Total Reads and Mapped Reads (TOP2B and P53)
Last updated: 2025-08-15
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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 4.3.2Loading required package: limmaWarning: package 'limma' was built under R version 4.3.1library(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, meltlibrary(tidyverse)Warning: package 'tidyverse' was built under R version 4.3.2Warning: package 'tidyr' was built under R version 4.3.3Warning: package 'readr' was built under R version 4.3.3Warning: package 'purrr' was built under R version 4.3.3Warning: package 'dplyr' was built under R version 4.3.2Warning: package 'stringr' was built under R version 4.3.2Warning: 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()
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✖ dplyr::lag() masks stats::lag()
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ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(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_factorlibrary(biomaRt)Warning: package 'biomaRt' was built under R version 4.3.2library(cowplot)Warning: package 'cowplot' was built under R version 4.3.2
Attaching package: 'cowplot'
The following object is masked from 'package:lubridate':
stamplibrary(ggrepel)Warning: package 'ggrepel' was built under R version 4.3.3library(corrplot)Warning: package 'corrplot' was built under R version 4.3.3corrplot 0.95 loadedlibrary(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, unitslibrary(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"))
| 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")
)
| 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")
)
| 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")
)
| 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")
)
| 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
| 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
| 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")
)
| 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")
)
| 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")
)
| 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")
)
| 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")
)
| Version | Author | Date |
|---|---|---|
| ffe6f24 | sayanpaul01 | 2025-08-15 |
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