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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(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)
library(limma)
library(epitools)
library(circlize)

Figure S13: H3K27-acetylated regions are enriched at transcription start sites.

knitr::include_graphics("assets/Fig\ S13.png", error=FALSE)

Version Author Date
50f3de9 E. Renee Matthews 2025-02-21
knitr::include_graphics("docs/assets/Fig\ S13.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...            2025-08-11 2:11:40 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...            2025-08-11 2:11:40 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...            2025-08-11 2:11:41 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...            2025-08-11 2:11:41 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...            2025-08-11 2:11:41 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...            2025-08-11 2:11:41 PM 
plot_grid(a1,a2, b1,b2,c1,c2, axis="l",align = "hv",nrow=3, ncol=2)

Version Author Date
77a569b E. Renee Matthews 2025-02-27

Figure S14: H3K27ac CUT&Tag sequencing data separate by time and drug treatment.

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)

Version Author Date
bbb4cff reneeisnowhere 2025-08-11

Figure S15: Change in H3K27ac enrichment in response to drugs is highly correlated across ACs.

Figure S15A: 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             0
AC.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))

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plot_grid(v3,v4,  rel_widths =c(.8,1))

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plot_grid(v5,v6,  rel_widths =c(.8,1))

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plot_grid(v7,v8,  rel_widths =c(.8,1))

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Figure S15B: Correlation of H3K27ac log fold change across all regions.

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)

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Figure S16: Shared ATAC and H3K27-acetylation region response to TOP2i clusters by time.

annotated_DARs<- readRDS("data/Final_four_data/re_analysis/DOX_DAR_annotated_peaks_chipannno.RDS")

Collapsed_peaks <- annotated_DARs$DOX_24 %>% as.data.frame()

final_peaks_gr <- Collapsed_peaks %>% 
  dplyr::select(seqnames:mcols.genes,annotation:distanceToTSS) %>% 
  GRanges()

final_peaks <- Collapsed_peaks 


H3K27ac_gr <- readRDS("data/Final_four_data/re_analysis/H3K27ac_granges_df.RDS")

ol_peaks <- join_overlap_intersect(final_peaks_gr, H3K27ac_gr)
ATAC_region_count <- ol_peaks %>% 
  as.data.frame() %>% 
  dplyr::rename("Peakid"=mcols.genes) %>% 
  distinct(Peakid)
H3K27ac_region_count <- ol_peaks %>% 
  as.data.frame() %>% 
  distinct(Geneid)
Overlap_regions <- ol_peaks %>% 
  as.data.frame() %>% 
   dplyr::rename("Peakid"=mcols.genes) %>% 
  distinct(Peakid,Geneid)

H3K27ac_toplist_results <- readRDS("data/Final_four_data/re_analysis/Toptable_results_H3K27ac_data.RDS")

H3K27ac_toptable_list <- bind_rows(H3K27ac_toplist_results, .id = "group")

ATAC_toptable_results <- readRDS("data/Final_four_data/re_analysis/Toptable_results.RDS")

ATAC_toptable_list <- bind_rows(ATAC_toptable_results, .id = "group")


hr3_K27 <-  H3K27ac_toptable_list %>% 
    separate_wider_delim(., group, names=c("trt","time"), delim="_") %>% 
  dplyr::select(trt,time:logFC) %>% 
  dplyr::filter(time=="3") %>% 
  pivot_wider(., id_cols=c(genes), 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 <- H3K27ac_toptable_list %>% 
  separate_wider_delim(., group, names=c("trt","time"), delim="_") %>% 
  dplyr::select(trt,time:logFC) %>% 
  dplyr::filter(time=="24") %>% 
  pivot_wider(., id_cols=c(genes), 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("genes"="genes")) 


hr3_ATAC <- ATAC_toptable_list %>% 
  dplyr::select(group:logFC) %>% 
    separate_wider_delim(., group, names=c("trt","time"), delim="_") %>% 
  dplyr::filter(time=="3") %>% 
  pivot_wider(., id_cols=c(time, genes), names_from = trt, values_from = logFC) %>% 
  dplyr::select(!TRZ) %>% 
  dplyr::rename(DOX_3_ATAC=DOX, DNR_3_ATAC=DNR,EPI_3_ATAC=EPI,MTX_3_ATAC=MTX) %>% 
  dplyr::rename("peak"=genes)

hr24_ATAC <- ATAC_toptable_list %>% 
  dplyr::select(group:logFC) %>% 
    separate_wider_delim(., group, names=c("trt","time"), delim="_") %>% 
  dplyr::filter(time=="24") %>% 
  pivot_wider(., id_cols=c(time, genes), names_from = trt, values_from = logFC) %>% 
  dplyr::select(!TRZ) %>% 
  dplyr::rename(DOX_24_ATAC=DOX, DNR_24_ATAC=DNR,EPI_24_ATAC=EPI,MTX_24_ATAC=MTX) %>%
  dplyr::rename("peak"=genes)

ATAC_LFC_df <- hr3_ATAC %>% 
  left_join(., hr24_ATAC, by=c("peak"="peak")) %>%
   dplyr::select(!time.x) %>% 
  dplyr::select(!time.y)
K27_ATAC_mat <- Overlap_regions %>%
  left_join(.,ATAC_LFC_df,by=c("Peakid"="peak")) %>% 
  left_join(.,K27_LFC_df, by=c("Geneid"="genes")) %>%
  distinct(Peakid,Geneid,.keep_all = TRUE) %>% 
  tidyr::unite(name,Peakid:Geneid, sep="_") %>% 
  column_to_rownames("name") %>% 
  as.matrix()  


pearson_cor_mat <- cor(K27_ATAC_mat,method = "pearson", use = "pairwise.complete.obs")

ComplexHeatmap::Heatmap(pearson_cor_mat, 
                        column_title = "Pearson H3K27ac/ATAC LFC correlation")

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bbb4cff reneeisnowhere 2025-08-11
77a569b E. Renee Matthews 2025-02-27

Figure S17: Shared accessible and H3K27-acetylated chromatin regions have a significant correlation with nearby gene expression at 24 hours.

toplistall_RNA <- readRDS("data/other_papers/toplistall_RNA.RDS") %>% 
  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::select(!TRZ) %>% 
  dplyr::rename(DOX_3_RNA=DOX, DNR_3_RNA=DNR,EPI_3_RNA=EPI,MTX_3_RNA=MTX)
  

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::select(!TRZ) %>% 
  dplyr::rename(DOX_24_RNA=DOX, DNR_24_RNA=DNR,EPI_24_RNA=EPI,MTX_24_RNA=MTX)

RNA_LFC_df <- hr3_RNA %>% 
  left_join(., hr24_RNA, by=c("SYMBOL"="SYMBOL","ENTREZID"="ENTREZID")) 
 

All_data_overlaps <- Overlap_regions %>% 
  left_join(., (final_peaks %>% 
              dplyr::select(mcols.genes, geneId,distanceToTSS)), 
            by=c("Peakid"= "mcols.genes")) %>% 
  left_join(.,ATAC_LFC_df,by=c("Peakid"="peak")) %>% 
  left_join(.,K27_LFC_df, by=c("Geneid"="genes")) %>%
  left_join(., RNA_LFC_df, by = c("geneId"="ENTREZID")) %>% 
  dplyr::rename("NCBI_gene"=geneId)

Only_2kb_ATAC_K27_RNA <- All_data_overlaps %>% 
  dplyr::filter(distanceToTSS > -2000 & distanceToTSS < 2000) 

All_data_2kb_mat <- Only_2kb_ATAC_K27_RNA %>% 
  tidyr::unite(name, Peakid, Geneid, NCBI_gene,SYMBOL,distanceToTSS, sep="_") %>%
  column_to_rownames(., "name") %>%
  as.matrix() 

H3K27_med_3 <- read_csv("data/Final_four_data/re_analysis/median_3_lfc_H3K27ac_norm.csv") %>% 
  dplyr::select(H3K27ac_peak,med_Kac_3h_lfc)
H3K27_med_24 <- read_csv("data/Final_four_data/re_analysis/median_24_lfc_H3K27ac_norm.csv")%>% 
  dplyr::select(H3K27ac_peak,med_Kac_24h_lfc)
ATAC_med_3 <- read_csv("data/Final_four_data/re_analysis/median_3_lfc_norm.csv")%>% 
  dplyr::select(peak,med_3h_lfc)
ATAC_med_24 <- read_csv("data/Final_four_data/re_analysis/median_24_lfc_norm.csv")%>% 
  dplyr::select(peak,med_24h_lfc)
RNA_median_3 <- readRDS("data/other_papers/RNA_median_3_lfc.RDS") %>% 
  dplyr::select(RNA_3h_lfc,ENTREZID)
RNA_median_24 <- readRDS("data/other_papers/RNA_median_24_lfc.RDS") %>% 
  dplyr::select(RNA_24h_lfc,ENTREZID)

Median_df_2kb <- Only_2kb_ATAC_K27_RNA %>% 
  dplyr::select(Peakid, Geneid,NCBI_gene) %>% 
  mutate(NCBI_gene = gsub("[:,]", ";", NCBI_gene)) %>% 
  separate_longer_delim(NCBI_gene, delim = ";") %>% 
  distinct() %>% 
  left_join(H3K27_med_3,by = c("Geneid"="H3K27ac_peak")) %>% 
  left_join(H3K27_med_24,by = c("Geneid"="H3K27ac_peak")) %>% 
  left_join(ATAC_med_3,by = c("Peakid"="peak")) %>% 
  left_join(ATAC_med_24,by = c("Peakid"="peak")) %>%
  left_join(RNA_median_3, by = c("NCBI_gene"="ENTREZID")) %>% 
  left_join(RNA_median_24, by = c("NCBI_gene"="ENTREZID")) 

Median_df_2kb %>% 
  ggplot(., aes(y=med_Kac_3h_lfc, x=med_3h_lfc))+
  ggrastr::geom_point_rast()+
   sm_statCorr(corr_method = 'pearson')+
   ggtitle("Correlation of 2kb ATAC regions and H3K27ac regions 3 hours")+
  xlab("ATAC peak med LFC")+
   ylab("H3K27ac med LFC")

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77a569b E. Renee Matthews 2025-02-27
Median_df_2kb %>% 
  ggplot(., aes(y=med_Kac_24h_lfc, x=med_24h_lfc))+
   ggrastr::geom_point_rast()+
   sm_statCorr(corr_method = 'pearson')+
   ggtitle("Correlation of 2kb ATAC regions and H3K27ac regions 24 hours")+
  xlab("ATAC peak med LFC")+
   ylab("H3K27ac med LFC")

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77a569b E. Renee Matthews 2025-02-27
no_H3K27ac_overlap <-final_peaks %>%
  dplyr::rename("Peakid"=mcols.genes) %>% 
  dplyr::filter(!Peakid %in% Overlap_regions$Peakid) %>% 
  dplyr::select (Peakid,geneId,distanceToTSS) %>% 
left_join(ATAC_med_3,by = c("Peakid"="peak")) %>% 
  left_join(ATAC_med_24,by = c("Peakid"="peak")) %>%
  left_join(RNA_median_3, by = c("geneId"="ENTREZID")) %>% 
  left_join(RNA_median_24, by = c("geneId"="ENTREZID")) %>% 
  dplyr::filter(distanceToTSS > -2000 & distanceToTSS < 2000)

no_H3K27ac_overlap %>% 
ggplot(., aes(y=RNA_3h_lfc, x=med_3h_lfc))+
  ggrastr::geom_point_rast()+ 
   sm_statCorr(corr_method = 'pearson')+
   ggtitle("3 hour 2kb ATAC & RNA expressed, no H3K27ac overlap")+
  xlab("ATAC peak med LFC")+
   ylab("RNA med LFC")

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bbb4cff reneeisnowhere 2025-08-11
77a569b E. Renee Matthews 2025-02-27
no_H3K27ac_overlap %>% 
ggplot(., aes(y=RNA_24h_lfc, x=med_24h_lfc))+
  ggrastr::geom_point_rast()+
   sm_statCorr(corr_method = 'pearson')+
   ggtitle("24 hour 2kb ATAC & RNA expressed, no H3K27ac overlap")+
  xlab("ATAC peak med LFC")+
   ylab("RNA med LFC")

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bbb4cff reneeisnowhere 2025-08-11
77a569b E. Renee Matthews 2025-02-27

Figure S18: Drug response associates with time and molecular phenotype.

pearson_cor_mat_2kb <- cor(All_data_2kb_mat,method = "pearson", use = "pairwise.complete.obs")


ComplexHeatmap::Heatmap(pearson_cor_mat_2kb, 
                        column_title = "Pearson RNA/ATAC/H3K27ac LFC correlation using +/- 2 kb from TSS ")

Version Author Date
bbb4cff reneeisnowhere 2025-08-11
77a569b E. Renee Matthews 2025-02-27

Figure S19: DARs are present within AC-induced cardiotoxicity SNP-containing TADs.

### Pulling the all regions granges list from the motif list of lists
Motif_list_gr <- readRDS("data/Final_four_data/re_analysis/Motif_list_granges.RDS")
### no change motif_list_gr names so they do not overwrite the dataframes
names(Motif_list_gr) <- paste0(names(Motif_list_gr), "_gr")

### this pulls out the all_regions_gr granges frame I made previously with 155,557 regions listed
list2env(Motif_list_gr[10],envir= .GlobalEnv)
<environment: R_GlobalEnv>
annotated_DARs<- readRDS("data/Final_four_data/re_analysis/DOX_DAR_annotated_peaks_chipannno.RDS")

Left_ventricle_TAD <- import(con = "C://Users/renee/Downloads/hg38.TADs/hg38/VentricleLeft_STL003_Leung_2015-raw_TADs.txt", format = "bed",genome="hg38")
mcols(Left_ventricle_TAD)$TAD_id <- paste0("TAD_", seq_along(Left_ventricle_TAD))


Schneider_all_SNPS <- read_delim("data/other_papers/Schneider_all_SNPS.txt", 
    delim = "\t", escape_double = FALSE, 
    trim_ws = TRUE)

Schneider_all_SNPS_df <- Schneider_all_SNPS %>%
  dplyr::rename("RSID"="#Uploaded_variation") %>% 
  dplyr::select(RSID,Location,SYMBOL,Gene, SOURCE) %>%
  distinct(RSID,Location,SYMBOL,.keep_all = TRUE) %>% 
  dplyr::rename("Close_SYMBOL"="SYMBOL") %>% 
  dplyr::filter(!str_starts(Location, "H")) %>% 
  separate_wider_delim(Location,delim=":",names=c("Chr","Coords")) %>% 
  separate_wider_delim(Coords,delim= "-", names= c("Start","End")) %>% 
  mutate(Chr=paste0("chr",Chr)) %>% 
  group_by(RSID) %>% 
  reframe(Chr=unique(Chr),
            Start=unique(Start),
            End=unique(End),
            Close_SYMBOL=paste(unique(Close_SYMBOL),collapse=";"),
            Gene=paste(Gene,collapse=";"),
            SOURCE=paste(SOURCE,collapse=";")
            ) %>% 
  GRanges() %>% as.data.frame 

schneider_gr <-Schneider_all_SNPS_df%>%
  dplyr::select(seqnames,start,end,RSID:SOURCE) %>%
  distinct() %>% 
  GRanges()

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()
all_results_pivot <- all_results %>% 
dplyr::select(genes,logFC,source) %>% 
  pivot_wider(., id_cols = genes, names_from = source, values_from = logFC) %>% 
  dplyr::select(genes,DOX_3,EPI_3,DNR_3,MTX_3,TRZ_3,DOX_24,EPI_24,DNR_24,MTX_24,TRZ_24)


Assigned_genes_toPeak <- annotated_DARs$DOX_24 %>% as.data.frame() %>% 
  dplyr::select(mcols.genes,annotation, geneId, distanceToTSS) %>% 
  dplyr::rename("Peakid"=mcols.genes)

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"))

Peak_gene_RNA_LFC <- Assigned_genes_toPeak %>% 
  left_join(., RNA_results, by =c("geneId"="ENTREZID"))

entrez_ids <- Assigned_genes_toPeak$geneId  


gene_info <- AnnotationDbi::select(
  org.Hs.eg.db,
  keys = entrez_ids,
  columns = c("SYMBOL"),
  keytype = "ENTREZID"
)
gene_info_collapsed <- gene_info %>%
  group_by(ENTREZID) %>%
  summarise(SYMBOL = paste(unique(SYMBOL), collapse = ","), .groups = "drop")

toplistall_RNA <- readRDS("data/other_papers/toplistall_RNA.RDS") %>% 
  mutate(logFC = logFC*(-1))

Assigned_genes_toPeak <- annotated_DARs$DOX_24 %>% as.data.frame() %>% 
  dplyr::select(mcols.genes,annotation, geneId, distanceToTSS) %>% 
  dplyr::rename("Peakid"=mcols.genes)

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"))

Peak_gene_RNA_LFC <- Assigned_genes_toPeak %>% 
  left_join(., RNA_results, by =c("geneId"="ENTREZID"))



DOX_24_DAR <- as.data.frame(annotated_DARs$DOX_24)
EPI_24_DAR <- as.data.frame(annotated_DARs$EPI_24)
DNR_24_DAR <- as.data.frame(annotated_DARs$DNR_24)
MTX_24_DAR <- as.data.frame(annotated_DARs$MTX_24)

DOX_3_DAR <- as.data.frame(annotated_DARs$DOX_3)
EPI_3_DAR <- as.data.frame(annotated_DARs$EPI_3)
DNR_3_DAR <- as.data.frame(annotated_DARs$DNR_3)
MTX_3_DAR <- as.data.frame(annotated_DARs$MTX_3)

DOX_DAR_sig <- DOX_24_DAR %>%
  dplyr::filter(mcols.adj.P.Val<0.05) %>% 
  distinct (mcols.genes) %>% 
  dplyr::rename("Peakid"="mcols.genes")
DOX_DAR_sig_3 <- DOX_3_DAR %>%
  dplyr::filter(mcols.adj.P.Val<0.05) %>% 
  distinct (mcols.genes) %>% 
  dplyr::rename("Peakid"="mcols.genes")

EPI_DAR_sig <- EPI_24_DAR %>%
  dplyr::filter(mcols.adj.P.Val<0.05) %>% 
  distinct (mcols.genes) %>% 
  dplyr::rename("Peakid"="mcols.genes")

EPI_DAR_sig_3 <- EPI_3_DAR %>%
  dplyr::filter(mcols.adj.P.Val<0.05) %>% 
  distinct (mcols.genes) %>% 
  dplyr::rename("Peakid"="mcols.genes")

DNR_DAR_sig <- DNR_24_DAR %>%
  dplyr::filter(mcols.adj.P.Val<0.05) %>% 
  distinct (mcols.genes) %>% 
  dplyr::rename("Peakid"="mcols.genes")
DNR_DAR_sig_3 <- DNR_3_DAR %>%
  dplyr::filter(mcols.adj.P.Val<0.05) %>% 
  distinct (mcols.genes) %>% 
  dplyr::rename("Peakid"="mcols.genes")

MTX_DAR_sig <- MTX_24_DAR %>%
  dplyr::filter(mcols.adj.P.Val<0.05) %>% 
  distinct (mcols.genes) %>% 
  dplyr::rename("Peakid"="mcols.genes")
MTX_DAR_sig_3 <- MTX_3_DAR %>%
  dplyr::filter(mcols.adj.P.Val<0.05) %>% 
  distinct (mcols.genes) %>% 
  dplyr::rename("Peakid"="mcols.genes")

snp_tad_df <-
  join_overlap_inner(schneider_gr, Left_ventricle_TAD) %>%
  as_tibble() %>%
  dplyr::select(RSID, snp_start = start, snp_chr = seqnames, TAD_id)


peak_tad_df <-
join_overlap_inner(all_regions_gr, Left_ventricle_TAD) %>%
  as_tibble() %>%
  dplyr::select(Peakid, peak_start = start, peak_chr = seqnames, TAD_id)

peak_snp_pairs <- peak_tad_df %>%
  inner_join(snp_tad_df, by = "TAD_id")
test_ol <- join_overlap_intersect(Left_ventricle_TAD, schneider_gr)
df <- as.data.frame(test_ol, row.names = NULL)
TAD_SNP_ol <- test_ol %>% as.data.frame() %>% 
  distinct(TAD_id, RSID)


peak_snp_pairs_dist <- peak_snp_pairs %>%
  mutate(distance = abs(peak_start - snp_start)) %>% 
  mutate(sig_24= if_else(Peakid %in% DOX_DAR_sig$Peakid, "sig","not_sig"))


Cardiotox_gwas_df <- peak_snp_pairs_dist %>% 
  dplyr::filter(sig_24=="sig") %>% 
  group_by(TAD_id,RSID) %>% 
  slice_min(order_by = distance, with_ties = FALSE) %>%
  ungroup() %>% 
  arrange(snp_chr,snp_start) %>% 
  left_join(., all_results_pivot, by=c("Peakid"="genes")) %>% 
  tidyr::unite(., name,Peakid,RSID)


Cardiotox_gwas_collaped_df <-
peak_snp_pairs_dist %>% 
  dplyr::filter(sig_24=="sig") %>% 
  group_by(TAD_id,RSID) %>% 
  slice_min(order_by = distance, with_ties = FALSE) %>%
  ungroup() %>% 
  arrange(snp_chr,snp_start) %>% 
  group_by(Peakid, peak_chr, peak_start, TAD_id, sig_24) %>%
  summarise(
    min_distance = min(distance),
    mean_distance = mean(distance),
    snp_list = paste(unique(RSID), collapse = ","),
    .groups = "drop"
  ) %>% 
  left_join(., all_results_pivot, by=c("Peakid"="genes")) %>% 
  left_join(., Peak_gene_RNA_LFC, by=c("Peakid"="Peakid")) %>% 
  left_join(.,gene_info_collapsed, by=c("geneId"="ENTREZID")) %>% 
  mutate(SYMBOL=if_else(is.na(SYMBOL.x),SYMBOL.y,if_else(SYMBOL.x==SYMBOL.y, SYMBOL.x,paste0(SYMBOL.x,"_",SYMBOL.y)))) %>% 
  tidyr::unite(., name,Peakid,SYMBOL,snp_list) %>% 
  mutate(snp_dist=case_when(min_distance <2000 ~"2kb",
                            min_distance > 2000 & min_distance<20000 ~ "20kb",
                            min_distance >20000 ~">20kb"))
  
left_ventricle_ol <- join_overlap_inner(all_regions_gr ,Left_ventricle_TAD) %>% 
  as.data.frame() %>% 
  distinct(Peakid,.keep_all = TRUE) %>% 
  dplyr::filter(TAD_id %in% TAD_SNP_ol$TAD_id)

# peak_df <- as.data.frame(left_ventricle_ol)
# SNP_df <- as.data.frame(snp_ol)


reds <- colorRampPalette(brewer.pal(9, "Reds")[3:9])(12)
greens <- colorRampPalette(brewer.pal(9, "Greens")[3:9])(12)
blues <- colorRampPalette(brewer.pal(9, "Blues")[3:9])(12)
purples <- colorRampPalette(brewer.pal(9, "Purples")[3:9])(12)
oranges <- colorRampPalette(brewer.pal(9, "Oranges")[3:9])(12)



tads <- unique(peak_snp_pairs$TAD_id)
num_tads <- length(tads)

color_spectrum <- c(reds, greens, blues, purples, oranges)[1:num_tads]

if (num_tads > length(color_spectrum)) {
  stop("Not enough colors for TADs. Add more palettes.")
}
tad_colors <- color_spectrum[1:num_tads]
names(tad_colors) <- tads  # Assign color names to TAD IDs

peak_snp_pairs_dist <- peak_snp_pairs %>%
  mutate(distance = abs(peak_start - snp_start)) %>%
  mutate(sig_24= if_else(Peakid %in% DOX_DAR_sig$Peakid, "sig","not_sig"))


ATAC_all_adj.pvals <- all_results%>%
dplyr::select(source,genes,adj.P.Val) %>%
    pivot_wider(id_cols=genes, values_from = adj.P.Val, names_from = source)
# saveRDS(ATAC_all_adj.pvals,"data/Final_four_data/re_analysis/ATAC_all_adj_pvals.RDS")
sig_mat_cardiotox <- ATAC_all_adj.pvals %>%
  dplyr::filter(genes %in% peak_snp_pairs_dist$Peakid) %>% 
  left_join(peak_snp_pairs_dist, by=c("genes"="Peakid")) %>% 
  group_by(TAD_id,RSID) %>% 
  slice_min(order_by = distance, with_ties = FALSE) %>%
  ungroup() %>% 
  arrange(snp_chr,snp_start) %>% 
   group_by(genes, peak_chr, peak_start, TAD_id, sig_24) %>%
  summarise(
    min_distance = min(distance),
    mean_distance = mean(distance),
    snp_list = paste(unique(RSID), collapse = ","),
    .groups = "drop"
  ) %>% 
  left_join(ATAC_all_adj.pvals) %>% 
  tidyr::unite(., name,genes,snp_list) %>% 
  dplyr::select(name, DNR_3:TRZ_24) %>% 
  column_to_rownames("name") %>% 
  as.matrix()

AR_Cardiotox_gwas_collaped_df <-
peak_snp_pairs_dist %>% 
  # dplyr::filter(sig_24=="sig") %>% 
  group_by(TAD_id,RSID) %>% 
  slice_min(order_by = distance, with_ties = FALSE) %>%
  ungroup() %>% 
  arrange(snp_chr,snp_start) %>% 
  group_by(Peakid, peak_chr, peak_start, TAD_id, sig_24) %>%
  summarise(
    min_distance = min(distance),
    mean_distance = mean(distance),
    snp_list = paste(unique(RSID), collapse = ","),
    .groups = "drop"
  ) %>% 
  left_join(., all_results_pivot, by=c("Peakid"="genes")) %>% 
  tidyr::unite(., name,Peakid,snp_list) %>%
  mutate(snp_dist=case_when(min_distance <2000 ~"2kb",
                            min_distance > 2000 & min_distance<20000 ~ "20kb",
                            min_distance >20000 ~">20kb"))


Cardotox_mat_2 <-   AR_Cardiotox_gwas_collaped_df %>%
  dplyr::select(name,DOX_3:TRZ_24) %>% 
  column_to_rownames("name") %>% 
  as.matrix()

annot_map_df_2 <- AR_Cardiotox_gwas_collaped_df %>% 
  dplyr::select(name,snp_dist,sig_24) %>% 
  column_to_rownames("name") 
annot_map_2 <-
  ComplexHeatmap::rowAnnotation(
    snp_dist=AR_Cardiotox_gwas_collaped_df$snp_dist,
    TAD_id=AR_Cardiotox_gwas_collaped_df$TAD_id,
    DOX_24hr_DAR=AR_Cardiotox_gwas_collaped_df$sig_24,
    col= list(snp_dist=c("2kb"="goldenrod4",
                               "20kb"="pink",
                               ">20kb"="tan2"),
              TAD_id=tad_colors))
# all.equal(rownames(sig_mat_cardiotox), rownames(Cardotox_mat_2))
# all.equal(colnames(sig_mat_cardiotox), colnames(Cardotox_mat_2))
# 
# setdiff(colnames(sig_mat_cardiotox), colnames(Cardotox_mat_2))
# setdiff(colnames(Cardotox_mat_2), colnames(sig_mat_cardiotox))
# 
# intersect(colnames(sig_mat_cardiotox), colnames(Cardotox_mat_2))
# setdiff(colnames(sig_mat_cardiotox), colnames(Cardotox_mat_2))
# setdiff(colnames(Cardotox_mat_2), colnames(sig_mat_cardiotox))

simply_map_lfc_2 <- ComplexHeatmap::Heatmap(Cardotox_mat_2,
                        left_annotation = annot_map_2,
                        show_row_names = TRUE,
                       row_names_max_width= ComplexHeatmap::max_text_width(rownames(Cardotox_mat_2),                                                        gp=gpar(fontsize=14)),
                        heatmap_legend_param = list(direction = "horizontal"),
                       
                        show_column_names = TRUE,
                        cluster_rows = FALSE,
                        cluster_columns = FALSE,
                        cell_fun = function(j, i, x, y, width, height, fill) {
  rowname <- rownames(Cardotox_mat_2)[i]
  colname <- colnames(Cardotox_mat_2)[j]
  if (!is.na(sig_mat_cardiotox[rowname, colname]) &&
      sig_mat_cardiotox[rowname, colname] < 0.05) {
    grid.text("*", x, y, gp = gpar(fontsize = 20))
  }
})




ComplexHeatmap::draw(simply_map_lfc_2, 
     merge_legend = TRUE, 
     heatmap_legend_side = "left", 
    annotation_legend_side = "left")

Version Author Date
bbb4cff reneeisnowhere 2025-08-11

Figure S20: DARs overlap AF SNPs

gwas_HF <- readRDS("data/other_papers/HF_gwas_association_downloaded_2025_01_23_EFO_0003144_withChildTraits.RDS")

gwas_ARR <- readRDS("data/other_papers/AF_gwas_association_downloaded_2025_01_23_EFO_0000275.RDS")

gwas_IHD   <- readRDS("data/other_papers/IHD_IHD_gwas_association_downloaded_2025_06_26_EFO_1001375_withChildTraits")
gwas_CAD <- readRDS( "data/CAD_gwas_dataframe.RDS")
gwas_ACresp <- readRDS("data/gwas_3_dataframe.RDS")
Short_gwas_gr <-
  gwas_ARR %>% 
          distinct(SNPS,.keep_all = TRUE) %>%
          dplyr::select(CHR_ID, CHR_POS,SNPS) %>% 
          mutate(gwas="AF") %>% 
   rbind(gwas_HF %>% 
          distinct(SNPS,.keep_all = TRUE) %>%
          dplyr::select(CHR_ID, CHR_POS,SNPS) %>% 
          mutate(gwas="HF")) %>% 
  rbind(gwas_IHD %>% 
          distinct(SNPS,.keep_all = TRUE) %>%
          dplyr::select(CHR_ID, CHR_POS,SNPS) %>% 
          mutate(gwas="IHD")) %>% 
  rbind(gwas_CAD %>% 
          distinct(SNPS,.keep_all = TRUE) %>%
          dplyr::select(CHR_ID, CHR_POS,SNPS) %>% 
          mutate(gwas="CAD")) %>% 
  
  na.omit() %>% 
 mutate(seqnames=paste0("chr",CHR_ID), CHR_POS=as.numeric(CHR_POS)) %>% 
  na.omit() %>%
   mutate(start=CHR_POS, end=CHR_POS, width=1) %>% 
  GRanges()

all_regions_peak <- all_results %>%
  dplyr::select(source,genes, logFC,adj.P.Val) %>% 
  mutate("Peakid"=genes) %>% 
  dplyr::filter(source=="DOX_3") %>% 
  distinct(Peakid) 

AF_ol_peaks <-   join_overlap_inner(all_regions_gr, Short_gwas_gr) %>%
  as.data.frame() %>% 
  dplyr::filter(gwas =="AF") %>% 
  distinct(Peakid)

HF_ol_peaks <-   join_overlap_inner(all_regions_gr, Short_gwas_gr) %>%
  as.data.frame() %>% 
  dplyr::filter(gwas =="HF") %>% 
  distinct(Peakid)

IHD_ol_peaks <-   join_overlap_inner(all_regions_gr, Short_gwas_gr) %>%
  as.data.frame() %>% 
  dplyr::filter(gwas =="IHD") %>% 
  distinct(Peakid)

CAD_ol_peaks <-   join_overlap_inner(all_regions_gr, Short_gwas_gr) %>%
  as.data.frame() %>% 
  dplyr::filter(gwas =="CAD") %>% 
  distinct(Peakid)
HF_AF_ol <- join_overlap_inner(all_regions_gr, Short_gwas_gr) %>%
  as.data.frame() %>% 
  dplyr::filter(gwas =="AF"|gwas=="HF")

SNP_overlaps <- join_overlap_inner(all_regions_gr, Short_gwas_gr) %>%
  as.data.frame()

all_results_LFC <- all_results %>%  dplyr::select(source,genes,logFC) %>%
    pivot_wider(id_cols=genes, values_from = logFC, names_from = source) %>% 
  dplyr::rename("Peakid"=genes)


ATAC_all_adj.pvals <- readRDS("data/Final_four_data/re_analysis/ATAC_all_adj_pvals.RDS")

AF_heatmap_df <- SNP_overlaps %>% 
  as.data.frame() %>% 
  dplyr::filter(gwas=="AF") %>% 
  group_by(Peakid) %>%
  summarise(SNPS=paste(unique(SNPS),collapse = ";"))

AF_mat <- AF_heatmap_df %>% 
  left_join(., all_results_LFC, by=c("Peakid"="Peakid")) %>% 
  tidyr::unite(., name,Peakid,SNPS) %>% 
  column_to_rownames("name") %>% 
  as.matrix()

AF_sig_mat <- AF_heatmap_df %>% 
  left_join(., ATAC_all_adj.pvals, by=c("Peakid"="genes")) %>% 
  tidyr::unite(., name,Peakid,SNPS) %>% 
  column_to_rownames("name") %>% 
  as.matrix()


desired_order <- c("DOX_3", "EPI_3", "DNR_3", "MTX_3", "TRZ_3", "DOX_24", "EPI_24", "DNR_24", "MTX_24", "TRZ_24")

AF_mat <- AF_mat[, desired_order]
AF_sig_mat <- AF_sig_mat[, desired_order]  # reorder your significance matrix the same way


simply_AF_lfc <- ComplexHeatmap::Heatmap(AF_mat,
                                         
                        show_row_names = TRUE,
                       row_names_max_width=
ComplexHeatmap::max_text_width(rownames(AF_mat),                                                        gp=gpar(fontsize=14)),
                        heatmap_legend_param = list(direction = "horizontal"),
column_title = "AF_SNPS",
                       
                        show_column_names = TRUE,
                        cluster_rows = FALSE,
                        cluster_columns = FALSE,
 cell_fun = function(j, i, x, y, width, height, fill) {
  rowname <- rownames(AF_mat)[i]
  colname <- colnames(AF_mat)[j]
  if (!is.na(AF_sig_mat[rowname, colname]) &&
      AF_sig_mat[rowname, colname] < 0.05) {
    grid.text("*", x, y, gp = gpar(fontsize = 20))
  }
})



ComplexHeatmap::draw(simply_AF_lfc, 
     merge_legend = TRUE, 
     heatmap_legend_side = "left", 
    annotation_legend_side = "left")

Version Author Date
bbb4cff reneeisnowhere 2025-08-11

Figure S21: DARs overlap HF SNPs

HF_heatmap_df <- SNP_overlaps %>% 
  as.data.frame() %>% 
  dplyr::filter(gwas=="HF") %>% 
  group_by(Peakid) %>%
  summarise(SNPS=paste(unique(SNPS),collapse = ";"))


HF_mat <- HF_heatmap_df %>% 
  left_join(., all_results_LFC, by=c("Peakid"="Peakid")) %>% 
  tidyr::unite(., name,Peakid,SNPS) %>% 
  column_to_rownames("name") %>% 
  as.matrix()

HF_sig_mat <- HF_heatmap_df %>% 
  left_join(., ATAC_all_adj.pvals, by=c("Peakid"="genes")) %>% 
  tidyr::unite(., name,Peakid,SNPS) %>% 
  column_to_rownames("name") %>% 
  as.matrix()
desired_order <- c("DOX_3", "EPI_3", "DNR_3", "MTX_3", "TRZ_3", "DOX_24", "EPI_24", "DNR_24", "MTX_24", "TRZ_24")
HF_mat <- HF_mat[, desired_order]
HF_sig_mat <- HF_sig_mat[, desired_order]

simply_HF_lfc <- ComplexHeatmap::Heatmap(HF_mat,
                                         
                        show_row_names = TRUE,
                       row_names_max_width=
ComplexHeatmap::max_text_width(rownames(HF_mat),                                                        gp=gpar(fontsize=14)),
                        heatmap_legend_param = list(direction = "horizontal"),
column_title = "HF_SNPS",
                       
                        show_column_names = TRUE,
                        cluster_rows = FALSE,
                        cluster_columns = FALSE,
                        cell_fun = function(j, i, x, y, width, height, fill) {
  rowname <- rownames(HF_mat)[i]
  colname <- colnames(HF_mat)[j]
  if (!is.na(HF_sig_mat[rowname, colname]) &&
      HF_sig_mat[rowname, colname] < 0.05) {
    grid.text("*", x, y, gp = gpar(fontsize = 20))
  }
})


ComplexHeatmap::draw(simply_HF_lfc, 
     merge_legend = TRUE, 
     heatmap_legend_side = "left", 
    annotation_legend_side = "left")

Version Author Date
bbb4cff reneeisnowhere 2025-08-11

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] circlize_0.4.16                         
 [7] epitools_0.5-10.1                       
 [8] eulerr_7.0.2                            
 [9] vargen_0.2.3                            
[10] devtools_2.4.5                          
[11] usethis_3.1.0                           
[12] readxl_1.4.5                            
[13] smplot2_0.2.5                           
[14] cowplot_1.2.0                           
[15] ComplexHeatmap_2.22.0                   
[16] ggrepel_0.9.6                           
[17] plyranges_1.26.0                        
[18] ggsignif_0.6.4                          
[19] genomation_1.38.0                       
[20] edgeR_4.4.2                             
[21] limma_3.62.2                            
[22] ggpubr_0.6.1                            
[23] BiocParallel_1.40.2                     
[24] ggVennDiagram_1.5.4                     
[25] scales_1.4.0                            
[26] VennDiagram_1.7.3                       
[27] futile.logger_1.4.3                     
[28] gridExtra_2.3                           
[29] ggfortify_0.4.19                        
[30] rtracklayer_1.66.0                      
[31] org.Hs.eg.db_3.20.0                     
[32] TxDb.Hsapiens.UCSC.hg38.knownGene_3.20.0
[33] GenomicFeatures_1.58.0                  
[34] AnnotationDbi_1.68.0                    
[35] Biobase_2.66.0                          
[36] GenomicRanges_1.58.0                    
[37] GenomeInfoDb_1.42.3                     
[38] IRanges_2.40.1                          
[39] S4Vectors_0.44.0                        
[40] BiocGenerics_0.52.0                     
[41] ChIPseeker_1.42.1                       
[42] RColorBrewer_1.1-3                      
[43] broom_1.0.9                             
[44] kableExtra_1.4.0                        
[45] lubridate_1.9.4                         
[46] forcats_1.0.0                           
[47] stringr_1.5.1                           
[48] dplyr_1.1.4                             
[49] purrr_1.1.0                             
[50] readr_2.1.5                             
[51] tidyr_1.3.1                             
[52] tibble_3.3.0                            
[53] ggplot2_3.5.2                           
[54] tidyverse_2.0.0                         
[55] 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] doParallel_1.0.17                      
  [7] profvis_0.4.0                          
  [8] tools_4.4.2                            
  [9] backports_1.5.0                        
 [10] R6_2.6.1                               
 [11] mgcv_1.9-3                             
 [12] lazyeval_0.2.2                         
 [13] GetoptLong_1.0.5                       
 [14] urlchecker_1.0.1                       
 [15] withr_3.0.2                            
 [16] cli_3.6.5                              
 [17] textshaping_1.0.1                      
 [18] formatR_1.14                           
 [19] Cairo_1.6-2                            
 [20] labeling_0.4.3                         
 [21] sass_0.4.10                            
 [22] Rsamtools_2.22.0                       
 [23] systemfonts_1.2.3                      
 [24] yulab.utils_0.2.0                      
 [25] foreign_0.8-90                         
 [26] DOSE_4.0.1                             
 [27] svglite_2.2.1                          
 [28] R.utils_2.13.0                         
 [29] dichromat_2.0-0.1                      
 [30] sessioninfo_1.2.3                      
 [31] plotrix_3.8-4                          
 [32] pwr_1.3-0                              
 [33] rstudioapi_0.17.1                      
 [34] impute_1.80.0                          
 [35] RSQLite_2.4.2                          
 [36] generics_0.1.4                         
 [37] gridGraphics_0.5-1                     
 [38] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
 [39] shape_1.4.6.1                          
 [40] vroom_1.6.5                            
 [41] gtools_3.9.5                           
 [42] car_3.1-3                              
 [43] GO.db_3.20.0                           
 [44] Matrix_1.7-3                           
 [45] ggbeeswarm_0.7.2                       
 [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_1.6.2                      
 [56] blob_1.2.4                             
 [57] promises_1.3.3                         
 [58] crayon_1.5.3                           
 [59] miniUI_0.1.2                           
 [60] ggtangle_0.0.7                         
 [61] lattice_0.22-7                         
 [62] KEGGREST_1.46.0                        
 [63] magick_2.8.7                           
 [64] pillar_1.11.0                          
 [65] knitr_1.50                             
 [66] fgsea_1.32.4                           
 [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_0.2.0                            
 [74] remotes_2.5.0                          
 [75] data.table_1.17.8                      
 [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_0.52                              
 [83] mime_0.13                              
 [84] S4Arrays_1.6.0                         
 [85] iterators_1.0.14                       
 [86] statmod_1.5.0                          
 [87] ellipsis_0.3.2                         
 [88] nlme_3.1-168                           
 [89] ggtree_3.14.0                          
 [90] bit64_4.6.0-1                          
 [91] rprojroot_2.1.0                        
 [92] bslib_0.9.0                            
 [93] vipor_0.4.7                            
 [94] rpart_4.1.24                           
 [95] KernSmooth_2.23-26                     
 [96] colorspace_2.1-1                       
 [97] DBI_1.2.3                              
 [98] Hmisc_5.2-3                            
 [99] nnet_7.3-20                            
[100] seqPattern_1.38.0                      
[101] ggrastr_1.0.2                          
[102] tidyselect_1.2.1                       
[103] processx_3.8.6                         
[104] bit_4.6.0                              
[105] compiler_4.4.2                         
[106] curl_6.4.0                             
[107] git2r_0.36.2                           
[108] htmlTable_2.4.3                        
[109] xml2_1.3.8                             
[110] DelayedArray_0.32.0                    
[111] checkmate_2.3.2                        
[112] caTools_1.18.3                         
[113] callr_3.7.6                            
[114] digest_0.6.37                          
[115] rmarkdown_2.29                         
[116] htmltools_0.5.8.1                      
[117] pkgconfig_2.0.3                        
[118] base64enc_0.1-3                        
[119] MatrixGenerics_1.18.1                  
[120] fastmap_1.2.0                          
[121] htmlwidgets_1.6.4                      
[122] rlang_1.1.6                            
[123] GlobalOptions_0.1.2                    
[124] UCSC.utils_1.2.0                       
[125] shiny_1.11.1                           
[126] farver_2.1.2                           
[127] jquerylib_0.1.4                        
[128] zoo_1.8-14                             
[129] jsonlite_2.0.0                         
[130] GOSemSim_2.32.0                        
[131] R.oo_1.27.1                            
[132] RCurl_1.98-1.17                        
[133] magrittr_2.0.3                         
[134] Formula_1.2-5                          
[135] GenomeInfoDbData_1.2.13                
[136] ggplotify_0.1.2                        
[137] patchwork_1.3.1                        
[138] Rcpp_1.1.0                             
[139] ape_5.8-1                              
[140] stringi_1.8.7                          
[141] zlibbioc_1.52.0                        
[142] pkgbuild_1.4.8                         
[143] plyr_1.8.9                             
[144] parallel_4.4.2                         
[145] splines_4.4.2                          
[146] hms_1.1.3                              
[147] locfit_1.5-9.12                        
[148] ps_1.9.1                               
[149] igraph_2.1.4                           
[150] pkgload_1.4.0                          
[151] reshape2_1.4.4                         
[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] foreach_1.5.2                          
[158] httpuv_1.6.16                          
[159] clue_0.3-66                            
[160] gridBase_0.4-7                         
[161] xtable_1.8-4                           
[162] restfulr_0.0.16                        
[163] tidytree_0.4.6                         
[164] rstatix_0.7.2                          
[165] later_1.4.2                            
[166] viridisLite_0.4.2                      
[167] aplot_0.2.8                            
[168] beeswarm_0.4.0                         
[169] memoise_2.0.1                          
[170] GenomicAlignments_1.42.0               
[171] cluster_2.1.8.1                        
[172] timechange_0.3.0