Figure 6
Renee Matthews
2025-02-25
Last updated: 2025-08-07
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Knit directory: ATAC_learning/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 8b026da | reneeisnowhere | 2025-08-07 | wflow_publish("analysis/Figure_6.Rmd") |
| html | a197856 | reneeisnowhere | 2025-05-01 | Build site. |
| html | 99ea869 | E. Renee Matthews | 2025-02-26 | Build site. |
| Rmd | 3af930f | E. Renee Matthews | 2025-02-26 | wflow_publish("analysis/Figure_6.Rmd") |
Figure 6: Drug-responsive regions overlap SNPs associated with atrial fibrillation
knitr::include_graphics("assets/Figure\ 6.png", error=FALSE)
knitr::include_graphics("docs/assets/Figure\ 6.png",error = FALSE)
library(tidyverse)
library(kableExtra)
library(broom)
library(RColorBrewer)
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)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()
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()
DOX_3_sig <-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)
DOX_24_sig <-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)
EPI_3_sig <-all_results %>%
dplyr::select(source,genes, logFC,adj.P.Val) %>%
mutate("Peakid"=genes) %>%
dplyr::filter(source=="EPI_3") %>%
dplyr::filter(adj.P.Val<0.05)
EPI_24_sig <-all_results %>%
dplyr::select(source,genes, logFC,adj.P.Val) %>%
mutate("Peakid"=genes) %>%
dplyr::filter(source=="EPI_24") %>%
dplyr::filter(adj.P.Val<0.05)
DNR_3_sig <-all_results %>%
dplyr::select(source,genes, logFC,adj.P.Val) %>%
mutate("Peakid"=genes) %>%
dplyr::filter(source=="DNR_3") %>%
dplyr::filter(adj.P.Val<0.05)
DNR_24_sig <-all_results %>%
dplyr::select(source,genes, logFC,adj.P.Val) %>%
mutate("Peakid"=genes) %>%
dplyr::filter(source=="DNR_24") %>%
dplyr::filter(adj.P.Val<0.05)
MTX_3_sig <-all_results %>%
dplyr::select(source,genes, logFC,adj.P.Val) %>%
mutate("Peakid"=genes) %>%
dplyr::filter(source=="MTX_3") %>%
dplyr::filter(adj.P.Val<0.05)
MTX_24_sig <-all_results %>%
dplyr::select(source,genes, logFC,adj.P.Val) %>%
mutate("Peakid"=genes) %>%
dplyr::filter(source=="MTX_24") %>%
dplyr::filter(adj.P.Val<0.05)
all_regions_gr <- all_results %>%
dplyr::select(source,genes, logFC,adj.P.Val) %>%
mutate("Peakid"=genes) %>%
dplyr::filter(source=="DOX_3") %>%
distinct(Peakid) %>%
separate_wider_delim(., cols="Peakid",names = c("seqnames","start","end"), delim= ".", cols_remove = FALSE) %>%
makeGRangesFromDataFrame(.,keep.extra.columns=TRUE)
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().
gwas_annote_df <-all_regions_peak %>%
mutate(AF_status=case_when(Peakid %in% AF_ol_peaks$Peakid ~"AF_peak",
TRUE~ "not_AF_peak"),
HF_status=case_when(Peakid %in% HF_ol_peaks$Peakid ~"HF_peak",
TRUE~ "not_HF_peak"),
CAD_status=case_when(Peakid %in% CAD_ol_peaks$Peakid ~"CAD_peak",
TRUE~ "not_CAD_peak"),
IHD_status=case_when(Peakid %in% IHD_ol_peaks$Peakid ~"IHD_peak",
TRUE~ "not_IHD_peak")) %>%
mutate(DOX_3=case_when(Peakid %in% DOX_3_sig$Peakid ~"sig_peak",
TRUE~ "not_sig_peak")) %>%
mutate(DOX_24=case_when(Peakid %in% DOX_24_sig$Peakid ~"sig_peak",
TRUE~ "not_sig_peak")) %>%
mutate(EPI_3=case_when(Peakid %in% EPI_3_sig$Peakid ~"sig_peak",
TRUE~ "not_sig_peak")) %>%
mutate(EPI_24=case_when(Peakid %in% EPI_24_sig$Peakid ~"sig_peak",
TRUE~ "not_sig_peak")) %>%
mutate(DNR_3=case_when(Peakid %in% DNR_3_sig$Peakid ~"sig_peak",
TRUE~ "not_sig_peak")) %>%
mutate(DNR_24=case_when(Peakid %in% DNR_24_sig$Peakid ~"sig_peak",
TRUE~ "not_sig_peak")) %>%
mutate(MTX_3=case_when(Peakid %in% MTX_3_sig$Peakid ~"sig_peak",
TRUE~ "not_sig_peak")) %>%
mutate(MTX_24=case_when(Peakid %in% MTX_24_sig$Peakid ~"sig_peak",
TRUE~ "not_sig_peak")) DOX_darsnp_3_AF <- gwas_annote_df %>%
group_by(DOX_3,AF_status) %>%
tally() %>%
pivot_wider(., id_cols=DOX_3, names_from = AF_status, values_from =n, values_fill =0) %>%
arrange(desc(DOX_3)) %>%
column_to_rownames("DOX_3") %>%
fisher.test(.)
DOX_darsnp_3_HF <- gwas_annote_df %>%
group_by(DOX_3,HF_status) %>%
tally() %>%
pivot_wider(., id_cols=DOX_3, names_from = HF_status, values_from =n, values_fill =0) %>%
arrange(desc(DOX_3)) %>%
column_to_rownames("DOX_3") %>%
fisher.test(.)
DOX_darsnp_3_CAD <- gwas_annote_df %>%
group_by(DOX_3,CAD_status) %>%
tally() %>%
pivot_wider(., id_cols=DOX_3, names_from = CAD_status, values_from =n, values_fill =0) %>%
arrange(desc(DOX_3)) %>%
column_to_rownames("DOX_3") %>%
fisher.test(.)
DOX_darsnp_3_IHD <- gwas_annote_df %>%
group_by(DOX_3,IHD_status) %>%
tally() %>%
pivot_wider(., id_cols=DOX_3, names_from = IHD_status, values_from = n, values_fill = 0) %>%
arrange(desc(DOX_3)) %>%
column_to_rownames("DOX_3") %>%
fisher.test(.)
DOX_darsnp_24_AF <- gwas_annote_df %>%
group_by(DOX_24,AF_status) %>%
tally() %>%
pivot_wider(., id_cols=DOX_24, names_from = AF_status, values_from = n, values_fill = 0) %>%
arrange(desc(DOX_24)) %>%
column_to_rownames("DOX_24") %>%
fisher.test(.)
DOX_darsnp_24_HF <- gwas_annote_df %>%
group_by(DOX_24,HF_status) %>%
tally() %>%
pivot_wider(., id_cols=DOX_24, names_from = HF_status, values_from =n, values_fill =0) %>%
arrange(desc(DOX_24)) %>%
column_to_rownames("DOX_24") %>%
fisher.test(.)
DOX_darsnp_24_CAD <- gwas_annote_df %>%
group_by(DOX_24,CAD_status) %>%
tally() %>%
pivot_wider(., id_cols=DOX_24, names_from = CAD_status, values_from =n, values_fill =0) %>%
arrange(desc(DOX_24)) %>%
column_to_rownames("DOX_24") %>%
fisher.test(.)
DOX_darsnp_24_IHD <- gwas_annote_df %>%
group_by(DOX_24,IHD_status) %>%
tally() %>%
pivot_wider(., id_cols=DOX_24, names_from = IHD_status, values_from =n, values_fill =0) %>%
arrange(desc(DOX_24)) %>%
column_to_rownames("DOX_24") %>%
fisher.test(.)EPI_darsnp_3_AF <- gwas_annote_df %>%
group_by(EPI_3,AF_status) %>%
tally() %>%
pivot_wider(., id_cols=EPI_3, names_from = AF_status, values_from =n, values_fill =0) %>%
arrange(desc(EPI_3)) %>%
column_to_rownames("EPI_3") %>%
fisher.test(.)
EPI_darsnp_3_HF <- gwas_annote_df %>%
group_by(EPI_3,HF_status) %>%
tally() %>%
pivot_wider(., id_cols=EPI_3, names_from = HF_status, values_from =n, values_fill =0) %>%
arrange(desc(EPI_3)) %>%
column_to_rownames("EPI_3") %>%
fisher.test(.)
EPI_darsnp_3_CAD <- gwas_annote_df %>%
group_by(EPI_3,CAD_status) %>%
tally() %>%
pivot_wider(., id_cols=EPI_3, names_from = CAD_status, values_from =n, values_fill =0) %>%
arrange(desc(EPI_3)) %>%
column_to_rownames("EPI_3") %>%
fisher.test(.)
EPI_darsnp_3_IHD <- gwas_annote_df %>%
group_by(EPI_3,IHD_status) %>%
tally() %>%
pivot_wider(., id_cols=EPI_3, names_from = IHD_status, values_from =n, values_fill =0) %>%
arrange(desc(EPI_3)) %>%
column_to_rownames("EPI_3") %>%
fisher.test(.)
EPI_darsnp_24_AF <- gwas_annote_df %>%
group_by(EPI_24,AF_status) %>%
tally() %>%
pivot_wider(., id_cols=EPI_24, names_from = AF_status, values_from =n, values_fill =0) %>%
arrange(desc(EPI_24)) %>%
column_to_rownames("EPI_24") %>%
fisher.test(.)
EPI_darsnp_24_HF <- gwas_annote_df %>%
group_by(EPI_24,HF_status) %>%
tally() %>%
pivot_wider(., id_cols=EPI_24, names_from = HF_status, values_from =n, values_fill =0) %>%
arrange(desc(EPI_24)) %>%
column_to_rownames("EPI_24") %>%
fisher.test(.)
EPI_darsnp_24_CAD <- gwas_annote_df %>%
group_by(EPI_24,CAD_status) %>%
tally() %>%
pivot_wider(., id_cols=EPI_24, names_from = CAD_status, values_from =n, values_fill =0) %>%
arrange(desc(EPI_24)) %>%
column_to_rownames("EPI_24") %>%
fisher.test(.)
EPI_darsnp_24_IHD <- gwas_annote_df %>%
group_by(EPI_24,IHD_status) %>%
tally() %>%
pivot_wider(., id_cols=EPI_24, names_from = IHD_status, values_from =n, values_fill =0) %>%
arrange(desc(EPI_24)) %>%
column_to_rownames("EPI_24") %>%
fisher.test(.)DNR_darsnp_3_AF <- gwas_annote_df %>%
group_by(DNR_3,AF_status) %>%
tally() %>%
pivot_wider(., id_cols=DNR_3, names_from = AF_status, values_from =n, values_fill =0) %>%
arrange(desc(DNR_3)) %>%
column_to_rownames("DNR_3") %>%
fisher.test(.)
DNR_darsnp_3_HF <- gwas_annote_df %>%
group_by(DNR_3,HF_status) %>%
tally() %>%
pivot_wider(., id_cols=DNR_3, names_from = HF_status, values_from =n, values_fill =0) %>%
arrange(desc(DNR_3)) %>%
column_to_rownames("DNR_3") %>%
fisher.test(.)
DNR_darsnp_3_CAD <- gwas_annote_df %>%
group_by(DNR_3,CAD_status) %>%
tally() %>%
pivot_wider(.,id_cols= DNR_3, names_from = CAD_status, values_from =n, values_fill =0) %>%
arrange(desc(DNR_3)) %>%
column_to_rownames("DNR_3") %>%
fisher.test(.)
DNR_darsnp_3_IHD <- gwas_annote_df %>%
group_by(DNR_3,IHD_status) %>%
tally() %>%
pivot_wider(., id_cols=DNR_3, names_from = IHD_status, values_from =n, values_fill =0) %>%
arrange(desc(DNR_3)) %>%
column_to_rownames("DNR_3") %>%
fisher.test(.)
DNR_darsnp_24_AF <- gwas_annote_df %>%
group_by(DNR_24,AF_status) %>%
tally() %>%
pivot_wider(., id_cols=DNR_24, names_from = AF_status, values_from =n, values_fill =0) %>%
arrange(desc(DNR_24)) %>%
column_to_rownames("DNR_24") %>%
fisher.test(.)
DNR_darsnp_24_HF <- gwas_annote_df %>%
group_by(DNR_24,HF_status) %>%
tally() %>%
pivot_wider(., id_cols=DNR_24, names_from = HF_status, values_from =n, values_fill =0) %>%
arrange(desc(DNR_24)) %>%
column_to_rownames("DNR_24") %>%
fisher.test(.)
DNR_darsnp_24_CAD <- gwas_annote_df %>%
group_by(DNR_24,CAD_status) %>%
tally() %>%
pivot_wider(., id_cols=DNR_24, names_from = CAD_status, values_from =n, values_fill =0) %>%
arrange(desc(DNR_24)) %>%
column_to_rownames("DNR_24") %>%
fisher.test(.)
DNR_darsnp_24_IHD <- gwas_annote_df %>%
group_by(DNR_24,IHD_status) %>%
tally() %>%
pivot_wider(., id_cols=DNR_24, names_from = IHD_status, values_from =n, values_fill =0) %>%
arrange(desc(DNR_24)) %>%
column_to_rownames("DNR_24") %>%
fisher.test(.)MTX_darsnp_3_AF <- gwas_annote_df %>%
group_by(MTX_3,AF_status) %>%
tally() %>%
pivot_wider(., id_cols=MTX_3, names_from = AF_status, values_from =n, values_fill =0) %>%
arrange(desc(MTX_3)) %>%
column_to_rownames("MTX_3") %>%
fisher.test(.)
MTX_darsnp_3_HF <- gwas_annote_df %>%
group_by(MTX_3,HF_status) %>%
tally() %>%
pivot_wider(., id_cols=MTX_3, names_from = HF_status, values_from =n, values_fill =0) %>%
arrange(desc(MTX_3)) %>%
column_to_rownames("MTX_3") %>%
fisher.test(.)
MTX_darsnp_3_CAD <- gwas_annote_df %>%
group_by(MTX_3,CAD_status) %>%
tally() %>%
pivot_wider(., id_cols=MTX_3, names_from = CAD_status, values_from =n, values_fill =0) %>%
arrange(desc(MTX_3)) %>%
column_to_rownames("MTX_3") %>%
fisher.test(.)
MTX_darsnp_3_IHD <- gwas_annote_df %>%
group_by(MTX_3,IHD_status) %>%
tally() %>%
pivot_wider(., id_cols=MTX_3, names_from = IHD_status, values_from =n, values_fill =0) %>%
arrange(desc(MTX_3)) %>%
column_to_rownames("MTX_3") %>%
fisher.test(.)
MTX_darsnp_24_AF <- gwas_annote_df %>%
group_by(MTX_24,AF_status) %>%
tally() %>%
pivot_wider(., id_cols=MTX_24, names_from = AF_status, values_from =n, values_fill =0) %>%
arrange(desc(MTX_24)) %>%
column_to_rownames("MTX_24") %>%
fisher.test(.)
MTX_darsnp_24_HF <- gwas_annote_df %>%
group_by(MTX_24,HF_status) %>%
tally() %>%
pivot_wider(., id_cols=MTX_24, names_from = HF_status, values_from =n, values_fill =0) %>%
arrange(desc(MTX_24)) %>%
column_to_rownames("MTX_24") %>%
fisher.test(.)
MTX_darsnp_24_CAD <- gwas_annote_df %>%
group_by(MTX_24,CAD_status) %>%
tally() %>%
pivot_wider(., id_cols=MTX_24, names_from = CAD_status, values_from =n, values_fill =0) %>%
arrange(desc(MTX_24)) %>%
column_to_rownames("MTX_24") %>%
fisher.test(.)
MTX_darsnp_24_IHD <- gwas_annote_df %>%
group_by(MTX_24,IHD_status) %>%
tally() %>%
pivot_wider(., id_cols=MTX_24, names_from = IHD_status, values_from =n, values_fill =0) %>%
arrange(desc(MTX_24)) %>%
column_to_rownames("MTX_24") %>%
fisher.test(.)Collecting all data to display:
All_24_results <- mget(ls(pattern = "*_darsnp_24*"))
All_3_results <- mget(ls(pattern = "*_darsnp_3*"))
# All_dar_results <- mget(ls(pattern = "*_darsnp_*"))
# convert to data frames with metadata
combined_df_24 <- bind_rows(
lapply(names(All_24_results), function(name) {
res <- All_24_results[[name]]
parts <- strsplit(name, "_")[[1]]
# convert htest to data.frame
data.frame(
p.value = res$p.value,
estimate = if (!is.null(res$estimate)) unname(res$estimate) else NA,
conf.low = if (!is.null(res$conf.int)) res$conf.int[1] else NA,
conf.high = if (!is.null(res$conf.int)) res$conf.int[2] else NA,
drug = parts[1],
time = parts[3],
population = parts[4],
stringsAsFactors = FALSE
)
})
)
combined_df_3 <- bind_rows(
lapply(names(All_3_results), function(name) {
res <- All_3_results[[name]]
parts <- strsplit(name, "_")[[1]]
# convert htest to data.frame
data.frame(
p.value = res$p.value,
estimate = if (!is.null(res$estimate)) unname(res$estimate) else NA,
conf.low = if (!is.null(res$conf.int)) res$conf.int[1] else NA,
conf.high = if (!is.null(res$conf.int)) res$conf.int[2] else NA,
drug = parts[1],
time = parts[3],
population = parts[4],
stringsAsFactors = FALSE
)
})
)Plotting the figure:
Figure 6.A. Enrichment of CVD SNPs in DARs
combined_df_3 %>%
mutate(group=paste0(drug,"_",time)) %>%
# separate_wider_delim(.,cols="group", names = c("trt","time"), delim = "_",cols_remove = FALSE) %>%
mutate(time= factor(time, levels =c("3","24")),
drug=factor(drug, levels= c("DOX", "EPI", "DNR", "MTX"))) %>%
mutate(population=factor(population, levels= c("AF","HF","IHD","CAD"))) %>%
mutate(
significant = case_when(
p.value < 0.001 ~ "***",
p.value < 0.01 ~ "**",
p.value < 0.05 ~ "*",
TRUE ~ ""
)
) %>%
ggplot(., aes(x = drug, y = estimate)) +
geom_point(aes(color = drug), size=4)+
geom_errorbar(aes(ymin = conf.low, ymax = conf.high), width = 0.2) +
geom_hline(yintercept = 1, linetype = "dashed", color = "gray40") +
geom_text(
aes(y = conf.high + 0.1 * estimate, label = significant),
hjust = 0, # aligns text to the left of the y point
size = 4,
color = "black"
)+
labs(
title = "Odds Ratio of SNP enrichment by type",
y = "Odds Ratio (95% confidence interval)",
x = "treatment"
) +
# coord_flip()+
theme_bw() +
facet_grid(rows = vars(population), cols = vars(time), scales = "free_y")+
theme(
text = element_text(size = 12),
plot.title = element_text(hjust = 0.5)
)
Figure 6.B. Chromatin accessibility response to drugs at AF SNPs
AF_peak_list <- gwas_annote_df %>%
dplyr::filter(AF_status=="AF_peak")
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)
drug_pal <- c("#8B006D","#DF707E","#F1B72B", "#3386DD","#707031","#41B333")
all_results_pivot %>%
dplyr::filter(genes %in% AF_peak_list$Peakid) %>%
pivot_longer(!genes, names_to = "sample",values_to="log_FC") %>%
separate_wider_delim(sample, names= c("trt","time"), delim= "_", cols_remove =FALSE) %>%
mutate(trt= factor(trt, levels= c("DOX","EPI","DNR","MTX","TRZ")),
log_FC=abs(log_FC),
time=factor(time, levels = c("3","24"))) %>%
ggplot(., aes(x=trt, y = log_FC))+
geom_boxplot(aes(fill = trt))+
# geom_point()+
geom_signif(comparisons = list(
c("TRZ", "DOX"),
c("TRZ", "EPI"),
c("TRZ", "DNR"),
c("TRZ", "MTX")),
step_increase = 0.1,
map_signif_level = FALSE,
test = "wilcox.test")+
ylim(-1,6)+
theme_classic()+
ggtitle(paste0("logFC of AF overlapping regions, n = ",length(AF_peak_list$Peakid))) +
facet_wrap(~time)+
scale_fill_manual(values= drug_pal)
Figure 6.C. CDKN1A loci
**see above image.
Figure 6.D. Chromatin accessibility at rs3176326
ATAC_counts <- readRDS("data/Final_four_data/ATAC_filtered_raw_counts_allsamples.RDS") %>%
cpm(., log = TRUE) %>%
as.data.frame() %>%
rename_with(.,~gsub(pattern = "Ind1_75", replacement = "D_",.)) %>%
rename_with(.,~gsub(pattern = "Ind2_87", replacement = "A_",.)) %>%
rename_with(.,~gsub(pattern = "Ind3_77", replacement = "B_",.)) %>%
rename_with(.,~gsub(pattern = "Ind6_71", replacement = "C_",.)) %>%
rename_with(.,~gsub( "DX" ,'DOX',.)) %>%
rename_with(.,~gsub( "DA" ,'DNR',.)) %>%
rename_with(.,~gsub( "E" ,'EPI',.)) %>%
rename_with(.,~gsub( "T" ,'TRZ',.)) %>%
rename_with(.,~gsub( "M" ,'MTX',.)) %>%
rename_with(.,~gsub( "V" ,'VEH',.)) %>%
rename_with(.,~gsub("24h","_24h",.)) %>%
rename_with(.,~gsub("3h","_3h",.)) %>%
rownames_to_column("Peakid")
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")drug_pal <- c("#8B006D","#DF707E","#F1B72B", "#3386DD","#707031","#41B333")
ATAC_counts <- readRDS("data/Final_four_data/ATAC_filtered_raw_counts_allsamples.RDS") %>%
cpm(., log = TRUE) %>%
as.data.frame() %>%
rename_with(.,~gsub(pattern = "Ind1_75", replacement = "D_",.)) %>%
rename_with(.,~gsub(pattern = "Ind2_87", replacement = "A_",.)) %>%
rename_with(.,~gsub(pattern = "Ind3_77", replacement = "B_",.)) %>%
rename_with(.,~gsub(pattern = "Ind6_71", replacement = "C_",.)) %>%
rename_with(.,~gsub( "DX" ,'DOX',.)) %>%
rename_with(.,~gsub( "DA" ,'DNR',.)) %>%
rename_with(.,~gsub( "E" ,'EPI',.)) %>%
rename_with(.,~gsub( "T" ,'TRZ',.)) %>%
rename_with(.,~gsub( "M" ,'MTX',.)) %>%
rename_with(.,~gsub( "V" ,'VEH',.)) %>%
rename_with(.,~gsub("24h","_24h",.)) %>%
rename_with(.,~gsub("3h","_3h",.)) %>%
rownames_to_column("Peakid")
filt_raw_counts_noY <- ATAC_counts[!grepl("chrY",rownames(ATAC_counts)),]
# ATAC_adj.pvals <-all_results %>%
# dplyr::select(source,genes,adj.P.Val) %>%
# dplyr::filter(genes %in% SNP_DAR_overlap_direct$Peakid) %>%
# separate(source, into = c("trt", "time")) %>%
# mutate(
# time = paste0(time, "h"), # convert "3" → "3h"
# trt = factor(trt, levels = c("DOX", "EPI", "DNR", "MTX", "TRZ")),
# group=paste0(trt,"_",time)) %>%
# mutate(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"))) %>%
# dplyr::rename("Peakid"=genes)
filt_raw_counts_noY %>%
dplyr::filter(Peakid =="chr6.36678380.36679788") %>%
pivot_longer(., cols= !Peakid, names_to = "sample",values_to = "log2cpm") %>%
separate_wider_delim(, cols=sample, names =c("ind","trt","time"),delim="_",cols_remove = FALSE) %>%
mutate(
time = factor(time, levels = c("3h", "24h")),
trt = factor(trt, levels = c("DOX", "EPI", "DNR", "MTX", "TRZ", "VEH"))
) %>%
ggplot(aes(x = time, y = log2cpm)) +
geom_boxplot(aes(fill = trt)) +
scale_fill_manual(values = drug_pal) +
theme_bw() +
facet_wrap(~Peakid, scales="free_y")+
ylab("log2 cpm ATAC regions") 
Figure 6.E. H3K27 acetylation at rs3178326
K27_counts <- readRDS("data/Final_four_data/re_analysis/H3K27ac_final_23_raw_counts.RDS") %>%
cpm(., log = TRUE) %>%
as.data.frame()
K27_counts %>%
rownames_to_column("AC_Peakid") %>%
dplyr::filter(AC_Peakid =="chr6.36674959.36687167") %>%
pivot_longer(., cols= !AC_Peakid, names_to = "sample",values_to = "log2cpm") %>%
separate_wider_delim(, cols=sample, names =c("ind","trt","time"),delim="_",cols_remove = FALSE) %>%
mutate(
time = factor(time, levels = c("3","24"), labels= c("3h", "24h")),
trt = factor(trt, levels = c("DOX", "EPI", "DNR", "MTX", "TRZ", "VEH"))
) %>%
ggplot(aes(x = time, y = log2cpm)) +
geom_boxplot(aes(fill = trt)) +
scale_fill_manual(values = drug_pal) +
theme_bw() +
facet_wrap(~AC_Peakid, scales="free_y")+
ylab("log2 cpm H3K27ac regions") 
Figure 6.F. Gene expression of rs3176326 heart eGene
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 =="1026") %>%
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 CDKN1A")+
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] vargen_0.2.3
[2] devtools_2.4.5
[3] usethis_3.1.0
[4] readxl_1.4.5
[5] smplot2_0.2.5
[6] cowplot_1.1.3
[7] ComplexHeatmap_2.22.0
[8] ggrepel_0.9.6
[9] plyranges_1.26.0
[10] ggsignif_0.6.4
[11] genomation_1.38.0
[12] edgeR_4.4.2
[13] limma_3.62.2
[14] ggpubr_0.6.1
[15] BiocParallel_1.40.2
[16] ggVennDiagram_1.5.4
[17] scales_1.4.0
[18] VennDiagram_1.7.3
[19] futile.logger_1.4.3
[20] gridExtra_2.3
[21] ggfortify_0.4.18
[22] rtracklayer_1.66.0
[23] org.Hs.eg.db_3.20.0
[24] TxDb.Hsapiens.UCSC.hg38.knownGene_3.20.0
[25] GenomicFeatures_1.58.0
[26] AnnotationDbi_1.68.0
[27] Biobase_2.66.0
[28] GenomicRanges_1.58.0
[29] GenomeInfoDb_1.42.3
[30] IRanges_2.40.1
[31] S4Vectors_0.44.0
[32] BiocGenerics_0.52.0
[33] RColorBrewer_1.1-3
[34] broom_1.0.8
[35] kableExtra_1.4.0
[36] lubridate_1.9.4
[37] forcats_1.0.0
[38] stringr_1.5.1
[39] dplyr_1.1.4
[40] purrr_1.0.4
[41] readr_2.1.5
[42] tidyr_1.3.1
[43] tibble_3.3.0
[44] ggplot2_3.5.2
[45] tidyverse_2.0.0
[46] workflowr_1.7.1
loaded via a namespace (and not attached):
[1] later_1.4.2 BiocIO_1.16.0
[3] bitops_1.0-9 cellranger_1.1.0
[5] rpart_4.1.24 XML_3.99-0.18
[7] lifecycle_1.0.4 rstatix_0.7.2
[9] doParallel_1.0.17 rprojroot_2.0.4
[11] processx_3.8.6 lattice_0.22-7
[13] backports_1.5.0 magrittr_2.0.3
[15] Hmisc_5.2-3 sass_0.4.10
[17] rmarkdown_2.29 remotes_2.5.0
[19] jquerylib_0.1.4 yaml_2.3.10
[21] plotrix_3.8-4 httpuv_1.6.16
[23] sessioninfo_1.2.3 pkgbuild_1.4.8
[25] DBI_1.2.3 pkgload_1.4.0
[27] abind_1.4-8 zlibbioc_1.52.0
[29] RCurl_1.98-1.17 nnet_7.3-20
[31] git2r_0.36.2 circlize_0.4.16
[33] GenomeInfoDbData_1.2.13 svglite_2.2.1
[35] codetools_0.2-20 DelayedArray_0.32.0
[37] xml2_1.3.8 tidyselect_1.2.1
[39] shape_1.4.6.1 UCSC.utils_1.2.0
[41] farver_2.1.2 base64enc_0.1-3
[43] matrixStats_1.5.0 GenomicAlignments_1.42.0
[45] jsonlite_2.0.0 GetoptLong_1.0.5
[47] ellipsis_0.3.2 Formula_1.2-5
[49] iterators_1.0.14 systemfonts_1.2.3
[51] foreach_1.5.2 tools_4.4.2
[53] Rcpp_1.1.0 glue_1.8.0
[55] SparseArray_1.6.2 xfun_0.52
[57] MatrixGenerics_1.18.1 withr_3.0.2
[59] formatR_1.14 fastmap_1.2.0
[61] callr_3.7.6 digest_0.6.37
[63] mime_0.13 timechange_0.3.0
[65] R6_2.6.1 seqPattern_1.38.0
[67] textshaping_1.0.1 colorspace_2.1-1
[69] dichromat_2.0-0.1 RSQLite_2.4.1
[71] generics_0.1.4 data.table_1.17.6
[73] htmlwidgets_1.6.4 httr_1.4.7
[75] S4Arrays_1.6.0 whisker_0.4.1
[77] pkgconfig_2.0.3 gtable_0.3.6
[79] blob_1.2.4 impute_1.80.0
[81] XVector_0.46.0 htmltools_0.5.8.1
[83] carData_3.0-5 profvis_0.4.0
[85] pwr_1.3-0 clue_0.3-66
[87] png_0.1-8 knitr_1.50
[89] lambda.r_1.2.4 rstudioapi_0.17.1
[91] tzdb_0.5.0 reshape2_1.4.4
[93] rjson_0.2.23 checkmate_2.3.2
[95] curl_6.4.0 zoo_1.8-14
[97] cachem_1.1.0 GlobalOptions_0.1.2
[99] KernSmooth_2.23-26 miniUI_0.1.2
[101] parallel_4.4.2 foreign_0.8-90
[103] restfulr_0.0.16 pillar_1.11.0
[105] vctrs_0.6.5 urlchecker_1.0.1
[107] promises_1.3.3 car_3.1-3
[109] xtable_1.8-4 cluster_2.1.8.1
[111] htmlTable_2.4.3 evaluate_1.0.4
[113] cli_3.6.5 locfit_1.5-9.12
[115] compiler_4.4.2 futile.options_1.0.1
[117] Rsamtools_2.22.0 rlang_1.1.6
[119] crayon_1.5.3 labeling_0.4.3
[121] ps_1.9.1 getPass_0.2-4
[123] plyr_1.8.9 fs_1.6.6
[125] stringi_1.8.7 viridisLite_0.4.2
[127] gridBase_0.4-7 Biostrings_2.74.1
[129] Matrix_1.7-3 BSgenome_1.74.0
[131] patchwork_1.3.1 hms_1.1.3
[133] bit64_4.6.0-1 shiny_1.11.1
[135] KEGGREST_1.46.0 statmod_1.5.0
[137] SummarizedExperiment_1.36.0 memoise_2.0.1
[139] bslib_0.9.0 bit_4.6.0