TOP2B and Snyder data
Renee Matthews
2025-06-09
Last updated: 2025-08-06
Checks: 7 0
Knit directory: ATAC_learning/
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library(tidyverse)
library(kableExtra)
library(broom)
library(RColorBrewer)
library(ChIPseeker)
library("TxDb.Hsapiens.UCSC.hg38.knownGene")
library("org.Hs.eg.db")
library(rtracklayer)
library(edgeR)
library(ggfortify)
library(limma)
library(readr)
library(BiocGenerics)
library(gridExtra)
library(VennDiagram)
library(scales)
library(BiocParallel)
library(ggpubr)
library(devtools)
library(biomaRt)
library(eulerr)
library(smplot2)
library(genomation)
library(ggsignif)
library(plyranges)
library(ggrepel)
library(epitools)
library(circlize)
library(readxl)
library(regioneR)
library(GenomicRanges)Loading in dataframes
toptable_results <- readRDS("data/Final_four_data/re_analysis/Toptable_results.RDS")
all_results <- toptable_results %>%
imap(~ .x %>% tibble::rownames_to_column(var = "rowname") %>%
mutate(source = .y)) %>%
bind_rows()
Top2b_peaks <- import(con="data/other_papers/ChIP3_TOP2B_CM_87-1.bed",format = "bed",genome="hg38")
Motif_list_gr <- readRDS( "data/Final_four_data/re_analysis/Motif_list_granges.RDS")
list2env(Motif_list_gr,envir = .GlobalEnv)<environment: R_GlobalEnv>df_list <- plyr::llply(Motif_list_gr[c(1:9)], as.data.frame)
list2env(df_list,envir=.GlobalEnv)<environment: R_GlobalEnv>Left_ventricle <- import(con = "C://Users/renee/Downloads/hg38.TADs/hg38/VentricleLeft_STL003_Leung_2015-raw_TADs.txt", format = "bed",genome="hg38")
DOX_3_sig_gr <-
all_results %>%
dplyr::select(source,genes, logFC,adj.P.Val) %>%
mutate("Peakid"=genes) %>%
dplyr::filter(source=="DOX_3") %>%
dplyr::filter(adj.P.Val<0.05) %>%
separate_wider_delim(., cols="genes",names = c("seqnames","start","end"), delim= ".") %>%
GRanges()
DOX_24_sig_gr <-
all_results %>%
dplyr::select(source,genes, logFC,adj.P.Val) %>%
mutate("Peakid"=genes) %>%
dplyr::filter(source=="DOX_24") %>%
dplyr::filter(adj.P.Val<0.05) %>%
separate_wider_delim(., cols="genes",names = c("seqnames","start","end"), delim= ".") %>%
GRanges() Overlapping Top2b and all ATAC regions
Allregion_ol <- join_overlap_intersect(all_regions,Top2b_peaks)%>%
as.data.frame() %>%
distinct(Peakid,.keep_all = TRUE)
left_ventricle_ol <- join_overlap_intersect(all_regions ,Left_ventricle) %>%
as.data.frame() %>%
distinct(Peakid,.keep_all = TRUE)
TOP2b_overlap <- join_overlap_intersect(all_regions,Top2b_peaks)%>%
as.data.frame() %>%
distinct(name,.keep_all = TRUE)
# join_overlap_intersect(all_regions,Top2b_peaks)%>%
# as.data.frame() %>%
# group_by(Peakid) %>%
# tally() %>%
# dplyr::filter(n>1
# )Proportion of Tob2b peaks found in ATAC peak by treatment:
annotated_regions <- all_results %>%
dplyr::filter(source=="DOX_3"|source=="DOX_24") %>%
dplyr::select(source,genes,logFC,adj.P.Val) %>%
pivot_wider(.,id_cols=genes,names_from = source, values_from = c(logFC, adj.P.Val)) %>%
mutate(top_2b_ol=case_when(genes %in% Allregion_ol$Peakid~"TOP2b_peak",
TRUE ~"not_TOP2b_peak")) %>%
mutate(sig_3=if_else(adj.P.Val_DOX_3<0.05,"sig","not_sig"),
sig_24=if_else(adj.P.Val_DOX_24<0.05,"sig","not_sig")) %>%
mutate(sig_3=factor(sig_3,levels=c("sig","not_sig")),
sig_24=factor(sig_24,levels=c("sig","not_sig"))) %>%
mutate(TAD_ol=case_when(genes%in% left_ventricle_ol$Peakid~"TAD_peak",
TRUE ~"not_TAD_peak"))
annotated_regions %>%
group_by(sig_3,top_2b_ol) %>% tally%>%
pivot_wider(., id_cols = sig_3, names_from = top_2b_ol, values_from = n)# A tibble: 2 × 3
# Groups: sig_3 [2]
sig_3 TOP2b_peak not_TOP2b_peak
<fct> <int> <int>
1 sig 185 3288
2 not_sig 5036 147048annotated_regions %>%
group_by(sig_24,top_2b_ol) %>% tally %>%
pivot_wider(., id_cols = sig_24, names_from = top_2b_ol, values_from = n)# A tibble: 2 × 3
# Groups: sig_24 [2]
sig_24 TOP2b_peak not_TOP2b_peak
<fct> <int> <int>
1 sig 1772 63048
2 not_sig 3449 87288annotated_regions %>%
group_by(sig_3,top_2b_ol) %>% tally%>%
pivot_wider(., id_cols = sig_3, names_from = top_2b_ol, values_from = n) %>%
column_to_rownames("sig_3") %>%
as.matrix() %>%
chisq.test(.)
Pearson's Chi-squared test with Yates' continuity correction
data: .
X-squared = 41.903, df = 1, p-value = 9.59e-11annotated_regions %>%
group_by(sig_24,top_2b_ol) %>% tally%>%
pivot_wider(., id_cols = sig_24, names_from = top_2b_ol, values_from = n) %>%
column_to_rownames("sig_24") %>%
as.matrix() %>%
chisq.test(.)
Pearson's Chi-squared test with Yates' continuity correction
data: .
X-squared = 132.47, df = 1, p-value < 2.2e-16annotated_regions %>%
group_by(sig_3,top_2b_ol) %>%
ggplot(., aes(x=sig_3, fill=top_2b_ol))+
geom_bar(position="fill")+
theme_bw()+
ggtitle(" 3 hour DARs and TOP2b")+
ylab("proportion")
annotated_regions %>%
group_by(sig_24,top_2b_ol) %>%
ggplot(., aes(x=sig_24, fill=top_2b_ol))+
geom_bar(position="fill")+
theme_bw()+
ggtitle(" 24 hour DARs and TOP2b")+
ylab("proportion")
top2b_3hr_or <- annotated_regions %>%
group_by(sig_3,top_2b_ol) %>%
tally %>%
pivot_wider(., id_cols=sig_3, names_from = top_2b_ol, values_from = n) %>%
column_to_rownames("sig_3") %>%
as.matrix() %>%
epitools::oddsratio(., method="wald")
top2b_24hr_or <- annotated_regions %>%
group_by(sig_24,top_2b_ol) %>%
tally %>%
pivot_wider(., id_cols=sig_24, names_from = top_2b_ol, values_from = n) %>%
column_to_rownames("sig_24") %>%
as.matrix() %>%
epitools::oddsratio(., method="wald")
ggplot_format_odds <- data.frame("or_value" = c(top2b_3hr_or$measure[2, "estimate"],top2b_24hr_or$measure[2, "estimate"]),
"lower_ci" = c(top2b_3hr_or$measure[2, "lower"],top2b_24hr_or$measure[2, "lower"]),
"upper_ci" = c(top2b_3hr_or$measure[2, "upper"],top2b_24hr_or$measure[2, "upper"]),
"p_value" = c(top2b_3hr_or$p.value[2,"chi.square"],top2b_24hr_or$p.value[2,"chi.square"]),
group=c("3hour","24hour"))
ggplot_format_odds %>%
mutate(group=factor(group, levels= c("3hour","24hour"))) %>%
ggplot(., aes(x = group, y = or_value)) +
geom_point(size = 3) +
geom_errorbar(aes(ymin = lower_ci, ymax = upper_ci), width = 0.2) +
geom_hline(yintercept = 1, linetype = "dashed", color = "gray40") +
labs(
title = "Odds Ratio of TOP2b Peak Overlap",
y = "Odds Ratio (95% confidence interval)",
x = "time"
) +
theme_bw() +
theme(
text = element_text(size = 12),
plot.title = element_text(hjust = 0.5)
)
annotated_DARs<- readRDS("data/Final_four_data/re_analysis/DOX_DAR_annotated_peaks_chipannno.RDS")
gene_N_peak <-
annotated_DARs$DOX_3 %>%
as.data.frame() %>%
dplyr::select(mcols.genes,annotation, geneId:distanceToTSS)
toplistall_RNA <- readRDS("data/other_papers/toplistall_RNA.RDS") %>%
mutate(logFC = logFC*(-1))
RNA_results <-
toplistall_RNA %>%
dplyr::select(time:logFC) %>%
tidyr::unite("sample",time, id) %>%
pivot_wider(., id_cols = c(ENTREZID,SYMBOL),names_from = sample, values_from = logFC) %>%
rename_with(~ str_replace(., "hours", "RNA"))
RNA_adj.pvals <-
toplistall_RNA %>%
dplyr::select(time:SYMBOL,adj.P.Val) %>%
tidyr::unite("sample",id, time) %>%
pivot_wider(., id_cols = c(ENTREZID,SYMBOL),names_from = sample, values_from = adj.P.Val) %>%
rename_with(~ str_replace(., "hours", "pval"))my_DOX_data <- all_results %>%
dplyr::filter(source=="DOX_3"|source=="DOX_24") %>%
dplyr::select(source,genes,logFC,adj.P.Val) %>%
pivot_wider(.,id_cols=genes,names_from = source, values_from = c(logFC, adj.P.Val))
my_EPI_data <- all_results %>%
dplyr::filter(source=="EPI_3"|source=="EPI_24") %>%
dplyr::select(source,genes,logFC,adj.P.Val) %>%
pivot_wider(.,id_cols=genes,names_from = source, values_from = c(logFC, adj.P.Val))
my_DNR_data <- all_results %>%
dplyr::filter(source=="DNR_3"|source=="DNR_24") %>%
dplyr::select(source,genes,logFC,adj.P.Val) %>%
pivot_wider(.,id_cols=genes,names_from = source, values_from = c(logFC, adj.P.Val))
my_MTX_data <- all_results %>%
dplyr::filter(source=="MTX_3"|source=="MTX_24") %>%
dplyr::select(source,genes,logFC,adj.P.Val) %>%
pivot_wider(.,id_cols=genes,names_from = source, values_from = c(logFC, adj.P.Val))adding DOX_sig and other trts for enrichment with top2b
TSS_listed_df <- annotated_regions %>%
left_join(.,my_EPI_data,by=c("genes"="genes")) %>%
mutate(EPI_sig_3=if_else(adj.P.Val_EPI_3<0.05,"sig","not_sig"),
EPI_sig_24=if_else(adj.P.Val_EPI_24<0.05,"sig","not_sig")) %>%
mutate(EPI_sig_3=factor(EPI_sig_3,levels=c("sig","not_sig")),
EPI_sig_24=factor(EPI_sig_24,levels=c("sig","not_sig"))) %>%
left_join(.,my_DNR_data,by=c("genes"="genes")) %>%
mutate(DNR_sig_3=if_else(adj.P.Val_DNR_3<0.05,"sig","not_sig"),
DNR_sig_24=if_else(adj.P.Val_DNR_24<0.05,"sig","not_sig")) %>%
mutate(DNR_sig_3=factor(DNR_sig_3,levels=c("sig","not_sig")),
DNR_sig_24=factor(DNR_sig_24,levels=c("sig","not_sig"))) %>%
left_join(.,my_MTX_data,by=c("genes"="genes")) %>%
mutate(MTX_sig_3=if_else(adj.P.Val_MTX_3<0.05,"sig","not_sig"),
MTX_sig_24=if_else(adj.P.Val_MTX_24<0.05,"sig","not_sig")) %>%
mutate(MTX_sig_3=factor(MTX_sig_3,levels=c("sig","not_sig")),
MTX_sig_24=factor(MTX_sig_24,levels=c("sig","not_sig"))) %>%
dplyr::rename("DOX_sig_3"=sig_3, "DOX_sig_24"= sig_24) %>%
left_join(., gene_N_peak, by= c("genes"="mcols.genes"))make the odds ratio dataframe
DOX_top2b_3hr_or <-
TSS_listed_df %>%
group_by(DOX_sig_3,top_2b_ol) %>%
tally %>%
pivot_wider(., id_cols=DOX_sig_3, names_from = top_2b_ol, values_from = n) %>% column_to_rownames("DOX_sig_3") %>%
as.matrix() %>%
fisher.test(.)
DOX_top2b_24hr_or <- TSS_listed_df %>%
group_by(DOX_sig_24,top_2b_ol) %>%
tally %>%
pivot_wider(., id_cols=DOX_sig_24, names_from = top_2b_ol, values_from = n) %>%
column_to_rownames("DOX_sig_24") %>%
as.matrix() %>%
fisher.test(.)
EPI_top2b_3hr_or <-
TSS_listed_df %>%
group_by(EPI_sig_3,top_2b_ol) %>%
tally %>%
pivot_wider(., id_cols=EPI_sig_3, names_from = top_2b_ol, values_from = n) %>% column_to_rownames("EPI_sig_3") %>%
as.matrix() %>%
fisher.test(.)
EPI_top2b_24hr_or <- TSS_listed_df %>%
group_by(EPI_sig_24,top_2b_ol) %>%
tally %>%
pivot_wider(., id_cols=EPI_sig_24, names_from = top_2b_ol, values_from = n) %>%
column_to_rownames("EPI_sig_24") %>%
as.matrix() %>%
fisher.test(.)
DNR_top2b_3hr_or <-
TSS_listed_df %>%
group_by(DNR_sig_3,top_2b_ol) %>%
tally %>%
pivot_wider(., id_cols=DNR_sig_3, names_from = top_2b_ol, values_from = n) %>% column_to_rownames("DNR_sig_3") %>%
as.matrix() %>%
fisher.test(.)
DNR_top2b_24hr_or <- TSS_listed_df %>%
group_by(DNR_sig_24,top_2b_ol) %>%
tally %>%
pivot_wider(., id_cols=DNR_sig_24, names_from = top_2b_ol, values_from = n) %>%
column_to_rownames("DNR_sig_24") %>%
as.matrix() %>%
fisher.test(.)
MTX_top2b_3hr_or <-
TSS_listed_df %>%
group_by(MTX_sig_3,top_2b_ol) %>%
tally %>%
pivot_wider(., id_cols=MTX_sig_3, names_from = top_2b_ol, values_from = n) %>% column_to_rownames("MTX_sig_3") %>%
as.matrix() %>%
fisher.test(.)
MTX_top2b_24hr_or <- TSS_listed_df %>%
group_by(MTX_sig_24,top_2b_ol) %>%
tally %>%
pivot_wider(., id_cols=MTX_sig_24, names_from = top_2b_ol, values_from = n) %>%
column_to_rownames("MTX_sig_24") %>%
as.matrix() %>%
fisher.test(.)# Define the variable names
var_names <- c("DOX_top2b_3hr_or", "DOX_top2b_24hr_or",
"EPI_top2b_3hr_or", "EPI_top2b_24hr_or",
"DNR_top2b_3hr_or", "DNR_top2b_24hr_or",
"MTX_top2b_3hr_or", "MTX_top2b_24hr_or")
# Optional: label for grouping
group_labels <- c("DOX_3hr", "DOX_24hr", "EPI_3hr", "EPI_24hr", "DNR_3hr", "DNR_24hr", "MTX_3hr", "MTX_24hr")
# Build the data frame
OR_all_trt_result_df <- do.call(rbind, lapply(seq_along(var_names), function(i) {
var <- get(var_names[i])
data.frame(
or_value = unname(var$estimate), # remove the name "odds ratio"
lower_ci = var$conf.int[1],
upper_ci = var$conf.int[2],
p_value = var$p.value,
group = group_labels[i]
)
}))OR_all_trt_result_df %>%
separate_wider_delim(.,cols="group", names = c("trt","time"), delim = "_",cols_remove = FALSE) %>%
mutate(time= factor(time, levels =c("3hr","24hr")),
trt=factor(trt, levels= c("DOX", "EPI", "DNR", "MTX"))) %>%
# mutate(significant=if_else(p_value <0.05,"TRUE","FALSE")) %>%
mutate(
significant = case_when(
p_value < 0.001 ~ "***",
p_value < 0.01 ~ "**",
p_value < 0.05 ~ "*",
TRUE ~ ""
)
) %>%
ggplot(., aes(x = trt, y = or_value)) +
geom_point(aes(color = trt), size=4)+
geom_errorbar(aes(ymin = lower_ci, ymax = upper_ci), width = 0.2) +
geom_hline(yintercept = 1, linetype = "dashed", color = "gray40") +
geom_text(
aes(y = upper_ci + 0.1 * or_value, label = significant),
hjust = 0, # aligns text to the left of the y point
size = 4,
color = "black"
)+
labs(
title = "Odds Ratio of TOP2b Peak Overlap",
y = "Odds Ratio (95% confidence interval)",
x = "treatment"
) +
# coord_flip()+
theme_classic() +
facet_wrap(~time)+
theme(
text = element_text(size = 12),
plot.title = element_text(hjust = 0.5)
)
top2b_overlap <- join_overlap_inner(all_regions,Top2b_peaks)
AR_total <- length(unique(all_regions$Peakid))
Top2B_total <- length(unique(Top2b_peaks$name))
overlap_n <- length(unique(top2b_overlap$Peakid))
fit_top2b <- euler(c(
"ARs" = AR_total-overlap_n,
"Top2B" = Top2B_total-length(unique(top2b_overlap$name)),
"ARs&Top2B" = overlap_n
))
plot(fit_top2b, fills = list(fill = c("skyblue", "lightcoral"), alpha = 0.6),
labels = FALSE, edges = TRUE, quantities = TRUE,
main = "Overlap between AR and TOP2B peaks")
length(intersect(unique(top2b_overlap$Peakid), unique(all_regions$Peakid))) # should match overlap_n[1] 5221significance of Snyder data (Heart left-ventricle)
Snyder_41peaks <- read.delim("data/other_papers/ENCFF966JZT_bed_Snyder_41peaks.bed",header=TRUE) %>%
GRanges()
genome <- BSgenome.Hsapiens.UCSC.hg38
# perm_test_hlv <- permTest(A= all_regions,
# B= Snyder_41peaks,
# ntimes=1000,
# randomize.function=randomizeRegions,
# evaluate.function = numOverlaps,
# genome=genome,
# count.once= TRUE,
# verbose = TRUE)
# saveRDS(perm_test_hlv,"data/Final_four_data/re_analysis/perm_test_results_HLV.RDS")
perm_test_hlv <- readRDS("data/Final_four_data/re_analysis/perm_test_results_HLV.RDS")
perm_test_hlv$numOverlaps
P-value: 0.000999000999000999
Z-score: 760.9593
Number of iterations: 1000
Alternative: greater
Evaluation of the original region set: 66927
Evaluation function: numOverlaps
Randomization function: randomizeRegions
attr(,"class")
[1] "permTestResultsList"plot(perm_test_hlv)
plot(perm_test_hlv, xlim = range(perm_test_hlv$numOverlaps$permuted))
TOP2B and TOP2A expression (log2cpm from RNA data)
drug_pal <- c("#8B006D","#DF707E","#F1B72B", "#3386DD","#707031","#41B333")
RNA_counts <- readRDS("data/other_papers/cpmcount.RDS") %>%
dplyr::rename_with(.,~gsub(pattern="Da",replacement="DNR",.)) %>%
dplyr::rename_with(.,~gsub(pattern="Do",replacement="DOX",.)) %>%
dplyr::rename_with(.,~gsub(pattern="Ep",replacement="EPI",.)) %>%
dplyr::rename_with(.,~gsub(pattern="Mi",replacement="MTX",.)) %>%
dplyr::rename_with(.,~gsub(pattern="Tr",replacement="TRZ",.)) %>%
dplyr::rename_with(.,~gsub(pattern="Ve",replacement="VEH",.)) %>%
rownames_to_column("ENTREZID")
RNA_results %>%
dplyr::filter(SYMBOL=="TOP2B")# A tibble: 1 × 12
ENTREZID SYMBOL `24_RNA_DNR` `24_RNA_DOX` `24_RNA_EPI` `24_RNA_MTX`
<chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 7155 TOP2B -0.940 -0.793 -0.936 -0.394
# ℹ 6 more variables: `24_RNA_TRZ` <dbl>, `3_RNA_DNR` <dbl>, `3_RNA_DOX` <dbl>,
# `3_RNA_EPI` <dbl>, `3_RNA_MTX` <dbl>, `3_RNA_TRZ` <dbl>RNA_counts %>%
dplyr::filter(ENTREZID =="7153") %>%
pivot_longer(cols = !ENTREZID, names_to = "sample", values_to = "counts") %>%
separate("sample", into = c("trt","ind","time")) %>%
mutate(time=factor(time, levels = c("3h","24h"))) %>%
mutate(trt=factor(trt, levels= c("DOX","EPI","DNR","MTX","TRZ","VEH"))) %>%
ggplot(., aes (x = time, y=counts))+
geom_boxplot(aes(fill=trt))+
# facet_wrap(~SYMBOL, scales="free_y")+
scale_fill_manual(values = drug_pal)+
ggtitle("RNA Log2cpm of TOP2a")+
theme_bw()+
ylab("log2 cpm RNA")
RNA_counts %>%
dplyr::filter(ENTREZID =="7155") %>%
pivot_longer(cols = !ENTREZID, names_to = "sample", values_to = "counts") %>%
separate("sample", into = c("trt","ind","time")) %>%
mutate(time=factor(time, levels = c("3h","24h"))) %>%
mutate(trt=factor(trt, levels= c("DOX","EPI","DNR","MTX","TRZ","VEH"))) %>%
ggplot(., aes (x = time, y=counts))+
geom_boxplot(aes(fill=trt))+
# facet_wrap(~SYMBOL, scales="free_y")+
scale_fill_manual(values = drug_pal)+
ggtitle("RNA Log2cpm of TOP2b")+
theme_bw()+
ylab("log2 cpm RNA")
# RNA_counts %>%
# dplyr::filter(ENTREZID =="7155")
#
# RNA_results %>%
# dplyr::filter(ENTREZID =="7155")Expression of genes in specific TAD region
(related to figure 5)
TAD_102_genes <- c("TNFSF18","PRDX6", "TEX50", "KLHL20", "DARS2", "TNFSF4", "SLC9C2", "ANKRD45", "CENPL", "GAS5")
TAD_plot_genes <- RNA_results %>%
dplyr::filter(SYMBOL%in% TAD_102_genes) %>%
dplyr::select(ENTREZID,SYMBOL)
### Filter pvalues
clean_RNA_adj.pvals <- RNA_adj.pvals %>%
# dplyr::filter(ENTREZID=="55157") %>%
pivot_longer(cols = contains("pval"), names_to = "sample", values_to = "adj.p.val") %>%
separate(sample, into = c("trt", "time","pval")) %>%
mutate(
time = paste0(time, "h"), # convert "3" → "3h"
trt = factor(trt, levels = c("DOX", "EPI", "DNR", "MTX", "TRZ", "VEH"))
)
for (gene in TAD_plot_genes$ENTREZID) {
SYMBOL <- TAD_plot_genes$SYMBOL[TAD_plot_genes$ENTREZID == gene]
# Filter and plot
gene_plot <- RNA_counts %>%
filter(ENTREZID == gene) %>%
pivot_longer(cols = !ENTREZID, names_to = "sample", values_to = "counts") %>%
separate(sample, into = c("trt", "ind", "time")) %>%
mutate(
time = factor(time, levels = c("3h", "24h")),
trt = factor(trt, levels = c("DOX", "EPI", "DNR", "MTX", "TRZ", "VEH"))
) %>%
ggplot(aes(x = time, y = counts)) +
geom_boxplot(aes(fill = trt)) +
scale_fill_manual(values = drug_pal) +
theme_bw() +
ylab("log2 cpm RNA") +
ggtitle(paste0("RNA Log2cpm of ", SYMBOL))
plot(gene_plot)
}
RNA_pval_clean <- RNA_adj.pvals %>%
pivot_longer(cols = contains("pval"), names_to = "sample", values_to = "adj.p.val") %>%
separate(sample, into = c("trt", "time","pval")) %>%
mutate(
time = paste0(time, "h"), # convert "3" → "3h"
trt = factor(trt, levels = c("DOX", "EPI", "DNR", "MTX", "TRZ", "VEH")),
group = paste0(trt, "_", time),
group = factor(group, levels = c(
"DOX_3h", "EPI_3h", "DNR_3h", "MTX_3h", "TRZ_3h", "VEH_3h",
"DOX_24h", "EPI_24h", "DNR_24h", "MTX_24h", "TRZ_24h", "VEH_24h"
))
)
for (gene in TAD_plot_genes$ENTREZID) {
SYMBOL <- TAD_plot_genes$SYMBOL[TAD_plot_genes$ENTREZID == gene]
# Prep expression data
gene_expr <- RNA_counts %>%
filter(ENTREZID == gene) %>%
pivot_longer(cols = !ENTREZID, names_to = "sample", values_to = "counts") %>%
separate(sample, into = c("trt", "ind", "time")) %>%
mutate(
time = paste0(time), # if already "3h"/"24h"
group = paste0(trt, "_", time),
group = factor(group, levels = c(
"DOX_3h", "EPI_3h", "DNR_3h", "MTX_3h", "TRZ_3h", "VEH_3h",
"DOX_24h", "EPI_24h", "DNR_24h", "MTX_24h", "TRZ_24h", "VEH_24h"
))
)
# Get gene-specific p-values
gene_pvals <- RNA_pval_clean %>%
filter(ENTREZID == gene)
# Merge in p-values by group
gene_plot_data <- left_join(gene_expr, gene_pvals, by = c("ENTREZID", "group"))
# Create label position below box
label_positions <- gene_plot_data %>%
group_by(group) %>%
summarise(y = min(counts, na.rm = TRUE) - 0.5, .groups = "drop")
gene_plot_data <- left_join(gene_plot_data, label_positions, by = "group")
gene_plot_data <- gene_plot_data %>%
separate(group, into = c("trt", "time"), sep = "_", remove = FALSE)
# Plot
gene_plot <- ggplot(gene_plot_data, aes(x = group, y = counts)) +
geom_boxplot(aes(fill = trt)) +
geom_text(
aes(y = y,
label = ifelse(trt != "VEH" & !is.na(adj.p.val),
paste0("", signif(adj.p.val, 2)),
"")),
size = 3,
vjust = 1.2
) +
scale_fill_manual(values = drug_pal) +
theme_bw() +
ggtitle(paste0("RNA Log2cpm of ", SYMBOL)) +
ylab("log2 cpm RNA") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
plot(gene_plot)
}
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] grid stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] BSgenome.Hsapiens.UCSC.hg38_1.4.5
[2] BSgenome_1.74.0
[3] BiocIO_1.16.0
[4] Biostrings_2.74.1
[5] XVector_0.46.0
[6] regioneR_1.38.0
[7] readxl_1.4.5
[8] circlize_0.4.16
[9] epitools_0.5-10.1
[10] ggrepel_0.9.6
[11] plyranges_1.26.0
[12] ggsignif_0.6.4
[13] genomation_1.38.0
[14] smplot2_0.2.5
[15] eulerr_7.0.2
[16] biomaRt_2.62.1
[17] devtools_2.4.5
[18] usethis_3.1.0
[19] ggpubr_0.6.1
[20] BiocParallel_1.40.2
[21] scales_1.4.0
[22] VennDiagram_1.7.3
[23] futile.logger_1.4.3
[24] gridExtra_2.3
[25] ggfortify_0.4.18
[26] edgeR_4.4.2
[27] limma_3.62.2
[28] rtracklayer_1.66.0
[29] org.Hs.eg.db_3.20.0
[30] TxDb.Hsapiens.UCSC.hg38.knownGene_3.20.0
[31] GenomicFeatures_1.58.0
[32] AnnotationDbi_1.68.0
[33] Biobase_2.66.0
[34] GenomicRanges_1.58.0
[35] GenomeInfoDb_1.42.3
[36] IRanges_2.40.1
[37] S4Vectors_0.44.0
[38] BiocGenerics_0.52.0
[39] ChIPseeker_1.42.1
[40] RColorBrewer_1.1-3
[41] broom_1.0.8
[42] kableExtra_1.4.0
[43] lubridate_1.9.4
[44] forcats_1.0.0
[45] stringr_1.5.1
[46] dplyr_1.1.4
[47] purrr_1.0.4
[48] readr_2.1.5
[49] tidyr_1.3.1
[50] tibble_3.3.0
[51] ggplot2_3.5.2
[52] tidyverse_2.0.0
[53] workflowr_1.7.1
loaded via a namespace (and not attached):
[1] fs_1.6.6
[2] matrixStats_1.5.0
[3] bitops_1.0-9
[4] enrichplot_1.26.6
[5] httr_1.4.7
[6] profvis_0.4.0
[7] tools_4.4.2
[8] backports_1.5.0
[9] utf8_1.2.6
[10] R6_2.6.1
[11] lazyeval_0.2.2
[12] urlchecker_1.0.1
[13] withr_3.0.2
[14] prettyunits_1.2.0
[15] cli_3.6.5
[16] textshaping_1.0.1
[17] formatR_1.14
[18] labeling_0.4.3
[19] sass_0.4.10
[20] Rsamtools_2.22.0
[21] systemfonts_1.2.3
[22] yulab.utils_0.2.0
[23] foreign_0.8-90
[24] DOSE_4.0.1
[25] svglite_2.2.1
[26] R.utils_2.13.0
[27] dichromat_2.0-0.1
[28] sessioninfo_1.2.3
[29] plotrix_3.8-4
[30] pwr_1.3-0
[31] impute_1.80.0
[32] rstudioapi_0.17.1
[33] RSQLite_2.4.1
[34] shape_1.4.6.1
[35] generics_0.1.4
[36] gridGraphics_0.5-1
[37] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
[38] gtools_3.9.5
[39] car_3.1-3
[40] GO.db_3.20.0
[41] Matrix_1.7-3
[42] abind_1.4-8
[43] R.methodsS3_1.8.2
[44] lifecycle_1.0.4
[45] whisker_0.4.1
[46] yaml_2.3.10
[47] carData_3.0-5
[48] SummarizedExperiment_1.36.0
[49] gplots_3.2.0
[50] qvalue_2.38.0
[51] SparseArray_1.6.2
[52] BiocFileCache_2.14.0
[53] blob_1.2.4
[54] promises_1.3.3
[55] crayon_1.5.3
[56] miniUI_0.1.2
[57] ggtangle_0.0.7
[58] lattice_0.22-7
[59] cowplot_1.1.3
[60] KEGGREST_1.46.0
[61] pillar_1.11.0
[62] knitr_1.50
[63] fgsea_1.32.4
[64] rjson_0.2.23
[65] boot_1.3-31
[66] codetools_0.2-20
[67] fastmatch_1.1-6
[68] glue_1.8.0
[69] getPass_0.2-4
[70] ggfun_0.1.9
[71] data.table_1.17.6
[72] remotes_2.5.0
[73] vctrs_0.6.5
[74] png_0.1-8
[75] treeio_1.30.0
[76] cellranger_1.1.0
[77] gtable_0.3.6
[78] cachem_1.1.0
[79] xfun_0.52
[80] S4Arrays_1.6.0
[81] mime_0.13
[82] statmod_1.5.0
[83] ellipsis_0.3.2
[84] nlme_3.1-168
[85] ggtree_3.14.0
[86] bit64_4.6.0-1
[87] filelock_1.0.3
[88] progress_1.2.3
[89] rprojroot_2.0.4
[90] bslib_0.9.0
[91] rpart_4.1.24
[92] KernSmooth_2.23-26
[93] Hmisc_5.2-3
[94] colorspace_2.1-1
[95] DBI_1.2.3
[96] seqPattern_1.38.0
[97] nnet_7.3-20
[98] tidyselect_1.2.1
[99] processx_3.8.6
[100] bit_4.6.0
[101] compiler_4.4.2
[102] curl_6.4.0
[103] git2r_0.36.2
[104] httr2_1.1.2
[105] htmlTable_2.4.3
[106] xml2_1.3.8
[107] DelayedArray_0.32.0
[108] checkmate_2.3.2
[109] caTools_1.18.3
[110] callr_3.7.6
[111] rappdirs_0.3.3
[112] digest_0.6.37
[113] rmarkdown_2.29
[114] base64enc_0.1-3
[115] htmltools_0.5.8.1
[116] pkgconfig_2.0.3
[117] MatrixGenerics_1.18.1
[118] dbplyr_2.5.0
[119] fastmap_1.2.0
[120] GlobalOptions_0.1.2
[121] rlang_1.1.6
[122] htmlwidgets_1.6.4
[123] UCSC.utils_1.2.0
[124] shiny_1.11.1
[125] farver_2.1.2
[126] jquerylib_0.1.4
[127] zoo_1.8-14
[128] jsonlite_2.0.0
[129] GOSemSim_2.32.0
[130] R.oo_1.27.1
[131] RCurl_1.98-1.17
[132] magrittr_2.0.3
[133] Formula_1.2-5
[134] GenomeInfoDbData_1.2.13
[135] ggplotify_0.1.2
[136] patchwork_1.3.1
[137] Rcpp_1.1.0
[138] ape_5.8-1
[139] stringi_1.8.7
[140] zlibbioc_1.52.0
[141] plyr_1.8.9
[142] pkgbuild_1.4.8
[143] parallel_4.4.2
[144] splines_4.4.2
[145] hms_1.1.3
[146] polylabelr_0.3.0
[147] locfit_1.5-9.12
[148] ps_1.9.1
[149] igraph_2.1.4
[150] reshape2_1.4.4
[151] pkgload_1.4.0
[152] futile.options_1.0.1
[153] XML_3.99-0.18
[154] evaluate_1.0.4
[155] lambda.r_1.2.4
[156] tzdb_0.5.0
[157] httpuv_1.6.16
[158] polyclip_1.10-7
[159] gridBase_0.4-7
[160] xtable_1.8-4
[161] restfulr_0.0.16
[162] tidytree_0.4.6
[163] rstatix_0.7.2
[164] later_1.4.2
[165] viridisLite_0.4.2
[166] aplot_0.2.8
[167] memoise_2.0.1
[168] GenomicAlignments_1.42.0
[169] cluster_2.1.8.1
[170] timechange_0.3.0