3.1

library(limma)
library(RColorBrewer)
library(gridExtra)
library(reshape2)
library(data.table)

Warning: package 'data.table' was built under R version 4.3.3

Attaching package: 'data.table'

The following objects are masked from 'package:reshape2':

    dcast, melt

library(tidyverse)

Warning: package 'tidyverse' was built under R version 4.3.2

Warning: package 'tidyr' was built under R version 4.3.3

Warning: package 'readr' was built under R version 4.3.3

Warning: package 'purrr' was built under R version 4.3.3

Warning: package 'dplyr' was built under R version 4.3.2

Warning: package 'stringr' was built under R version 4.3.2

Warning: package 'lubridate' was built under R version 4.3.3

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.4     ✔ readr     2.1.5
✔ forcats   1.0.0     ✔ stringr   1.5.1
✔ ggplot2   3.5.2     ✔ tibble    3.2.1
✔ lubridate 1.9.4     ✔ tidyr     1.3.1
✔ purrr     1.0.4     

── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::between()     masks data.table::between()
✖ dplyr::combine()     masks gridExtra::combine()
✖ dplyr::filter()      masks stats::filter()
✖ dplyr::first()       masks data.table::first()
✖ lubridate::hour()    masks data.table::hour()
✖ lubridate::isoweek() masks data.table::isoweek()
✖ dplyr::lag()         masks stats::lag()
✖ dplyr::last()        masks data.table::last()
✖ lubridate::mday()    masks data.table::mday()
✖ lubridate::minute()  masks data.table::minute()
✖ lubridate::month()   masks data.table::month()
✖ lubridate::quarter() masks data.table::quarter()
✖ lubridate::second()  masks data.table::second()
✖ purrr::transpose()   masks data.table::transpose()
✖ lubridate::wday()    masks data.table::wday()
✖ lubridate::week()    masks data.table::week()
✖ lubridate::yday()    masks data.table::yday()
✖ lubridate::year()    masks data.table::year()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(scales)

Warning: package 'scales' was built under R version 4.3.2

Attaching package: 'scales'

The following object is masked from 'package:purrr':

    discard

The following object is masked from 'package:readr':

    col_factor

library(biomaRt)

Warning: package 'biomaRt' was built under R version 4.3.2

library(cowplot)

Warning: package 'cowplot' was built under R version 4.3.2

Attaching package: 'cowplot'

The following object is masked from 'package:lubridate':

    stamp

library(ggrepel)

Warning: package 'ggrepel' was built under R version 4.3.3

library(corrplot)

Warning: package 'corrplot' was built under R version 4.3.3

corrplot 0.95 loaded

library(Hmisc)

Warning: package 'Hmisc' was built under R version 4.3.3

Attaching package: 'Hmisc'

The following objects are masked from 'package:dplyr':

    src, summarize

The following objects are masked from 'package:base':

    format.pval, units

library(ggpubr)

Warning: package 'ggpubr' was built under R version 4.3.1

Attaching package: 'ggpubr'

The following object is masked from 'package:cowplot':

    get_legend

📍 2. Load Data

align<-read.csv("data/ChIP Seq Summary stat TOP2B P53.csv")

map<-data.frame(align)

map$Treatment<- factor(map$Treatment, levels = c("VEH_TOP2B", "DOX_TOP2B", "VEH_p53", "DOX_p53", "VEH_Input_P53", "DOX_Input_P53"))

📍 3. Define Color Palettes

drug_palc <- c("#8B006D","#DF707E","#F1B72B", "#3386DD","#707031","#41B333")

Ind_palc <- c("#ffbe0b","#ff006e","#fb5607", "#8338ec","#3a86ff","#4a4e69")

Treat_palc <- c("#ffbe0b","#ff006e","#fb5607", "#8338ec", "#800080","#FFC0CB")

Map_palc <- c("#9b19f5","#e6d800", "#b3d4ff")

Combined_palc <- c("#FF0000","#00FF00","#0000FF","#FFFF00","#FF00FF","#00FFFF","#FFA500","#800080","#FFC0CB","#A52A2A","#808080","#FFD700")

Type_palc <- c("#800080","#FFD700")

📍 4. Prepare Data

# Factor Sample_name to maintain order
map$Sample.Det<-factor(map$Sample.Det,levels = map$Sample.Det)

📍 5. Plot Total Reads by Sample

map %>%
  #mutate(Drug=factor(Drug,levels=c("CX-5461","DOX","VEH"))) %>%
  #mutate(Conc.=factor(Conc.,levels=c("0.1","0.5"))) %>%
  #mutate(Time=factor(Time,levels=c("3","24","48"))) %>%
  #group_by(Drug,Conc.,Time) %>%
  ggplot(., aes (x =Sample.Det, y=Total.Reads..before.trimming., fill = Ind))+
  geom_col()+
  #geom_hline(aes(yintercept=20000000))+
  scale_fill_manual(values=Ind_palc)+
  scale_y_continuous(labels = label_number(suffix = " M", scale = 1e-6))+
  ggtitle(expression("Total number of reads by sample"))+
  xlab("")+
  ylab(expression("ChIP -sequencing reads"))+
  theme_bw()+
  theme(plot.title = element_text(size = rel(2), hjust = 0.5),
        axis.title = element_text(size = 15, color = "black"),
        axis.ticks = element_line(linewidth = 1.5),
        axis.line = element_line(linewidth = 1.5),
        axis.text.y = element_text(size =10, color = "black", angle = 0, hjust = 0.8, vjust = 0.5),
        axis.text.x = element_text(size =10, color = "black", angle = 90, hjust = 1, vjust = 0.2),
        #strip.text.x = element_text(size = 15, color = "black", face = "bold"),
        strip.text.y = element_text(color = "white"))

Past versions of plot-total-reads-1.png

Version	Author	Date
ffe6f24	sayanpaul01	2025-08-15

📌 Total Reads by Individuals

map %>%
  ggplot(aes(x = Ind, y = Total.Reads..before.trimming., fill = Ind)) +
  geom_boxplot() +
  scale_fill_manual(values = Ind_palc) +
  scale_y_continuous(labels = label_number(suffix = " M", scale = 1e-6),
                     limits = c(0, NA)) +
  ggtitle(expression("Total number of reads by Individual")) +
  xlab("") +
  ylab(expression("ChIP -sequencing reads")) +
  theme_bw() +
  theme(
    plot.title  = element_text(size = rel(2), hjust = 0.5),
    axis.title  = element_text(size = 15, color = "black"),
    axis.ticks  = element_line(linewidth = 1.5),
    axis.line   = element_line(linewidth = 1.5),
    axis.text.y = element_text(size = 10, color = "black"),
    axis.text.x = element_text(size = 10, color = "black", angle = 90,
                               hjust = 1, vjust = 0.2),
    strip.text.y = element_text(color = "white")
  )

Past versions of "generate_boxplot indv-1.png"

Version	Author	Date
ffe6f24	sayanpaul01	2025-08-15

📌 Total Reads by Treatment

map %>%
  ggplot(aes(x = Treatment, y = Total.Reads..before.trimming., fill = Treatment)) +
  geom_boxplot() +
  scale_fill_manual(values = Treat_palc) +
  scale_y_continuous(labels = label_number(suffix = " M", scale = 1e-6),
                     limits = c(0, NA)) +
  ggtitle(expression("Total number of reads by Treatment")) +
  xlab("") +
  ylab(expression("ChIP -sequencing reads")) +
  theme_bw() +
  theme(
    plot.title  = element_text(size = rel(2), hjust = 0.5),
    axis.title  = element_text(size = 15, color = "black"),
    axis.ticks  = element_line(linewidth = 1.5),
    axis.line   = element_line(linewidth = 1.5),
    axis.text.y = element_text(size = 10, color = "black"),
    axis.text.x = element_text(size = 10, color = "black", angle = 90,
                               hjust = 1, vjust = 0.2),
    strip.text.y = element_text(color = "white")
  )

Past versions of "generate_boxplot treatment-1.png"

Version	Author	Date
ffe6f24	sayanpaul01	2025-08-15

📌 Total Reads by Sample type

map %>%
  ggplot(aes(x = Type, y = Total.Reads..before.trimming., fill = Type)) +
  geom_boxplot() +
  scale_fill_manual(values = Type_palc) +
  scale_y_continuous(labels = label_number(suffix = " M", scale = 1e-6),
                     limits = c(0, NA)) +
  ggtitle(expression("Total number of reads by Sample Type")) +
  xlab("") +
  ylab(expression("ChIP -sequencing reads")) +
  theme_bw() +
  theme(
    plot.title  = element_text(size = rel(2), hjust = 0.5),
    axis.title  = element_text(size = 15, color = "black"),
    axis.ticks  = element_line(linewidth = 1.5),
    axis.line   = element_line(linewidth = 1.5),
    axis.text.y = element_text(size = 10, color = "black"),
    axis.text.x = element_text(size = 10, color = "black", angle = 90,
                               hjust = 1, vjust = 0.2),
    strip.text.y = element_text(color = "white")
  )

Past versions of "generate_boxplot type-1.png"

Version	Author	Date
ffe6f24	sayanpaul01	2025-08-15

📌 Total Reads by Individuals and Sample type

map %>%
  ggplot(aes(x = Ind, y = Total.Reads..before.trimming., fill = Type)) +
  geom_boxplot() +
  scale_fill_manual(values = Type_palc) +
  scale_y_continuous(labels = label_number(suffix = " M", scale = 1e-6),
                     limits = c(0, NA)) +
  ggtitle(expression("Total number of reads by Individual and Sample Type")) +
  xlab("") +
  ylab(expression("ChIP -sequencing reads")) +
  theme_bw() +
  theme(
    plot.title  = element_text(size = rel(2), hjust = 0.5),
    axis.title  = element_text(size = 15, color = "black"),
    axis.ticks  = element_line(linewidth = 1.5),
    axis.line   = element_line(linewidth = 1.5),
    axis.text.y = element_text(size = 10, color = "black"),
    axis.text.x = element_text(size = 10, color = "black", angle = 90,
                               hjust = 1, vjust = 0.2),
    strip.text.y = element_text(color = "white")
  )

Past versions of "generate_boxplot Ind and type-1.png"

Version	Author	Date
ffe6f24	sayanpaul01	2025-08-15

📌 Number of reads before and after trimming

library(dplyr)
library(tidyr)
library(ggplot2)
library(scales)

# Keep sample order as in file
map$Sample.Det <- factor(map$Sample.Det, levels = map$Sample.Det)

# Make sure read counts are numeric (in case CSV parsed as character)
map <- map %>%
  mutate(
    `Total.Reads..before.trimming.` = as.numeric(`Total.Reads..before.trimming.`),
    `Total.reads..after.Trimming.`  = as.numeric(`Total.reads..after.Trimming.`)
  )

# Compute kept fraction for annotation
map <- map %>%
  mutate(kept_frac = `Total.reads..after.Trimming.` / `Total.Reads..before.trimming.`)

# Long format for before vs after
map_long <- map %>%
  pivot_longer(
    cols = c(`Total.Reads..before.trimming.`, `Total.reads..after.Trimming.`),
    names_to = "TrimStage", values_to = "Reads"
  ) %>%
  mutate(
    TrimStage = factor(
      TrimStage,
      levels = c("Total.Reads..before.trimming.", "Total.reads..after.Trimming."),
      labels = c("Before trimming", "After trimming")
    )
  )

# High-contrast (colorblind-safe) colors
stage_colors <- c("Before trimming" = "#1f78b4",  # deep blue
                  "After trimming"  = "#ff7f00")  # bright orange

# Base plot
p <- ggplot(map_long, aes(x = Sample.Det, y = Reads, fill = TrimStage)) +
  geom_col(position = position_dodge(width = 0.8), width = 0.75, color = "black") +
  scale_fill_manual(values = stage_colors) +
  scale_y_continuous(labels = label_number(suffix = " M", scale = 1e-6)) +
  ggtitle(expression("Total number of reads by sample: before vs after trimming")) +
  xlab("") +
  ylab(expression("ChIP-sequencing reads")) +
  theme_bw() +
  theme(
    plot.title  = element_text(size = rel(2), hjust = 0.5),
    axis.title  = element_text(size = 15, color = "black"),
    axis.ticks  = element_line(linewidth = 1.5),
    axis.line   = element_line(linewidth = 1.5),
    axis.text.y = element_text(size = 10, color = "black"),
    axis.text.x = element_text(size = 10, color = "black", angle = 90, hjust = 1, vjust = 0.2),
    legend.title = element_blank()
  )
p

Past versions of "plot trimmed reads-1.png"

Version	Author	Date
ffe6f24	sayanpaul01	2025-08-15

📌 Mean Sequencing Reads Before and After Trimming by Individual, Sample Type, and Antibody

library(dplyr)
library(tidyr)
library(ggplot2)
library(scales)
library(stringr)

# --- Prep -------------------------------------------------------------

# Use your existing data frame name: map (change to df if needed)
df <- map

# Ensure required columns exist / are numeric
df <- df %>%
  mutate(
    `Total.Reads..before.trimming.` = as.numeric(`Total.Reads..before.trimming.`),
    `Total.reads..after.Trimming.`  = as.numeric(`Total.reads..after.Trimming.`)
  )

# Define Tx if your data uses 'Treatment'
if (!"Tx" %in% names(df) && "Treatment" %in% names(df)) {
  df <- df %>% mutate(Tx = Treatment)
}

# Normalize factors you care about
df <- df %>%
  mutate(
    Ab   = ifelse(is.na(Ab) | Ab == "", "Input", as.character(Ab)),
    Type = factor(Type, levels = c("ChIP_DNA", "Input_DNA"))  # adjust if your labels differ
  )

# Helper: label function for Y axis (millions)
reads_lab <- label_number(suffix = " M", scale = 1e-6)

# Kept fraction (After / Before)
df <- df %>%
  mutate(kept_frac = `Total.reads..after.Trimming.` / `Total.Reads..before.trimming.`)

# --- Group, summarise, pivot -----------------------------------------

group_sum <- df %>%
  group_by(Ind, Type, Ab, Tx) %>%
  summarise(
    before_mean = mean(`Total.Reads..before.trimming.`, na.rm = TRUE),
    after_mean  = mean(`Total.reads..after.Trimming.`,  na.rm = TRUE),
    kept_pct    = 100 * mean(kept_frac, na.rm = TRUE),
    .groups     = "drop"
  ) %>%
  pivot_longer(
    cols      = c(before_mean, after_mean),
    names_to  = "stage",
    values_to = "reads"
  ) %>%
  mutate(
    stage = factor(stage, levels = c("before_mean", "after_mean"),
                          labels = c("Before", "After")),
    facet_id = paste0(Ind, "_", Type, "_", Ab)
  )

# Order facets by Ind then Type (cleaner viewing)
facet_levels <- group_sum %>%
  distinct(Ind, Type, Ab, facet_id) %>%
  arrange(Ind, Type, Ab) %>%
  pull(facet_id)

group_sum <- group_sum %>%
  mutate(facet_id = factor(facet_id, levels = facet_levels))

# Optional: prettier strip labels (multi-line)
strip_labeller <- function(ids) {
  parts <- str_split(ids, "_", n = 3, simplify = TRUE)
  paste0("Ind: ", parts[,1], "\nType: ", parts[,2], "\nAb: ", parts[,3])
}

# Colors for Before / After
before_after_colors <- c("Before" = "#1f78b4", "After" = "#ff7f00")

# --- Plot -------------------------------------------------------------

p_group <- ggplot(group_sum, aes(x = Tx, y = reads, fill = stage)) +
  geom_col(position = position_dodge(width = 0.8), width = 0.7, color = "black") +
  scale_fill_manual(values = before_after_colors) +
  facet_wrap(~ facet_id, scales = "free_y",
             labeller = labeller(facet_id = strip_labeller)) +
  scale_y_continuous(labels = reads_lab) +
  labs(
    x = "Treatment",
    y = "Mean reads",
    title = "Mean reads by Individual × Sample Type × Antibody"
  ) +
  theme_bw(base_size = 12) +
  theme(
    legend.title = element_blank(),
    plot.title = element_text(hjust = 0.5, face = "bold"),
    strip.text  = element_text(size = 10)
  )

p_group

Past versions of "plot mean reads-1.png"

Version	Author	Date
ffe6f24	sayanpaul01	2025-08-15

📌 Duplication percentage by Sample

map %>%
  ggplot(aes(x = Sample.Det, y = Duplication.percentage, fill = Ind)) +
  geom_col(color = "black") +
  scale_fill_manual(values = Ind_palc) +
  scale_y_continuous(limits = c(0, 100), expand = c(0, 0)) +
  ggtitle(expression("Duplication Percentage by Sample")) +
  xlab("") +
  ylab(expression("Duplication percentage")) +
  theme_bw() +
  theme(
    plot.title  = element_text(size = rel(2), hjust = 0.5),
    axis.title  = element_text(size = 15, color = "black"),
    axis.ticks  = element_line(linewidth = 1.5),
    axis.line   = element_line(linewidth = 1.5),
    axis.text.y = element_text(size = 10, color = "black"),
    axis.text.x = element_text(size = 10, color = "black", angle = 90,
                               hjust = 1, vjust = 0.2),
    strip.text.y = element_text(color = "white")
  )

Past versions of Dup-1.png

Version	Author	Date
ffe6f24	sayanpaul01	2025-08-15

📌 Duplication percentage by Individual

map %>%
  ggplot(aes(x = Ind, y = Duplication.percentage, fill = Ind)) +
  geom_boxplot() +
  scale_fill_manual(values = Ind_palc) +
  scale_y_continuous(limits = c(0, 100), labels = function(x) paste0(x, "%")) +
  ggtitle(expression("Duplication percentage by Individual")) +
  xlab("") +
  ylab(expression("Duplication percentage")) +
  theme_bw() +
  theme(
    plot.title  = element_text(size = rel(2), hjust = 0.5),
    axis.title  = element_text(size = 15, color = "black"),
    axis.ticks  = element_line(linewidth = 1.5),
    axis.line   = element_line(linewidth = 1.5),
    axis.text.y = element_text(size = 10, color = "black"),
    axis.text.x = element_text(size = 10, color = "black", angle = 90,
                               hjust = 1, vjust = 0.2),
    strip.text.y = element_text(color = "white")
  )

Past versions of Dup1-1.png

Version	Author	Date
ffe6f24	sayanpaul01	2025-08-15

📌 Duplication percentage by Treatment

map %>%
  ggplot(aes(x = Treatment, y = Duplication.percentage, fill = Treatment)) +
  geom_boxplot() +
  scale_fill_manual(values = Treat_palc) +
  scale_y_continuous(limits = c(0, 100), labels = function(x) paste0(x, "%")) +
  ggtitle(expression("Duplication percentage by Treatment")) +
  xlab("") +
  ylab(expression("Duplication percentage")) +
  theme_bw() +
  theme(
    plot.title  = element_text(size = rel(2), hjust = 0.5),
    axis.title  = element_text(size = 15, color = "black"),
    axis.ticks  = element_line(linewidth = 1.5),
    axis.line   = element_line(linewidth = 1.5),
    axis.text.y = element_text(size = 10, color = "black"),
    axis.text.x = element_text(size = 10, color = "black", angle = 90,
                               hjust = 1, vjust = 0.2),
    strip.text.y = element_text(color = "white")
  )

Past versions of Dup2-1.png

Version	Author	Date
ffe6f24	sayanpaul01	2025-08-15

📌 Duplication percentage by Sample type

map %>%
  ggplot(aes(x = Type, y = Duplication.percentage, fill = Type)) +
  geom_boxplot() +
  scale_fill_manual(values = Type_palc) +
  scale_y_continuous(limits = c(0, 100), labels = function(x) paste0(x, "%")) +
  ggtitle(expression("Duplication percentage by Sample Type")) +
  xlab("") +
  ylab(expression("Duplication percentage")) +
  theme_bw() +
  theme(
    plot.title  = element_text(size = rel(2), hjust = 0.5),
    axis.title  = element_text(size = 15, color = "black"),
    axis.ticks  = element_line(linewidth = 1.5),
    axis.line   = element_line(linewidth = 1.5),
    axis.text.y = element_text(size = 10, color = "black"),
    axis.text.x = element_text(size = 10, color = "black", angle = 90,
                               hjust = 1, vjust = 0.2),
    strip.text.y = element_text(color = "white")
  )

Past versions of Dup3-1.png

Version	Author	Date
ffe6f24	sayanpaul01	2025-08-15

📌 Duplication percentage by Individual and Sample type

map %>%
  ggplot(aes(x = Ind, y = Duplication.percentage, fill = Type)) +
  geom_boxplot() +
  scale_fill_manual(values = Type_palc) +
  scale_y_continuous(limits = c(0, 100), labels = function(x) paste0(x, "%")) +
  ggtitle(expression("Duplication percentage by Individual and Sample Type")) +
  xlab("") +
  ylab(expression("Duplication percentage")) +
  theme_bw() +
  theme(
    plot.title  = element_text(size = rel(2), hjust = 0.5),
    axis.title  = element_text(size = 15, color = "black"),
    axis.ticks  = element_line(linewidth = 1.5),
    axis.line   = element_line(linewidth = 1.5),
    axis.text.y = element_text(size = 10, color = "black"),
    axis.text.x = element_text(size = 10, color = "black", angle = 90,
                               hjust = 1, vjust = 0.2),
    strip.text.y = element_text(color = "white")
  )

Past versions of Dup4-1.png

Version	Author	Date
ffe6f24	sayanpaul01	2025-08-15

📌 Mapping Summary

library(dplyr)
library(tidyr)
library(ggplot2)
library(scales)

# If not already done, read with check.names=TRUE so we have dot-style names
# map <- read.csv("data/ChIP Seq Summary stat TOP2B P53.csv", check.names = TRUE)

# Keep sample order as in the file
map$Sample.Det <- factor(map$Sample.Det, levels = map$Sample.Det)

# Ensure numeric (CSV can import as character)
map <- map %>%
  mutate(
    Total.Reads..before.trimming. = as.numeric(Total.Reads..before.trimming.),
    Total.reads..after.Trimming.  = as.numeric(Total.reads..after.Trimming.),
    Mapped.Reads                  = as.numeric(Mapped.Reads),
    Unmapped.reads                = as.numeric(Unmapped.reads)
  )

# Long format for the 4 metrics
metric_order <- c("Total.Reads..before.trimming.",
                  "Total.reads..after.Trimming.",
                  "Mapped.Reads",
                  "Unmapped.reads")

map_long4 <- map %>%
  dplyr::select(Sample.Det, all_of(metric_order)) %>%
  pivot_longer(cols = all_of(metric_order), names_to = "Metric", values_to = "Reads") %>%
  mutate(
    Metric = factor(
      Metric,
      levels = metric_order,
      labels = c("Before trimming", "After trimming", "Mapped reads", "Unmapped reads")
    )
  )


# Colors (colorblind-friendly)
metric_cols <- c(
  "Before trimming" = "#1f78b4",  # blue
  "After trimming"  = "#ff7f00",  # orange
  "Mapped reads"    = "#33a02c",  # green
  "Unmapped reads"  = "#6a737b"   # gray
)

# Plot: grouped bars per sample
ggplot(map_long4, aes(x = Sample.Det, y = Reads, fill = Metric)) +
  geom_col(position = position_dodge(width = 0.85), width = 0.72, color = "black") +
  scale_fill_manual(values = metric_cols) +
  scale_y_continuous(labels = label_number(suffix = " M", scale = 1e-6)) +
  labs(
    title = "Total reads per sample: Before vs After, Mapped vs Unmapped",
    x = NULL,
    y = "Reads"
  ) +
  theme_bw() +
  theme(
    plot.title  = element_text(size = rel(2), hjust = 0.5),
    axis.title  = element_text(size = 15, color = "black"),
    axis.ticks  = element_line(linewidth = 1.2),
    axis.line   = element_line(linewidth = 1.2),
    axis.text.y = element_text(size = 10, color = "black"),
    axis.text.x = element_text(size = 10, color = "black", angle = 90, hjust = 1, vjust = 0.2),
    legend.title = element_blank()
  )

Past versions of mapping-1.png

Version	Author	Date
3c1ffe7	sayanpaul01	2025-08-17

📌 Total, mapped and unmapped reads by individual

library(dplyr)
library(tidyr)
library(ggplot2)
library(scales)

# Keep original dot-style names (assumes you read with check.names=TRUE)
# map <- read.csv("data/ChIP Seq Summary stat TOP2B P53.csv", check.names = TRUE)

# Make sure numeric
map <- map %>%
  mutate(
    Total.Reads..before.trimming. = as.numeric(Total.Reads..before.trimming.),
    Mapped.Reads                 = as.numeric(Mapped.Reads),
    Unmapped.reads               = as.numeric(Unmapped.reads)
  )

comp_cols <- c(
  "Total reads" = "#1f78b4",  # blue
  "Mapped reads"    = "#33a02c",  # green
  "Unmapped reads"  = "#6a737b"   # gray
)


# Long format for the 3 metrics
metric_order  <- c("Total.Reads..before.trimming.", "Mapped.Reads", "Unmapped.reads")
metric_labels <- c("Total reads", "Mapped reads", "Unmapped reads")

map_long3 <- map %>%
  pivot_longer(
    cols = all_of(metric_order),
    names_to = "Metric",
    values_to = "Reads"
  ) %>%
  mutate(
    Metric = factor(Metric, levels = metric_order, labels = metric_labels)
  )


ggplot(map_long3, aes(x = Ind, y = Reads, fill = Metric)) +
  geom_boxplot(position = position_dodge(width = 0.75)) +
  scale_fill_manual(values = comp_cols) +
  scale_y_continuous(labels = label_number(suffix = " M", scale = 1e-6)) +
  labs(title = "Reads by Individual: Total, mapped and Unmapped",
       x = NULL, y = "Reads") +
  theme_bw() +
  theme(plot.title = element_text(size = rel(1.8), hjust = 0.5),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.2))

Past versions of mapping1-1.png

Version	Author	Date
3c1ffe7	sayanpaul01	2025-08-17

📌 Total, mapped and unmapped reads by treatment

ggplot(map_long3, aes(x = Treatment, y = Reads, fill = Metric)) +
  geom_boxplot(position = position_dodge(width = 0.75)) +
  scale_fill_manual(values = comp_cols) +
  scale_y_continuous(labels = label_number(suffix = " M", scale = 1e-6)) +
  labs(title = "Reads by Treatment: Total, mapped and Unmapped",
       x = NULL, y = "Reads") +
  theme_bw() +
  theme(plot.title = element_text(size = rel(1.8), hjust = 0.5),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.2))

Past versions of mapping2-1.png

Version	Author	Date
3c1ffe7	sayanpaul01	2025-08-17

📌 Total, mapped and unmapped reads by Sample type

ggplot(map_long3, aes(x = Type, y = Reads, fill = Metric)) +
  geom_boxplot(position = position_dodge(width = 0.75)) +
  scale_fill_manual(values = comp_cols) +
  scale_y_continuous(labels = label_number(suffix = " M", scale = 1e-6)) +
  labs(title = "Reads by Sample Type: Total, mapped and Unmapped",
       x = NULL, y = "Reads") +
  theme_bw() +
  theme(plot.title = element_text(size = rel(1.8), hjust = 0.5),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.2))

Past versions of mapping3-1.png

Version	Author	Date
3c1ffe7	sayanpaul01	2025-08-17

📌 Total, mapped and unmapped reads by individual and sample type

library(dplyr)
library(tidyr)
library(ggplot2)
library(scales)

# Make sure numeric
map <- map %>%
  mutate(
    Total.Reads..before.trimming. = as.numeric(Total.Reads..before.trimming.),
    Mapped.Reads                  = as.numeric(Mapped.Reads),
    Unmapped.reads                = as.numeric(Unmapped.reads)
  )

# Colors for boxplot fill
comp_cols <- c(
  "Total reads" = "#1f78b4",  # blue
  "Mapped reads"    = "#33a02c",  # green
  "Unmapped reads"  = "#6a737b"   # gray
)


# Reshape into long format
map_long <- map %>%
  dplyr::select(Ind, Type,
                Total.Reads..before.trimming., Mapped.Reads, Unmapped.reads) %>%
  tidyr::pivot_longer(
    cols = c(Total.Reads..before.trimming., Mapped.Reads, Unmapped.reads),
    names_to = "Metric", values_to = "Reads"
  ) %>%
  dplyr::mutate(
    Metric = factor(Metric,
      levels = c("Total.Reads..before.trimming.", "Mapped.Reads", "Unmapped.reads"),
      labels = c("Total reads", "Mapped reads", "Unmapped reads"))
  )

# ---- Plot ----
ggplot(map_long, aes(x = Ind, y = Reads, fill = Metric)) +
  geom_boxplot(outlier.size = 0.8, position = position_dodge(width = 0.75)) +
  scale_fill_manual(values = comp_cols) +
  scale_y_continuous(labels = label_number(suffix = " M", scale = 1e-6)) +
  facet_wrap(~ Type) +
  labs(
    title = "Read mapping by Individual and Sample Type",
    x = "Individual", y = "Reads"
  ) +
  theme_bw() +
  theme(
    plot.title   = element_text(size = rel(1.6), hjust = 0.5),
    legend.title = element_blank(),
    strip.text   = element_text(size = 11, face = "bold"),
    axis.text.x  = element_text(angle = 90, hjust = 1, vjust = 0.2)
  )

Past versions of mapping4-1.png

Version	Author	Date
3c1ffe7	sayanpaul01	2025-08-17

📌 Total, Mapped, Unmapped, and Deduplicated and uniquely mapped Reads per Sample

library(dplyr)
library(tidyr)
library(ggplot2)
library(scales)

# ---- Load data (already read into "map") ----
# If needed, uncomment the following line to read fresh:
# map <- read.csv("data/ChIP Seq Summary stat TOP2B P53.csv", check.names = TRUE)

# Convert to tibble for tidyverse
map <- tibble::as_tibble(map)

# Keep sample order as in the file
map$Sample.Det <- factor(map$Sample.Det, levels = map$Sample.Det)

# Ensure numeric for all metrics
map <- map %>%
  mutate(
    Total.Reads..before.trimming.              = as.numeric(Total.Reads..before.trimming.),
    Total.reads..after.Trimming.               = as.numeric(Total.reads..after.Trimming.),
    Mapped.Reads                               = as.numeric(Mapped.Reads),
    Reads.after.deduplication                  = as.numeric(Reads.after.deduplication),
    Uniquely.mapped.reads.before.deduplication = as.numeric(Uniquely.mapped.reads.before.deduplication),
    Uniquely.mapped.reads.after.deduplication  = as.numeric(Uniquely.mapped.reads.after.deduplication),
    Unmapped.reads                             = as.numeric(Unmapped.reads)
  )

# ---- Define order, labels, and colors ----
metric_order <- c(
  "Total.Reads..before.trimming.",
  "Total.reads..after.Trimming.",
  "Mapped.Reads",
  "Reads.after.deduplication",
  "Uniquely.mapped.reads.before.deduplication",
  "Uniquely.mapped.reads.after.deduplication",
  "Unmapped.reads"
)

metric_labels <- c(
  "Total.Reads..before.trimming."              = "Before trimming",
  "Total.reads..after.Trimming."               = "After trimming",
  "Mapped.Reads"                               = "Mapped",
  "Reads.after.deduplication"                  = "After deduplication",
  "Uniquely.mapped.reads.before.deduplication" = "Uniquely mapped (pre-dedup)",
  "Uniquely.mapped.reads.after.deduplication"  = "Uniquely mapped (post-dedup)",
  "Unmapped.reads"                             = "Unmapped"
)

metric_colors <- c(
  "Before trimming"              = "#1f78b4",  # blue
  "After trimming"               = "#ff7f00",  # orange
  "Mapped"                       = "#33a02c",  # green
  "After deduplication"          = "#b15928",  # brown
  "Uniquely mapped (pre-dedup)"  = "#6a3d9a",  # purple
  "Uniquely mapped (post-dedup)" = "#e31a1c",  # red
  "Unmapped"                     = "#6a737b"   # gray
)

# ---- Reshape to long format ----
map_long <- map %>%
  dplyr::select(Sample.Det, dplyr::all_of(metric_order)) %>%
  tidyr::pivot_longer(
    cols = dplyr::all_of(metric_order),
    names_to = "Metric", values_to = "Reads"
  ) %>%
  dplyr::mutate(
    Metric = factor(Metric, levels = metric_order, labels = metric_labels)
  )


# ---- Plot ----
ggplot(map_long, aes(x = Sample.Det, y = Reads, fill = Metric)) +
  geom_col(position = position_dodge(width = 0.9), width = 0.8, color = "black") +
  scale_fill_manual(values = metric_colors) +
  scale_y_continuous(labels = label_number(suffix = " M", scale = 1e-6)) +
  labs(
    title = "Read statistics per sample",
    x = "Sample",
    y = "Number of reads"
  ) +
  theme_bw() +
  theme(
    plot.title   = element_text(size = rel(1.6), hjust = 0.5),
    axis.text.x  = element_text(size = 8, angle = 90, hjust = 1, vjust = 0.2),
    legend.title = element_blank()
  )

Past versions of dedup-1.png

Version	Author	Date
abe25eb	sayanpaul01	2025-08-18

📌 Total, Mapped, Unmapped, and Deduplicated and uniquely mapped Reads per Individual

library(dplyr)
library(tidyr)
library(ggplot2)
library(scales)

# ---- Make sure numeric ----
map <- map %>%
  mutate(
    Total.Reads..before.trimming.              = as.numeric(Total.Reads..before.trimming.),
    Mapped.Reads                               = as.numeric(Mapped.Reads),
    Reads.after.deduplication                  = as.numeric(Reads.after.deduplication),
    Uniquely.mapped.reads.before.deduplication = as.numeric(Uniquely.mapped.reads.before.deduplication),
    Uniquely.mapped.reads.after.deduplication  = as.numeric(Uniquely.mapped.reads.after.deduplication)
  )

# ---- Define order, labels, and colors ----
metric_order <- c(
  "Total.Reads..before.trimming.",
  "Mapped.Reads",
  "Reads.after.deduplication",
  "Uniquely.mapped.reads.before.deduplication",
  "Uniquely.mapped.reads.after.deduplication"
)

metric_labels <- c(
  "Total.Reads..before.trimming."              = "Total reads",
  "Mapped.Reads"                               = "Mapped reads",
  "Reads.after.deduplication"                  = "Deduplicated reads",
  "Uniquely.mapped.reads.before.deduplication" = "Uniquely mapped (pre-dedup)",
  "Uniquely.mapped.reads.after.deduplication"  = "Uniquely mapped (post-dedup)"
)

metric_colors <- c(
  "Total reads"                  = "#1f78b4",  # blue
  "Mapped reads"                 = "#33a02c",  # green
  "Deduplicated reads"           = "#b15928",  # brown
  "Uniquely mapped (pre-dedup)"  = "#6a3d9a",  # purple
  "Uniquely mapped (post-dedup)" = "#e31a1c"   # red
)

# ---- Reshape to long format ----
map_long <- map %>%
  dplyr::select(Ind, dplyr::all_of(metric_order)) %>%
  tidyr::pivot_longer(cols = dplyr::all_of(metric_order),
               names_to = "Metric", values_to = "Reads") %>%
  dplyr::mutate(
    Metric = factor(Metric, levels = metric_order, labels = metric_labels)
  )

# ---- Plot ----
ggplot(map_long, aes(x = Ind, y = Reads, fill = Metric)) +
  geom_boxplot(position = position_dodge(width = 0.8)) +
  scale_fill_manual(values = metric_colors) +
  scale_y_continuous(labels = label_number(suffix = " M", scale = 1e-6)) +
  labs(
    title = "Read statistics by Individual",
    x = "Individual",
    y = "Number of reads"
  ) +
  theme_bw() +
  theme(
    plot.title   = element_text(size = rel(1.6), hjust = 0.5),
    legend.title = element_blank(),
    axis.text.x  = element_text(size = 10, angle = 45, hjust = 1)
  )

Past versions of dedup1-1.png

Version	Author	Date
abe25eb	sayanpaul01	2025-08-18

📌 Total, Mapped, Unmapped, and Deduplicated and uniquely mapped Reads per treatment

# ---- Long format ----
map_long_tx <- map %>%
  dplyr::select(Treatment, dplyr::all_of(metric_order)) %>%
  tidyr::pivot_longer(
    cols = dplyr::all_of(metric_order),
    names_to = "Metric", values_to = "Reads"
  ) %>%
  dplyr::mutate(
    Metric = factor(Metric, levels = metric_order, labels = metric_labels)
  )

# ---- Plot ----
ggplot(map_long_tx, aes(x = Treatment, y = Reads, fill = Metric)) +
  geom_boxplot(position = position_dodge(width = 0.75)) +
  scale_fill_manual(values = metric_colors) +
  scale_y_continuous(labels = label_number(suffix = " M", scale = 1e-6)) +
  labs(
    title = "Reads by Treatment: Total, Mapped, Deduplicated, and Uniquely Mapped",
    x = NULL, y = "Reads"
  ) +
  theme_bw() +
  theme(
    plot.title   = element_text(size = rel(1.8), hjust = 0.5),
    legend.title = element_blank(),
    axis.text.x  = element_text(angle = 90, hjust = 1, vjust = 0.2)
  )

Past versions of dedup2-1.png

Version	Author	Date
abe25eb	sayanpaul01	2025-08-18

📌 Total, Mapped, Unmapped, and Deduplicated and uniquely mapped Reads per sample type

# ---- Long format by Sample Type ----
map_long_type <- map %>%
  dplyr::select(Type, dplyr::all_of(metric_order)) %>%
  tidyr::pivot_longer(
    cols = dplyr::all_of(metric_order),
    names_to = "Metric", values_to = "Reads"
  ) %>%
  dplyr::mutate(
    Metric = factor(Metric, levels = metric_order, labels = metric_labels)
  )

# ---- Plot ----
ggplot(map_long_type, aes(x = Type, y = Reads, fill = Metric)) +
  geom_boxplot(position = position_dodge(width = 0.75)) +
  scale_fill_manual(values = metric_colors) +
  scale_y_continuous(labels = label_number(suffix = " M", scale = 1e-6)) +
  labs(
    title = "Reads by Sample Type: Total, Mapped, Deduplicated, and Uniquely Mapped",
    x = NULL, y = "Reads"
  ) +
  theme_bw() +
  theme(
    plot.title   = element_text(size = rel(1.8), hjust = 0.5),
    legend.title = element_blank(),
    axis.text.x  = element_text(angle = 0, hjust = 0.5, vjust = 0.5, size = 11)
  )

Past versions of dedup3-1.png

Version	Author	Date
abe25eb	sayanpaul01	2025-08-18

📌 Total, Mapped, Unmapped, and Deduplicated and uniquely mapped Reads per sample type and individuals

library(dplyr)
library(tidyr)
library(ggplot2)
library(scales)

# Make sure numeric
map <- map %>%
  mutate(
    Total.Reads..before.trimming.              = as.numeric(Total.Reads..before.trimming.),
    Mapped.Reads                               = as.numeric(Mapped.Reads),
    Reads.after.deduplication                  = as.numeric(Reads.after.deduplication),
    Uniquely.mapped.reads.before.deduplication = as.numeric(Uniquely.mapped.reads.before.deduplication),
    Uniquely.mapped.reads.after.deduplication  = as.numeric(Uniquely.mapped.reads.after.deduplication)
  )

# Colors for boxplot fill (5 metrics)
comp_cols <- c(
  "Total reads"                  = "#1f78b4",  # blue
  "Mapped reads"                 = "#33a02c",  # green
  "Deduplicated reads"           = "#b15928",  # brown
  "Uniquely mapped (pre-dedup)"  = "#6a3d9a",  # purple
  "Uniquely mapped (post-dedup)" = "#e31a1c"   # red
)

# Reshape into long format
map_long <- map %>%
  dplyr::select(
    Ind, Type,
    Total.Reads..before.trimming., Mapped.Reads,
    Reads.after.deduplication,
    Uniquely.mapped.reads.before.deduplication,
    Uniquely.mapped.reads.after.deduplication
  ) %>%
  tidyr::pivot_longer(
    cols = c(
      Total.Reads..before.trimming., Mapped.Reads,
      Reads.after.deduplication,
      Uniquely.mapped.reads.before.deduplication,
      Uniquely.mapped.reads.after.deduplication
    ),
    names_to = "Metric", values_to = "Reads"
  ) %>%
  dplyr::mutate(
    Metric = factor(
      Metric,
      levels = c(
        "Total.Reads..before.trimming.", "Mapped.Reads",
        "Reads.after.deduplication",
        "Uniquely.mapped.reads.before.deduplication",
        "Uniquely.mapped.reads.after.deduplication"
      ),
      labels = c(
        "Total reads", "Mapped reads",
        "Deduplicated reads",
        "Uniquely mapped (pre-dedup)",
        "Uniquely mapped (post-dedup)"
      )
    )
  )

# ---- Plot (same style as yours) ----
ggplot(map_long, aes(x = Ind, y = Reads, fill = Metric)) +
  geom_boxplot(outlier.size = 0.8, position = position_dodge(width = 0.75)) +
  scale_fill_manual(values = comp_cols) +
  scale_y_continuous(labels = label_number(suffix = " M", scale = 1e-6)) +
  facet_wrap(~ Type) +
  labs(
    title = "Read statistics by Individual and Sample Type",
    x = "Individual", y = "Number of reads"
  ) +
  theme_bw() +
  theme(
    plot.title   = element_text(size = rel(1.6), hjust = 0.5),
    legend.title = element_blank(),
    strip.text   = element_text(size = 11, face = "bold"),
    axis.text.x  = element_text(angle = 90, hjust = 1, vjust = 0.2)
  )

Past versions of dedup4-1.png

Version	Author	Date
abe25eb	sayanpaul01	2025-08-18

📌 Comparison of VEH and DOX reads across ChIP and Input samples

library(dplyr)
library(tidyr)
library(ggplot2)
library(scales)

# ---- Ensure numeric ----
map <- map %>%
  mutate(
    Total.Reads..before.trimming.              = as.numeric(Total.Reads..before.trimming.),
    Mapped.Reads                               = as.numeric(Mapped.Reads),
    Reads.after.deduplication                  = as.numeric(Reads.after.deduplication),
    Uniquely.mapped.reads.before.deduplication = as.numeric(Uniquely.mapped.reads.before.deduplication),
    Nuclear.reads.before.dedup                 = as.numeric(Nuclear.reads.before.dedup),
    Uniquely.mapped.reads.after.deduplication  = as.numeric(Uniquely.mapped.reads.after.deduplication)
  )

# ---- Metric definitions ----
metric_order <- c(
  "Total.Reads..before.trimming.",
  "Mapped.Reads",
  "Reads.after.deduplication",
  "Uniquely.mapped.reads.before.deduplication",
  "Nuclear.reads.before.dedup",
  "Uniquely.mapped.reads.after.deduplication"
)

metric_labels <- c(
  "Total.Reads..before.trimming."              = "total_reads",
  "Mapped.Reads"                               = "mapped_reads",
  "Reads.after.deduplication"                  = "dedup_reads",
  "Uniquely.mapped.reads.before.deduplication" = "unique_pre_dedup",
  "Nuclear.reads.before.dedup"                 = "nuclear_pre_dedup",
  "Uniquely.mapped.reads.after.deduplication"  = "unique_post_dedup"
)

# ---- Reshape ----
long5 <- map %>%
  dplyr::select(Ind, Type, Treatment, dplyr::all_of(metric_order)) %>%
  pivot_longer(
    cols = dplyr::all_of(metric_order),
    names_to = "Metric", values_to = "Reads"
  ) %>%
  mutate(
    Metric = factor(Metric, levels = metric_order, labels = metric_labels),
    Ind    = factor(Ind, levels = sort(unique(Ind))),
    Tx     = ifelse(grepl("^VEH", Treatment), "VEH", "DOX"),
    Tx     = factor(Tx, levels = c("VEH", "DOX")),
    # build clean facet names: e.g. VEH_TOP2B_ChIP
    Facet  = paste(Tx, gsub("VEH_|DOX_", "", Treatment), Type, sep = "_"),
    Facet  = factor(Facet, levels = unique(paste(Tx, gsub("VEH_|DOX_", "", Treatment), Type, sep = "_")))
  ) %>%
  droplevels()

# ---- Colors ----
Ind_palc <- c("#ffbe0b","#ff006e","#fb5607","#8338ec","#3a86ff","#4a4e69")
tx_cols  <- c("VEH" = "#1f77b4", "DOX" = "#d62728")

# ---- Plot ----
ggplot(long5, aes(x = Metric, y = Reads)) +
  geom_boxplot(aes(fill = Tx),
               color = "black", width = 0.65, outlier.shape = NA,
               position = position_dodge(width = 0.75)) +
  geom_point(aes(color = Ind, group = Tx),
             position = position_dodge(width = 0.75),
             size = 2, alpha = 0.9) +
  scale_color_manual(values = Ind_palc, name = "Ind") +
  scale_fill_manual(values = tx_cols, name = "Tx") +
  scale_y_continuous(labels = label_number(suffix = " M", scale = 1e-6)) +
  facet_wrap(~Facet, scales = "free_y", ncol = 3) +
  labs(
    title = "Read metrics across treatments and sample types",
    x = NULL,
    y = "read_count"
  ) +
  theme_bw() +
  theme(
    plot.title   = element_text(size = rel(1.5), hjust = 0.5),
    axis.text.x  = element_text(angle = 30, hjust = 1),
    strip.text.x = element_text(face = "bold")
  )

Past versions of dedup5-1.png

Version	Author	Date
841c632	sayanpaul01	2025-08-19

Session information

sessionInfo()

R version 4.3.0 (2023-04-21 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
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] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] ggpubr_0.6.0       Hmisc_5.2-3        corrplot_0.95      ggrepel_0.9.6     
 [5] cowplot_1.1.3      biomaRt_2.58.2     scales_1.3.0       lubridate_1.9.4   
 [9] forcats_1.0.0      stringr_1.5.1      dplyr_1.1.4        purrr_1.0.4       
[13] readr_2.1.5        tidyr_1.3.1        tibble_3.2.1       ggplot2_3.5.2     
[17] tidyverse_2.0.0    data.table_1.17.0  reshape2_1.4.4     gridExtra_2.3     
[21] RColorBrewer_1.1-3 edgeR_4.0.16       limma_3.58.1      

loaded via a namespace (and not attached):
 [1] DBI_1.2.3               bitops_1.0-9            rlang_1.1.3            
 [4] magrittr_2.0.3          git2r_0.36.2            compiler_4.3.0         
 [7] RSQLite_2.3.9           png_0.1-8               vctrs_0.6.5            
[10] pkgconfig_2.0.3         crayon_1.5.3            fastmap_1.2.0          
[13] backports_1.5.0         dbplyr_2.5.0            XVector_0.42.0         
[16] labeling_0.4.3          promises_1.3.2          rmarkdown_2.29         
[19] tzdb_0.5.0              bit_4.6.0               xfun_0.52              
[22] zlibbioc_1.48.2         cachem_1.1.0            GenomeInfoDb_1.38.8    
[25] jsonlite_2.0.0          progress_1.2.3          blob_1.2.4             
[28] later_1.3.2             broom_1.0.8             prettyunits_1.2.0      
[31] cluster_2.1.8.1         R6_2.6.1                bslib_0.9.0            
[34] stringi_1.8.3           car_3.1-3               rpart_4.1.24           
[37] jquerylib_0.1.4         Rcpp_1.0.12             knitr_1.50             
[40] base64enc_0.1-3         IRanges_2.36.0          httpuv_1.6.15          
[43] nnet_7.3-20             timechange_0.3.0        tidyselect_1.2.1       
[46] abind_1.4-8             rstudioapi_0.17.1       yaml_2.3.10            
[49] curl_6.2.2              lattice_0.22-7          plyr_1.8.9             
[52] Biobase_2.62.0          withr_3.0.2             KEGGREST_1.42.0        
[55] evaluate_1.0.3          foreign_0.8-90          BiocFileCache_2.10.2   
[58] xml2_1.3.8              Biostrings_2.70.3       pillar_1.10.2          
[61] filelock_1.0.3          carData_3.0-5           whisker_0.4.1          
[64] checkmate_2.3.2         stats4_4.3.0            generics_0.1.3         
[67] rprojroot_2.0.4         RCurl_1.98-1.17         S4Vectors_0.40.2       
[70] hms_1.1.3               munsell_0.5.1           glue_1.7.0             
[73] tools_4.3.0             ggsignif_0.6.4          locfit_1.5-9.12        
[76] fs_1.6.3                XML_3.99-0.18           grid_4.3.0             
[79] AnnotationDbi_1.64.1    colorspace_2.1-0        GenomeInfoDbData_1.2.11
[82] htmlTable_2.4.3         Formula_1.2-5           cli_3.6.1              
[85] rappdirs_0.3.3          workflowr_1.7.1         gtable_0.3.6           
[88] rstatix_0.7.2           sass_0.4.10             digest_0.6.34          
[91] BiocGenerics_0.48.1     farver_2.1.2            htmlwidgets_1.6.4      
[94] memoise_2.0.1           htmltools_0.5.8.1       lifecycle_1.0.4        
[97] httr_1.4.7              statmod_1.5.0           bit64_4.6.0-1