Figure 5

Last updated: 2025-08-07

Checks: 7 0

Knit directory: ATAC_learning/

This reproducible R Markdown analysis was created with workflowr (version 1.7.1). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

R Markdown file: up-to-date

Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.

Environment: empty

Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.

Seed: set.seed(20231016)

The command set.seed(20231016) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.

Session information: recorded

Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

Cache: none

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

File paths: relative

Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.

Repository version: 4fff354

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.

The results in this page were generated with repository version 4fff354. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:


Ignored files:
    Ignored:    .RData
    Ignored:    .Rhistory
    Ignored:    .Rproj.user/
    Ignored:    analysis/H3K27ac_integration_noM.Rmd
    Ignored:    data/ACresp_SNP_table.csv
    Ignored:    data/ARR_SNP_table.csv
    Ignored:    data/All_merged_peaks.tsv
    Ignored:    data/CAD_gwas_dataframe.RDS
    Ignored:    data/CTX_SNP_table.csv
    Ignored:    data/Collapsed_expressed_NG_peak_table.csv
    Ignored:    data/DEG_toplist_sep_n45.RDS
    Ignored:    data/FRiP_first_run.txt
    Ignored:    data/Final_four_data/
    Ignored:    data/Frip_1_reads.csv
    Ignored:    data/Frip_2_reads.csv
    Ignored:    data/Frip_3_reads.csv
    Ignored:    data/Frip_4_reads.csv
    Ignored:    data/Frip_5_reads.csv
    Ignored:    data/Frip_6_reads.csv
    Ignored:    data/GO_KEGG_analysis/
    Ignored:    data/HF_SNP_table.csv
    Ignored:    data/Ind1_75DA24h_dedup_peaks.csv
    Ignored:    data/Ind1_TSS_peaks.RDS
    Ignored:    data/Ind1_firstfragment_files.txt
    Ignored:    data/Ind1_fragment_files.txt
    Ignored:    data/Ind1_peaks_list.RDS
    Ignored:    data/Ind1_summary.txt
    Ignored:    data/Ind2_TSS_peaks.RDS
    Ignored:    data/Ind2_fragment_files.txt
    Ignored:    data/Ind2_peaks_list.RDS
    Ignored:    data/Ind2_summary.txt
    Ignored:    data/Ind3_TSS_peaks.RDS
    Ignored:    data/Ind3_fragment_files.txt
    Ignored:    data/Ind3_peaks_list.RDS
    Ignored:    data/Ind3_summary.txt
    Ignored:    data/Ind4_79B24h_dedup_peaks.csv
    Ignored:    data/Ind4_TSS_peaks.RDS
    Ignored:    data/Ind4_V24h_fraglength.txt
    Ignored:    data/Ind4_fragment_files.txt
    Ignored:    data/Ind4_fragment_filesN.txt
    Ignored:    data/Ind4_peaks_list.RDS
    Ignored:    data/Ind4_summary.txt
    Ignored:    data/Ind5_TSS_peaks.RDS
    Ignored:    data/Ind5_fragment_files.txt
    Ignored:    data/Ind5_fragment_filesN.txt
    Ignored:    data/Ind5_peaks_list.RDS
    Ignored:    data/Ind5_summary.txt
    Ignored:    data/Ind6_TSS_peaks.RDS
    Ignored:    data/Ind6_fragment_files.txt
    Ignored:    data/Ind6_peaks_list.RDS
    Ignored:    data/Ind6_summary.txt
    Ignored:    data/Knowles_4.RDS
    Ignored:    data/Knowles_5.RDS
    Ignored:    data/Knowles_6.RDS
    Ignored:    data/LiSiLTDNRe_TE_df.RDS
    Ignored:    data/MI_gwas.RDS
    Ignored:    data/SNP_GWAS_PEAK_MRC_id
    Ignored:    data/SNP_GWAS_PEAK_MRC_id.csv
    Ignored:    data/SNP_gene_cat_list.tsv
    Ignored:    data/SNP_supp_schneider.RDS
    Ignored:    data/TE_info/
    Ignored:    data/TFmapnames.RDS
    Ignored:    data/all_TSSE_scores.RDS
    Ignored:    data/all_four_filtered_counts.txt
    Ignored:    data/aln_run1_results.txt
    Ignored:    data/anno_ind1_DA24h.RDS
    Ignored:    data/anno_ind4_V24h.RDS
    Ignored:    data/annotated_gwas_SNPS.csv
    Ignored:    data/background_n45_he_peaks.RDS
    Ignored:    data/cardiac_muscle_FRIP.csv
    Ignored:    data/cardiomyocyte_FRIP.csv
    Ignored:    data/col_ng_peak.csv
    Ignored:    data/cormotif_full_4_run.RDS
    Ignored:    data/cormotif_full_4_run_he.RDS
    Ignored:    data/cormotif_full_6_run.RDS
    Ignored:    data/cormotif_full_6_run_he.RDS
    Ignored:    data/cormotif_probability_45_list.csv
    Ignored:    data/cormotif_probability_45_list_he.csv
    Ignored:    data/cormotif_probability_all_6_list.csv
    Ignored:    data/cormotif_probability_all_6_list_he.csv
    Ignored:    data/datasave.RDS
    Ignored:    data/embryo_heart_FRIP.csv
    Ignored:    data/enhancer_list_ENCFF126UHK.bed
    Ignored:    data/enhancerdata/
    Ignored:    data/filt_Peaks_efit2.RDS
    Ignored:    data/filt_Peaks_efit2_bl.RDS
    Ignored:    data/filt_Peaks_efit2_n45.RDS
    Ignored:    data/first_Peaksummarycounts.csv
    Ignored:    data/first_run_frag_counts.txt
    Ignored:    data/full_bedfiles/
    Ignored:    data/gene_ref.csv
    Ignored:    data/gwas_1_dataframe.RDS
    Ignored:    data/gwas_2_dataframe.RDS
    Ignored:    data/gwas_3_dataframe.RDS
    Ignored:    data/gwas_4_dataframe.RDS
    Ignored:    data/gwas_5_dataframe.RDS
    Ignored:    data/high_conf_peak_counts.csv
    Ignored:    data/high_conf_peak_counts.txt
    Ignored:    data/high_conf_peaks_bl_counts.txt
    Ignored:    data/high_conf_peaks_counts.txt
    Ignored:    data/hits_files/
    Ignored:    data/hyper_files/
    Ignored:    data/hypo_files/
    Ignored:    data/ind1_DA24hpeaks.RDS
    Ignored:    data/ind1_TSSE.RDS
    Ignored:    data/ind2_TSSE.RDS
    Ignored:    data/ind3_TSSE.RDS
    Ignored:    data/ind4_TSSE.RDS
    Ignored:    data/ind4_V24hpeaks.RDS
    Ignored:    data/ind5_TSSE.RDS
    Ignored:    data/ind6_TSSE.RDS
    Ignored:    data/initial_complete_stats_run1.txt
    Ignored:    data/left_ventricle_FRIP.csv
    Ignored:    data/median_24_lfc.RDS
    Ignored:    data/median_3_lfc.RDS
    Ignored:    data/mergedPeads.gff
    Ignored:    data/mergedPeaks.gff
    Ignored:    data/motif_list_full
    Ignored:    data/motif_list_n45
    Ignored:    data/motif_list_n45.RDS
    Ignored:    data/multiqc_fastqc_run1.txt
    Ignored:    data/multiqc_fastqc_run2.txt
    Ignored:    data/multiqc_genestat_run1.txt
    Ignored:    data/multiqc_genestat_run2.txt
    Ignored:    data/my_hc_filt_counts.RDS
    Ignored:    data/my_hc_filt_counts_n45.RDS
    Ignored:    data/n45_bedfiles/
    Ignored:    data/n45_files
    Ignored:    data/other_papers/
    Ignored:    data/peakAnnoList_1.RDS
    Ignored:    data/peakAnnoList_2.RDS
    Ignored:    data/peakAnnoList_24_full.RDS
    Ignored:    data/peakAnnoList_24_n45.RDS
    Ignored:    data/peakAnnoList_3.RDS
    Ignored:    data/peakAnnoList_3_full.RDS
    Ignored:    data/peakAnnoList_3_n45.RDS
    Ignored:    data/peakAnnoList_4.RDS
    Ignored:    data/peakAnnoList_5.RDS
    Ignored:    data/peakAnnoList_6.RDS
    Ignored:    data/peakAnnoList_Eight.RDS
    Ignored:    data/peakAnnoList_full_motif.RDS
    Ignored:    data/peakAnnoList_n45_motif.RDS
    Ignored:    data/siglist_full.RDS
    Ignored:    data/siglist_n45.RDS
    Ignored:    data/summarized_peaks_dataframe.txt
    Ignored:    data/summary_peakIDandReHeat.csv
    Ignored:    data/test.list.RDS
    Ignored:    data/testnames.txt
    Ignored:    data/toplist_6.RDS
    Ignored:    data/toplist_full.RDS
    Ignored:    data/toplist_full_DAR_6.RDS
    Ignored:    data/toplist_n45.RDS
    Ignored:    data/trimmed_seq_length.csv
    Ignored:    data/unclassified_full_set_peaks.RDS
    Ignored:    data/unclassified_n45_set_peaks.RDS
    Ignored:    data/xstreme/

Untracked files:
    Untracked:  RNA_seq_integration.Rmd
    Untracked:  Rplot.pdf
    Untracked:  Sig_meta
    Untracked:  analysis/.gitignore
    Untracked:  analysis/Cormotif_analysis_testing diff.Rmd
    Untracked:  analysis/Diagnosis-tmm.Rmd
    Untracked:  analysis/Expressed_RNA_associations.Rmd
    Untracked:  analysis/IF_counts_20x.Rmd
    Untracked:  analysis/Jaspar_motif_DAR_paper.Rmd
    Untracked:  analysis/LFC_corr.Rmd
    Untracked:  analysis/SVA.Rmd
    Untracked:  analysis/Tan2020.Rmd
    Untracked:  analysis/making_master_peaks_list.Rmd
    Untracked:  analysis/my_hc_filt_counts.csv
    Untracked:  code/Concatenations_for_export.R
    Untracked:  code/IGV_snapshot_code.R
    Untracked:  code/LongDARlist.R
    Untracked:  code/just_for_Fun.R
    Untracked:  my_plot.pdf
    Untracked:  my_plot.png
    Untracked:  output/cormotif_probability_45_list.csv
    Untracked:  output/cormotif_probability_all_6_list.csv
    Untracked:  setup.RData

Unstaged changes:
    Modified:   ATAC_learning.Rproj
    Modified:   analysis/AC_shared_analysis.Rmd
    Modified:   analysis/AF_HF_SNPs.Rmd
    Modified:   analysis/Cardiotox_SNPs.Rmd
    Modified:   analysis/Cormotif_analysis.Rmd
    Modified:   analysis/DEG_analysis.Rmd
    Modified:   analysis/Figure_4.Rmd
    Modified:   analysis/H3K27ac_integration.Rmd
    Modified:   analysis/Jaspar_motif.Rmd
    Modified:   analysis/Jaspar_motif_ff.Rmd
    Modified:   analysis/SNP_TAD_peaks.Rmd
    Modified:   analysis/TE_analysis_norm.Rmd
    Modified:   analysis/final_four_analysis.Rmd
    Modified:   analysis/index.Rmd

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

These are the previous versions of the repository in which changes were made to the R Markdown (analysis/Figure_5.Rmd) and HTML (docs/Figure_5.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File	Version	Author	Date	Message
Rmd	4fff354	reneeisnowhere	2025-08-07	first recommit
html	a197856	reneeisnowhere	2025-05-01	Build site.
html	ec84c84	E. Renee Matthews	2025-03-05	Build site.
Rmd	055f576	E. Renee Matthews	2025-03-05	update png
html	86820a3	E. Renee Matthews	2025-02-25	Build site.
Rmd	dc3d330	E. Renee Matthews	2025-02-25	wflow_publish("analysis/Figure_5.Rmd")

Package Loading:

library(tidyverse)
library(kableExtra)
library(broom)
library(RColorBrewer)
library(ChIPseeker)
library(ChIPpeakAnno)
library("TxDb.Hsapiens.UCSC.hg38.knownGene")
library("org.Hs.eg.db")
library(rtracklayer)
library(edgeR)
library(ggfortify)
library(limma)
library(readr)
library(BiocGenerics)
library(gridExtra)
library(VennDiagram)
library(scales)
library(BiocParallel)
library(ggpubr)
library(devtools)
library(biomaRt)
library(eulerr)
library(smplot2)
library(genomation)
library(ggsignif)
library(plyranges)
library(ggrepel)
library(epitools)
library(circlize)
library(readxl)
library(ComplexHeatmap)

Figure 5: Drug-responsive regions are near AC-induced cardiotoxicity-associated SNPs

knitr::include_graphics("assets/Figure\ 5.png", error=FALSE)

Version	Author	Date
b33af76	reneeisnowhere	2025-05-01
ca5c73f	E. Renee Matthews	2025-02-26
50f3de9	E. Renee Matthews	2025-02-21

knitr::include_graphics("docs/assets/Figure\ 5.png",error = FALSE)

Figure 5.A. Chromatin response at AC-induced cardiotoxicity SNPs

### 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")

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)

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)


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"))


entrez_ids <- Assigned_genes_toPeak$geneId  


DOX_DAR_24hr_table <- annotated_DARs$DOX_24 %>% 
  as.data.frame()

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")


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"))



snp_peak_ol <- join_overlap_inner(all_regions_gr,schneider_gr)
  SNP_DAR_overlap_direct <- snp_peak_ol %>% 
    as.data.frame() %>% 
      mutate(Dox_24=if_else(Peakid %in% DOX_DAR_sig$Peakid,"yes","no")) %>% 
  mutate(Epi_24=if_else(Peakid %in% EPI_DAR_sig$Peakid,"yes","no")) %>% 
  mutate(Dnr_24=if_else(Peakid %in% DNR_DAR_sig$Peakid,"yes","no")) %>% 
  mutate(MTx_24=if_else(Peakid %in% MTX_DAR_sig$Peakid,"yes","no")) %>% 
    mutate(Dox_3=if_else(Peakid %in% DOX_DAR_sig_3$Peakid,"yes","no")) %>% 
  mutate(Epi_3=if_else(Peakid %in% EPI_DAR_sig_3$Peakid,"yes","no")) %>% 
  mutate(Dnr_3=if_else(Peakid %in% DNR_DAR_sig_3$Peakid,"yes","no")) %>% 
  mutate(Mtx_3=if_else(Peakid %in% MTX_DAR_sig_3$Peakid,"yes","no")) %>% 
    dplyr::select(Peakid,RSID,Dox_24:Mtx_3) 
  
  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)

SNP_DAR_overlap_mat <-
SNP_DAR_overlap_direct %>% 
  dplyr::select(Peakid,RSID) %>% 
  left_join(., snp_tad_df,by= c("RSID"="RSID")) %>% 
  dplyr::select(Peakid, TAD_id, RSID) %>% 
 left_join(., all_results_pivot, by=c("Peakid"="genes")) %>% 
  tidyr::unite(., name,Peakid,RSID) 

SNP_DAR_sig_mat <-   SNP_DAR_overlap_direct %>% 
    dplyr::select(Peakid,RSID) %>% 
  left_join(., snp_tad_df,by= c("RSID"="RSID")) %>% 
  dplyr::select(Peakid, TAD_id, RSID) %>% 
    left_join(., ATAC_all_adj.pvals, by=c("Peakid"="genes")) %>% 
    tidyr::unite(., name,Peakid,RSID) %>% 
    column_to_rownames("name") %>% 
  as.matrix()


Cardotox_mat_3 <-   SNP_DAR_overlap_mat %>%
  dplyr::select(name,DOX_3:TRZ_24) %>% 
  column_to_rownames("name") %>% 
  as.matrix()

annot_map_df_3 <- SNP_DAR_overlap_mat %>% 
  dplyr::select(name,TAD_id) %>% 
  column_to_rownames("name") 
annot_map_3 <-
  ComplexHeatmap::rowAnnotation(TAD_id=SNP_DAR_overlap_mat$TAD_id)


simply_map_lfc_3 <- ComplexHeatmap::Heatmap(Cardotox_mat_3,
                        #                   col = col_fun,
                        left_annotation = annot_map_3,
                        column_title="Cardiotox SNP direct overlaps",
                        show_row_names = TRUE,
                       row_names_max_width= ComplexHeatmap::max_text_width(rownames(Cardotox_mat_3),                                                        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_3)[i]
  colname <- colnames(Cardotox_mat_3)[j]

  if (!is.na(SNP_DAR_sig_mat[rowname, colname]) &&
      SNP_DAR_sig_mat[rowname, colname] < 0.05) {
    grid.text("*", x, y, gp = gpar(fontsize = 20))
  }
})




ComplexHeatmap::draw(simply_map_lfc_3, 
     merge_legend = TRUE, 
      
     heatmap_legend_side = "left", 
    annotation_legend_side = "left")

Version	Author	Date
86820a3	E. Renee Matthews	2025-02-25

Figure 5.B. Distance between chromatin regions and SNPs

peak_snp_pairs_dist_DOX_3 <- peak_snp_pairs %>%
  mutate(distance = abs(peak_start - snp_start)) %>% 
  mutate(sig_3= if_else(Peakid %in% DOX_DAR_sig_3$Peakid, "sig","not_sig"))

peak_snp_pairs_dist_EPI_3 <- peak_snp_pairs %>%
  mutate(distance = abs(peak_start - snp_start)) %>% 
  mutate(sig_3= if_else(Peakid %in% EPI_DAR_sig_3$Peakid, "sig","not_sig"))

peak_snp_pairs_dist_DNR_3 <- peak_snp_pairs %>%
  mutate(distance = abs(peak_start - snp_start)) %>% 
  mutate(sig_3= if_else(Peakid %in% DNR_DAR_sig_3$Peakid, "sig","not_sig"))

peak_snp_pairs_dist_MTX_3 <- peak_snp_pairs %>%
  mutate(distance = abs(peak_start - snp_start)) %>% 
  mutate(sig_3= if_else(Peakid %in% MTX_DAR_sig_3$Peakid, "sig","not_sig"))
peak_snp_pairs_dist_MTX <- peak_snp_pairs %>%
  mutate(distance = abs(peak_start - snp_start)) %>% 
  mutate(sig_24= if_else(Peakid %in% MTX_DAR_sig$Peakid, "sig","not_sig"))

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

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


drug_pal <- c("#8B006D","#DF707E","#F1B72B", "#3386DD","#707031","#41B333")


SNP_TAD_dist_DF_3 <- bind_rows((peak_snp_pairs_dist_MTX_3 %>% 
             mutate(trt="MTX")),
          (peak_snp_pairs_dist_DOX_3 %>%
               mutate(trt="DOX"))) %>% 
  bind_rows(.,(peak_snp_pairs_dist_EPI_3 %>% 
                 mutate(trt="EPI"))) %>% 
  bind_rows(.,(peak_snp_pairs_dist_DNR_3 %>% 
                 mutate(trt="DNR"))) %>% 
  mutate(trt=factor(trt,levels=c("DOX","EPI","DNR","MTX"))) %>% 
   mutate(sig_3=factor(sig_3, levels= c("sig","not_sig"))) 

SNP_TAD_dist_DF_3%>% 
  ggplot(., aes(x= interaction(sig_3,trt), y=distance))+
  geom_boxplot(aes(fill=trt))+
  theme_bw()+
  geom_signif(comparisons = list(c("sig.DOX", "not_sig.DOX"),
                                 c("sig.EPI","not_sig.EPI"),
                                 c("sig.DNR", "not_sig.DNR"),
                                 c("sig.MTX", "not_sig.MTX")),
                              # step_increase = 0.1, 
              map_signif_level = FALSE, 
              test = "wilcox.test")+
  ggtitle("ALL dist 3 hours")+
  scale_fill_manual(values=drug_pal)

SNP_TAD_dist_DF <- bind_rows((peak_snp_pairs_dist_MTX %>% 
             mutate(trt="MTX")),
          (peak_snp_pairs_dist %>%
               mutate(trt="DOX"))) %>% 
  bind_rows(.,(peak_snp_pairs_dist_EPI %>% 
                 mutate(trt="EPI"))) %>% 
  bind_rows(.,(peak_snp_pairs_dist_DNR %>% 
                 mutate(trt="DNR"))) %>% 
  mutate(trt=factor(trt,levels=c("DOX","EPI","DNR","MTX"))) %>% 
   mutate(sig_24=factor(sig_24, levels= c("sig","not_sig"))) 

SNP_TAD_dist_DF%>% 
  ggplot(., aes(x= interaction(sig_24,trt), y=distance))+
  geom_boxplot(aes(fill=trt))+
  theme_bw()+
  geom_signif(comparisons = list(c("sig.DOX", "not_sig.DOX"),
                                 c("sig.EPI","not_sig.EPI"),
                                 c("sig.DNR", "not_sig.DNR"),
                                 c("sig.MTX", "not_sig.MTX")),
                              # step_increase = 0.1, 
              map_signif_level = FALSE, 
              test = "wilcox.test")+
  ggtitle("ALL dist 24 hours")+
  scale_fill_manual(values=drug_pal)

Figure 5.C. Example TAD region with 3 cardiotox SNPs

** see image above.

Figure 5.D. Chromatin accessibility within 10 kb of rs10753081

raw_counts <- read_delim("data/Final_four_data/re_analysis/Raw_unfiltered_counts.tsv",delim="\t") %>% 
  column_to_rownames("Peakid") %>% 
  as.matrix()

lcpm <- cpm(raw_counts, log= TRUE)
  ### for determining the basic cutoffs
filt_raw_counts <- raw_counts[rowMeans(lcpm)> 0,]

filt_raw_counts_noY <- filt_raw_counts[!grepl("chrY",rownames(filt_raw_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)
# ATAC_counts_lcpm <- filt_raw_counts_noY %>%
#   cpm(., log = TRUE) %>% 
#   as.data.frame() %>% 
#   rownames_to_column("Peakid")

filt_raw_counts_noY %>%
  cpm(., log = TRUE) %>% 
  as.data.frame() %>% 
  rownames_to_column("Peakid") %>% 
  dplyr::filter(Peakid =="chr1.173473597.173473889") %>% 
  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 5.E. Gene expression of rs10753801 heart eGene

drug_pal <- c("#8B006D","#DF707E","#F1B72B", "#3386DD","#707031","#41B333")
RNA_counts <- readRDS("data/other_papers/cpmcount.RDS") %>%
  dplyr::rename_with(.,~gsub(pattern="Da",replacement="DNR",.)) %>% 
 dplyr::rename_with(.,~gsub(pattern="Do",replacement="DOX",.)) %>% 
  dplyr::rename_with(.,~gsub(pattern="Ep",replacement="EPI",.)) %>% 
   dplyr::rename_with(.,~gsub(pattern="Mi",replacement="MTX",.)) %>% 
    dplyr::rename_with(.,~gsub(pattern="Tr",replacement="TRZ",.)) %>% 
       dplyr::rename_with(.,~gsub(pattern="Ve",replacement="VEH",.)) %>% 
  rownames_to_column("ENTREZID")
RNA_counts %>% 
  dplyr::filter(ENTREZID =="55157") %>% 
  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 DARS2")+
  theme_bw()+
  ylab("log2 cpm RNA")

Figure 5.F. Chromatin accessibility and TOP2B binding at the TSS of DARS2