Supplemental Figures 12-19
Renee Matthews
2025-02-27
Last updated: 2025-02-27
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Knit directory: ATAC_learning/
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|---|---|---|---|---|
| Rmd | 8435f1f | E. Renee Matthews | 2025-02-27 | adding figures |
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| Rmd | 337980a | E. Renee Matthews | 2025-02-27 | updates to plot |
| Rmd | 37c7d47 | E. Renee Matthews | 2025-02-27 | wflow_git_commit("analysis/Supp_Fig_12-19.Rmd") |
library(tidyverse)
library(kableExtra)
library(broom)
library(RColorBrewer)
library(ChIPseeker)
library("TxDb.Hsapiens.UCSC.hg38.knownGene")
library("org.Hs.eg.db")
library(rtracklayer)
library(ggfortify)
library(readr)
library(BiocGenerics)
library(gridExtra)
library(VennDiagram)
library(scales)
library(ggVennDiagram)
library(BiocParallel)
library(ggpubr)
library(edgeR)
library(genomation)
library(ggsignif)
library(plyranges)
library(ggrepel)
library(ComplexHeatmap)
library(cowplot)
library(smplot2)
library(readxl)
library(devtools)
library(vargen)
library(eulerr)Figure S12: H3K27ac Enrichment at TSS
knitr::include_graphics("assets/Fig\ S12.png", error=FALSE)
| Version | Author | Date |
|---|---|---|
| 50f3de9 | E. Renee Matthews | 2025-02-21 |
knitr::include_graphics("docs/assets/Fig\ S12.png",error = FALSE)
txdb <- TxDb.Hsapiens.UCSC.hg38.knownGene
###taken from Peak_calling rmd
# first get peakfiles (using .narrowPeak files from MACS2 calling) and upload functions
TSS = getBioRegion(TxDb=txdb, upstream=2000, downstream=2000, by = "gene",
type = "start_site")
#### EXAMPLE OF CODE #####
# ind4_V24hpeaks_gr <- prepGRangeObj(ind4_V24hpeaks)
# ind1_DA24hpeaks_gr <- prepGRangeObj((ind1_DA24hpeaks))
# H3K27ac_list <- GRangesList(ind1_DA24hpeaks_gr, ind4_V24hpeaks_gr)
# # ##plotting the TSS average window (making an overlap of each using Epi_list as list holder)
# H3K27ac_list_tagMatrix = lapply(H3K27ac_list, getTagMatrix, windows = TSS)
# plotAvgProf(H3K27ac_list_tagMatrix, xlim=c(-3000, 3000), ylab = "Count Frequency")
#plotPeakProf(H3K27ac_list_tagMatrix, facet = "none", conf = 0.95)
## What I did here: I called all my narrowpeak files
peakfiles1 <- choose.files()
##these were practice for getting file names and shortening for the for loop below
# testname <- basename(peakfiles1[1])
# str_split_i(testname, "_",3)
##This loop first established a list then (because I already knew the list had 12 files)
## I then imported each of these onto that list. Once I had the list, I stored it as
## an R object,
IndA_peaks <- list()
for (file in 1:8){
testname <- basename(peakfiles1[file])
banana_peel <- str_split_i(testname, "_",3)
IndA_peaks[[banana_peel]] <- readPeakFile(peakfiles1[file])
}
saveRDS(IndA_peaks, "data/Final_four_data/H3K27ac_files/IndA_peaks_list.RDS")
# I then called annotatePeak on that list object, and stored that as a R object for later retrieval.)
peakAnnoList_1 <- lapply(IndA_peaks, annotatePeak, tssRegion =c(-2000,2000), TxDb= txdb)
saveRDS(peakAnnoList_1, "data/Final_four_data/H3K27ac_files/IndA_peakAnnoList.RDS")
promoter <- getPromoters(TxDb=txdb, upstream=3000, downstream=3000)
tagMatrix_C <- lapply(IndC_peaks,getTagMatrix, windows=promoter)
plotAvgProf(tagMatrix_C, xlim = c(-3000,3000),xlab = "Genomic Region (5'->3')", ylab = "Read Count Frequency")
saveRDS(tagMatrix_C, "data/Final_four_data/H3K27ac_files/IndC_tagMatrix.RDS")Making the plot
##load tagMatrix files from above
tagMatrix_A <- readRDS("data/Final_four_data/H3K27ac_files/IndA_tagMatrix.RDS")
tagMatrix_B <- readRDS("data/Final_four_data/H3K27ac_files/IndB_tagMatrix.RDS")
tagMatrix_C <- readRDS("data/Final_four_data/H3K27ac_files/IndC_tagMatrix.RDS")
###making the plots and storing the 3 hour as an object
a1<- plotAvgProf(tagMatrix_A[c(1,3,5,7)], xlim=c(-3000, 3000), ylab = "Count Frequency")+ ggtitle("3 hour Individual A" )+coord_cartesian(xlim=c(-2000,2000))>> plotting figure... 2025-02-27 5:56:37 PM b1 <- plotAvgProf(tagMatrix_B[c(1,3,4,7)], xlim=c(-3000, 3000), ylab = "Count Frequency")+ ggtitle("3 hour Individual B" )+coord_cartesian(xlim=c(-2000,2000))>> plotting figure... 2025-02-27 5:56:37 PM c1 <- plotAvgProf(tagMatrix_C[c(1,4,6,8)], xlim=c(-3000, 3000), ylab = "Count Frequency")+ ggtitle("3 hour Individual C" )+coord_cartesian(xlim=c(-2000,2000))>> plotting figure... 2025-02-27 5:56:38 PM ### making the plots and storing the 24 hour as an object
a2<- plotAvgProf(tagMatrix_A[c(2,4,6,8)], xlim=c(-3000, 3000), ylab = "Count Frequency")+ ggtitle("24 hour Individual A" )+coord_cartesian(xlim=c(-2000,2000))>> plotting figure... 2025-02-27 5:56:38 PM b2 <- plotAvgProf(tagMatrix_B[c(2,5,6,8)], xlim=c(-3000, 3000), ylab = "Count Frequency")+ ggtitle("24 hour Individual B" )+coord_cartesian(xlim=c(-2000,2000))>> plotting figure... 2025-02-27 5:56:38 PM c2 <- plotAvgProf(tagMatrix_C[c(2,3,5,7,9)], xlim=c(-3000, 3000), ylab = "Count Frequency")+ ggtitle("24 hour Individual C" )+coord_cartesian(xlim=c(-2000,2000))>> plotting figure... 2025-02-27 5:56:38 PM plot_grid(a1,a2, b1,b2,c1,c2, axis="l",align = "hv",nrow=3, ncol=2)
Figure S13: Correlation between H3K27ac enrichment across samples
H3K27ac_counts <- read_delim("data/Final_four_data/H3K27ac_files/H3K27ac_counts_file.txt", delim= "\t")
corr_lcpmH3K27ac <- H3K27ac_counts %>%
column_to_rownames("Geneid") %>%
cpm(.,log=TRUE) %>%
cor()
filmat_groupmat_col <- data.frame(timeset = colnames(corr_lcpmH3K27ac))
counts_corr_mat <- filmat_groupmat_col %>%
separate(timeset, into = c("indv","trt","time"), sep= "_") %>%
mutate(time = factor(time, levels = c("3", "24"), labels= c("3 hours","24 hours"))) %>%
mutate(trt = factor(trt, levels = c("DOX","EPI", "DNR", "MTX","VEH"))) %>%
mutate(class = if_else(trt == "DNR", "AC", if_else(
trt == "DOX", "AC", if_else(trt == "EPI", "AC", "nAC")
))) %>%
mutate(TOP2i = if_else(trt == "DNR", "yes", if_else(
trt == "DOX", "yes", if_else(trt == "EPI", "yes", if_else(trt == "MTX", "yes", "no"))))) %>%
mutate(indv=factor(indv, levels = c("A","B","C")))
mat_colors <- list(
trt= c("#F1B72B","#8B006D","#DF707E","#3386DD","#41B333"),
indv=c(A="#1B9E77",B= "#D95F02" ,C="#7570B3"),
time=c("pink", "chocolate4"),
class=c("yellow1","darkorange1"),
TOP2i =c("darkgreen","lightgreen"))
names(mat_colors$trt) <- unique(counts_corr_mat$trt)
names(mat_colors$indv) <- unique(counts_corr_mat$indv)
names(mat_colors$time) <- unique(counts_corr_mat$time)
names(mat_colors$class) <- unique(counts_corr_mat$class)
names(mat_colors$TOP2i) <- unique(counts_corr_mat$TOP2i)
htanno <- ComplexHeatmap::HeatmapAnnotation(df = counts_corr_mat, col = mat_colors)
ComplexHeatmap::Heatmap(corr_lcpmH3K27ac, top_annotation = htanno)
Figure S14: H3K27-acetylation response is highly correlated across ACs
Figure S14A: Differentially enriched H3K27ac regions across drug treatments
Code for Differential analysis
### group without veh3hr and veh24hr individual samples
# group_23s <- c( 1,2,4,5,6,7,10,1,2,3,4,7,8,9,10,1,2,3,5,6,7,8,9)
H3K27ac_counts_file <- read_delim("data/Final_four_data/H3K27ac_files/H3K27ac_counts_file.txt", delim= "\t")
PCA_H3_mat_23s <- H3K27ac_counts_file %>%
##removing C_VEH_3 and B_VEH_24 columns
dplyr::select(Geneid,B_DNR_24:B_MTX_24, B_VEH_3:C_VEH_24) %>%
column_to_rownames("Geneid") %>%
as.matrix()
anno_H3_mat_23s <-
data.frame(timeset=colnames(PCA_H3_mat_23s)) %>%
mutate(sample = timeset) %>%
separate(timeset, into = c("indv","trt","time"), sep= "_") %>%
mutate(time = factor(time, levels = c("3", "24"), labels= c("3 hours","24 hours"))) %>%
mutate(trt = factor(trt, levels = c("DOX","EPI", "DNR", "MTX","VEH")))
lcpm_h3_23s <- cpm(PCA_H3_mat_23s, log=TRUE) ### for determining the basic cutoffs
row_means23s <- rowMeans(lcpm_h3_23s)
h3_23s_filtered <- PCA_H3_mat_23s[row_means23s >0,]
filt_H3_matrix_lcpm_23s <- cpm(h3_23s_filtered, log=TRUE)
### no difference in between filtered and unfiltered numbers
for_group_23s <- data.frame(timeset=colnames(PCA_H3_mat_23s)) %>%
mutate(sample = timeset) %>%
separate(timeset, into = c("indv","trt","time"), sep= "_") %>%
unite("test", trt:time,sep="_", remove = FALSE)
# dge_23s <- DGEList.data.frame(counts = PCA_H3_mat_23s, group = group_23s, genes = lcpm_h3_23s_filtered)
# group_1 <- for_group_23s$test
#
# dge_23s$group$indv <- for_group_23s$indv
# dge_23s$group$time <- for_group_23s$time
# dge_23s$group$trt <- for_group_23s$trt
# #
# indv_23s <- for_group_23s$indv
# mm <- model.matrix(~0 +group_1)
# colnames(mm) <- c("DNR_24", "DNR_3", "DOX_24","DOX_3","EPI_24", "EPI_3","MTX_24", "MTX_3","VEH_24", "VEH_3")
#
# y <- voom(dge_23s$counts, mm,plot =TRUE)
#
# corfit <- duplicateCorrelation(y, mm, block = indv_23s)
#
# v <- voom(dge_23s$counts, mm, block = indv_23s, correlation = corfit$consensus)
#
# fit <- lmFit(v, mm, block = indv_23s, correlation = corfit$consensus)
# # colnames(mm) <- c("DNR_24","DNR_3","DOX_24","DOX_3","EPI_24","EPI_3","MTX_24","MTX_3","TRZ_24","TRZ_3","VEH_24", "VEH_3")
# #
# #
# cm <- makeContrasts(
# DNR_3.VEH_3 = DNR_3-VEH_3,
# DOX_3.VEH_3 = DOX_3-VEH_3,
# EPI_3.VEH_3 = EPI_3-VEH_3,
# MTX_3.VEH_3 = MTX_3-VEH_3,
# DNR_24.VEH_24 =DNR_24-VEH_24,
# DOX_24.VEH_24= DOX_24-VEH_24,
# EPI_24.VEH_24= EPI_24-VEH_24,
# MTX_24.VEH_24= MTX_24-VEH_24,
# levels = mm)
#
# vfit <- lmFit(y, mm)
# #
# vfit<- contrasts.fit(vfit, contrasts=cm)
#
# efit4_raodah_23s <- eBayes(vfit)efit_raodah_23s <- readRDS("data/Final_four_data/efit4_raodah_23s.RDS")
results = decideTests(efit_raodah_23s)
summary(results) DNR_3.VEH_3 DOX_3.VEH_3 EPI_3.VEH_3 MTX_3.VEH_3 DNR_24.VEH_24
Down 424 2 0 0 2007
NotSig 19502 20135 20137 20137 16669
Up 211 0 0 0 1461
DOX_24.VEH_24 EPI_24.VEH_24 MTX_24.VEH_24
Down 126 55 0
NotSig 19756 19997 20137
Up 255 85 0AC.DNR_3.top= topTable(efit_raodah_23s, coef=1, adjust.method="BH", number=Inf, sort.by="p")
AC.DOX_3.top= topTable(efit_raodah_23s, coef=2, adjust.method="BH", number=Inf, sort.by="p")
AC.EPI_3.top= topTable(efit_raodah_23s, coef=3, adjust.method="BH", number=Inf, sort.by="p")
AC.MTX_3.top= topTable(efit_raodah_23s, coef=4, adjust.method="BH", number=Inf, sort.by="p")
AC.DNR_24.top= topTable(efit_raodah_23s, coef=5, adjust.method="BH", number=Inf, sort.by="p")
AC.DOX_24.top= topTable(efit_raodah_23s, coef=6, adjust.method="BH", number=Inf, sort.by="p")
AC.EPI_24.top= topTable(efit_raodah_23s, coef=7, adjust.method="BH", number=Inf, sort.by="p")
AC.MTX_24.top= topTable(efit_raodah_23s, coef=8, adjust.method="BH", number=Inf, sort.by="p")
plot_filenames <- c("AC.DNR_3.top","AC.DOX_3.top","AC.EPI_3.top","AC.MTX_3.top",
"AC.DNR_24.top","AC.DOX_24.top","AC.EPI_24.top",
"AC.MTX_24.top")
plot_files <- c( AC.DNR_3.top,AC.DOX_3.top,AC.EPI_3.top,AC.MTX_3.top,
AC.DNR_24.top,AC.DOX_24.top,AC.EPI_24.top,
AC.MTX_24.top)
volcanosig <- function(df, psig.lvl) {
df <- df %>%
mutate(threshold = ifelse(adj.P.Val > psig.lvl, "A", ifelse(adj.P.Val <= psig.lvl & logFC<=0,"B","C")))
ggplot(df, aes(x=logFC, y=-log10(P.Value))) +
geom_point(aes(color=threshold))+
xlab(expression("Log"[2]*" FC"))+
ylab(expression("-log"[10]*"P Value"))+
scale_color_manual(values = c("black", "red","blue"))+
ylim(0,12)+
xlim(-5,5)+
theme_cowplot()+
theme(legend.position = "none",
plot.title = element_text(size = rel(1.5), hjust = 0.5),
axis.title = element_text(size = rel(0.8)))
}
v1 <- volcanosig(AC.DNR_3.top, 0.05)+ ggtitle("H3K27ac DNR 3 hour")
v2 <- volcanosig(AC.DNR_24.top, 0.05)+ ggtitle("H3K27ac DNR 24 hour")+ylab("")
v3 <- volcanosig(AC.DOX_3.top, 0.05)+ ggtitle("H3K27ac DOX 3 hour")
v4 <- volcanosig(AC.DOX_24.top, 0.05)+ ggtitle("H3K27ac DOX 24 hour")+ylab("")
v5 <- volcanosig(AC.EPI_3.top, 0.05)+ ggtitle("H3K27ac EPI 3 hour")
v6 <- volcanosig(AC.EPI_24.top, 0.05)+ ggtitle("H3K27ac EPI 24 hour")+ylab("")
v7 <- volcanosig(AC.MTX_3.top, 0.05)+ ggtitle("H3K27ac MTX 3 hour")
v8 <- volcanosig(AC.MTX_24.top, 0.05)+ ggtitle("H3K27ac MTX 24 hour")+ylab("")
plot_grid(v1,v2, rel_widths =c(.8,1))
plot_grid(v3,v4, rel_widths =c(.8,1))
plot_grid(v5,v6, rel_widths =c(.8,1))
plot_grid(v7,v8, rel_widths =c(.8,1))
#### Figure S14B:
FCmatrix_23s <- subset(efit_raodah_23s$coefficients)
colnames(FCmatrix_23s) <-
c("DNR\n3h",
"DOX\n3h",
"EPI\n3h",
"MTX\n3h",
"DNR\n24h",
"DOX\n24h",
"EPI\n24h",
"MTX\n24h"
)
mat_col_FC <-
data.frame(
time = c(rep("3 hours", 4), rep("24 hours", 4)),
class = (c(
"AC", "AC", "AC", "nAC", "AC", "AC", "AC", "nAC"
)))
rownames(mat_col_FC) <- colnames(FCmatrix_23s)
mat_colors_FC <-
list(
time = c("pink", "chocolate4"),
class = c("yellow1", "lightgreen"))
names(mat_colors_FC$time) <- unique(mat_col_FC$time)
names(mat_colors_FC$class) <- unique(mat_col_FC$class)
# names(mat_colors_FC$TOP2i) <- unique(mat_col_FC$TOP2i)
corrFC_23s <- cor(FCmatrix_23s)
htanno_23s <- HeatmapAnnotation(df = mat_col_FC, col = mat_colors_FC)
Heatmap(corrFC_23s, top_annotation = htanno_23s)
Figure S15:
Figure S15 A & B:
###now adjusted for 23 samples:
H3K27ac_counts_file <- read_delim("data/Final_four_data/H3K27ac_files/H3K27ac_counts_file.txt", delim= "\t")
PCA_H3_mat_23s <- H3K27ac_counts_file %>%
##removing C_VEH_3 and B_VEH_24 columns
dplyr::select(Geneid,B_DNR_24:B_MTX_24, B_VEH_3:C_VEH_24) %>%
column_to_rownames("Geneid") %>%
as.matrix()
anno_H3_mat_23s <-
data.frame(timeset=colnames(PCA_H3_mat_23s)) %>%
mutate(sample = timeset) %>%
separate(timeset, into = c("indv","trt","time"), sep= "_") %>%
mutate(time = factor(time, levels = c("3", "24"), labels= c("3 hours","24 hours"))) %>%
mutate(trt = factor(trt, levels = c("DOX","EPI", "DNR", "MTX","VEH")))
for_group3 <- data.frame(timeset=colnames(PCA_H3_mat_23s)) %>%
mutate(sample = timeset) %>%
separate(timeset, into = c("indv","trt","time"), sep= "_") %>%
tidyr::unite("test", trt:time,sep="_", remove = FALSE)
### for 3 individuals {2,3,6}
group_3 <- c( 1,2,4,5,6,7,10,1,2,3,4,7,8,9,10,1,2,3,5,6,7,8,9)
group_fac_3 <- group_3
groupid_3 <- as.numeric(group_fac_3)
label <- for_group3$sample
compid_3 <- data.frame(c1= c(2,4,6,8,1,3,5,7), c2 = c( 10,10,10,10,9,9,9,9))
y_TMM_cpm_3 <- cpm(PCA_H3_mat_23s, log = TRUE)
colnames(y_TMM_cpm_3) <- label
# set.seed(31415)
# cormotif_initial_3 <- cormotiffit(exprs = y_TMM_cpm_3, groupid = groupid_3, compid = compid_3, K=1:6, max.iter = 500, runtype = "logCPM")
##results from the K1:6 run:
cormotif_initial_23s <- readRDS("data/Final_four_data/cormotif_3_raodah_run_23s.RDS")
Cormotif::plotIC(cormotif_initial_23s)
Figure S15 C:
Cormotif::plotMotif(cormotif_initial_23s)
Figure S15 D:
drug_pal <- c("#8B006D","#DF707E","#F1B72B", "#3386DD","#41B333")
H3K27ac_counts_file <- read_delim("data/Final_four_data/H3K27ac_files/H3K27ac_counts_file.txt", delim= "\t")
lcpm_h3_23s <- H3K27ac_counts_file %>%
##removing C_VEH_3 and B_VEH_24 columns
dplyr::select(Geneid,B_DNR_24:B_MTX_24, B_VEH_3:C_VEH_24) %>%
column_to_rownames("Geneid") %>%
as.matrix() %>%
cpm(., log = TRUE)
NR_ac <- readRDS("data/Final_four_data/H3K27ac_files/NR_23s.RDS")
ESR_ac <- readRDS("data/Final_four_data/H3K27ac_files/ESR_23s.RDS")
EAR_ac <- readRDS("data/Final_four_data/H3K27ac_files/EAR_23s.RDS")
LR_ac <- readRDS("data/Final_four_data/H3K27ac_files/LR_23s.RDS")
set.seed(31415)
sample_peaks <- rbind(
"NR_1"=sample_n(NR_ac,size = 2),
"ESR_2"=sample_n(ESR_ac,size=2),
"EAR_3"=sample_n(EAR_ac,size=2),
"LR_4"=sample_n(LR_ac, size=2))
sample_peaks_choice <- sample_peaks %>%
dplyr::filter(rownames(.)=="NR_1.2"|
rownames(.)=="ESR_2.1"|
rownames(.)=="EAR_3.1"|
rownames(.)=="LR_4.1")
lcpm_h3_23s%>%
as.data.frame() %>%
rownames_to_column("Peakid")%>%
dplyr::filter(Peakid %in% sample_peaks_choice$Peakid) %>%
pivot_longer(., cols = !Peakid, names_to = "samples",values_to = "logcpm") %>%
separate_wider_delim(., samples, delim = "_",names=c("ind","trt","time"), cols_remove = FALSE) %>%
left_join(., (sample_peaks %>%
rownames_to_column("cluster") %>%
dplyr::select(cluster,Peakid)),
by=c("Peakid"="Peakid")) %>%
mutate(trt=factor(trt, levels = c("DOX","EPI","DNR","MTX","TRZ", "VEH")),
time= factor(time, levels = c("3","24"))) %>%
ggplot(aes(x=time,y=logcpm))+
geom_boxplot(aes(fill=trt))+
# geom_point(aes(color=ind))+
facet_wrap(~Peakid+cluster,scales="free_y", nrow=4, ncol=2)+
ggtitle(" H3K27ac acetylation")+
scale_fill_manual(values = drug_pal)+
theme_bw()+
ylab("log2 cpm H3K27ac")
Figure S16: Shared ATAC and H3K27-acetylation regions cluster by time
toplist_atac_lfc <- readRDS("data/Final_four_data/toplist_ff.RDS")
hr3_ATAC <- toplist_atac_lfc %>%
dplyr::select(trt:logFC) %>%
dplyr::filter(time=="3 hours") %>%
pivot_wider(., id_cols = c("peak"),names_from = trt,values_from = logFC) %>%
dplyr::rename(DOX_3_ATAC=DOX, DNR_3_ATAC=DNR,EPI_3_ATAC=EPI,MTX_3_ATAC=MTX, TRZ_3_ATAC=TRZ)
hr24_ATAC <- toplist_atac_lfc %>%
dplyr::select(trt:logFC) %>%
dplyr::filter(time=="24 hours") %>%
pivot_wider(., id_cols = c("peak"),names_from = trt,values_from = logFC) %>%
dplyr::rename(DOX_24_ATAC=DOX, DNR_24_ATAC=DNR,EPI_24_ATAC=EPI,MTX_24_ATAC=MTX, TRZ_24_ATAC=TRZ)
ATAC_LFC_df <- hr3_ATAC %>%
left_join(., hr24_ATAC, by=c("peak"="peak")) %>%
dplyr::select(peak:MTX_3_ATAC,DNR_24_ATAC:MTX_24_ATAC)
toplist_ac_lfc <- readRDS("data/Final_four_data/toplist_ac_lfc.RDS")
hr3_K27 <- toplist_ac_lfc %>%
dplyr::filter(time=="3") %>%
dplyr::select(Peakid:logFC) %>%
###something weird in peaknames... additional numbers? so I will remove them
mutate(Peakid=str_replace(Peakid, "\\.\\.\\.[0=9]*","/")) %>%
separate(Peakid, into=c("Geneid",NA),sep="/") %>%
pivot_wider(., id_cols=c(Geneid), names_from = "trt", values_from = "logFC") %>%
dplyr::rename(DOX_3_K27=DOX, DNR_3_K27=DNR,EPI_3_K27=EPI,MTX_3_K27=MTX)
hr24_K27 <- toplist_ac_lfc %>%
dplyr::filter(time=="24") %>%
dplyr::select(Peakid:logFC) %>%
###something weird in peaknames... additional numbers? so I will remove them
mutate(Peakid=str_replace(Peakid, "\\.\\.\\.[0=9]*","/")) %>%
separate(Peakid, into=c("Geneid",NA),sep="/") %>%
pivot_wider(., id_cols=c(Geneid), names_from = "trt", values_from = "logFC") %>%
dplyr::rename(DOX_24_K27=DOX, DNR_24_K27=DNR,EPI_24_K27=EPI,MTX_24_K27=MTX)
K27_LFC_df <- hr3_K27 %>%
left_join(., hr24_K27, by=c("Geneid"="Geneid"))
overlap_df_ggplot <- readRDS("data/Final_four_data/LFC_ATAC_K27ac.RDS") %>%
dplyr::select(peakid,Geneid) %>%
left_join(.,ATAC_LFC_df, by=c("peakid"="peak")) %>%
left_join(.,K27_LFC_df, by= c("Geneid"="Geneid"))
counts_corr <-
overlap_df_ggplot %>%
tidyr::unite(.,"name",peakid,Geneid,sep="_") %>%
column_to_rownames("name") %>%
cor()
ComplexHeatmap::Heatmap(counts_corr)
Figure S17:
RNA_ATAC_overlap_df_ggplot <- readRDS("data/Final_four_data/LFC_ATAC_K27ac.RDS") %>%
dplyr::select(peakid,Geneid)
Collapsed_new_peaks <- read_delim("data/Final_four_data/collapsed_new_peaks.txt", delim = "\t", col_names = TRUE)
RNA_median_3_lfc <- readRDS("data/other_papers/RNA_median_3_lfc.RDS")
RNA_median_24_lfc <- readRDS("data/other_papers/RNA_median_24_lfc.RDS")
overlap_df_ggplot <- readRDS("data/Final_four_data/LFC_ATAC_K27ac.RDS")
ATAC_median_24_lfc <- read_csv("data/Final_four_data/median_24_lfc.csv")
ATAC_median_3_lfc <- read_csv("data/Final_four_data/median_3_lfc.csv")
anti_joined_lfc <- ATAC_median_24_lfc %>%
dplyr::filter(!peak %in% RNA_ATAC_overlap_df_ggplot$peakid) %>%
left_join(., (Collapsed_new_peaks %>% dplyr::select(Peakid, NCBI_gene:dist_to_NG)), by = c("peak"="Peakid")) %>%
left_join(.,ATAC_median_3_lfc,by = c("peak"="peak")) %>%
left_join(., RNA_median_3_lfc ,
by=c("SYMBOL"="SYMBOL", "NCBI_gene"="ENTREZID")) %>%
left_join(., RNA_median_24_lfc,
by=c("SYMBOL"="SYMBOL", "NCBI_gene"="ENTREZID"))
joined_LFC_df <-
RNA_ATAC_overlap_df_ggplot %>%
left_join(.,(Collapsed_new_peaks %>%
dplyr::select(Peakid,dist_to_NG, NCBI_gene:SYMBOL)),
by=c("peakid"="Peakid")) %>%
left_join(., RNA_median_3_lfc ,
by=c("SYMBOL"="SYMBOL", "NCBI_gene"="ENTREZID")) %>%
left_join(., RNA_median_24_lfc,
by=c("SYMBOL"="SYMBOL", "NCBI_gene"="ENTREZID")) %>%
left_join(.,ATAC_median_3_lfc,by = c("peakid"="peak")) %>%
left_join(.,ATAC_median_24_lfc,by = c("peakid"="peak")) %>%
dplyr::filter(dist_to_NG > -2000| dist_to_NG <2000)
joined_LFC_df %>%
dplyr::filter(dist_to_NG>-2000 & dist_to_NG<2000) %>%
ggplot(., aes(x=med_3h_lfc,y=RNA_3h_lfc))+
geom_point()+
sm_statCorr(corr_method = 'pearson')+
ggtitle("Correlation of shared 2kb H3K27ac peaks and ATAC_peaks at 3 hours")+
ylab("H3K27ac peak LFC")+
xlab("ATAC-seq log FC")
joined_LFC_df %>%
dplyr::filter(dist_to_NG>-2000 & dist_to_NG<2000) %>%
ggplot(., aes(x=med_24h_lfc,y=RNA_24h_lfc))+
geom_point()+
sm_statCorr(corr_method = 'pearson')+
ggtitle("Correlation of shared 2kb H3K27ac peaks and ATAC_peaks at 24 hours")+
ylab("H3K27ac peak LFC")+
xlab("ATAC-seq log FC")
anti_joined_lfc %>%
dplyr::filter(dist_to_NG>-2000 & dist_to_NG<2000) %>%
# dplyr::filter(peak %in% ATAC_LFC_df$peak) %>%
ggplot(., aes(x=med_3h_lfc,y=RNA_3h_lfc))+
geom_point()+
sm_statCorr(corr_method = 'pearson')+
ggtitle("ATAC-seq regions without H3K27ac overlap at 3 hours")+
ylab("RNA expression LFC")+
xlab("ATAC-seq log FC")
anti_joined_lfc %>%
dplyr::filter(dist_to_NG>-2000 & dist_to_NG<2000) %>%
ggplot(., aes(x=med_24h_lfc,y=RNA_24h_lfc))+
geom_point()+
sm_statCorr(corr_method = 'pearson')+
ggtitle("Correlation of ATAC peaks not shared with H3K27ac and RNA-NG at 24 hours")+
xlab("ATAC peak LFC")+
ylab("RNA LFC")
Figure S18:
toplistall_RNA <- readRDS("data/other_papers/toplistall_RNA.RDS")
toplistall_RNA <- toplistall_RNA %>%
mutate(logFC = logFC*(-1))
hr3_RNA <- toplistall_RNA %>%
dplyr::select(time:logFC) %>%
dplyr::filter(time=="3_hours") %>%
pivot_wider(., id_cols=c(ENTREZID,SYMBOL), names_from = id, values_from = logFC) %>%
dplyr::rename(DOX_3_RNA=DOX, DNR_3_RNA=DNR,EPI_3_RNA=EPI,MTX_3_RNA=MTX, TRZ_3_RNA=TRZ)
hr24_RNA <- toplistall_RNA %>%
dplyr::select(time:logFC) %>%
dplyr::filter(time=="24_hours") %>%
pivot_wider(., id_cols=c(ENTREZID,SYMBOL), names_from = id, values_from = logFC) %>%
dplyr::rename(DOX_24_RNA=DOX, DNR_24_RNA=DNR,EPI_24_RNA=EPI,MTX_24_RNA=MTX, TRZ_24_RNA=TRZ)
RNA_LFC_df <- hr3_RNA %>%
left_join(., hr24_RNA, by=c("SYMBOL"="SYMBOL","ENTREZID"="ENTREZID")) %>%
dplyr::select(ENTREZID:MTX_3_RNA,DNR_24_RNA:MTX_24_RNA)
combo_lfc <- readRDS("data/Final_four_data/LFC_ATAC_K27ac.RDS") %>%
dplyr::select(peakid,Geneid) %>%
left_join(.,ATAC_LFC_df, by=c("peakid"="peak")) %>%
left_join(.,K27_LFC_df, by= c("Geneid"="Geneid"))
combo_corr <- combo_lfc %>%
left_join(.,(Collapsed_new_peaks %>%
dplyr::select(Peakid, NCBI_gene,SYMBOL)), by=c("peakid"="Peakid")) %>%
left_join(., RNA_LFC_df, by=c("SYMBOL"="SYMBOL","NCBI_gene"="ENTREZID")) %>%
tidyr::unite(., "name",peakid,Geneid,NCBI_gene,SYMBOL,sep = "_") %>%
column_to_rownames("name") %>%
na.omit() %>%
cor()
ComplexHeatmap::Heatmap(combo_corr)
###: Figure S19
drug_pal <- c("#8B006D","#DF707E","#F1B72B", "#3386DD","#707031","#41B333")
PRDX6_info <- Collapsed_new_peaks %>% dplyr::filter(SYMBOL=="PRDX6") %>%
distinct(NCBI_gene,SYMBOL)
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_counts %>%
dplyr::filter(ENTREZID == PRDX6_info$NCBI_gene) %>%
pivot_longer(cols = !ENTREZID, names_to = "sample", values_to = "counts") %>%
left_join(., PRDX6_info, by =c("ENTREZID"="NCBI_gene")) %>%
separate("sample", into = c("trt","ind","time")) %>%
mutate(time=factor(time, levels = c("3h","24h"))) %>%
mutate(trt=factor(trt, levels= c("DOX","EPI","DNR","MTX","TRZ","VEH"))) %>%
ggplot(., aes (x = time, y=counts))+
geom_boxplot(aes(fill=trt))+
facet_wrap(~SYMBOL, scales="free_y")+
ggtitle("RNA log 2 cpm of expressed gene")+
scale_fill_manual(values = drug_pal)+
theme_bw()+
ylab("log2 cpm RNA")
PRUNE2_info <- Collapsed_new_peaks %>% dplyr::filter(SYMBOL=="PRUNE2") %>%
distinct(NCBI_gene,SYMBOL)
RNA_counts %>%
dplyr::filter(ENTREZID == PRUNE2_info$NCBI_gene) %>%
pivot_longer(cols = !ENTREZID, names_to = "sample", values_to = "counts") %>%
left_join(., PRUNE2_info, by =c("ENTREZID"="NCBI_gene")) %>%
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")+
ggtitle("RNA log 2 cpm of expressed gene")+
scale_fill_manual(values = drug_pal)+
theme_bw()+
ylab("log2 cpm RNA")
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] eulerr_7.0.2
[2] vargen_0.2.3
[3] devtools_2.4.5
[4] usethis_3.1.0
[5] readxl_1.4.3
[6] smplot2_0.2.5
[7] cowplot_1.1.3
[8] ComplexHeatmap_2.22.0
[9] ggrepel_0.9.6
[10] plyranges_1.26.0
[11] ggsignif_0.6.4
[12] genomation_1.38.0
[13] edgeR_4.4.1
[14] limma_3.62.2
[15] ggpubr_0.6.0
[16] BiocParallel_1.40.0
[17] ggVennDiagram_1.5.2
[18] scales_1.3.0
[19] VennDiagram_1.7.3
[20] futile.logger_1.4.3
[21] gridExtra_2.3
[22] ggfortify_0.4.17
[23] rtracklayer_1.66.0
[24] org.Hs.eg.db_3.20.0
[25] TxDb.Hsapiens.UCSC.hg38.knownGene_3.20.0
[26] GenomicFeatures_1.58.0
[27] AnnotationDbi_1.68.0
[28] Biobase_2.66.0
[29] GenomicRanges_1.58.0
[30] GenomeInfoDb_1.42.1
[31] IRanges_2.40.1
[32] S4Vectors_0.44.0
[33] BiocGenerics_0.52.0
[34] ChIPseeker_1.42.0
[35] RColorBrewer_1.1-3
[36] broom_1.0.7
[37] kableExtra_1.4.0
[38] lubridate_1.9.4
[39] forcats_1.0.0
[40] stringr_1.5.1
[41] dplyr_1.1.4
[42] purrr_1.0.2
[43] readr_2.1.5
[44] tidyr_1.3.1
[45] tibble_3.2.1
[46] ggplot2_3.5.1
[47] tidyverse_2.0.0
[48] workflowr_1.7.1
loaded via a namespace (and not attached):
[1] fs_1.6.5
[2] matrixStats_1.5.0
[3] bitops_1.0-9
[4] enrichplot_1.26.6
[5] httr_1.4.7
[6] doParallel_1.0.17
[7] profvis_0.4.0
[8] tools_4.4.2
[9] backports_1.5.0
[10] R6_2.5.1
[11] mgcv_1.9-1
[12] lazyeval_0.2.2
[13] GetoptLong_1.0.5
[14] urlchecker_1.0.1
[15] withr_3.0.2
[16] preprocessCore_1.68.0
[17] cli_3.6.3
[18] formatR_1.14
[19] Cormotif_1.52.0
[20] labeling_0.4.3
[21] sass_0.4.9
[22] Rsamtools_2.22.0
[23] systemfonts_1.2.1
[24] yulab.utils_0.2.0
[25] foreign_0.8-88
[26] DOSE_4.0.0
[27] svglite_2.1.3
[28] R.utils_2.12.3
[29] sessioninfo_1.2.2
[30] plotrix_3.8-4
[31] BSgenome_1.74.0
[32] pwr_1.3-0
[33] rstudioapi_0.17.1
[34] impute_1.80.0
[35] RSQLite_2.3.9
[36] generics_0.1.3
[37] gridGraphics_0.5-1
[38] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
[39] shape_1.4.6.1
[40] BiocIO_1.16.0
[41] vroom_1.6.5
[42] gtools_3.9.5
[43] car_3.1-3
[44] GO.db_3.20.0
[45] Matrix_1.7-2
[46] abind_1.4-8
[47] R.methodsS3_1.8.2
[48] lifecycle_1.0.4
[49] whisker_0.4.1
[50] yaml_2.3.10
[51] carData_3.0-5
[52] SummarizedExperiment_1.36.0
[53] gplots_3.2.0
[54] qvalue_2.38.0
[55] SparseArray_1.6.1
[56] blob_1.2.4
[57] promises_1.3.2
[58] crayon_1.5.3
[59] miniUI_0.1.1.1
[60] ggtangle_0.0.6
[61] lattice_0.22-6
[62] KEGGREST_1.46.0
[63] magick_2.8.5
[64] pillar_1.10.1
[65] knitr_1.49
[66] fgsea_1.32.2
[67] rjson_0.2.23
[68] boot_1.3-31
[69] codetools_0.2-20
[70] fastmatch_1.1-6
[71] glue_1.8.0
[72] getPass_0.2-4
[73] ggfun_0.1.8
[74] remotes_2.5.0
[75] data.table_1.16.4
[76] vctrs_0.6.5
[77] png_0.1-8
[78] treeio_1.30.0
[79] cellranger_1.1.0
[80] gtable_0.3.6
[81] cachem_1.1.0
[82] xfun_0.50
[83] mime_0.12
[84] S4Arrays_1.6.0
[85] iterators_1.0.14
[86] statmod_1.5.0
[87] ellipsis_0.3.2
[88] nlme_3.1-167
[89] ggtree_3.14.0
[90] bit64_4.6.0-1
[91] rprojroot_2.0.4
[92] bslib_0.8.0
[93] affyio_1.76.0
[94] rpart_4.1.24
[95] KernSmooth_2.23-26
[96] colorspace_2.1-1
[97] DBI_1.2.3
[98] Hmisc_5.2-2
[99] nnet_7.3-20
[100] seqPattern_1.38.0
[101] tidyselect_1.2.1
[102] processx_3.8.5
[103] bit_4.5.0.1
[104] compiler_4.4.2
[105] curl_6.2.0
[106] git2r_0.35.0
[107] htmlTable_2.4.3
[108] xml2_1.3.6
[109] DelayedArray_0.32.0
[110] checkmate_2.3.2
[111] caTools_1.18.3
[112] affy_1.84.0
[113] callr_3.7.6
[114] digest_0.6.37
[115] rmarkdown_2.29
[116] XVector_0.46.0
[117] htmltools_0.5.8.1
[118] pkgconfig_2.0.3
[119] base64enc_0.1-3
[120] MatrixGenerics_1.18.1
[121] fastmap_1.2.0
[122] htmlwidgets_1.6.4
[123] rlang_1.1.5
[124] GlobalOptions_0.1.2
[125] UCSC.utils_1.2.0
[126] shiny_1.10.0
[127] farver_2.1.2
[128] jquerylib_0.1.4
[129] zoo_1.8-12
[130] jsonlite_1.8.9
[131] GOSemSim_2.32.0
[132] R.oo_1.27.0
[133] RCurl_1.98-1.16
[134] magrittr_2.0.3
[135] Formula_1.2-5
[136] GenomeInfoDbData_1.2.13
[137] ggplotify_0.1.2
[138] patchwork_1.3.0
[139] munsell_0.5.1
[140] Rcpp_1.0.14
[141] ape_5.8-1
[142] stringi_1.8.4
[143] zlibbioc_1.52.0
[144] pkgbuild_1.4.6
[145] plyr_1.8.9
[146] parallel_4.4.2
[147] Biostrings_2.74.1
[148] splines_4.4.2
[149] hms_1.1.3
[150] circlize_0.4.16
[151] locfit_1.5-9.10
[152] ps_1.8.1
[153] igraph_2.1.4
[154] pkgload_1.4.0
[155] reshape2_1.4.4
[156] futile.options_1.0.1
[157] XML_3.99-0.18
[158] evaluate_1.0.3
[159] BiocManager_1.30.25
[160] lambda.r_1.2.4
[161] tzdb_0.4.0
[162] foreach_1.5.2
[163] httpuv_1.6.15
[164] clue_0.3-66
[165] gridBase_0.4-7
[166] xtable_1.8-4
[167] restfulr_0.0.15
[168] tidytree_0.4.6
[169] rstatix_0.7.2
[170] later_1.4.1
[171] viridisLite_0.4.2
[172] aplot_0.2.4
[173] memoise_2.0.1
[174] GenomicAlignments_1.42.0
[175] cluster_2.1.8
[176] timechange_0.3.0