SNP & TAD
Renee Matthews
2025-06-10
Last updated: 2025-07-29
Checks: 7 0
Knit directory: ATAC_learning/
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| Rmd | ad110ea | reneeisnowhere | 2025-07-29 | updates |
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| Rmd | b838697 | reneeisnowhere | 2025-07-21 | adding in SNP overlap heatmap |
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| Rmd | 0fa75c3 | reneeisnowhere | 2025-07-09 | wflow_publish("analysis/SNP_TAD_peaks.Rmd") |
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)
library(gwascat)
library(liftOver)Loading data frames
### 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")
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)$TAD_id <- paste0("TAD_", seq_along(Left_ventricle_TAD))
# mcols(Left_ventricle_TAD)$name <- Left_ventricle_TAD$TAD_id
##exporting the LEFt ventricle tad info for IGV visualization
# export(Left_ventricle_TAD, con = "data/Final_four_data/re_analysis/Other_bed_files/TAD_regions.bed", format="bed")
Schneider_all_SNPS <- read_delim("data/other_papers/Schneider_all_SNPS.txt",
delim = "\t", escape_double = FALSE,
trim_ws = TRUE)
Schneider_all_SNPS_df <- Schneider_all_SNPS %>%
dplyr::rename("RSID"="#Uploaded_variation") %>%
dplyr::select(RSID,Location,SYMBOL,Gene, SOURCE) %>%
distinct(RSID,Location,SYMBOL,.keep_all = TRUE) %>%
dplyr::rename("Close_SYMBOL"="SYMBOL") %>%
dplyr::filter(!str_starts(Location, "H")) %>%
separate_wider_delim(Location,delim=":",names=c("Chr","Coords")) %>%
separate_wider_delim(Coords,delim= "-", names= c("Start","End")) %>%
mutate(Chr=paste0("chr",Chr)) %>%
group_by(RSID) %>%
reframe(Chr=unique(Chr),
Start=unique(Start),
End=unique(End),
Close_SYMBOL=paste(unique(Close_SYMBOL),collapse=";"),
Gene=paste(Gene,collapse=";"),
SOURCE=paste(SOURCE,collapse=";")
) %>%
GRanges() %>% as.data.frame
schneider_gr <-Schneider_all_SNPS_df%>%
dplyr::select(seqnames,start,end,RSID:SOURCE) %>%
distinct() %>%
GRanges()
# export(schneider_gr, con = "data/Final_four_data/re_analysis/Other_bed_files/CardiotoxSNPs.bed", format="bed")
toptable_results <- readRDS("data/Final_four_data/re_analysis/Toptable_results.RDS")
all_results <- toptable_results %>%
imap(~ .x %>% tibble::rownames_to_column(var = "rowname") %>%
mutate(source = .y)) %>%
bind_rows()
all_results_pivot <- all_results %>%
dplyr::select(genes,logFC,source) %>%
pivot_wider(., id_cols = genes, names_from = source, values_from = logFC) %>%
dplyr::select(genes,DOX_3,EPI_3,DNR_3,MTX_3,TRZ_3,DOX_24,EPI_24,DNR_24,MTX_24,TRZ_24)
toplistall_RNA <- readRDS("data/other_papers/toplistall_RNA.RDS") %>%
mutate(logFC = logFC*(-1))
Assigned_genes_toPeak <- annotated_DARs$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$geneId
gene_info <- AnnotationDbi::select(
org.Hs.eg.db,
keys = entrez_ids,
columns = c("SYMBOL"),
keytype = "ENTREZID"
)
gene_info_collapsed <- gene_info %>%
group_by(ENTREZID) %>%
summarise(SYMBOL = paste(unique(SYMBOL), collapse = ","), .groups = "drop")
DOX_DAR_24hr_table <- annotated_DARs$DOX_24 %>%
as.data.frame()
Top2b_peaks <- import(con="data/other_papers/ChIP3_TOP2B_CM_87-1.bed",format = "bed",genome="hg38")
# for_export <- DOX_24_DAR%>%
# # as.data.frame() %>%
# ### mark significance with color
# mutate(sig_24=if_else(mcols.adj.P.Val<0.05,"TRUE","FALSE")) %>%
# dplyr::select(seqnames:mcols.genes,sig_24) %>%
# GRanges
#
# ### add to the granges thingy for exporting
# mcols(for_export)$itemRgb <- ifelse(mcols(for_export)$sig_24,
# "255,0,0", # red for significant
# "190,190,190") # gray for not significant
# mcols(for_export)$sig_24 <- as.logical(mcols(for_export)$sig_24)
# mcols(for_export)$itemRgb <- as.character(mcols(for_export)$itemRgb)
#
# bed_df <- data.frame(
# seqnames = seqnames(for_export),
# start = start(for_export) - 1, # BED is 0-based
# end = end(for_export),
# strand = "*",
# thickStart = start(for_export) - 1,
# thickEnd = end(for_export),
# itemRgb = ifelse(mcols(for_export)$sig_24, "255,0,0", "190,190,190"),
# stringsAsFactors = FALSE)
# # )
#
# write.table(bed_df,
# file = "data/Final_four_data/re_analysis/Other_bed_files/DOX_24hour_sig_notsig_regions.bed",
# quote = FALSE, sep = "\t", row.names = FALSE, col.names = FALSE)Enrichment test of sig DAR and non-sig DAR of DOX within SNP-containing TADS
test_ol <- join_overlap_intersect(Left_ventricle_TAD, schneider_gr)
df <- as.data.frame(test_ol, row.names = NULL)
TAD_SNP_ol <- test_ol %>% as.data.frame() %>%
distinct(TAD_id, RSID)
peak_ol <- join_overlap_intersect(all_regions_gr, Left_ventricle_TAD)
TAD_SNP_Peak_ol <- peak_ol %>%
as.data.frame() %>%
dplyr::filter(TAD_id %in% TAD_SNP_ol$TAD_id)
snp_ol <- join_overlap_inner(schneider_gr, Left_ventricle_TAD)
TAD_peak_ol <- peak_ol %>%
as.data.frame() %>%
distinct(Peakid,.keep_all = TRUE)
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$TAD_id)
peak_df <- as.data.frame(left_ventricle_ol)
SNP_df <- as.data.frame(snp_ol)
peak_snp_pairs <- inner_join(peak_df, SNP_df, by = "TAD_id", suffix = c(".peak", ".snp")) %>%
mutate(
peak_center = (start.peak + end.peak) / 2,
distance = abs(peak_center - start.snp) # or any metric you prefer
)reds <- colorRampPalette(brewer.pal(9, "Reds")[3:9])(12)
greens <- colorRampPalette(brewer.pal(9, "Greens")[3:9])(12)
blues <- colorRampPalette(brewer.pal(9, "Blues")[3:9])(12)
purples <- colorRampPalette(brewer.pal(9, "Purples")[3:9])(12)
oranges <- colorRampPalette(brewer.pal(9, "Oranges")[3:9])(12)
tads <- unique(peak_snp_pairs$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
#ha <- HeatmapAnnotation(TAD = df$TAD_id, col = list(TAD = tad_colors))Top2b_overlap_regions <-join_overlap_inner(all_regions_gr ,Top2b_peaks) %>%
as.data.frame() %>%
distinct(Peakid,.keep_all = TRUE) DOX_24_DAR <- as.data.frame(annotated_DARs$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$MTX_3)
TAD_count_df <- DOX_24_DAR %>%
dplyr::select(mcols.genes, mcols.adj.P.Val,annotation:distanceToTSS) %>%
mutate(sig_24=if_else(mcols.adj.P.Val<0.05,"sig","not_sig")) %>%
mutate(sig_24=factor(sig_24, levels = c("sig","not_sig"))) %>%
mutate(TAD_all_status=if_else(mcols.genes %in% peak_ol$Peakid,"TAD_peak","not_TAD_peak")) %>%
mutate(SNP_TAD_status= if_else(mcols.genes %in% TAD_SNP_Peak_ol$Peakid,"SNP_TAD","not_SNP_TAD")) %>%
mutate(Top2b_peak= if_else(mcols.genes %in% Top2b_overlap_regions$Peakid, "TOP2B_peak","not_TOP2B_peak"))
TAD_count_df %>% #dplyr::filter(TAD_all_status=="TAD_peak") %>%
group_by(sig_24,SNP_TAD_status,TAD_all_status) %>%
tally #%>% # A tibble: 6 × 4
# Groups: sig_24, SNP_TAD_status [4]
sig_24 SNP_TAD_status TAD_all_status n
<fct> <chr> <chr> <int>
1 sig SNP_TAD TAD_peak 2047
2 sig not_SNP_TAD TAD_peak 56865
3 sig not_SNP_TAD not_TAD_peak 5908
4 not_sig SNP_TAD TAD_peak 3111
5 not_sig not_SNP_TAD TAD_peak 78627
6 not_sig not_SNP_TAD not_TAD_peak 8999 # pivot_wider(., id_cols = sig_24, names_from = SNP_TAD_status, values_from = n)
# print("Odds ratio of SNP_TADs across DOXall regions, regardless of TAD_status")
# TAD_count_df %>% #dplyr::filter(TAD_all_status=="TAD_peak") %>%
# group_by(sig_24,SNP_TAD_status) %>%
# tally %>%
# pivot_wider(., id_cols = sig_24, names_from = SNP_TAD_status, values_from = n) %>%
# column_to_rownames( "sig_24") %>% as.matrix() %>%
# # chisq.test()
# epitools::oddsratio()
print("Odds ratio testing proportion SNP-containing TADs of sig-DOX DARs vs non-sig DARs at 24 hours")[1] "Odds ratio testing proportion SNP-containing TADs of sig-DOX DARs vs non-sig DARs at 24 hours"TAD_count_df %>% dplyr::filter(TAD_all_status=="TAD_peak") %>%
group_by(sig_24,SNP_TAD_status) %>%
tally %>%
pivot_wider(., id_cols = sig_24, names_from = SNP_TAD_status, values_from = n) %>%
column_to_rownames( "sig_24") %>% as.matrix() %>%
# chisq.test()
epitools::oddsratio(method = "wald")$data
SNP_TAD not_SNP_TAD Total
sig 2047 56865 58912
not_sig 3111 78627 81738
Total 5158 135492 140650
$measure
NA
odds ratio with 95% C.I. estimate lower upper
sig 1.000000 NA NA
not_sig 0.909797 0.8595486 0.9629828
$p.value
NA
two-sided midp.exact fisher.exact chi.square
sig NA NA NA
not_sig 0.00107761 0.001098901 0.001105101
$correction
[1] FALSE
attr(,"method")
[1] "Unconditional MLE & normal approximation (Wald) CI"TAD_count_df %>%
dplyr::filter(TAD_all_status=="TAD_peak") %>%
group_by(sig_24,SNP_TAD_status) %>%
tally ()%>%
mutate(sig_24=factor(sig_24, levels = c("sig","not_sig"))) %>%
ggplot(.,aes(x=sig_24, y= n,fill=SNP_TAD_status))+
geom_col(position="fill")+
theme_bw()+
ggtitle("Proportion of significant regions by 24 hours")+
ylab("proportion")
# TAD_count_df %>%
# dplyr::filter(TAD_all_status=="TAD_peak") %>%
#
# group_by(sig_24,SNP_TAD_status) %>%
# tally ()%>%
# mutate(sig_24=factor(sig_24, levels = c("sig","not_sig"))) %>%
# ggplot(.,aes(x=SNP_TAD_status, y= n,fill=sig_24))+
# geom_col(position="fill")+
# theme_bw()+
# ggtitle("Proportion of significant regions by 24 hours")+
# ylab("proportion")Proportion of DARs that overlap TOP2B peaks in a TAD
TAD_count_df %>%
dplyr::filter((TAD_all_status=="TAD_peak")) %>%
dplyr::filter(SNP_TAD_status=="SNP_TAD") %>%
group_by(SNP_TAD_status, Top2b_peak, sig_24) %>%
tally() %>%
pivot_wider(., id_cols=sig_24, names_from = Top2b_peak, values_from = n) %>%
print() %>%
column_to_rownames("sig_24") %>%
fisher.test()# A tibble: 2 × 3
sig_24 TOP2B_peak not_TOP2B_peak
<fct> <int> <int>
1 sig 32 2015
2 not_sig 121 2990
Fisher's Exact Test for Count Data
data: .
p-value = 8.347e-07
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
0.2560764 0.5863054
sample estimates:
odds ratio
0.3924942 Calculating Distance to TAD-SNP from peak
DOX 24 hours
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")
MCF7_DAR_snp_pairs_dist <- readRDS("data/Final_four_data/re_analysis/MCF7_DAR_snp_pairs_dist.RDS") %>%
dplyr::rename("Peakid"=names) %>%
mutate(sig_24="MCF7_DAR")
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"))
peak_snp_pairs_dist %>%
mutate(sig_24=factor(sig_24, levels= c("sig","not_sig"))) %>%
ggplot(., aes(x= sig_24, y=distance))+
geom_boxplot()+
theme_bw()+
geom_signif(comparisons = list(c("sig", "not_sig")),
map_signif_level = FALSE, test = "wilcox.test")+
ggtitle("DOX 24 hour distances of DAR-SNP pairs and non-DAR-SNP pairs")
wilcox.test(distance ~ sig_24, data = peak_snp_pairs_dist)
Wilcoxon rank sum test with continuity correction
data: distance by sig_24
W = 9463083, p-value = 0.002185
alternative hypothesis: true location shift is not equal to 0Cardiotox_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"))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$Peakid, "sig","not_sig"))
peak_snp_pairs_dist_DOX_3 %>%
mutate(sig_3=factor(sig_3, levels= c("sig","not_sig"))) %>%
ggplot(., aes(x= sig_3, y=distance))+
geom_boxplot()+
theme_bw()+
geom_signif(comparisons = list(c("sig", "not_sig")),
map_signif_level = FALSE, test = "wilcox.test")
| Version | Author | Date |
|---|---|---|
| 1429820 | reneeisnowhere | 2025-07-21 |
wilcox.test(distance ~ sig_3, data = peak_snp_pairs_dist_DOX_3)
Wilcoxon rank sum test with continuity correction
data: distance by sig_3
W = 837367, p-value = 0.0241
alternative hypothesis: true location shift is not equal to 0Cardiotox_gwas_EPI <- peak_snp_pairs_dist_DOX_3 %>%
dplyr::filter(sig_3=="sig") %>%
group_by(TAD_id,RSID) %>%
slice_min(order_by = distance, with_ties = FALSE) %>%
ungroup() %>%
arrange(snp_chr,snp_start) %>%
left_join(., all_results_pivot, by=c("Peakid"="genes")) %>%
tidyr::unite(., name,Peakid,RSID)Looking at SNPs that directly overlap DARs
snp_peak_ol <- join_overlap_inner(all_regions_gr,schneider_gr)
SNP_DAR_overlap_direct <- snp_peak_ol %>%
as.data.frame() %>%
mutate(Dox_24=if_else(Peakid %in% DOX_DAR_sig$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$Peakid,"yes","no")) %>%
dplyr::select(Peakid,RSID,Dox_24:Mtx_3)
SNP_DAR_overlap_direct Peakid RSID Dox_24 Epi_24 Dnr_24 MTx_24 Dox_3 Epi_3
1 chr18.58336709.58336869 rs12051934 yes yes yes no no no
2 chr2.176086015.176086512 rs6752623 no no no no no no
3 chr22.46249819.46250733 rs7291763 no no yes no no no
4 chr7.134834642.134835353 rs7777356 no yes no no no yes
Dnr_3 Mtx_3
1 no no
2 no no
3 no no
4 yes noDOX_MCF7 added
bind_rows(MCF7_DAR_snp_pairs_dist,peak_snp_pairs_dist) %>%
mutate(sig_24=factor(sig_24, levels= c("sig","not_sig", "MCF7_DAR"))) %>% ggplot(., aes(x= sig_24, y=distance))+
geom_boxplot()+
theme_bw()+
geom_signif(comparisons = list(c("sig", "not_sig"),
c("sig","MCF7_DAR"),
c("not_sig","MCF7_DAR")),
step_increase = 0.1,
map_signif_level = FALSE,
test = "wilcox.test")+
ggtitle("DOX 24 hour distances of DAR-SNP pairs\n and non-DAR-SNP pairs with MCF7 DARs")
EPI 24 hours
peak_snp_pairs_dist_EPI <- peak_snp_pairs %>%
mutate(distance = abs(peak_start - snp_start)) %>%
mutate(sig_24= if_else(Peakid %in% EPI_DAR_sig$Peakid, "sig","not_sig"))
peak_snp_pairs_dist_EPI %>%
mutate(sig_24=factor(sig_24, levels= c("sig","not_sig"))) %>%
ggplot(., aes(x= sig_24, y=distance))+
geom_boxplot()+
theme_bw()+
geom_signif(comparisons = list(c("sig", "not_sig")),
map_signif_level = FALSE, test = "wilcox.test")
| Version | Author | Date |
|---|---|---|
| 1a9df02 | reneeisnowhere | 2025-07-09 |
wilcox.test(distance ~ sig_24, data = peak_snp_pairs_dist)
Wilcoxon rank sum test with continuity correction
data: distance by sig_24
W = 9463083, p-value = 0.002185
alternative hypothesis: true location shift is not equal to 0Cardiotox_gwas_EPI <- peak_snp_pairs_dist_EPI %>%
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)EPI_MCF7 added
bind_rows(MCF7_DAR_snp_pairs_dist,peak_snp_pairs_dist_EPI) %>%
mutate(sig_24=factor(sig_24, levels= c("sig","not_sig", "MCF7_DAR"))) %>% ggplot(., aes(x= sig_24, y=distance))+
geom_boxplot()+
theme_bw()+
geom_signif(comparisons = list(c("sig", "not_sig"),
c("sig","MCF7_DAR"),
c("not_sig","MCF7_DAR")),
step_increase = 0.1,
map_signif_level = FALSE,
test = "wilcox.test")+
ggtitle("EPI 24 hour distances of DAR-SNP pairs\n and non-DAR-SNP pairs with MCF7 DARs")
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$Peakid, "sig","not_sig"))
peak_snp_pairs_dist_EPI_3 %>%
mutate(sig_3=factor(sig_3, levels= c("sig","not_sig"))) %>%
ggplot(., aes(x= sig_3, y=distance))+
geom_boxplot()+
theme_bw()+
geom_signif(comparisons = list(c("sig", "not_sig")),
map_signif_level = FALSE, test = "wilcox.test")
| Version | Author | Date |
|---|---|---|
| 1429820 | reneeisnowhere | 2025-07-21 |
wilcox.test(distance ~ sig_3, data = peak_snp_pairs_dist_EPI_3)
Wilcoxon rank sum test with continuity correction
data: distance by sig_3
W = 3249493, p-value = 3.114e-05
alternative hypothesis: true location shift is not equal to 0Cardiotox_gwas_EPI_3 <- peak_snp_pairs_dist_EPI_3 %>%
dplyr::filter(sig_3=="sig") %>%
group_by(TAD_id,RSID) %>%
slice_min(order_by = distance, with_ties = FALSE) %>%
ungroup() %>%
arrange(snp_chr,snp_start) %>%
left_join(., all_results_pivot, by=c("Peakid"="genes")) %>%
tidyr::unite(., name,Peakid,RSID)DNR 24 hours
peak_snp_pairs_dist_DNR <- peak_snp_pairs %>%
mutate(distance = abs(peak_start - snp_start)) %>%
mutate(sig_24= if_else(Peakid %in% DNR_DAR_sig$Peakid, "sig","not_sig"))
peak_snp_pairs_dist_DNR %>%
mutate(sig_24=factor(sig_24, levels= c("sig","not_sig"))) %>%
ggplot(., aes(x= sig_24, y=distance))+
geom_boxplot()+
theme_bw()+
geom_signif(comparisons = list(c("sig", "not_sig")),
map_signif_level = FALSE, test = "wilcox.test")+
ggtitle("DNR 24 hour distances of DAR-SNP pairs and non-DAR-SNP pairs")
| Version | Author | Date |
|---|---|---|
| 1a9df02 | reneeisnowhere | 2025-07-09 |
wilcox.test(distance ~ sig_24, data = peak_snp_pairs_dist)
Wilcoxon rank sum test with continuity correction
data: distance by sig_24
W = 9463083, p-value = 0.002185
alternative hypothesis: true location shift is not equal to 0Cardiotox_gwas_DNR <- peak_snp_pairs_dist_DNR %>%
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)DNR_MCF7 added
bind_rows(MCF7_DAR_snp_pairs_dist,peak_snp_pairs_dist_DNR) %>%
mutate(sig_24=factor(sig_24, levels= c("sig","not_sig", "MCF7_DAR"))) %>% ggplot(., aes(x= sig_24, y=distance))+
geom_boxplot()+
theme_bw()+
geom_signif(comparisons = list(c("sig", "not_sig"),
c("sig","MCF7_DAR"),
c("not_sig","MCF7_DAR")),
step_increase = 0.1,
map_signif_level = FALSE,
test = "wilcox.test")+
ggtitle("DNR 24 hour distances of DAR-SNP pairs\n and non-DAR-SNP pairs with MCF7 DARs")
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$Peakid, "sig","not_sig"))
peak_snp_pairs_dist_DNR_3 %>%
mutate(sig_3=factor(sig_3, levels= c("sig","not_sig"))) %>%
ggplot(., aes(x= sig_3, y=distance))+
geom_boxplot()+
theme_bw()+
geom_signif(comparisons = list(c("sig", "not_sig")),
map_signif_level = FALSE, test = "wilcox.test")+
ggtitle("3 hour DNR")
| Version | Author | Date |
|---|---|---|
| 1429820 | reneeisnowhere | 2025-07-21 |
wilcox.test(distance ~ sig_3, data = peak_snp_pairs_dist_DNR_3)
Wilcoxon rank sum test with continuity correction
data: distance by sig_3
W = 4576878, p-value = 0.02023
alternative hypothesis: true location shift is not equal to 0Cardiotox_gwas_DNR_3 <- peak_snp_pairs_dist_DNR_3 %>%
dplyr::filter(sig_3=="sig") %>%
group_by(TAD_id,RSID) %>%
slice_min(order_by = distance, with_ties = FALSE) %>%
ungroup() %>%
arrange(snp_chr,snp_start) %>%
left_join(., all_results_pivot, by=c("Peakid"="genes")) %>%
tidyr::unite(., name,Peakid,RSID)MTX 24 hours
peak_snp_pairs_dist_MTX <- peak_snp_pairs %>%
mutate(distance = abs(peak_start - snp_start)) %>%
mutate(sig_24= if_else(Peakid %in% MTX_DAR_sig$Peakid, "sig","not_sig"))
peak_snp_pairs_dist_MTX %>%
mutate(sig_24=factor(sig_24, levels= c("sig","not_sig"))) %>%
ggplot(., aes(x= sig_24, y=distance))+
geom_boxplot()+
theme_bw()+
geom_signif(comparisons = list(c("sig", "not_sig")),
map_signif_level = FALSE, test = "wilcox.test")+
ggtitle("MTX 24 hour distances of DAR-SNP pairs and non-DAR-SNP pairs")
| Version | Author | Date |
|---|---|---|
| 1a9df02 | reneeisnowhere | 2025-07-09 |
wilcox.test(distance ~ sig_24, data = peak_snp_pairs_dist)
Wilcoxon rank sum test with continuity correction
data: distance by sig_24
W = 9463083, p-value = 0.002185
alternative hypothesis: true location shift is not equal to 0Cardiotox_gwas_MTX <- peak_snp_pairs_dist_MTX %>%
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)MTX_MCF7 added
bind_rows(MCF7_DAR_snp_pairs_dist,peak_snp_pairs_dist_MTX) %>%
mutate(sig_24=factor(sig_24, levels= c("sig","not_sig", "MCF7_DAR"))) %>% ggplot(., aes(x= sig_24, y=distance))+
geom_boxplot()+
theme_bw()+
geom_signif(comparisons = list(c("sig", "not_sig"),
c("sig","MCF7_DAR"),
c("not_sig","MCF7_DAR")),
step_increase = 0.1,
map_signif_level = FALSE,
test = "wilcox.test")+
ggtitle("MTX 24 hour distances of DAR-SNP pairs\n and non-DAR-SNP pairs with MCF7 DARs")
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_3 %>%
mutate(sig_3=factor(sig_3, levels= c("sig","not_sig"))) %>%
ggplot(., aes(x= sig_3, y=distance))+
geom_boxplot()+
theme_bw()+
geom_signif(comparisons = list(c("sig", "not_sig")),
map_signif_level = FALSE, test = "wilcox.test")+
ggtitle("3 hour MTX")
| Version | Author | Date |
|---|---|---|
| 1429820 | reneeisnowhere | 2025-07-21 |
wilcox.test(distance ~ sig_3, data = peak_snp_pairs_dist_MTX_3)
Wilcoxon rank sum test with continuity correction
data: distance by sig_3
W = 219052, p-value = 0.5511
alternative hypothesis: true location shift is not equal to 0Cardiotox_gwas_MTX_3 <- peak_snp_pairs_dist_MTX_3 %>%
dplyr::filter(sig_3=="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)Creating SNP_TAD distance DF
For combining the above 24 hour trt-distance to SNP data frames for box-plots
drug_pal <- c("#8B006D","#DF707E","#F1B72B", "#3386DD","#707031","#41B333")
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)
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)
| Version | Author | Date |
|---|---|---|
| 1429820 | reneeisnowhere | 2025-07-21 |
MCF7 tissue specific, non-tissue specific.
Here I am overlapping my data with the MCF7 DAR data. This will create DARS for each treatment that overlap MCF7 DARS (tissue-shared regions) and DARS that do not overlap MCF7 DARS (tissue-specific regions). I will then calculate the distance between the SNPs in the shared vs specific for each treatment.
MCF7_DARs_hyper <- read_excel("C:/Users/renee/Downloads/MCF7-doxATAC/Table 4.XLSX",
sheet = "hyper") %>% GRanges()
MCF7_DARs_hypo <- read_excel("C:/Users/renee/Downloads/MCF7-doxATAC/Table 4.XLSX",
sheet = "hypo") %>% GRanges()
# MCF7_ARsmcf7_1 <- read_excel("C:/Users/renee/Downloads/MCF7-doxATAC/Table 3.XLSX") %>%
# GRanges()
MCF7_DARs_hyper$names <- paste0("hyper_", seq_along(seqnames(MCF7_DARs_hyper)))
MCF7_DARs_hypo$names <- paste0("hypo_", seq_along(seqnames(MCF7_DARs_hypo)))
MCF7_DAR_all <- c(MCF7_DARs_hyper,MCF7_DARs_hypo)
ch = import.chain("C:/Users/renee/ATAC_folder/liftOver_genome/hg19ToHg38.over.chain")
# MCF7_ARsmcf7_1_LO <- as.data.frame(liftOver(MCF7_ARsmcf7_1,ch)) %>%
# GRanges()
MCF7_DARs_hyper_LO <- as.data.frame(liftOver(MCF7_DARs_hyper,ch)) %>%
GRanges()
MCF7_DARs_hypo_LO <- as.data.frame(liftOver(MCF7_DARs_hypo,ch)) %>%
GRanges()
MCF7_DAR_all_LO <- c(MCF7_DARs_hyper_LO,MCF7_DARs_hypo_LO)
MCF7DAR_AR_ol <- join_overlap_intersect(all_regions_gr, MCF7_DAR_all_LO)
DAR_AR_overlap_df <-MCF7DAR_AR_ol %>%
as.data.frame() %>%
distinct(Peakid)Attempting with DOX
peak_snp_pairs_dist %>%
dplyr::filter(sig_24 =="sig") %>%
mutate(specific=if_else(Peakid %in%DAR_AR_overlap_df$Peakid,"specific","not_specific")) %>%
mutate(specific=factor(specific,levels= c("specific","not_specific"))) %>%
ggplot(.,aes(x=specific, y=distance))+
geom_boxplot()+
theme_bw()+
geom_signif(comparisons = list(c("specific", "not_specific")),
step_increase = 0.1,
map_signif_level = FALSE,
test = "wilcox.test")+
ggtitle("DOX 24 hour distances of DAR-SNP pairs\n that are specific for iPSC-CMs or not-specific")
| Version | Author | Date |
|---|---|---|
| 1429820 | reneeisnowhere | 2025-07-21 |
peak_snp_pairs_dist_EPI %>%
dplyr::filter(sig_24 =="sig") %>%
mutate(specific=if_else(Peakid %in%DAR_AR_overlap_df$Peakid,"specific","not_specific")) %>%
mutate(specific=factor(specific,levels= c("specific","not_specific"))) %>%
ggplot(.,aes(x=specific, y=distance))+
geom_boxplot()+
theme_bw()+
geom_signif(comparisons = list(c("specific", "not_specific")),
step_increase = 0.1,
map_signif_level = FALSE,
test = "wilcox.test")+
ggtitle("EPI 24 hour distances of DAR-SNP pairs\n that are specific for iPSC-CMs or not-specific")
| Version | Author | Date |
|---|---|---|
| 1429820 | reneeisnowhere | 2025-07-21 |
peak_snp_pairs_dist_DNR %>%
dplyr::filter(sig_24 =="sig") %>%
mutate(specific=if_else(Peakid %in%DAR_AR_overlap_df$Peakid,"specific","not_specific")) %>%
mutate(specific=factor(specific,levels= c("specific","not_specific"))) %>%
ggplot(.,aes(x=specific, y=distance))+
geom_boxplot()+
theme_bw()+
geom_signif(comparisons = list(c("specific", "not_specific")),
step_increase = 0.1,
map_signif_level = FALSE,
test = "wilcox.test")+
ggtitle("DNR 24 hour distances of DAR-SNP pairs\n that are specific for iPSC-CMs or not-specific")
| Version | Author | Date |
|---|---|---|
| 1429820 | reneeisnowhere | 2025-07-21 |
peak_snp_pairs_dist_MTX %>%
dplyr::filter(sig_24 =="sig") %>%
mutate(specific=if_else(Peakid %in%DAR_AR_overlap_df$Peakid,"specific","not_specific")) %>%
mutate(specific=factor(specific,levels= c("specific","not_specific"))) %>%
ggplot(.,aes(x=specific, y=distance))+
geom_boxplot()+
theme_bw()+
geom_signif(comparisons = list(c("specific", "not_specific")),
step_increase = 0.1,
map_signif_level = FALSE,
test = "wilcox.test")+
ggtitle("MTX 24 hour distances of DAR-SNP pairs\n that are specific for iPSC-CMs or not-specific")
| Version | Author | Date |
|---|---|---|
| 1429820 | reneeisnowhere | 2025-07-21 |
peak_snp_pairs_dist %>%
dplyr::filter(!Peakid %in% DAR_AR_overlap_df$Peakid) %>%
mutate(sig_24=factor(sig_24,levels = c ("sig","not_sig"))) %>%
ggplot(.,aes(x=sig_24, y=distance))+
geom_boxplot()+
theme_bw()+
geom_signif(comparisons = list(c("sig", "not_sig")),
step_increase = 0.1,
map_signif_level = FALSE,
test = "wilcox.test")+
ggtitle("DOX 24 hour distances of DAR-SNP pairs\n that are specific for iPSC-CMs")
| Version | Author | Date |
|---|---|---|
| 1429820 | reneeisnowhere | 2025-07-21 |
peak_snp_pairs_dist_EPI %>%
dplyr::filter(!Peakid %in% DAR_AR_overlap_df$Peakid) %>%
mutate(sig_24=factor(sig_24,levels = c ("sig","not_sig"))) %>%
ggplot(.,aes(x=sig_24, y=distance))+
geom_boxplot()+
theme_bw()+
geom_signif(comparisons = list(c("sig", "not_sig")),
step_increase = 0.1,
map_signif_level = FALSE,
test = "wilcox.test")+
ggtitle("EPI 24 hour distances of DAR-SNP pairs\n that are specific for iPSC-CMs")
| Version | Author | Date |
|---|---|---|
| 1429820 | reneeisnowhere | 2025-07-21 |
peak_snp_pairs_dist_DNR %>%
dplyr::filter(!Peakid %in% DAR_AR_overlap_df$Peakid) %>%
mutate(sig_24=factor(sig_24,levels = c ("sig","not_sig"))) %>%
ggplot(.,aes(x=sig_24, y=distance))+
geom_boxplot()+
theme_bw()+
geom_signif(comparisons = list(c("sig", "not_sig")),
step_increase = 0.1,
map_signif_level = FALSE,
test = "wilcox.test")+
ggtitle("DNR 24 hour distances of DAR-SNP pairs\n that are specific for iPSC-CMs")
| Version | Author | Date |
|---|---|---|
| 1429820 | reneeisnowhere | 2025-07-21 |
peak_snp_pairs_dist_MTX %>%
dplyr::filter(!Peakid %in% DAR_AR_overlap_df$Peakid) %>%
mutate(sig_24=factor(sig_24,levels = c ("sig","not_sig"))) %>%
ggplot(.,aes(x=sig_24, y=distance))+
geom_boxplot()+
theme_bw()+
geom_signif(comparisons = list(c("sig", "not_sig")),
step_increase = 0.1,
map_signif_level = FALSE,
test = "wilcox.test")+
ggtitle("MTX 24 hour distances of DAR-SNP pairs\n that are specific for iPSC-CMs")
| Version | Author | Date |
|---|---|---|
| 1429820 | reneeisnowhere | 2025-07-21 |
Attempt at heatmap with RNA expression, decided not to use or plot here.
Cardotox_mat <- Cardiotox_gwas_collaped_df %>%
dplyr::select(name,DOX_3:TRZ_24,`3_RNA_DOX`,`3_RNA_EPI`,`3_RNA_DNR`,`3_RNA_MTX`,`3_RNA_TRZ`,`24_RNA_DOX`,`24_RNA_EPI`,`24_RNA_DNR`,`24_RNA_MTX`,`24_RNA_TRZ`) %>%
column_to_rownames("name") %>%
as.matrix()
annot_map_df <- Cardiotox_gwas_collaped_df %>%
dplyr::select(name,snp_dist) %>%
column_to_rownames("name")
annot_map <-
rowAnnotation(
snp_dist=Cardiotox_gwas_collaped_df$snp_dist,
TAD_id=Cardiotox_gwas_collaped_df$TAD_id,
col= list(snp_dist=c("2kb"="goldenrod4",
"20kb"="pink",
">20kb"="tan2"),
TAD_id=tad_colors))
simply_map_lfc <- ComplexHeatmap::Heatmap(Cardotox_mat,
# col = col_fun,
left_annotation = annot_map,
show_row_names = TRUE,
row_names_max_width= ComplexHeatmap::max_text_width(rownames(Cardotox_mat), gp=gpar(fontsize=14)),
heatmap_legend_param = list(direction = "horizontal"),
show_column_names = TRUE,
cluster_rows = FALSE,
cluster_columns = FALSE)
ComplexHeatmap::draw(simply_map_lfc,
merge_legend = TRUE,
heatmap_legend_side = "left",
annotation_legend_side = "left")Alternative of final graph
ATAC_all_adj.pvals <- all_results%>%
dplyr::select(source,genes,adj.P.Val) %>%
pivot_wider(id_cols=genes, values_from = adj.P.Val, names_from = source)
# saveRDS(ATAC_all_adj.pvals,"data/Final_four_data/re_analysis/ATAC_all_adj_pvals.RDS")
sig_mat_cardiotox <- ATAC_all_adj.pvals %>%
dplyr::filter(genes %in% peak_snp_pairs_dist$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")) %>%
# 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,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$sig_24,
col= list(snp_dist=c("2kb"="goldenrod4",
"20kb"="pink",
">20kb"="tan2"),
TAD_id=tad_colors))
simply_map_lfc_2 <- ComplexHeatmap::Heatmap(Cardotox_mat_2,
left_annotation = annot_map_2,
show_row_names = TRUE,
row_names_max_width= ComplexHeatmap::max_text_width(rownames(Cardotox_mat_2), gp=gpar(fontsize=14)),
heatmap_legend_param = list(direction = "horizontal"),
show_column_names = TRUE,
cluster_rows = FALSE,
cluster_columns = FALSE,
cell_fun = function(j, i, x, y, width, height, fill) {
if (!is.na(sig_mat_cardiotox[i, j]) && sig_mat_cardiotox[i, j] <0.05) {
grid.text("*", x, y, gp = gpar(fontsize = 20)) # Add star if significant
} })
ComplexHeatmap::draw(simply_map_lfc_2,
merge_legend = TRUE,
heatmap_legend_side = "left",
annotation_legend_side = "left")
ATAC_all_adj.pvals %>%
dplyr::filter(genes %in% peak_snp_pairs_dist$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() DNR_3
chr1.108829511.108829805_rs10857972,rs6704266 0.2604386136
chr1.173473597.173473889_rs4916358,rs10798282,rs10753081 0.9941823649
chr1.19640454.19641134_rs7523079 0.2751816852
chr1.19644587.19645224_rs7522389 0.1110929630
chr10.16792579.16793007_rs7068265,rs7068881 0.1754416544
chr10.76209088.76209420_rs7094302 0.6062032046
chr11.124863016.124863456_rs7111513 0.9095139649
chr11.124865334.124865878_rs5017048 0.8068193757
chr11.133816825.133817041_rs10894749 0.9576490254
chr11.36409327.36409838_rs10836550 0.4507242896
chr11.36420818.36421386_rs1365120 0.1671941818
chr12.120317069.120317911_rs16950058 0.8463250455
chr12.120325389.120326108_rs5634 0.5597350198
chr12.120367731.120368309_rs2238161 0.0496726775
chr12.19222646.19223206_rs7399293 0.7530113056
chr13.100722024.100722184_rs12863645 0.1369620985
chr13.46774697.46774984_rs1745836 0.1103175482
chr13.64945176.64945505_rs925558,rs1513001,rs7994896 0.3060666798
chr13.91724572.91724918_rs342715,rs171060 0.0506322122
chr13.99908163.99908652_rs9557321 0.1382557035
chr14.55721341.55721700_rs2184559 0.9748799763
chr15.23483710.23484436_rs28684656,rs28714259,rs1875910 0.0975418891
chr15.93645160.93645583_rs4238475 0.9900179537
chr16.13378005.13378513_rs2188738,rs1018891,rs886218,rs1468171 0.2887664071
chr17.51461539.51461982_rs10514983 0.5052014759
chr18.32469230.32469379_rs1458866 0.7599187815
chr2.104528125.104528417_rs11123997 0.8552557266
chr2.12759282.12759669_rs6722342,rs7596623 0.4098477471
chr2.12766229.12766820_rs13413220,rs13397389 0.4019189553
chr2.176086015.176086512_rs6752623 0.7542466264
chr2.41038390.41038934_rs6746423 0.3221761584
chr2.77457604.77458071_rs12614202,rs17014128 0.1955860057
chr20.22217873.22218175_rs76464104 0.7146552246
chr20.53735535.53736570_rs12480103 0.0039717853
chr22.46225398.46226192_rs4253763 0.0323062960
chr22.46233211.46233644_rs11090819 0.0061385813
chr22.46249819.46250733_rs7291763 0.2194815027
chr3.107240266.107241585_rs11710776 0.0039425887
chr3.118380545.118381165_rs779206,rs13087409 0.9685916399
chr3.193461783.193462089_rs9879227 0.3898544491
chr3.20682627.20683020_rs11715178,rs10510504 0.9624468773
chr4.137238844.137239180_rs13148112 0.2635250836
chr4.176615030.176615396_rs6553973 0.5016560972
chr4.32226261.32226503_rs13125385 0.7169684281
chr4.40731697.40732054_rs11727143 0.8934529766
chr4.40749770.40751218_rs10012367 0.4517700481
chr4.9880847.9881265_rs4697900,rs9684729,rs6449090 0.2945038500
chr5.102865289.102866656_rs32680 0.0543776655
chr5.102947036.102947471_rs6871111,rs6860588,rs3776874,rs11952361 0.2877953785
chr5.164619344.164619596_rs250594 0.0304643100
chr5.7415226.7416045_rs7710510 0.1140516622
chr5.7464212.7464571_rs6883259,rs766643 0.6488216182
chr6.160899655.160900401_rs2489944,rs2465874 0.8175996745
chr6.27026322.27026687_rs36022097 0.9851482375
chr6.32521585.32522598_rs115541994 0.5746006297
chr6.71712533.71713167_rs1204328 0.7516231731
chr7.10239630.10240386_rs17162825 0.0275441848
chr7.116499279.116500903_rs17138749 0.0004354869
chr7.134834642.134835353_rs7777356 0.0468197649
chr7.26147128.26148142_rs10248605 0.1920101330
chr8.141160879.141162083_rs428300 0.4304731801
chr8.15830942.15831184_rs11203734,rs7016422,rs12549742,rs2410329 0.0209345846
chr8.20115130.20115538_rs12548222 0.4403856042
chr8.69455933.69456585_rs12545665 0.5785563175
chr8.701154.701537_rs13273765 0.0847512055
chr8.81096615.81097088_rs16908579 0.3593169617
chr9.18632808.18633319_rs776777 0.4978362823
chr9.19720338.19720665_rs7869360,rs7855757 0.9888222941
DOX_3
chr1.108829511.108829805_rs10857972,rs6704266 0.28996020
chr1.173473597.173473889_rs4916358,rs10798282,rs10753081 0.79324477
chr1.19640454.19641134_rs7523079 0.10235976
chr1.19644587.19645224_rs7522389 0.76107014
chr10.16792579.16793007_rs7068265,rs7068881 0.52334599
chr10.76209088.76209420_rs7094302 0.87870169
chr11.124863016.124863456_rs7111513 0.87999911
chr11.124865334.124865878_rs5017048 0.94606145
chr11.133816825.133817041_rs10894749 0.69326462
chr11.36409327.36409838_rs10836550 0.87860628
chr11.36420818.36421386_rs1365120 0.22233972
chr12.120317069.120317911_rs16950058 0.72036630
chr12.120325389.120326108_rs5634 0.94148931
chr12.120367731.120368309_rs2238161 0.86619208
chr12.19222646.19223206_rs7399293 0.80126526
chr13.100722024.100722184_rs12863645 0.13362272
chr13.46774697.46774984_rs1745836 0.10024827
chr13.64945176.64945505_rs925558,rs1513001,rs7994896 0.31772047
chr13.91724572.91724918_rs342715,rs171060 0.30032478
chr13.99908163.99908652_rs9557321 0.33019997
chr14.55721341.55721700_rs2184559 0.81016947
chr15.23483710.23484436_rs28684656,rs28714259,rs1875910 0.07429395
chr15.93645160.93645583_rs4238475 0.93216164
chr16.13378005.13378513_rs2188738,rs1018891,rs886218,rs1468171 0.42769913
chr17.51461539.51461982_rs10514983 0.49879726
chr18.32469230.32469379_rs1458866 0.73139860
chr2.104528125.104528417_rs11123997 0.46759370
chr2.12759282.12759669_rs6722342,rs7596623 0.58364196
chr2.12766229.12766820_rs13413220,rs13397389 0.54471722
chr2.176086015.176086512_rs6752623 0.95595571
chr2.41038390.41038934_rs6746423 0.95043912
chr2.77457604.77458071_rs12614202,rs17014128 0.75361165
chr20.22217873.22218175_rs76464104 0.42265673
chr20.53735535.53736570_rs12480103 0.08343468
chr22.46225398.46226192_rs4253763 0.39046192
chr22.46233211.46233644_rs11090819 0.11013515
chr22.46249819.46250733_rs7291763 0.41625015
chr3.107240266.107241585_rs11710776 0.12136474
chr3.118380545.118381165_rs779206,rs13087409 0.66214382
chr3.193461783.193462089_rs9879227 0.42860660
chr3.20682627.20683020_rs11715178,rs10510504 0.99221246
chr4.137238844.137239180_rs13148112 0.91587514
chr4.176615030.176615396_rs6553973 0.97276867
chr4.32226261.32226503_rs13125385 0.78012067
chr4.40731697.40732054_rs11727143 0.99026586
chr4.40749770.40751218_rs10012367 0.91621966
chr4.9880847.9881265_rs4697900,rs9684729,rs6449090 0.21644958
chr5.102865289.102866656_rs32680 0.39419001
chr5.102947036.102947471_rs6871111,rs6860588,rs3776874,rs11952361 0.53263972
chr5.164619344.164619596_rs250594 0.37800156
chr5.7415226.7416045_rs7710510 0.59452990
chr5.7464212.7464571_rs6883259,rs766643 0.96323882
chr6.160899655.160900401_rs2489944,rs2465874 0.87685451
chr6.27026322.27026687_rs36022097 0.96215537
chr6.32521585.32522598_rs115541994 0.86310747
chr6.71712533.71713167_rs1204328 0.77580074
chr7.10239630.10240386_rs17162825 0.06055191
chr7.116499279.116500903_rs17138749 0.03946688
chr7.134834642.134835353_rs7777356 0.06100145
chr7.26147128.26148142_rs10248605 0.98340441
chr8.141160879.141162083_rs428300 0.66713952
chr8.15830942.15831184_rs11203734,rs7016422,rs12549742,rs2410329 0.20823369
chr8.20115130.20115538_rs12548222 0.60645494
chr8.69455933.69456585_rs12545665 0.58621301
chr8.701154.701537_rs13273765 0.14027293
chr8.81096615.81097088_rs16908579 0.54794256
chr9.18632808.18633319_rs776777 0.38921211
chr9.19720338.19720665_rs7869360,rs7855757 0.89538037
EPI_3
chr1.108829511.108829805_rs10857972,rs6704266 0.627792912
chr1.173473597.173473889_rs4916358,rs10798282,rs10753081 0.709914257
chr1.19640454.19641134_rs7523079 0.415719858
chr1.19644587.19645224_rs7522389 0.601889717
chr10.16792579.16793007_rs7068265,rs7068881 0.130614071
chr10.76209088.76209420_rs7094302 0.728255472
chr11.124863016.124863456_rs7111513 0.698068653
chr11.124865334.124865878_rs5017048 0.658177438
chr11.133816825.133817041_rs10894749 0.519814821
chr11.36409327.36409838_rs10836550 0.204486043
chr11.36420818.36421386_rs1365120 0.126493056
chr12.120317069.120317911_rs16950058 0.896505610
chr12.120325389.120326108_rs5634 0.972895082
chr12.120367731.120368309_rs2238161 0.332993063
chr12.19222646.19223206_rs7399293 0.755566564
chr13.100722024.100722184_rs12863645 0.097501269
chr13.46774697.46774984_rs1745836 0.041216934
chr13.64945176.64945505_rs925558,rs1513001,rs7994896 0.478988902
chr13.91724572.91724918_rs342715,rs171060 0.266646540
chr13.99908163.99908652_rs9557321 0.512417777
chr14.55721341.55721700_rs2184559 0.628238659
chr15.23483710.23484436_rs28684656,rs28714259,rs1875910 0.002865416
chr15.93645160.93645583_rs4238475 0.436573116
chr16.13378005.13378513_rs2188738,rs1018891,rs886218,rs1468171 0.031459171
chr17.51461539.51461982_rs10514983 0.399674713
chr18.32469230.32469379_rs1458866 0.679191345
chr2.104528125.104528417_rs11123997 0.898534678
chr2.12759282.12759669_rs6722342,rs7596623 0.536775555
chr2.12766229.12766820_rs13413220,rs13397389 0.748199265
chr2.176086015.176086512_rs6752623 0.995945463
chr2.41038390.41038934_rs6746423 0.907599244
chr2.77457604.77458071_rs12614202,rs17014128 0.277297264
chr20.22217873.22218175_rs76464104 0.615438543
chr20.53735535.53736570_rs12480103 0.060097328
chr22.46225398.46226192_rs4253763 0.046303317
chr22.46233211.46233644_rs11090819 0.079390213
chr22.46249819.46250733_rs7291763 0.632752727
chr3.107240266.107241585_rs11710776 0.009720301
chr3.118380545.118381165_rs779206,rs13087409 0.851331297
chr3.193461783.193462089_rs9879227 0.213935344
chr3.20682627.20683020_rs11715178,rs10510504 0.628774319
chr4.137238844.137239180_rs13148112 0.771367568
chr4.176615030.176615396_rs6553973 0.316307474
chr4.32226261.32226503_rs13125385 0.515065253
chr4.40731697.40732054_rs11727143 0.771939694
chr4.40749770.40751218_rs10012367 0.640910831
chr4.9880847.9881265_rs4697900,rs9684729,rs6449090 0.595412025
chr5.102865289.102866656_rs32680 0.231037136
chr5.102947036.102947471_rs6871111,rs6860588,rs3776874,rs11952361 0.392516335
chr5.164619344.164619596_rs250594 0.232154081
chr5.7415226.7416045_rs7710510 0.089960026
chr5.7464212.7464571_rs6883259,rs766643 0.564584818
chr6.160899655.160900401_rs2489944,rs2465874 0.852939108
chr6.27026322.27026687_rs36022097 0.849269257
chr6.32521585.32522598_rs115541994 0.835105641
chr6.71712533.71713167_rs1204328 0.398006470
chr7.10239630.10240386_rs17162825 0.003879779
chr7.116499279.116500903_rs17138749 0.005247476
chr7.134834642.134835353_rs7777356 0.006546935
chr7.26147128.26148142_rs10248605 0.063537276
chr8.141160879.141162083_rs428300 0.297732616
chr8.15830942.15831184_rs11203734,rs7016422,rs12549742,rs2410329 0.011869205
chr8.20115130.20115538_rs12548222 0.168385512
chr8.69455933.69456585_rs12545665 0.978951269
chr8.701154.701537_rs13273765 0.032741476
chr8.81096615.81097088_rs16908579 0.503472900
chr9.18632808.18633319_rs776777 0.635914438
chr9.19720338.19720665_rs7869360,rs7855757 0.871034405
MTX_3
chr1.108829511.108829805_rs10857972,rs6704266 0.94362679
chr1.173473597.173473889_rs4916358,rs10798282,rs10753081 0.99802465
chr1.19640454.19641134_rs7523079 0.48437917
chr1.19644587.19645224_rs7522389 0.11226483
chr10.16792579.16793007_rs7068265,rs7068881 0.56212854
chr10.76209088.76209420_rs7094302 0.73886646
chr11.124863016.124863456_rs7111513 0.48404517
chr11.124865334.124865878_rs5017048 0.80172074
chr11.133816825.133817041_rs10894749 0.83460367
chr11.36409327.36409838_rs10836550 0.61712982
chr11.36420818.36421386_rs1365120 0.06195368
chr12.120317069.120317911_rs16950058 0.83007788
chr12.120325389.120326108_rs5634 0.33879927
chr12.120367731.120368309_rs2238161 0.99849458
chr12.19222646.19223206_rs7399293 0.77288115
chr13.100722024.100722184_rs12863645 0.40520448
chr13.46774697.46774984_rs1745836 0.47094774
chr13.64945176.64945505_rs925558,rs1513001,rs7994896 0.98094184
chr13.91724572.91724918_rs342715,rs171060 0.64047737
chr13.99908163.99908652_rs9557321 0.90367908
chr14.55721341.55721700_rs2184559 0.93540948
chr15.23483710.23484436_rs28684656,rs28714259,rs1875910 0.06195368
chr15.93645160.93645583_rs4238475 0.91785720
chr16.13378005.13378513_rs2188738,rs1018891,rs886218,rs1468171 0.64181703
chr17.51461539.51461982_rs10514983 0.88083022
chr18.32469230.32469379_rs1458866 0.62913275
chr2.104528125.104528417_rs11123997 0.82707716
chr2.12759282.12759669_rs6722342,rs7596623 0.95995037
chr2.12766229.12766820_rs13413220,rs13397389 0.75742139
chr2.176086015.176086512_rs6752623 0.66588181
chr2.41038390.41038934_rs6746423 0.88641352
chr2.77457604.77458071_rs12614202,rs17014128 0.24139438
chr20.22217873.22218175_rs76464104 0.63615304
chr20.53735535.53736570_rs12480103 0.65896538
chr22.46225398.46226192_rs4253763 0.91036202
chr22.46233211.46233644_rs11090819 0.73870849
chr22.46249819.46250733_rs7291763 0.10613245
chr3.107240266.107241585_rs11710776 0.33473496
chr3.118380545.118381165_rs779206,rs13087409 0.39468863
chr3.193461783.193462089_rs9879227 0.74709279
chr3.20682627.20683020_rs11715178,rs10510504 0.83552292
chr4.137238844.137239180_rs13148112 0.53961070
chr4.176615030.176615396_rs6553973 0.32815975
chr4.32226261.32226503_rs13125385 0.57101791
chr4.40731697.40732054_rs11727143 0.88665099
chr4.40749770.40751218_rs10012367 0.57024703
chr4.9880847.9881265_rs4697900,rs9684729,rs6449090 0.30425568
chr5.102865289.102866656_rs32680 0.45625089
chr5.102947036.102947471_rs6871111,rs6860588,rs3776874,rs11952361 0.44068330
chr5.164619344.164619596_rs250594 0.98347369
chr5.7415226.7416045_rs7710510 0.96134937
chr5.7464212.7464571_rs6883259,rs766643 0.74209321
chr6.160899655.160900401_rs2489944,rs2465874 0.97225243
chr6.27026322.27026687_rs36022097 0.72606878
chr6.32521585.32522598_rs115541994 0.80282001
chr6.71712533.71713167_rs1204328 0.98124991
chr7.10239630.10240386_rs17162825 0.29934433
chr7.116499279.116500903_rs17138749 0.08841947
chr7.134834642.134835353_rs7777356 0.18102442
chr7.26147128.26148142_rs10248605 0.64095699
chr8.141160879.141162083_rs428300 0.68455466
chr8.15830942.15831184_rs11203734,rs7016422,rs12549742,rs2410329 0.11269613
chr8.20115130.20115538_rs12548222 0.43458342
chr8.69455933.69456585_rs12545665 0.97431132
chr8.701154.701537_rs13273765 0.57807309
chr8.81096615.81097088_rs16908579 0.80238348
chr9.18632808.18633319_rs776777 0.42825715
chr9.19720338.19720665_rs7869360,rs7855757 0.67064494
TRZ_3
chr1.108829511.108829805_rs10857972,rs6704266 0.9999782
chr1.173473597.173473889_rs4916358,rs10798282,rs10753081 0.9999782
chr1.19640454.19641134_rs7523079 0.9999782
chr1.19644587.19645224_rs7522389 0.9999782
chr10.16792579.16793007_rs7068265,rs7068881 0.9999782
chr10.76209088.76209420_rs7094302 0.9999782
chr11.124863016.124863456_rs7111513 0.9999782
chr11.124865334.124865878_rs5017048 0.9999782
chr11.133816825.133817041_rs10894749 0.9999782
chr11.36409327.36409838_rs10836550 0.9999782
chr11.36420818.36421386_rs1365120 0.9999782
chr12.120317069.120317911_rs16950058 0.9999782
chr12.120325389.120326108_rs5634 0.9999782
chr12.120367731.120368309_rs2238161 0.9999782
chr12.19222646.19223206_rs7399293 0.9999782
chr13.100722024.100722184_rs12863645 0.9999782
chr13.46774697.46774984_rs1745836 0.9999782
chr13.64945176.64945505_rs925558,rs1513001,rs7994896 0.9999782
chr13.91724572.91724918_rs342715,rs171060 0.9999782
chr13.99908163.99908652_rs9557321 0.9999782
chr14.55721341.55721700_rs2184559 0.9999782
chr15.23483710.23484436_rs28684656,rs28714259,rs1875910 0.9999782
chr15.93645160.93645583_rs4238475 0.9999782
chr16.13378005.13378513_rs2188738,rs1018891,rs886218,rs1468171 0.9999782
chr17.51461539.51461982_rs10514983 0.9999782
chr18.32469230.32469379_rs1458866 0.9999782
chr2.104528125.104528417_rs11123997 0.9999782
chr2.12759282.12759669_rs6722342,rs7596623 0.9999782
chr2.12766229.12766820_rs13413220,rs13397389 0.9999782
chr2.176086015.176086512_rs6752623 0.9999782
chr2.41038390.41038934_rs6746423 0.9999782
chr2.77457604.77458071_rs12614202,rs17014128 0.9999782
chr20.22217873.22218175_rs76464104 0.9999782
chr20.53735535.53736570_rs12480103 0.9999782
chr22.46225398.46226192_rs4253763 0.9999782
chr22.46233211.46233644_rs11090819 0.9999782
chr22.46249819.46250733_rs7291763 0.9999782
chr3.107240266.107241585_rs11710776 0.9999782
chr3.118380545.118381165_rs779206,rs13087409 0.9999782
chr3.193461783.193462089_rs9879227 0.9999782
chr3.20682627.20683020_rs11715178,rs10510504 0.9999782
chr4.137238844.137239180_rs13148112 0.9999782
chr4.176615030.176615396_rs6553973 0.9999782
chr4.32226261.32226503_rs13125385 0.9999782
chr4.40731697.40732054_rs11727143 0.9999782
chr4.40749770.40751218_rs10012367 0.9999782
chr4.9880847.9881265_rs4697900,rs9684729,rs6449090 0.9999782
chr5.102865289.102866656_rs32680 0.9999782
chr5.102947036.102947471_rs6871111,rs6860588,rs3776874,rs11952361 0.9999782
chr5.164619344.164619596_rs250594 0.9999782
chr5.7415226.7416045_rs7710510 0.9999782
chr5.7464212.7464571_rs6883259,rs766643 0.9999782
chr6.160899655.160900401_rs2489944,rs2465874 0.9999782
chr6.27026322.27026687_rs36022097 0.9999782
chr6.32521585.32522598_rs115541994 0.9999782
chr6.71712533.71713167_rs1204328 0.9999782
chr7.10239630.10240386_rs17162825 0.9999782
chr7.116499279.116500903_rs17138749 0.9999782
chr7.134834642.134835353_rs7777356 0.9999782
chr7.26147128.26148142_rs10248605 0.9999782
chr8.141160879.141162083_rs428300 0.9999782
chr8.15830942.15831184_rs11203734,rs7016422,rs12549742,rs2410329 0.9999782
chr8.20115130.20115538_rs12548222 0.9999782
chr8.69455933.69456585_rs12545665 0.9999782
chr8.701154.701537_rs13273765 0.9999782
chr8.81096615.81097088_rs16908579 0.9999782
chr9.18632808.18633319_rs776777 0.9999782
chr9.19720338.19720665_rs7869360,rs7855757 0.9999782
DNR_24
chr1.108829511.108829805_rs10857972,rs6704266 3.653165e-01
chr1.173473597.173473889_rs4916358,rs10798282,rs10753081 3.414595e-04
chr1.19640454.19641134_rs7523079 2.258959e-02
chr1.19644587.19645224_rs7522389 1.284788e-01
chr10.16792579.16793007_rs7068265,rs7068881 2.915057e-07
chr10.76209088.76209420_rs7094302 6.989744e-05
chr11.124863016.124863456_rs7111513 7.043568e-01
chr11.124865334.124865878_rs5017048 5.194175e-01
chr11.133816825.133817041_rs10894749 2.019903e-01
chr11.36409327.36409838_rs10836550 9.991173e-01
chr11.36420818.36421386_rs1365120 6.204942e-04
chr12.120317069.120317911_rs16950058 3.122067e-01
chr12.120325389.120326108_rs5634 9.454181e-01
chr12.120367731.120368309_rs2238161 7.491181e-02
chr12.19222646.19223206_rs7399293 4.739298e-01
chr13.100722024.100722184_rs12863645 7.478253e-01
chr13.46774697.46774984_rs1745836 7.803478e-01
chr13.64945176.64945505_rs925558,rs1513001,rs7994896 3.708060e-01
chr13.91724572.91724918_rs342715,rs171060 1.630170e-01
chr13.99908163.99908652_rs9557321 3.824181e-02
chr14.55721341.55721700_rs2184559 6.334068e-03
chr15.23483710.23484436_rs28684656,rs28714259,rs1875910 3.962670e-05
chr15.93645160.93645583_rs4238475 1.777890e-02
chr16.13378005.13378513_rs2188738,rs1018891,rs886218,rs1468171 2.727988e-03
chr17.51461539.51461982_rs10514983 2.881889e-01
chr18.32469230.32469379_rs1458866 5.533204e-01
chr2.104528125.104528417_rs11123997 2.379898e-02
chr2.12759282.12759669_rs6722342,rs7596623 2.178708e-02
chr2.12766229.12766820_rs13413220,rs13397389 2.030141e-04
chr2.176086015.176086512_rs6752623 6.773493e-01
chr2.41038390.41038934_rs6746423 1.109757e-05
chr2.77457604.77458071_rs12614202,rs17014128 9.910401e-01
chr20.22217873.22218175_rs76464104 2.370751e-02
chr20.53735535.53736570_rs12480103 2.754945e-01
chr22.46225398.46226192_rs4253763 7.221211e-05
chr22.46233211.46233644_rs11090819 5.820372e-06
chr22.46249819.46250733_rs7291763 1.285664e-04
chr3.107240266.107241585_rs11710776 6.211429e-01
chr3.118380545.118381165_rs779206,rs13087409 8.252985e-03
chr3.193461783.193462089_rs9879227 8.472228e-01
chr3.20682627.20683020_rs11715178,rs10510504 3.903876e-01
chr4.137238844.137239180_rs13148112 8.523507e-03
chr4.176615030.176615396_rs6553973 6.898894e-02
chr4.32226261.32226503_rs13125385 8.301549e-01
chr4.40731697.40732054_rs11727143 7.963452e-02
chr4.40749770.40751218_rs10012367 3.622968e-01
chr4.9880847.9881265_rs4697900,rs9684729,rs6449090 3.565007e-02
chr5.102865289.102866656_rs32680 1.018215e-01
chr5.102947036.102947471_rs6871111,rs6860588,rs3776874,rs11952361 2.602205e-06
chr5.164619344.164619596_rs250594 5.145805e-05
chr5.7415226.7416045_rs7710510 1.723460e-02
chr5.7464212.7464571_rs6883259,rs766643 3.098879e-03
chr6.160899655.160900401_rs2489944,rs2465874 4.020312e-02
chr6.27026322.27026687_rs36022097 3.769879e-03
chr6.32521585.32522598_rs115541994 4.042327e-02
chr6.71712533.71713167_rs1204328 2.657015e-03
chr7.10239630.10240386_rs17162825 3.560149e-04
chr7.116499279.116500903_rs17138749 1.597110e-02
chr7.134834642.134835353_rs7777356 6.953804e-02
chr7.26147128.26148142_rs10248605 7.987717e-01
chr8.141160879.141162083_rs428300 3.799798e-01
chr8.15830942.15831184_rs11203734,rs7016422,rs12549742,rs2410329 3.324496e-07
chr8.20115130.20115538_rs12548222 1.006294e-01
chr8.69455933.69456585_rs12545665 5.878684e-05
chr8.701154.701537_rs13273765 3.284827e-03
chr8.81096615.81097088_rs16908579 2.772015e-01
chr9.18632808.18633319_rs776777 4.468125e-03
chr9.19720338.19720665_rs7869360,rs7855757 1.200819e-02
DOX_24
chr1.108829511.108829805_rs10857972,rs6704266 5.414089e-01
chr1.173473597.173473889_rs4916358,rs10798282,rs10753081 5.101642e-04
chr1.19640454.19641134_rs7523079 5.044156e-02
chr1.19644587.19645224_rs7522389 9.963477e-02
chr10.16792579.16793007_rs7068265,rs7068881 7.171811e-07
chr10.76209088.76209420_rs7094302 4.306295e-05
chr11.124863016.124863456_rs7111513 2.556428e-02
chr11.124865334.124865878_rs5017048 4.975003e-01
chr11.133816825.133817041_rs10894749 1.878919e-01
chr11.36409327.36409838_rs10836550 5.445648e-01
chr11.36420818.36421386_rs1365120 1.860554e-02
chr12.120317069.120317911_rs16950058 6.946001e-02
chr12.120325389.120326108_rs5634 5.175766e-01
chr12.120367731.120368309_rs2238161 2.843598e-01
chr12.19222646.19223206_rs7399293 2.917495e-01
chr13.100722024.100722184_rs12863645 2.093724e-01
chr13.46774697.46774984_rs1745836 8.325236e-01
chr13.64945176.64945505_rs925558,rs1513001,rs7994896 7.745944e-02
chr13.91724572.91724918_rs342715,rs171060 1.779978e-01
chr13.99908163.99908652_rs9557321 3.726412e-02
chr14.55721341.55721700_rs2184559 2.531649e-01
chr15.23483710.23484436_rs28684656,rs28714259,rs1875910 4.650302e-03
chr15.93645160.93645583_rs4238475 8.588334e-02
chr16.13378005.13378513_rs2188738,rs1018891,rs886218,rs1468171 2.812001e-02
chr17.51461539.51461982_rs10514983 7.835901e-01
chr18.32469230.32469379_rs1458866 9.620150e-01
chr2.104528125.104528417_rs11123997 7.692048e-02
chr2.12759282.12759669_rs6722342,rs7596623 6.708164e-02
chr2.12766229.12766820_rs13413220,rs13397389 5.432938e-03
chr2.176086015.176086512_rs6752623 8.114451e-02
chr2.41038390.41038934_rs6746423 6.858211e-03
chr2.77457604.77458071_rs12614202,rs17014128 6.968894e-01
chr20.22217873.22218175_rs76464104 9.972604e-02
chr20.53735535.53736570_rs12480103 1.540196e-01
chr22.46225398.46226192_rs4253763 3.655236e-01
chr22.46233211.46233644_rs11090819 4.213655e-06
chr22.46249819.46250733_rs7291763 7.254337e-02
chr3.107240266.107241585_rs11710776 5.299319e-01
chr3.118380545.118381165_rs779206,rs13087409 1.816010e-02
chr3.193461783.193462089_rs9879227 7.037356e-01
chr3.20682627.20683020_rs11715178,rs10510504 2.512577e-01
chr4.137238844.137239180_rs13148112 6.937904e-02
chr4.176615030.176615396_rs6553973 9.848660e-01
chr4.32226261.32226503_rs13125385 6.958245e-01
chr4.40731697.40732054_rs11727143 8.199057e-01
chr4.40749770.40751218_rs10012367 9.311694e-01
chr4.9880847.9881265_rs4697900,rs9684729,rs6449090 6.523537e-01
chr5.102865289.102866656_rs32680 2.317915e-01
chr5.102947036.102947471_rs6871111,rs6860588,rs3776874,rs11952361 3.395487e-04
chr5.164619344.164619596_rs250594 2.588807e-03
chr5.7415226.7416045_rs7710510 1.421706e-01
chr5.7464212.7464571_rs6883259,rs766643 8.472525e-04
chr6.160899655.160900401_rs2489944,rs2465874 1.472753e-01
chr6.27026322.27026687_rs36022097 1.942271e-02
chr6.32521585.32522598_rs115541994 9.139523e-03
chr6.71712533.71713167_rs1204328 2.869743e-02
chr7.10239630.10240386_rs17162825 7.585279e-02
chr7.116499279.116500903_rs17138749 1.217002e-02
chr7.134834642.134835353_rs7777356 1.883007e-01
chr7.26147128.26148142_rs10248605 9.003688e-01
chr8.141160879.141162083_rs428300 5.742705e-01
chr8.15830942.15831184_rs11203734,rs7016422,rs12549742,rs2410329 4.234505e-05
chr8.20115130.20115538_rs12548222 3.558188e-01
chr8.69455933.69456585_rs12545665 1.755253e-04
chr8.701154.701537_rs13273765 3.824064e-01
chr8.81096615.81097088_rs16908579 3.685826e-01
chr9.18632808.18633319_rs776777 1.446595e-03
chr9.19720338.19720665_rs7869360,rs7855757 6.070919e-01
EPI_24
chr1.108829511.108829805_rs10857972,rs6704266 3.146685e-01
chr1.173473597.173473889_rs4916358,rs10798282,rs10753081 4.083676e-04
chr1.19640454.19641134_rs7523079 1.258701e-02
chr1.19644587.19645224_rs7522389 2.094945e-02
chr10.16792579.16793007_rs7068265,rs7068881 1.814685e-06
chr10.76209088.76209420_rs7094302 6.278692e-05
chr11.124863016.124863456_rs7111513 2.672714e-01
chr11.124865334.124865878_rs5017048 6.841295e-01
chr11.133816825.133817041_rs10894749 2.454422e-02
chr11.36409327.36409838_rs10836550 5.990061e-01
chr11.36420818.36421386_rs1365120 2.180274e-04
chr12.120317069.120317911_rs16950058 3.078297e-01
chr12.120325389.120326108_rs5634 6.709758e-01
chr12.120367731.120368309_rs2238161 4.294827e-02
chr12.19222646.19223206_rs7399293 9.681589e-01
chr13.100722024.100722184_rs12863645 7.117580e-01
chr13.46774697.46774984_rs1745836 5.460103e-01
chr13.64945176.64945505_rs925558,rs1513001,rs7994896 2.059572e-01
chr13.91724572.91724918_rs342715,rs171060 5.553094e-01
chr13.99908163.99908652_rs9557321 2.811388e-01
chr14.55721341.55721700_rs2184559 1.582115e-02
chr15.23483710.23484436_rs28684656,rs28714259,rs1875910 7.433648e-02
chr15.93645160.93645583_rs4238475 2.793523e-01
chr16.13378005.13378513_rs2188738,rs1018891,rs886218,rs1468171 2.119829e-03
chr17.51461539.51461982_rs10514983 9.947625e-01
chr18.32469230.32469379_rs1458866 8.123747e-01
chr2.104528125.104528417_rs11123997 4.754784e-03
chr2.12759282.12759669_rs6722342,rs7596623 2.560375e-02
chr2.12766229.12766820_rs13413220,rs13397389 1.466140e-04
chr2.176086015.176086512_rs6752623 2.730192e-01
chr2.41038390.41038934_rs6746423 2.705464e-03
chr2.77457604.77458071_rs12614202,rs17014128 1.341606e-02
chr20.22217873.22218175_rs76464104 2.532489e-03
chr20.53735535.53736570_rs12480103 8.756351e-01
chr22.46225398.46226192_rs4253763 8.978233e-02
chr22.46233211.46233644_rs11090819 5.119405e-06
chr22.46249819.46250733_rs7291763 7.275134e-02
chr3.107240266.107241585_rs11710776 2.995115e-01
chr3.118380545.118381165_rs779206,rs13087409 2.956848e-02
chr3.193461783.193462089_rs9879227 5.332866e-01
chr3.20682627.20683020_rs11715178,rs10510504 1.687950e-01
chr4.137238844.137239180_rs13148112 2.262285e-03
chr4.176615030.176615396_rs6553973 2.155857e-02
chr4.32226261.32226503_rs13125385 3.487420e-01
chr4.40731697.40732054_rs11727143 6.510506e-01
chr4.40749770.40751218_rs10012367 3.703610e-01
chr4.9880847.9881265_rs4697900,rs9684729,rs6449090 4.313594e-01
chr5.102865289.102866656_rs32680 5.569582e-01
chr5.102947036.102947471_rs6871111,rs6860588,rs3776874,rs11952361 5.021422e-04
chr5.164619344.164619596_rs250594 6.012673e-02
chr5.7415226.7416045_rs7710510 5.959757e-02
chr5.7464212.7464571_rs6883259,rs766643 1.565716e-03
chr6.160899655.160900401_rs2489944,rs2465874 5.664559e-01
chr6.27026322.27026687_rs36022097 1.768075e-01
chr6.32521585.32522598_rs115541994 2.489476e-01
chr6.71712533.71713167_rs1204328 2.896963e-01
chr7.10239630.10240386_rs17162825 4.425513e-03
chr7.116499279.116500903_rs17138749 2.101737e-02
chr7.134834642.134835353_rs7777356 1.173219e-02
chr7.26147128.26148142_rs10248605 1.566356e-01
chr8.141160879.141162083_rs428300 2.443513e-01
chr8.15830942.15831184_rs11203734,rs7016422,rs12549742,rs2410329 2.261059e-04
chr8.20115130.20115538_rs12548222 6.742704e-02
chr8.69455933.69456585_rs12545665 3.088158e-03
chr8.701154.701537_rs13273765 5.405245e-02
chr8.81096615.81097088_rs16908579 1.451063e-01
chr9.18632808.18633319_rs776777 3.813429e-02
chr9.19720338.19720665_rs7869360,rs7855757 4.255916e-01
MTX_24
chr1.108829511.108829805_rs10857972,rs6704266 0.143612204
chr1.173473597.173473889_rs4916358,rs10798282,rs10753081 0.166985401
chr1.19640454.19641134_rs7523079 0.386560760
chr1.19644587.19645224_rs7522389 0.083459693
chr10.16792579.16793007_rs7068265,rs7068881 0.007940749
chr10.76209088.76209420_rs7094302 0.000924940
chr11.124863016.124863456_rs7111513 0.120123879
chr11.124865334.124865878_rs5017048 0.307342372
chr11.133816825.133817041_rs10894749 0.368842737
chr11.36409327.36409838_rs10836550 0.231801783
chr11.36420818.36421386_rs1365120 0.027092796
chr12.120317069.120317911_rs16950058 0.172473549
chr12.120325389.120326108_rs5634 0.646604873
chr12.120367731.120368309_rs2238161 0.535934368
chr12.19222646.19223206_rs7399293 0.616081509
chr13.100722024.100722184_rs12863645 0.465107643
chr13.46774697.46774984_rs1745836 0.939257304
chr13.64945176.64945505_rs925558,rs1513001,rs7994896 0.532181117
chr13.91724572.91724918_rs342715,rs171060 0.837246328
chr13.99908163.99908652_rs9557321 0.761667808
chr14.55721341.55721700_rs2184559 0.162819249
chr15.23483710.23484436_rs28684656,rs28714259,rs1875910 0.217838852
chr15.93645160.93645583_rs4238475 0.476711994
chr16.13378005.13378513_rs2188738,rs1018891,rs886218,rs1468171 0.308763031
chr17.51461539.51461982_rs10514983 0.640685868
chr18.32469230.32469379_rs1458866 0.512354849
chr2.104528125.104528417_rs11123997 0.225281685
chr2.12759282.12759669_rs6722342,rs7596623 0.510320433
chr2.12766229.12766820_rs13413220,rs13397389 0.022657513
chr2.176086015.176086512_rs6752623 0.967536271
chr2.41038390.41038934_rs6746423 0.140763597
chr2.77457604.77458071_rs12614202,rs17014128 0.762871185
chr20.22217873.22218175_rs76464104 0.050031470
chr20.53735535.53736570_rs12480103 0.445159074
chr22.46225398.46226192_rs4253763 0.012437681
chr22.46233211.46233644_rs11090819 0.016770628
chr22.46249819.46250733_rs7291763 0.183926449
chr3.107240266.107241585_rs11710776 0.231162659
chr3.118380545.118381165_rs779206,rs13087409 0.494393320
chr3.193461783.193462089_rs9879227 0.515773248
chr3.20682627.20683020_rs11715178,rs10510504 0.309606866
chr4.137238844.137239180_rs13148112 0.577743705
chr4.176615030.176615396_rs6553973 0.897856151
chr4.32226261.32226503_rs13125385 0.993026830
chr4.40731697.40732054_rs11727143 0.961489010
chr4.40749770.40751218_rs10012367 0.675404619
chr4.9880847.9881265_rs4697900,rs9684729,rs6449090 0.848771811
chr5.102865289.102866656_rs32680 0.157419963
chr5.102947036.102947471_rs6871111,rs6860588,rs3776874,rs11952361 0.002836755
chr5.164619344.164619596_rs250594 0.286580523
chr5.7415226.7416045_rs7710510 0.636407880
chr5.7464212.7464571_rs6883259,rs766643 0.135326952
chr6.160899655.160900401_rs2489944,rs2465874 0.583409984
chr6.27026322.27026687_rs36022097 0.099562150
chr6.32521585.32522598_rs115541994 0.273102359
chr6.71712533.71713167_rs1204328 0.915814676
chr7.10239630.10240386_rs17162825 0.579189402
chr7.116499279.116500903_rs17138749 0.389947603
chr7.134834642.134835353_rs7777356 0.894533553
chr7.26147128.26148142_rs10248605 0.647506619
chr8.141160879.141162083_rs428300 0.883193455
chr8.15830942.15831184_rs11203734,rs7016422,rs12549742,rs2410329 0.005202160
chr8.20115130.20115538_rs12548222 0.047002399
chr8.69455933.69456585_rs12545665 0.004285771
chr8.701154.701537_rs13273765 0.318646385
chr8.81096615.81097088_rs16908579 0.635859465
chr9.18632808.18633319_rs776777 0.158385280
chr9.19720338.19720665_rs7869360,rs7855757 0.143104689
TRZ_24
chr1.108829511.108829805_rs10857972,rs6704266 0.9999654
chr1.173473597.173473889_rs4916358,rs10798282,rs10753081 0.9999654
chr1.19640454.19641134_rs7523079 0.9999654
chr1.19644587.19645224_rs7522389 0.9999654
chr10.16792579.16793007_rs7068265,rs7068881 0.9999654
chr10.76209088.76209420_rs7094302 0.9999654
chr11.124863016.124863456_rs7111513 0.9999654
chr11.124865334.124865878_rs5017048 0.9999654
chr11.133816825.133817041_rs10894749 0.9999654
chr11.36409327.36409838_rs10836550 0.9999654
chr11.36420818.36421386_rs1365120 0.9999654
chr12.120317069.120317911_rs16950058 0.9999654
chr12.120325389.120326108_rs5634 0.9999654
chr12.120367731.120368309_rs2238161 0.9999654
chr12.19222646.19223206_rs7399293 0.9999654
chr13.100722024.100722184_rs12863645 0.9999654
chr13.46774697.46774984_rs1745836 0.9999654
chr13.64945176.64945505_rs925558,rs1513001,rs7994896 0.9999654
chr13.91724572.91724918_rs342715,rs171060 0.9999654
chr13.99908163.99908652_rs9557321 0.9999654
chr14.55721341.55721700_rs2184559 0.9999654
chr15.23483710.23484436_rs28684656,rs28714259,rs1875910 0.9999654
chr15.93645160.93645583_rs4238475 0.9999654
chr16.13378005.13378513_rs2188738,rs1018891,rs886218,rs1468171 0.9999654
chr17.51461539.51461982_rs10514983 0.9999654
chr18.32469230.32469379_rs1458866 0.9999654
chr2.104528125.104528417_rs11123997 0.9999654
chr2.12759282.12759669_rs6722342,rs7596623 0.9999654
chr2.12766229.12766820_rs13413220,rs13397389 0.9999654
chr2.176086015.176086512_rs6752623 0.9999654
chr2.41038390.41038934_rs6746423 0.9999654
chr2.77457604.77458071_rs12614202,rs17014128 0.9999654
chr20.22217873.22218175_rs76464104 0.9999654
chr20.53735535.53736570_rs12480103 0.9999654
chr22.46225398.46226192_rs4253763 0.9999654
chr22.46233211.46233644_rs11090819 0.9999654
chr22.46249819.46250733_rs7291763 0.9999654
chr3.107240266.107241585_rs11710776 0.9999654
chr3.118380545.118381165_rs779206,rs13087409 0.9999654
chr3.193461783.193462089_rs9879227 0.9999654
chr3.20682627.20683020_rs11715178,rs10510504 0.9999654
chr4.137238844.137239180_rs13148112 0.9999654
chr4.176615030.176615396_rs6553973 0.9999654
chr4.32226261.32226503_rs13125385 0.9999654
chr4.40731697.40732054_rs11727143 0.9999654
chr4.40749770.40751218_rs10012367 0.9999654
chr4.9880847.9881265_rs4697900,rs9684729,rs6449090 0.9999654
chr5.102865289.102866656_rs32680 0.9999654
chr5.102947036.102947471_rs6871111,rs6860588,rs3776874,rs11952361 0.9999654
chr5.164619344.164619596_rs250594 0.9999654
chr5.7415226.7416045_rs7710510 0.9999654
chr5.7464212.7464571_rs6883259,rs766643 0.9999654
chr6.160899655.160900401_rs2489944,rs2465874 0.9999654
chr6.27026322.27026687_rs36022097 0.9999654
chr6.32521585.32522598_rs115541994 0.9999654
chr6.71712533.71713167_rs1204328 0.9999654
chr7.10239630.10240386_rs17162825 0.9999654
chr7.116499279.116500903_rs17138749 0.9999654
chr7.134834642.134835353_rs7777356 0.9999654
chr7.26147128.26148142_rs10248605 0.9999654
chr8.141160879.141162083_rs428300 0.9999654
chr8.15830942.15831184_rs11203734,rs7016422,rs12549742,rs2410329 0.9999654
chr8.20115130.20115538_rs12548222 0.9999654
chr8.69455933.69456585_rs12545665 0.9999654
chr8.701154.701537_rs13273765 0.9999654
chr8.81096615.81097088_rs16908579 0.9999654
chr9.18632808.18633319_rs776777 0.9999654
chr9.19720338.19720665_rs7869360,rs7855757 0.9999654AR_Cardiotox_gwas_collaped_df# A tibble: 68 × 18
name peak_chr peak_start TAD_id sig_24 min_distance mean_distance DOX_3
<chr> <fct> <int> <chr> <chr> <int> <dbl> <dbl>
1 chr1.10… chr1 108829511 TAD_71 not_s… 12303 13172 -1.27
2 chr1.17… chr1 173473597 TAD_1… sig 795 8481. 0.195
3 chr1.19… chr1 19640454 TAD_13 not_s… 4074 4074 -0.989
4 chr1.19… chr1 19644587 TAD_13 not_s… 2277 2277 0.241
5 chr10.1… chr10 16792579 TAD_1… sig 3839 4064. -0.310
6 chr10.7… chr10 76209088 TAD_1… sig 2753 2753 0.311
7 chr11.1… chr11 124863016 TAD_1… sig 465 465 0.188
8 chr11.1… chr11 124865334 TAD_1… not_s… 759 759 0.0823
9 chr11.1… chr11 133816825 TAD_1… not_s… 4468 4468 -0.585
10 chr11.3… chr11 36409327 TAD_1… not_s… 2984 2984 -0.171
# ℹ 58 more rows
# ℹ 10 more variables: EPI_3 <dbl>, DNR_3 <dbl>, MTX_3 <dbl>, TRZ_3 <dbl>,
# DOX_24 <dbl>, EPI_24 <dbl>, DNR_24 <dbl>, MTX_24 <dbl>, TRZ_24 <dbl>,
# snp_dist <chr>adding in Park data:
ParkSNPs <- readRDS("data/other_papers/ParkSNPs_pull_VEF.RDS")
ParkSNP_table <-
ParkSNPs %>%
dplyr::select(1:2) %>%
distinct() %>%
separate_wider_delim(.,Location,delim=":",names=c("chr","position"), cols_remove=FALSE) %>%
separate_wider_delim(.,position,delim="-",names=c("begin","term")) %>%
mutate(chr=paste0("chr",chr))
ParkSNP_gr <- ParkSNP_table %>%
mutate("start" = begin, "end"=term) %>%
GRanges()
Park_snp_tad_df <- join_overlap_inner(ParkSNP_gr, Left_ventricle_TAD) %>%
as_tibble() %>%
dplyr::rename("RSID"=X.Uploaded_variation) %>%
dplyr::select(RSID, snp_start = start, snp_chr = seqnames, TAD_id)
Park_snp_pairs <- peak_tad_df %>%
inner_join(Park_snp_tad_df, by = "TAD_id")
Park_snp_pairs %>%
distinct(RSID)
Park_snp_pairs_dist <- Park_snp_pairs %>%
mutate(distance = abs(peak_start - snp_start)) %>%
mutate(sig_24= if_else(Peakid %in% DOX_DAR_sig$Peakid, "sig","not_sig"))
Park_snp_pairs_dist %>%
mutate(sig_24=factor(sig_24, levels= c("sig","not_sig"))) %>%
ggplot(., aes(x= sig_24, y=distance))+
geom_boxplot()+
theme_bw()+
geom_signif(comparisons = list(c("sig", "not_sig")),
map_signif_level = FALSE, test = "wilcox.test")
wilcox.test(distance ~ sig_24, data = Park_snp_pairs_dist)
Park_Cardiotox_gwas_df <- Park_snp_pairs_dist %>%
dplyr::filter(sig_24=="sig") %>%
group_by(TAD_id,RSID) %>%
slice_min(order_by = distance, with_ties = FALSE) %>%
ungroup() %>%
arrange(snp_chr,snp_start) %>%
left_join(., all_results_pivot, by=c("Peakid"="genes")) %>%
tidyr::unite(., name,Peakid,RSID)Park_Cardiotox_gwas_collaped_df <-
Park_snp_pairs_dist %>%
# dplyr::filter(sig_24=="sig") %>%
group_by(TAD_id,RSID) %>%
slice_min(order_by = distance, with_ties = FALSE) %>%
ungroup() %>%
arrange(snp_chr,snp_start) %>%
group_by(Peakid, peak_chr, peak_start, TAD_id, sig_24) %>%
summarise(
min_distance = min(distance),
mean_distance = mean(distance),
snp_list = paste(unique(RSID), collapse = ","),
.groups = "drop"
) %>%
left_join(., all_results_pivot, by=c("Peakid"="genes")) %>%
# left_join(., Peak_gene_RNA_LFC, by=c("Peakid"="Peakid")) %>%
# left_join(.,gene_info_collapsed, by=c("geneId"="ENTREZID")) %>%
# mutate(SYMBOL=if_else(is.na(SYMBOL.x),SYMBOL.y,if_else(SYMBOL.x==SYMBOL.y, SYMBOL.x,paste0(SYMBOL.x,"_",SYMBOL.y)))) %>%
tidyr::unite(., name,Peakid,snp_list) %>%
mutate(snp_dist=case_when(min_distance <2000 ~"2kb",
min_distance > 2000 & min_distance<20000 ~ "20kb",
min_distance >20000 ~">20kb"))
Cardotox_mat_park <- Park_Cardiotox_gwas_collaped_df %>%
dplyr::select(name,DOX_3:TRZ_24) %>%
column_to_rownames("name") %>%
as.matrix()
annot_map_df_park <- Park_Cardiotox_gwas_collaped_df %>%
dplyr::select(name,snp_dist,sig_24) %>%
column_to_rownames("name")
annot_map_park <-
ComplexHeatmap::rowAnnotation(
snp_dist=Park_Cardiotox_gwas_collaped_df$snp_dist,
TAD_id=Park_Cardiotox_gwas_collaped_df$TAD_id,
DOX_24hr_DAR=Park_Cardiotox_gwas_collaped_df$sig_24,
col= list(snp_dist=c("2kb"="goldenrod4",
"20kb"="pink",
">20kb"="tan2")))
simply_map_lfc_park <- ComplexHeatmap::Heatmap(Cardotox_mat_park,
# col = col_fun,
left_annotation = annot_map_park,
show_row_names = TRUE,
row_names_max_width= ComplexHeatmap::max_text_width(rownames(Cardotox_mat_park), gp=gpar(fontsize=14)),
heatmap_legend_param = list(direction = "horizontal"),
show_column_names = TRUE,
cluster_rows = FALSE,
cluster_columns = FALSE)
ComplexHeatmap::draw(simply_map_lfc_park,
merge_legend = TRUE,
heatmap_legend_side = "left",
annotation_legend_side = "left")Accessibility changes of SNP- directly overlapping DARs
drug_pal <- c("#8B006D","#DF707E","#F1B72B", "#3386DD","#707031","#41B333")
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$Peakid) %>%
separate(source, into = c("trt", "time")) %>%
mutate(
time = paste0(time, "h"), # convert "3" → "3h"
trt = factor(trt, levels = c("DOX", "EPI", "DNR", "MTX", "TRZ")),
group=paste0(trt,"_",time)) %>%
mutate(group=factor(group,levels = c("DOX_3h", "EPI_3h", "DNR_3h", "MTX_3h", "TRZ_3h", "VEH_3h",
"DOX_24h", "EPI_24h", "DNR_24h", "MTX_24h", "TRZ_24h", "VEH_24h"))) %>%
dplyr::rename("Peakid"=genes)ATAC_counts_lcpm <- filt_raw_counts_noY %>%
cpm(., log = TRUE) %>%
as.data.frame() %>%
rownames_to_column("Peakid")for (peak in SNP_DAR_overlap_direct$Peakid) {
PEAK <- SNP_DAR_overlap_direct$Peakid[SNP_DAR_overlap_direct$Peakid == peak]
# Prep expression data
peak_expr <- ATAC_counts_lcpm %>%
filter(Peakid == peak) %>%
pivot_longer(cols = !Peakid, names_to = "sample", values_to = "lcpm") %>%
separate(sample, into = c("ind", "trt", "time")) %>%
mutate(
time = paste0(time), # if already "3h"/"24h"
group = paste0(trt, "_", time),
group = factor(group, levels = c(
"DOX_3h", "EPI_3h", "DNR_3h", "MTX_3h", "TRZ_3h", "VEH_3h",
"DOX_24h", "EPI_24h", "DNR_24h", "MTX_24h", "TRZ_24h", "VEH_24h"
))
)
# Get peak-specific p-values
peak_pvals <- ATAC_adj.pvals %>%
filter(Peakid==peak)
# Merge in p-values by group
peak_plot_data <- left_join(peak_expr, peak_pvals, by = c("Peakid", "group", "time"))
# Create label position below box
label_positions <- peak_plot_data %>%
group_by(group) %>%
summarise(y = min(lcpm, na.rm = TRUE) - 0.5, .groups = "drop")
peak_plot_data <- left_join(peak_plot_data, label_positions, by = "group")
peak_plot_data <- peak_plot_data %>%
separate(group, into = c("trt", "time"), sep = "_", remove = FALSE)
# Plot
peak_plot <- ggplot(peak_plot_data, aes(x = group, y = lcpm)) +
geom_boxplot(aes(fill = trt)) +
geom_text(
aes(y = y,
label = ifelse(trt != "VEH" & !is.na(adj.P.Val),
paste0("", signif(adj.P.Val, 2)),
"")),
size = 3,
vjust = 1.2
) +
scale_fill_manual(values = drug_pal) +
theme_bw() +
ggtitle(paste0("ATAC Log2cpm of ", PEAK)) +
ylab("log2 cpm ATAC") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
plot(peak_plot)
}
| Version | Author | Date |
|---|---|---|
| 1429820 | reneeisnowhere | 2025-07-21 |
# for (peak in SNP_DAR_overlap_direct$Peakid) {
# PEAK <- SNP_DAR_overlap_direct$Peakid[SNP_DAR_overlap_direct$Peakid == peak]
#
#
# # Filter and plot
# gene_plot <- ATAC_counts_lcpm %>%
# filter(Peakid == peak) %>%
# pivot_longer(cols = !Peakid, names_to = "sample", values_to = "lcpm") %>%
# separate(sample, into = c("trt", "ind", "time")) %>%
# mutate(
# time = factor(time, levels = c("3h", "24h")),
# trt = factor(trt, levels = c("DOX", "EPI", "DNR", "MTX", "TRZ", "VEH"))
# ) %>%
# ggplot(aes(x = time, y = counts)) +
# geom_boxplot(aes(fill = trt)) +
# scale_fill_manual(values = drug_pal) +
# theme_bw() +
# ylab("log2 cpm RNA") +
# ggtitle(paste0(" Log2cpm of ", PEAK))
#
# plot(gene_plot)
# }
#
filt_raw_counts_noY %>%
cpm(., log = TRUE) %>%
as.data.frame() %>%
rownames_to_column("Peakid") %>%
dplyr::filter(Peakid %in% SNP_DAR_overlap_direct$Peakid) %>%
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") 
| Version | Author | Date |
|---|---|---|
| 1429820 | reneeisnowhere | 2025-07-21 |
SNP DAR direct overlap heatmap
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) {
if (!is.na(SNP_DAR_sig_mat[i, j]) && SNP_DAR_sig_mat[i, j] <0.05) {
grid.text("*", x, y, gp = gpar(fontsize = 20)) # Add star if significant
} })
ComplexHeatmap::draw(simply_map_lfc_3,
merge_legend = TRUE,
heatmap_legend_side = "left",
annotation_legend_side = "left")
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] liftOver_1.30.0
[7] Homo.sapiens_1.3.1
[8] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
[9] GO.db_3.20.0
[10] OrganismDbi_1.48.0
[11] gwascat_2.38.0
[12] ComplexHeatmap_2.22.0
[13] readxl_1.4.5
[14] circlize_0.4.16
[15] epitools_0.5-10.1
[16] ggrepel_0.9.6
[17] plyranges_1.26.0
[18] ggsignif_0.6.4
[19] genomation_1.38.0
[20] smplot2_0.2.5
[21] eulerr_7.0.2
[22] biomaRt_2.62.1
[23] devtools_2.4.5
[24] usethis_3.1.0
[25] ggpubr_0.6.1
[26] BiocParallel_1.40.2
[27] scales_1.4.0
[28] VennDiagram_1.7.3
[29] futile.logger_1.4.3
[30] gridExtra_2.3
[31] ggfortify_0.4.18
[32] edgeR_4.4.2
[33] limma_3.62.2
[34] rtracklayer_1.66.0
[35] org.Hs.eg.db_3.20.0
[36] TxDb.Hsapiens.UCSC.hg38.knownGene_3.20.0
[37] GenomicFeatures_1.58.0
[38] AnnotationDbi_1.68.0
[39] Biobase_2.66.0
[40] ChIPpeakAnno_3.40.0
[41] GenomicRanges_1.58.0
[42] GenomeInfoDb_1.42.3
[43] IRanges_2.40.1
[44] S4Vectors_0.44.0
[45] BiocGenerics_0.52.0
[46] ChIPseeker_1.42.1
[47] RColorBrewer_1.1-3
[48] broom_1.0.8
[49] kableExtra_1.4.0
[50] lubridate_1.9.4
[51] forcats_1.0.0
[52] stringr_1.5.1
[53] dplyr_1.1.4
[54] purrr_1.0.4
[55] readr_2.1.5
[56] tidyr_1.3.1
[57] tibble_3.3.0
[58] ggplot2_3.5.2
[59] tidyverse_2.0.0
[60] workflowr_1.7.1
loaded via a namespace (and not attached):
[1] R.methodsS3_1.8.2 dichromat_2.0-0.1
[3] vroom_1.6.5 progress_1.2.3
[5] urlchecker_1.0.1 nnet_7.3-20
[7] vctrs_0.6.5 ggtangle_0.0.7
[9] digest_0.6.37 png_0.1-8
[11] shape_1.4.6.1 git2r_0.36.2
[13] magick_2.8.7 MASS_7.3-65
[15] reshape2_1.4.4 foreach_1.5.2
[17] httpuv_1.6.16 qvalue_2.38.0
[19] withr_3.0.2 xfun_0.52
[21] ggfun_0.1.9 ellipsis_0.3.2
[23] survival_3.8-3 memoise_2.0.1
[25] profvis_0.4.0 systemfonts_1.2.3
[27] tidytree_0.4.6 zoo_1.8-14
[29] GlobalOptions_0.1.2 gtools_3.9.5
[31] R.oo_1.27.1 Formula_1.2-5
[33] prettyunits_1.2.0 KEGGREST_1.46.0
[35] promises_1.3.3 httr_1.4.7
[37] rstatix_0.7.2 restfulr_0.0.16
[39] ps_1.9.1 rstudioapi_0.17.1
[41] UCSC.utils_1.2.0 miniUI_0.1.2
[43] generics_0.1.4 DOSE_4.0.1
[45] base64enc_0.1-3 processx_3.8.6
[47] curl_6.4.0 zlibbioc_1.52.0
[49] GenomeInfoDbData_1.2.13 SparseArray_1.6.2
[51] RBGL_1.82.0 xtable_1.8-4
[53] doParallel_1.0.17 evaluate_1.0.4
[55] S4Arrays_1.6.0 BiocFileCache_2.14.0
[57] hms_1.1.3 colorspace_2.1-1
[59] filelock_1.0.3 magrittr_2.0.3
[61] later_1.4.2 ggtree_3.14.0
[63] lattice_0.22-7 getPass_0.2-4
[65] XML_3.99-0.18 cowplot_1.1.3
[67] matrixStats_1.5.0 Hmisc_5.2-3
[69] pillar_1.11.0 nlme_3.1-168
[71] iterators_1.0.14 pwalign_1.2.0
[73] gridBase_0.4-7 caTools_1.18.3
[75] compiler_4.4.2 stringi_1.8.7
[77] SummarizedExperiment_1.36.0 GenomicAlignments_1.42.0
[79] plyr_1.8.9 crayon_1.5.3
[81] abind_1.4-8 gridGraphics_0.5-1
[83] locfit_1.5-9.12 bit_4.6.0
[85] fastmatch_1.1-6 whisker_0.4.1
[87] codetools_0.2-20 textshaping_1.0.1
[89] bslib_0.9.0 GetoptLong_1.0.5
[91] multtest_2.62.0 mime_0.13
[93] splines_4.4.2 Rcpp_1.1.0
[95] dbplyr_2.5.0 cellranger_1.1.0
[97] utf8_1.2.6 knitr_1.50
[99] blob_1.2.4 clue_0.3-66
[101] AnnotationFilter_1.30.0 fs_1.6.6
[103] checkmate_2.3.2 pkgbuild_1.4.8
[105] ggplotify_0.1.2 Matrix_1.7-3
[107] callr_3.7.6 statmod_1.5.0
[109] tzdb_0.5.0 svglite_2.2.1
[111] pkgconfig_2.0.3 tools_4.4.2
[113] cachem_1.1.0 RSQLite_2.4.1
[115] viridisLite_0.4.2 DBI_1.2.3
[117] impute_1.80.0 fastmap_1.2.0
[119] rmarkdown_2.29 Rsamtools_2.22.0
[121] sass_0.4.10 patchwork_1.3.1
[123] BiocManager_1.30.26 VariantAnnotation_1.52.0
[125] graph_1.84.1 carData_3.0-5
[127] rpart_4.1.24 farver_2.1.2
[129] yaml_2.3.10 MatrixGenerics_1.18.1
[131] foreign_0.8-90 cli_3.6.5
[133] txdbmaker_1.2.1 lifecycle_1.0.4
[135] lambda.r_1.2.4 sessioninfo_1.2.3
[137] backports_1.5.0 timechange_0.3.0
[139] gtable_0.3.6 rjson_0.2.23
[141] parallel_4.4.2 ape_5.8-1
[143] jsonlite_2.0.0 bitops_1.0-9
[145] bit64_4.6.0-1 pwr_1.3-0
[147] yulab.utils_0.2.0 futile.options_1.0.1
[149] jquerylib_0.1.4 GOSemSim_2.32.0
[151] R.utils_2.13.0 snpStats_1.56.0
[153] lazyeval_0.2.2 shiny_1.11.1
[155] htmltools_0.5.8.1 enrichplot_1.26.6
[157] rappdirs_0.3.3 formatR_1.14
[159] ensembldb_2.30.0 glue_1.8.0
[161] httr2_1.1.2 RCurl_1.98-1.17
[163] InteractionSet_1.34.0 rprojroot_2.0.4
[165] treeio_1.30.0 boot_1.3-31
[167] universalmotif_1.24.2 igraph_2.1.4
[169] R6_2.6.1 gplots_3.2.0
[171] labeling_0.4.3 cluster_2.1.8.1
[173] pkgload_1.4.0 regioneR_1.38.0
[175] aplot_0.2.8 DelayedArray_0.32.0
[177] tidyselect_1.2.1 plotrix_3.8-4
[179] ProtGenerics_1.38.0 htmlTable_2.4.3
[181] xml2_1.3.8 car_3.1-3
[183] seqPattern_1.38.0 KernSmooth_2.23-26
[185] data.table_1.17.6 htmlwidgets_1.6.4
[187] fgsea_1.32.4 rlang_1.1.6
[189] remotes_2.5.0 Cairo_1.6-2