AF_HF_SNPs
Renee Matthews
2025-05-09
Last updated: 2025-07-29
Checks: 7 0
Knit directory: ATAC_learning/
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Unstaged changes:
Modified: ATAC_learning.Rproj
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 9f23163 | reneeisnowhere | 2025-07-29 | updates to heatmaps |
| html | 2baaf88 | reneeisnowhere | 2025-07-28 | Build site. |
| Rmd | f5bad16 | reneeisnowhere | 2025-07-28 | adding odds ration plot |
| html | 88047ef | reneeisnowhere | 2025-07-28 | Build site. |
| Rmd | 3e7c4ce | reneeisnowhere | 2025-07-28 | BH_correction |
library(tidyverse)
library(kableExtra)
library(broom)
library(RColorBrewer)
library("TxDb.Hsapiens.UCSC.hg38.knownGene")
library("org.Hs.eg.db")
library(rtracklayer)
library(ggfortify)
library(readr)
library(BiocGenerics)
library(gridExtra)
library(VennDiagram)
library(scales)
library(ggVennDiagram)
library(BiocParallel)
library(ggpubr)
library(edgeR)
library(genomation)
library(ggsignif)
library(plyranges)
library(ggrepel)
library(ComplexHeatmap)
library(cowplot)
library(smplot2)
library(readxl)
library(devtools)
library(vargen)
library(liftOver)Loading Atrial Fibrillation and Heart Failure SNPs and I
gwas_HF <- readRDS("data/other_papers/HF_gwas_association_downloaded_2025_01_23_EFO_0003144_withChildTraits.RDS")
gwas_ARR <- readRDS("data/other_papers/AF_gwas_association_downloaded_2025_01_23_EFO_0000275.RDS")
gwas_IHD <- readRDS("data/other_papers/IHD_IHD_gwas_association_downloaded_2025_06_26_EFO_1001375_withChildTraits")
gwas_CAD <- readRDS( "data/CAD_gwas_dataframe.RDS")
gwas_ACresp <- readRDS("data/gwas_3_dataframe.RDS")
Short_gwas_gr <-
gwas_ARR %>%
distinct(SNPS,.keep_all = TRUE) %>%
dplyr::select(CHR_ID, CHR_POS,SNPS) %>%
mutate(gwas="AF") %>%
rbind(gwas_HF %>%
distinct(SNPS,.keep_all = TRUE) %>%
dplyr::select(CHR_ID, CHR_POS,SNPS) %>%
mutate(gwas="HF")) %>%
rbind(gwas_IHD %>%
distinct(SNPS,.keep_all = TRUE) %>%
dplyr::select(CHR_ID, CHR_POS,SNPS) %>%
mutate(gwas="IHD")) %>%
rbind(gwas_CAD %>%
distinct(SNPS,.keep_all = TRUE) %>%
dplyr::select(CHR_ID, CHR_POS,SNPS) %>%
mutate(gwas="CAD")) %>%
na.omit() %>%
mutate(seqnames=paste0("chr",CHR_ID), CHR_POS=as.numeric(CHR_POS)) %>%
na.omit() %>%
mutate(start=CHR_POS, end=CHR_POS, width=1) %>%
GRanges()Loading ATAC-seq regions
toptable_results <- readRDS("data/Final_four_data/re_analysis/Toptable_results.RDS")
all_results <- toptable_results %>%
imap(~ .x %>% tibble::rownames_to_column(var = "rowname") %>%
mutate(source = .y)) %>%
bind_rows()
DOX_3_sig <-all_results %>%
dplyr::select(source,genes, logFC,adj.P.Val) %>%
mutate("Peakid"=genes) %>%
dplyr::filter(source=="DOX_3") %>%
dplyr::filter(adj.P.Val<0.05)
DOX_24_sig <-all_results %>%
dplyr::select(source,genes, logFC,adj.P.Val) %>%
mutate("Peakid"=genes) %>%
dplyr::filter(source=="DOX_24") %>%
dplyr::filter(adj.P.Val<0.05)
EPI_3_sig <-all_results %>%
dplyr::select(source,genes, logFC,adj.P.Val) %>%
mutate("Peakid"=genes) %>%
dplyr::filter(source=="EPI_3") %>%
dplyr::filter(adj.P.Val<0.05)
EPI_24_sig <-all_results %>%
dplyr::select(source,genes, logFC,adj.P.Val) %>%
mutate("Peakid"=genes) %>%
dplyr::filter(source=="EPI_24") %>%
dplyr::filter(adj.P.Val<0.05)
DNR_3_sig <-all_results %>%
dplyr::select(source,genes, logFC,adj.P.Val) %>%
mutate("Peakid"=genes) %>%
dplyr::filter(source=="DNR_3") %>%
dplyr::filter(adj.P.Val<0.05)
DNR_24_sig <-all_results %>%
dplyr::select(source,genes, logFC,adj.P.Val) %>%
mutate("Peakid"=genes) %>%
dplyr::filter(source=="DNR_24") %>%
dplyr::filter(adj.P.Val<0.05)
MTX_3_sig <-all_results %>%
dplyr::select(source,genes, logFC,adj.P.Val) %>%
mutate("Peakid"=genes) %>%
dplyr::filter(source=="MTX_3") %>%
dplyr::filter(adj.P.Val<0.05)
MTX_24_sig <-all_results %>%
dplyr::select(source,genes, logFC,adj.P.Val) %>%
mutate("Peakid"=genes) %>%
dplyr::filter(source=="MTX_24") %>%
dplyr::filter(adj.P.Val<0.05)
all_regions_gr <- all_results %>%
dplyr::select(source,genes, logFC,adj.P.Val) %>%
mutate("Peakid"=genes) %>%
dplyr::filter(source=="DOX_3") %>%
distinct(Peakid) %>%
separate_wider_delim(., cols="Peakid",names = c("seqnames","start","end"), delim= ".", cols_remove = FALSE) %>%
makeGRangesFromDataFrame(.,keep.extra.columns=TRUE)
all_regions_peak <- all_results %>%
dplyr::select(source,genes, logFC,adj.P.Val) %>%
mutate("Peakid"=genes) %>%
dplyr::filter(source=="DOX_3") %>%
distinct(Peakid) 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_DARs_hyper$names <- paste0("hyper_", seq_along(seqnames(MCF7_DARs_hyper)))
MCF7_DARs_hypo$names <- paste0("hypo_", seq_along(seqnames(MCF7_DARs_hypo)))
MCF7_ARsmcf7_1 <- read_excel("C:/Users/renee/Downloads/MCF7-doxATAC/Table 3.XLSX") %>%
GRanges()ch = import.chain("C:/Users/renee/ATAC_folder/liftOver_genome/hg19ToHg38.over.chain")
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 <- c(MCF7_DARs_hyper_LO,MCF7_DARs_hypo_LO)
MCF7_ARsmcf7_1_LO <- as.data.frame(liftOver(MCF7_ARsmcf7_1,ch)) %>%
GRanges()Overlapping ATAC regions and SNPs
AF_ol_peaks <- join_overlap_inner(all_regions_gr, Short_gwas_gr) %>%
as.data.frame() %>%
dplyr::filter(gwas =="AF") %>%
distinct(Peakid)
HF_ol_peaks <- join_overlap_inner(all_regions_gr, Short_gwas_gr) %>%
as.data.frame() %>%
dplyr::filter(gwas =="HF") %>%
distinct(Peakid)
IHD_ol_peaks <- join_overlap_inner(all_regions_gr, Short_gwas_gr) %>%
as.data.frame() %>%
dplyr::filter(gwas =="IHD") %>%
distinct(Peakid)
CAD_ol_peaks <- join_overlap_inner(all_regions_gr, Short_gwas_gr) %>%
as.data.frame() %>%
dplyr::filter(gwas =="CAD") %>%
distinct(Peakid)
HF_AF_ol <- join_overlap_inner(all_regions_gr, Short_gwas_gr) %>%
as.data.frame() %>%
dplyr::filter(gwas =="AF"|gwas=="HF")
SNP_overlaps <- join_overlap_inner(all_regions_gr, Short_gwas_gr) %>%
as.data.frame() #%>% saveRDS(.,
# "data/Final_four_data/re_analysis/GWAS_overlaps_dataframe.RDS")gwas_annote_df <-all_regions_peak %>%
mutate(AF_status=case_when(Peakid %in% AF_ol_peaks$Peakid ~"AF_peak",
TRUE~ "not_AF_peak"),
HF_status=case_when(Peakid %in% HF_ol_peaks$Peakid ~"HF_peak",
TRUE~ "not_HF_peak"),
CAD_status=case_when(Peakid %in% CAD_ol_peaks$Peakid ~"CAD_peak",
TRUE~ "not_CAD_peak"),
IHD_status=case_when(Peakid %in% IHD_ol_peaks$Peakid ~"IHD_peak",
TRUE~ "not_IHD_peak")) %>%
mutate(DOX_3=case_when(Peakid %in% DOX_3_sig$Peakid ~"sig_peak",
TRUE~ "not_sig_peak")) %>%
mutate(DOX_24=case_when(Peakid %in% DOX_24_sig$Peakid ~"sig_peak",
TRUE~ "not_sig_peak")) %>%
mutate(EPI_3=case_when(Peakid %in% EPI_3_sig$Peakid ~"sig_peak",
TRUE~ "not_sig_peak")) %>%
mutate(EPI_24=case_when(Peakid %in% EPI_24_sig$Peakid ~"sig_peak",
TRUE~ "not_sig_peak")) %>%
mutate(DNR_3=case_when(Peakid %in% DNR_3_sig$Peakid ~"sig_peak",
TRUE~ "not_sig_peak")) %>%
mutate(DNR_24=case_when(Peakid %in% DNR_24_sig$Peakid ~"sig_peak",
TRUE~ "not_sig_peak")) %>%
mutate(MTX_3=case_when(Peakid %in% MTX_3_sig$Peakid ~"sig_peak",
TRUE~ "not_sig_peak")) %>%
mutate(MTX_24=case_when(Peakid %in% MTX_24_sig$Peakid ~"sig_peak",
TRUE~ "not_sig_peak"))
# saveRDS(gwas_annote_df,"data/Final_four_data/re_analysis/GWAS_SNP_annotations.RDS")Testing for enrichment: ### DOX enrichment tests
gwas_annote_df %>%
group_by(DOX_3,AF_status) %>%
tally() %>%
pivot_wider(., DOX_3, names_from = AF_status, values_from = n) %>%
arrange(desc(DOX_3)) %>%
print() %>%
column_to_rownames("DOX_3") %>%
fisher.test(.)# A tibble: 2 × 3
# Groups: DOX_3 [2]
DOX_3 AF_peak not_AF_peak
<chr> <int> <int>
1 sig_peak 5 3468
2 not_sig_peak 71 152013
Fisher's Exact Test for Count Data
data: .
p-value = 0.02774
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
0.9718879 7.5547025
sample estimates:
odds ratio
3.086745 DOX_darsnp_3_AF <- gwas_annote_df %>%
group_by(DOX_3,AF_status) %>%
tally() %>%
pivot_wider(., DOX_3, names_from = AF_status, values_from = n) %>%
arrange(desc(DOX_3)) %>%
column_to_rownames("DOX_3") %>%
fisher.test(.)gwas_annote_df %>%
group_by(DOX_24,AF_status) %>%
tally() %>%
pivot_wider(., DOX_24, names_from = AF_status, values_from = n) %>%
arrange(desc(DOX_24)) %>%
print() %>%
column_to_rownames("DOX_24") %>%
fisher.test(.)# A tibble: 2 × 3
# Groups: DOX_24 [2]
DOX_24 AF_peak not_AF_peak
<chr> <int> <int>
1 sig_peak 34 64786
2 not_sig_peak 42 90695
Fisher's Exact Test for Count Data
data: .
p-value = 0.6419
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
0.6991766 1.8248197
sample estimates:
odds ratio
1.133263 DOX_darsnp_24_AF <- gwas_annote_df %>%
group_by(DOX_24,AF_status) %>%
tally() %>%
pivot_wider(., DOX_24, names_from = AF_status, values_from = n) %>%
arrange(desc(DOX_24)) %>%
column_to_rownames("DOX_24") %>%
fisher.test(.)gwas_annote_df %>%
group_by(DOX_3,HF_status) %>%
tally() %>%
pivot_wider(., DOX_3, names_from = HF_status, values_from = n) %>%
arrange(desc(DOX_3)) %>%
print() %>%
column_to_rownames("DOX_3") %>%
fisher.test(.)# A tibble: 2 × 3
# Groups: DOX_3 [2]
DOX_3 HF_peak not_HF_peak
<chr> <int> <int>
1 sig_peak 1 3472
2 not_sig_peak 28 152056
Fisher's Exact Test for Count Data
data: .
p-value = 0.4805
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
0.038240 9.465039
sample estimates:
odds ratio
1.564073 DOX_darsnp_3_HF <- gwas_annote_df %>%
group_by(DOX_3,HF_status) %>%
tally() %>%
pivot_wider(., DOX_3, names_from = HF_status, values_from = n) %>%
arrange(desc(DOX_3)) %>%
column_to_rownames("DOX_3") %>%
fisher.test(.)gwas_annote_df %>%
group_by(DOX_24,HF_status) %>%
tally() %>%
pivot_wider(., DOX_24, names_from = HF_status, values_from = n) %>%
arrange(desc(DOX_24)) %>%
print() %>%
column_to_rownames("DOX_24") %>%
fisher.test(.)# A tibble: 2 × 3
# Groups: DOX_24 [2]
DOX_24 HF_peak not_HF_peak
<chr> <int> <int>
1 sig_peak 14 64806
2 not_sig_peak 15 90722
Fisher's Exact Test for Count Data
data: .
p-value = 0.5727
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
0.5842783 2.9036690
sample estimates:
odds ratio
1.306558 DOX_darsnp_24_HF <- gwas_annote_df %>%
group_by(DOX_24,HF_status) %>%
tally() %>%
pivot_wider(., DOX_24, names_from = HF_status, values_from = n) %>%
arrange(desc(DOX_24)) %>%
column_to_rownames("DOX_24") %>%
fisher.test(.)gwas_annote_df %>%
group_by(DOX_3,IHD_status) %>%
tally() %>%
pivot_wider(., DOX_3, names_from = IHD_status, values_from = n, values_fill = 0) %>%
arrange(desc(DOX_3))%>%
print() %>%
column_to_rownames("DOX_3") %>%
fisher.test(.)# A tibble: 2 × 3
# Groups: DOX_3 [2]
DOX_3 IHD_peak not_IHD_peak
<chr> <int> <int>
1 sig_peak 0 3473
2 not_sig_peak 7 152077
Fisher's Exact Test for Count Data
data: .
p-value = 1
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
0.00000 30.37975
sample estimates:
odds ratio
0 DOX_darsnp_3_IHD <- gwas_annote_df %>%
group_by(DOX_3,IHD_status) %>%
tally() %>%
pivot_wider(., DOX_3, names_from = IHD_status, values_from = n, values_fill = 0) %>%
arrange(desc(DOX_3))%>%
column_to_rownames("DOX_3") %>%
fisher.test(.)gwas_annote_df %>%
group_by(DOX_24,IHD_status) %>%
tally() %>%
pivot_wider(., DOX_24, names_from = IHD_status, values_from = n, values_fill = 0) %>%
arrange(desc(DOX_24)) %>%
print() %>%
column_to_rownames("DOX_24") %>%
fisher.test(.)# A tibble: 2 × 3
# Groups: DOX_24 [2]
DOX_24 IHD_peak not_IHD_peak
<chr> <int> <int>
1 sig_peak 3 64817
2 not_sig_peak 4 90733
Fisher's Exact Test for Count Data
data: .
p-value = 1
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
0.1537767 6.2053650
sample estimates:
odds ratio
1.049845 DOX_darsnp_24_IHD <- gwas_annote_df %>%
group_by(DOX_24,IHD_status) %>%
tally() %>%
pivot_wider(., DOX_24, names_from = IHD_status, values_from = n, values_fill = 0) %>%
arrange(desc(DOX_24)) %>%
column_to_rownames("DOX_24") %>%
fisher.test(.)gwas_annote_df %>%
group_by(DOX_3,CAD_status) %>%
tally() %>%
pivot_wider(., DOX_3, names_from = CAD_status, values_from = n) %>%
arrange(desc(DOX_3)) %>%
print() %>%
column_to_rownames("DOX_3") %>%
fisher.test(.)# A tibble: 2 × 3
# Groups: DOX_3 [2]
DOX_3 CAD_peak not_CAD_peak
<chr> <int> <int>
1 sig_peak 2 3471
2 not_sig_peak 136 151948
Fisher's Exact Test for Count Data
data: .
p-value = 0.7736
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
0.07714899 2.37238460
sample estimates:
odds ratio
0.6437719 DOX_darsnp_3_CAD <- gwas_annote_df %>%
group_by(DOX_3,CAD_status) %>%
tally() %>%
pivot_wider(., DOX_3, names_from = CAD_status, values_from = n) %>%
arrange(desc(DOX_3)) %>%
column_to_rownames("DOX_3") %>%
fisher.test(.)gwas_annote_df %>%
group_by(DOX_24,CAD_status) %>%
tally() %>%
pivot_wider(., DOX_24, names_from = CAD_status, values_from = n) %>%
arrange(desc(DOX_24)) %>%
print() %>%
column_to_rownames("DOX_24") %>%
fisher.test(.)# A tibble: 2 × 3
# Groups: DOX_24 [2]
DOX_24 CAD_peak not_CAD_peak
<chr> <int> <int>
1 sig_peak 59 64761
2 not_sig_peak 79 90658
Fisher's Exact Test for Count Data
data: .
p-value = 0.7962
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
0.7331472 1.4837782
sample estimates:
odds ratio
1.045516 DOX_darsnp_24_CAD <- gwas_annote_df %>%
group_by(DOX_24,CAD_status) %>%
tally() %>%
pivot_wider(., DOX_24, names_from = CAD_status, values_from = n) %>%
arrange(desc(DOX_24)) %>%
column_to_rownames("DOX_24") %>%
fisher.test(.)EPI enrichment tests
gwas_annote_df %>%
group_by(EPI_3,AF_status) %>%
tally() %>%
pivot_wider(., EPI_3, names_from = AF_status, values_from = n) %>%
arrange(desc(EPI_3)) %>%
print() %>%
column_to_rownames("EPI_3") %>%
fisher.test(.)# A tibble: 2 × 3
# Groups: EPI_3 [2]
EPI_3 AF_peak not_AF_peak
<chr> <int> <int>
1 sig_peak 14 14220
2 not_sig_peak 62 141261
Fisher's Exact Test for Count Data
data: .
p-value = 0.00924
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
1.159315 4.053255
sample estimates:
odds ratio
2.243075 EPI_darsnp_3_AF <- gwas_annote_df %>%
group_by(EPI_3,AF_status) %>%
tally() %>%
pivot_wider(., EPI_3, names_from = AF_status, values_from = n) %>%
arrange(desc(EPI_3)) %>%
column_to_rownames("EPI_3") %>%
fisher.test(.)gwas_annote_df %>%
group_by(EPI_24,AF_status) %>%
tally() %>%
pivot_wider(., EPI_24, names_from = AF_status, values_from = n) %>%
arrange(desc(EPI_24)) %>%
print() %>%
column_to_rownames("EPI_24") %>%
fisher.test(.)# A tibble: 2 × 3
# Groups: EPI_24 [2]
EPI_24 AF_peak not_AF_peak
<chr> <int> <int>
1 sig_peak 39 66462
2 not_sig_peak 37 89019
Fisher's Exact Test for Count Data
data: .
p-value = 0.1334
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
0.8768983 2.2766348
sample estimates:
odds ratio
1.411774 EPI_darsnp_24_AF <- gwas_annote_df %>%
group_by(EPI_24,AF_status) %>%
tally() %>%
pivot_wider(., EPI_24, names_from = AF_status, values_from = n) %>%
arrange(desc(EPI_24)) %>%
column_to_rownames("EPI_24") %>%
fisher.test(.)gwas_annote_df %>%
group_by(EPI_3,HF_status) %>%
tally() %>%
pivot_wider(., EPI_3, names_from = HF_status, values_from = n) %>%
arrange(desc(EPI_3)) %>%
print() %>%
column_to_rownames("EPI_3") %>%
fisher.test(.)# A tibble: 2 × 3
# Groups: EPI_3 [2]
EPI_3 HF_peak not_HF_peak
<chr> <int> <int>
1 sig_peak 3 14231
2 not_sig_peak 26 141297
Fisher's Exact Test for Count Data
data: .
p-value = 0.7452
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
0.2219052 3.7390450
sample estimates:
odds ratio
1.14563 EPI_darsnp_3_HF <- gwas_annote_df %>%
group_by(EPI_3,HF_status) %>%
tally() %>%
pivot_wider(., EPI_3, names_from = HF_status, values_from = n) %>%
arrange(desc(EPI_3)) %>%
column_to_rownames("EPI_3") %>%
fisher.test(.)gwas_annote_df %>%
group_by(EPI_24,HF_status) %>%
tally() %>%
pivot_wider(., EPI_24, names_from = HF_status, values_from = n) %>%
arrange(desc(EPI_24)) %>%
print() %>%
column_to_rownames("EPI_24") %>%
fisher.test(.)# A tibble: 2 × 3
# Groups: EPI_24 [2]
EPI_24 HF_peak not_HF_peak
<chr> <int> <int>
1 sig_peak 15 66486
2 not_sig_peak 14 89042
Fisher's Exact Test for Count Data
data: .
p-value = 0.3521
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
0.6457011 3.2086406
sample estimates:
odds ratio
1.434896 EPI_darsnp_24_HF <- gwas_annote_df %>%
group_by(EPI_24,HF_status) %>%
tally() %>%
pivot_wider(., EPI_24, names_from = HF_status, values_from = n) %>%
arrange(desc(EPI_24)) %>%
column_to_rownames("EPI_24") %>%
fisher.test(.)gwas_annote_df %>%
group_by(EPI_3,IHD_status) %>%
tally() %>%
pivot_wider(., EPI_3, names_from = IHD_status, values_from = n, values_fill = 0) %>%
arrange(desc(EPI_3))%>%
print() %>%
column_to_rownames("EPI_3") %>%
fisher.test(.)# A tibble: 2 × 3
# Groups: EPI_3 [2]
EPI_3 IHD_peak not_IHD_peak
<chr> <int> <int>
1 sig_peak 0 14234
2 not_sig_peak 7 141316
Fisher's Exact Test for Count Data
data: .
p-value = 1
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
0.000000 6.889309
sample estimates:
odds ratio
0 EPI_darsnp_3_IHD <- gwas_annote_df %>%
group_by(EPI_3,IHD_status) %>%
tally() %>%
pivot_wider(., EPI_3, names_from = IHD_status, values_from = n, values_fill = 0) %>%
arrange(desc(EPI_3))%>%
column_to_rownames("EPI_3") %>%
fisher.test(.)gwas_annote_df %>%
group_by(EPI_24,IHD_status) %>%
tally() %>%
pivot_wider(., EPI_24, names_from = IHD_status, values_from = n, values_fill = 0) %>%
arrange(desc(EPI_24)) %>%
print() %>%
column_to_rownames("EPI_24") %>%
fisher.test(.)# A tibble: 2 × 3
# Groups: EPI_24 [2]
EPI_24 IHD_peak not_IHD_peak
<chr> <int> <int>
1 sig_peak 3 66498
2 not_sig_peak 4 89052
Fisher's Exact Test for Count Data
data: .
p-value = 1
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
0.1471093 5.9381589
sample estimates:
odds ratio
1.004376 EPI_darsnp_24_IHD <- gwas_annote_df %>%
group_by(EPI_24,IHD_status) %>%
tally() %>%
pivot_wider(., EPI_24, names_from = IHD_status, values_from = n, values_fill = 0) %>%
arrange(desc(EPI_24)) %>%
column_to_rownames("EPI_24") %>%
fisher.test(.)gwas_annote_df %>%
group_by(EPI_3,CAD_status) %>%
tally() %>%
pivot_wider(., EPI_3, names_from = CAD_status, values_from = n) %>%
arrange(desc(EPI_3)) %>%
print() %>%
column_to_rownames("EPI_3") %>%
fisher.test(.)# A tibble: 2 × 3
# Groups: EPI_3 [2]
EPI_3 CAD_peak not_CAD_peak
<chr> <int> <int>
1 sig_peak 18 14216
2 not_sig_peak 120 141203
Fisher's Exact Test for Count Data
data: .
p-value = 0.1367
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
0.8537334 2.4575896
sample estimates:
odds ratio
1.489886 EPI_darsnp_3_CAD <- gwas_annote_df %>%
group_by(EPI_3,CAD_status) %>%
tally() %>%
pivot_wider(., EPI_3, names_from = CAD_status, values_from = n) %>%
arrange(desc(EPI_3)) %>%
column_to_rownames("EPI_3") %>%
fisher.test(.)gwas_annote_df %>%
group_by(EPI_24,CAD_status) %>%
tally() %>%
pivot_wider(., EPI_24, names_from = CAD_status, values_from = n) %>%
arrange(desc(EPI_24)) %>%
print() %>%
column_to_rownames("EPI_24") %>%
fisher.test(.)# A tibble: 2 × 3
# Groups: EPI_24 [2]
EPI_24 CAD_peak not_CAD_peak
<chr> <int> <int>
1 sig_peak 58 66443
2 not_sig_peak 80 88976
Fisher's Exact Test for Count Data
data: .
p-value = 0.9315
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
0.680080 1.378666
sample estimates:
odds ratio
0.9708687 EPI_darsnp_24_CAD <- gwas_annote_df %>%
group_by(EPI_24,CAD_status) %>%
tally() %>%
pivot_wider(., EPI_24, names_from = CAD_status, values_from = n) %>%
arrange(desc(EPI_24)) %>%
column_to_rownames("EPI_24") %>%
fisher.test(.)DNR enrichment tests
gwas_annote_df %>%
group_by(DNR_3,AF_status) %>%
tally() %>%
pivot_wider(., DNR_3, names_from = AF_status, values_from = n) %>%
arrange(desc(DNR_3)) %>%
print() %>%
column_to_rownames("DNR_3") %>%
fisher.test(.)# A tibble: 2 × 3
# Groups: DNR_3 [2]
DNR_3 AF_peak not_AF_peak
<chr> <int> <int>
1 sig_peak 21 22717
2 not_sig_peak 55 132764
Fisher's Exact Test for Count Data
data: .
p-value = 0.002992
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
1.281504 3.750823
sample estimates:
odds ratio
2.231432 DNR_darsnp_3_AF <- gwas_annote_df %>%
group_by(DNR_3,AF_status) %>%
tally() %>%
pivot_wider(., DNR_3, names_from = AF_status, values_from = n) %>%
arrange(desc(DNR_3)) %>%
column_to_rownames("DNR_3") %>%
fisher.test(.)gwas_annote_df %>%
group_by(DNR_24,AF_status) %>%
tally() %>%
pivot_wider(., DNR_24, names_from = AF_status, values_from = n) %>%
arrange(desc(DNR_24)) %>%
print() %>%
column_to_rownames("DNR_24") %>%
fisher.test(.)# A tibble: 2 × 3
# Groups: DNR_24 [2]
DNR_24 AF_peak not_AF_peak
<chr> <int> <int>
1 sig_peak 41 79954
2 not_sig_peak 35 75527
Fisher's Exact Test for Count Data
data: .
p-value = 0.7309
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
0.6875137 1.7898588
sample estimates:
odds ratio
1.106551 DNR_darsnp_24_AF <- gwas_annote_df %>%
group_by(DNR_24,AF_status) %>%
tally() %>%
pivot_wider(., DNR_24, names_from = AF_status, values_from = n) %>%
arrange(desc(DNR_24)) %>%
column_to_rownames("DNR_24") %>%
fisher.test(.)gwas_annote_df %>%
group_by(DNR_3,HF_status) %>%
tally() %>%
pivot_wider(., DNR_3, names_from = HF_status, values_from = n) %>%
arrange(desc(DNR_3)) %>%
print() %>%
column_to_rownames("DNR_3") %>%
fisher.test(.)# A tibble: 2 × 3
# Groups: DNR_3 [2]
DNR_3 HF_peak not_HF_peak
<chr> <int> <int>
1 sig_peak 6 22732
2 not_sig_peak 23 132796
Fisher's Exact Test for Count Data
data: .
p-value = 0.4252
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
0.5075285 3.8506709
sample estimates:
odds ratio
1.52394 DNR_darsnp_3_HF <- gwas_annote_df %>%
group_by(DNR_3,HF_status) %>%
tally() %>%
pivot_wider(., DNR_3, names_from = HF_status, values_from = n) %>%
arrange(desc(DNR_3)) %>%
column_to_rownames("DNR_3") %>%
fisher.test(.)gwas_annote_df %>%
group_by(DNR_24,HF_status) %>%
tally() %>%
pivot_wider(., DNR_24, names_from = HF_status, values_from = n) %>%
arrange(desc(DNR_24)) %>%
print() %>%
column_to_rownames("DNR_24") %>%
fisher.test(.)# A tibble: 2 × 3
# Groups: DNR_24 [2]
DNR_24 HF_peak not_HF_peak
<chr> <int> <int>
1 sig_peak 19 79976
2 not_sig_peak 10 75552
Fisher's Exact Test for Count Data
data: .
p-value = 0.1406
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
0.7940266 4.3218505
sample estimates:
odds ratio
1.794883 DNR_darsnp_24_HF <- gwas_annote_df %>%
group_by(DNR_24,HF_status) %>%
tally() %>%
pivot_wider(., DNR_24, names_from = HF_status, values_from = n) %>%
arrange(desc(DNR_24)) %>%
column_to_rownames("DNR_24") %>%
fisher.test(.)gwas_annote_df %>%
group_by(DNR_3,IHD_status) %>%
tally() %>%
pivot_wider(., DNR_3, names_from = IHD_status, values_from = n, values_fill = 0) %>%
arrange(desc(DNR_3))%>%
print() %>%
column_to_rownames("DNR_3") %>%
fisher.test(.)# A tibble: 2 × 3
# Groups: DNR_3 [2]
DNR_3 IHD_peak not_IHD_peak
<chr> <int> <int>
1 sig_peak 2 22736
2 not_sig_peak 5 132814
Fisher's Exact Test for Count Data
data: .
p-value = 0.2727
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
0.2224883 14.2718586
sample estimates:
odds ratio
2.336612 DNR_darsnp_3_IHD <- gwas_annote_df %>%
group_by(DNR_3,IHD_status) %>%
tally() %>%
pivot_wider(., DNR_3, names_from = IHD_status, values_from = n, values_fill = 0) %>%
arrange(desc(DNR_3))%>%
column_to_rownames("DNR_3") %>%
fisher.test(.)gwas_annote_df %>%
group_by(DNR_24,IHD_status) %>%
tally() %>%
pivot_wider(., DNR_24, names_from = IHD_status, values_from = n, values_fill = 0) %>%
arrange(desc(DNR_24)) %>%
print() %>%
column_to_rownames("DNR_24") %>%
fisher.test(.)# A tibble: 2 × 3
# Groups: DNR_24 [2]
DNR_24 IHD_peak not_IHD_peak
<chr> <int> <int>
1 sig_peak 3 79992
2 not_sig_peak 4 75558
Fisher's Exact Test for Count Data
data: .
p-value = 0.7194
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
0.1037956 4.1876739
sample estimates:
odds ratio
0.7084326 DNR_darsnp_24_IHD <- gwas_annote_df %>%
group_by(DNR_24,IHD_status) %>%
tally() %>%
pivot_wider(., DNR_24, names_from = IHD_status, values_from = n, values_fill = 0) %>%
arrange(desc(DNR_24)) %>%
column_to_rownames("DNR_24") %>%
fisher.test(.)gwas_annote_df %>%
group_by(DNR_3,CAD_status) %>%
tally() %>%
pivot_wider(., DNR_3, names_from = CAD_status, values_from = n) %>%
arrange(desc(DNR_3)) %>%
print() %>%
column_to_rownames("DNR_3") %>%
fisher.test(.)# A tibble: 2 × 3
# Groups: DNR_3 [2]
DNR_3 CAD_peak not_CAD_peak
<chr> <int> <int>
1 sig_peak 28 22710
2 not_sig_peak 110 132709
Fisher's Exact Test for Count Data
data: .
p-value = 0.06949
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
0.9452147 2.2698345
sample estimates:
odds ratio
1.487462 DNR_darsnp_3_CAD <- gwas_annote_df %>%
group_by(DNR_3,CAD_status) %>%
tally() %>%
pivot_wider(., DNR_3, names_from = CAD_status, values_from = n) %>%
arrange(desc(DNR_3)) %>%
column_to_rownames("DNR_3") %>%
fisher.test(.)gwas_annote_df %>%
group_by(DNR_24,CAD_status) %>%
tally() %>%
pivot_wider(., DNR_24, names_from = CAD_status, values_from = n) %>%
arrange(desc(DNR_24)) %>%
print() %>%
column_to_rownames("DNR_24") %>%
fisher.test(.)# A tibble: 2 × 3
# Groups: DNR_24 [2]
DNR_24 CAD_peak not_CAD_peak
<chr> <int> <int>
1 sig_peak 68 79927
2 not_sig_peak 70 75492
Fisher's Exact Test for Count Data
data: .
p-value = 0.6703
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
0.6473026 1.2999857
sample estimates:
odds ratio
0.9175381 DNR_darsnp_24_CAD <- gwas_annote_df %>%
group_by(DNR_24,CAD_status) %>%
tally() %>%
pivot_wider(., DNR_24, names_from = CAD_status, values_from = n) %>%
arrange(desc(DNR_24)) %>%
column_to_rownames("DNR_24") %>%
fisher.test(.)MTX enrichment tests
gwas_annote_df %>%
group_by(MTX_3,AF_status) %>%
tally() %>%
pivot_wider(., MTX_3, names_from = AF_status, values_from = n,values_fill = 0) %>%
arrange(desc(MTX_3)) %>%
print() %>%
column_to_rownames("MTX_3") %>%
fisher.test(.)# A tibble: 2 × 3
# Groups: MTX_3 [2]
MTX_3 AF_peak not_AF_peak
<chr> <int> <int>
1 sig_peak 0 804
2 not_sig_peak 76 154677
Fisher's Exact Test for Count Data
data: .
p-value = 1
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
0.000000 9.590161
sample estimates:
odds ratio
0 MTX_darsnp_3_AF <- gwas_annote_df %>%
group_by(MTX_3,AF_status) %>%
tally() %>%
pivot_wider(., MTX_3, names_from = AF_status, values_from = n,values_fill = 0) %>%
arrange(desc(MTX_3)) %>%
column_to_rownames("MTX_3") %>%
fisher.test(.)gwas_annote_df %>%
group_by(MTX_24,AF_status) %>%
tally() %>%
pivot_wider(., MTX_24, names_from = AF_status, values_from = n) %>%
arrange(desc(MTX_24)) %>%
print() %>%
column_to_rownames("MTX_24") %>%
fisher.test(.)# A tibble: 2 × 3
# Groups: MTX_24 [2]
MTX_24 AF_peak not_AF_peak
<chr> <int> <int>
1 sig_peak 20 24230
2 not_sig_peak 56 131251
Fisher's Exact Test for Count Data
data: .
p-value = 0.01633
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
1.099398 3.275113
sample estimates:
odds ratio
1.934478 MTX_darsnp_24_AF <- gwas_annote_df %>%
group_by(MTX_24,AF_status) %>%
tally() %>%
pivot_wider(., MTX_24, names_from = AF_status, values_from = n) %>%
arrange(desc(MTX_24)) %>%
column_to_rownames("MTX_24") %>%
fisher.test(.)gwas_annote_df %>%
group_by(MTX_3,HF_status) %>%
tally() %>%
pivot_wider(., MTX_3, names_from = HF_status, values_from = n) %>%
arrange(desc(MTX_3)) %>%
print() %>%
column_to_rownames("MTX_3") %>%
fisher.test(.)# A tibble: 2 × 3
# Groups: MTX_3 [2]
MTX_3 HF_peak not_HF_peak
<chr> <int> <int>
1 sig_peak 1 803
2 not_sig_peak 28 154725
Fisher's Exact Test for Count Data
data: .
p-value = 0.1395
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
0.1680849 41.7539207
sample estimates:
odds ratio
6.880335 MTX_darsnp_3_HF <- gwas_annote_df %>%
group_by(MTX_3,HF_status) %>%
tally() %>%
pivot_wider(., MTX_3, names_from = HF_status, values_from = n) %>%
arrange(desc(MTX_3)) %>%
column_to_rownames("MTX_3") %>%
fisher.test(.)gwas_annote_df %>%
group_by(MTX_24,HF_status) %>%
tally() %>%
pivot_wider(., MTX_24, names_from = HF_status, values_from = n) %>%
arrange(desc(MTX_24)) %>%
print() %>%
column_to_rownames("MTX_24") %>%
fisher.test(.)# A tibble: 2 × 3
# Groups: MTX_24 [2]
MTX_24 HF_peak not_HF_peak
<chr> <int> <int>
1 sig_peak 10 24240
2 not_sig_peak 19 131288
Fisher's Exact Test for Count Data
data: .
p-value = 0.009723
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
1.183787 6.443806
sample estimates:
odds ratio
2.850689 MTX_darsnp_24_HF <- gwas_annote_df %>%
group_by(MTX_24,HF_status) %>%
tally() %>%
pivot_wider(., MTX_24, names_from = HF_status, values_from = n) %>%
arrange(desc(MTX_24)) %>%
column_to_rownames("MTX_24") %>%
fisher.test(.)gwas_annote_df %>%
group_by(MTX_3,IHD_status) %>%
tally() %>%
pivot_wider(., MTX_3, names_from = IHD_status, values_from = n, values_fill = 0) %>%
arrange(desc(MTX_3))%>%
print() %>%
column_to_rownames("MTX_3") %>%
fisher.test(.)# A tibble: 2 × 3
# Groups: MTX_3 [2]
MTX_3 IHD_peak not_IHD_peak
<chr> <int> <int>
1 sig_peak 0 804
2 not_sig_peak 7 154746
Fisher's Exact Test for Count Data
data: .
p-value = 1
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
0.0000 133.7927
sample estimates:
odds ratio
0 MTX_darsnp_3_IHD <- gwas_annote_df %>%
group_by(MTX_3,IHD_status) %>%
tally() %>%
pivot_wider(., MTX_3, names_from = IHD_status, values_from = n, values_fill = 0) %>%
arrange(desc(MTX_3))%>%
column_to_rownames("MTX_3") %>%
fisher.test(.)gwas_annote_df %>%
group_by(MTX_24,IHD_status) %>%
tally() %>%
pivot_wider(., MTX_24, names_from = IHD_status, values_from = n, values_fill = 0) %>%
arrange(desc(MTX_24)) %>%
print() %>%
column_to_rownames("MTX_24") %>%
fisher.test(.)# A tibble: 2 × 3
# Groups: MTX_24 [2]
MTX_24 IHD_peak not_IHD_peak
<chr> <int> <int>
1 sig_peak 2 24248
2 not_sig_peak 5 131302
Fisher's Exact Test for Count Data
data: .
p-value = 0.2999
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
0.2062384 13.2298655
sample estimates:
odds ratio
2.165985 MTX_darsnp_24_IHD <- gwas_annote_df %>%
group_by(MTX_24,IHD_status) %>%
tally() %>%
pivot_wider(., MTX_24, names_from = IHD_status, values_from = n, values_fill = 0) %>%
arrange(desc(MTX_24)) %>%
column_to_rownames("MTX_24") %>%
fisher.test(.)gwas_annote_df %>%
group_by(MTX_3,CAD_status) %>%
tally() %>%
pivot_wider(., MTX_3, names_from = CAD_status, values_from = n, values_fill = 0) %>%
arrange(desc(MTX_3)) %>%
print() %>%
column_to_rownames("MTX_3") %>%
fisher.test(.)# A tibble: 2 × 3
# Groups: MTX_3 [2]
MTX_3 CAD_peak not_CAD_peak
<chr> <int> <int>
1 sig_peak 0 804
2 not_sig_peak 138 154615
Fisher's Exact Test for Count Data
data: .
p-value = 1
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
0.000000 5.222361
sample estimates:
odds ratio
0 MTX_darsnp_3_CAD <- gwas_annote_df %>%
group_by(MTX_3,CAD_status) %>%
tally() %>%
pivot_wider(., MTX_3, names_from = CAD_status, values_from = n, values_fill = 0) %>%
arrange(desc(MTX_3)) %>%
column_to_rownames("MTX_3") %>%
fisher.test(.)gwas_annote_df %>%
group_by(MTX_24,CAD_status) %>%
tally() %>%
pivot_wider(., MTX_24, names_from = CAD_status, values_from = n) %>%
arrange(desc(MTX_24)) %>%
print() %>%
column_to_rownames("MTX_24") %>%
fisher.test(.)# A tibble: 2 × 3
# Groups: MTX_24 [2]
MTX_24 CAD_peak not_CAD_peak
<chr> <int> <int>
1 sig_peak 28 24222
2 not_sig_peak 110 131197
Fisher's Exact Test for Count Data
data: .
p-value = 0.1275
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
0.8761135 2.1038816
sample estimates:
odds ratio
1.378725 MTX_darsnp_24_CAD <- gwas_annote_df %>%
group_by(MTX_24,CAD_status) %>%
tally() %>%
pivot_wider(., MTX_24, names_from = CAD_status, values_from = n) %>%
arrange(desc(MTX_24)) %>%
column_to_rownames("MTX_24") %>%
fisher.test(.)Collecting all data to display:
All_24_results <- mget(ls(pattern = "*_darsnp_24*"))
All_3_results <- mget(ls(pattern = "*_darsnp_3*"))
# All_dar_results <- mget(ls(pattern = "*_darsnp_*"))
# convert to data frames with metadata
combined_df_24 <- bind_rows(
lapply(names(All_24_results), function(name) {
res <- All_24_results[[name]]
parts <- strsplit(name, "_")[[1]]
# convert htest to data.frame
data.frame(
p.value = res$p.value,
estimate = if (!is.null(res$estimate)) unname(res$estimate) else NA,
conf.low = if (!is.null(res$conf.int)) res$conf.int[1] else NA,
conf.high = if (!is.null(res$conf.int)) res$conf.int[2] else NA,
drug = parts[1],
time = parts[3],
population = parts[4],
stringsAsFactors = FALSE
)
})
)
combined_df_3 <- bind_rows(
lapply(names(All_3_results), function(name) {
res <- All_3_results[[name]]
parts <- strsplit(name, "_")[[1]]
# convert htest to data.frame
data.frame(
p.value = res$p.value,
estimate = if (!is.null(res$estimate)) unname(res$estimate) else NA,
conf.low = if (!is.null(res$conf.int)) res$conf.int[1] else NA,
conf.high = if (!is.null(res$conf.int)) res$conf.int[2] else NA,
drug = parts[1],
time = parts[3],
population = parts[4],
stringsAsFactors = FALSE
)
})
)drug_pal <- c("#8B006D","#DF707E","#F1B72B", "#3386DD","#707031","#41B333")
combined_df_3 %>%
mutate(drug=factor(drug, levels = c("DOX","EPI","DNR","MTX")),
time=factor(time, levels = c("3","24")),
population=factor(population,levels = c("AF","HF","IHD","CAD"))) %>%
mutate(log10_pvalue=-log10(p.value)) %>%
ggplot(., aes(x=drug, y=log10_pvalue))+
geom_col(aes(fill=drug))+
geom_hline(yintercept= -log10(0.05), linetype = "dashed",color="black")+
theme_classic()+
facet_wrap(~time+population,nrow=2)+
xlab("treatment")+
ylab("log10 pvalue of fisher exact test")+
scale_fill_manual(values = drug_pal)
| Version | Author | Date |
|---|---|---|
| 88047ef | reneeisnowhere | 2025-07-28 |
combined_df_3 %>%
dplyr::filter(population!= "IHD" & population != "CAD") %>%
mutate(drug=factor(drug, levels = c("DOX","EPI","DNR","MTX")),
time=factor(time, levels = c("3","24")),
population=factor(population,levels = c("AF","HF","IHD", "CAD"))) %>%
group_by(time,drug) %>%
mutate(rank_val=rank(p.value, ties.method = "first")) %>%
mutate(BH_correction= p.adjust(p.value,method= "BH")) %>%
mutate(log10_pvalue=-log10(BH_correction)) %>%
ggplot(., aes(x=drug, y=log10_pvalue))+
geom_col(aes(fill=drug))+
geom_hline(yintercept= -log10(0.05), linetype = "dashed",color="black")+
theme_classic()+
facet_wrap(~time+population,nrow=2)+
xlab("treatment")+
ylab("log10 pvalue of fisher exact test")+
scale_fill_manual(values = drug_pal)
| Version | Author | Date |
|---|---|---|
| 88047ef | reneeisnowhere | 2025-07-28 |
OR of results
combined_df_3 %>%
mutate(group=paste0(drug,"_",time)) %>%
# separate_wider_delim(.,cols="group", names = c("trt","time"), delim = "_",cols_remove = FALSE) %>%
mutate(time= factor(time, levels =c("3","24")),
drug=factor(drug, levels= c("DOX", "EPI", "DNR", "MTX"))) %>%
mutate(population=factor(population, levels= c("AF","HF","IHD","CAD"))) %>%
mutate(
significant = case_when(
p.value < 0.001 ~ "***",
p.value < 0.01 ~ "**",
p.value < 0.05 ~ "*",
TRUE ~ ""
)
) %>%
ggplot(., aes(x = drug, y = estimate)) +
geom_point(aes(color = drug), size=4)+
geom_errorbar(aes(ymin = conf.low, ymax = conf.high), width = 0.2) +
geom_hline(yintercept = 1, linetype = "dashed", color = "gray40") +
geom_text(
aes(y = conf.high + 0.1 * estimate, label = significant),
hjust = 0, # aligns text to the left of the y point
size = 4,
color = "black"
)+
labs(
title = "Odds Ratio of SNP enrichment by type",
y = "Odds Ratio (95% confidence interval)",
x = "treatment"
) +
# coord_flip()+
theme_bw() +
facet_grid(rows = vars(population), cols = vars(time), scales = "free_y")+
theme(
text = element_text(size = 12),
plot.title = element_text(hjust = 0.5)
)
MCF7 enrichment tests
library(regioneR)
AF_gr <- Short_gwas_gr %>%
dplyr::filter(gwas == "AF")
HF_gr <- Short_gwas_gr %>%
dplyr::filter(gwas == "HF")
IHD_gr <- Short_gwas_gr %>%
dplyr::filter(gwas == "IHD")
CAD_gr <- Short_gwas_gr %>%
dplyr::filter(gwas == "CAD")
# AF_MCF7_ol_peaks <- join_overlap_inner(MCF7_DAR_all, Short_gwas_gr) %>%
# dplyr::filter(mcols(gwas =="AF")) %>%
# as.data.frame() %>%
# dplyr::filter(gwas =="AF") %>%
# distinct(names)
#
# HF_MCF7_ol_peaks <- join_overlap_inner(MCF7_DAR_all, Short_gwas_gr) %>%
# as.data.frame() %>%
# dplyr::filter(gwas =="HF") %>%
# distinct(names)
#
# IHD_MCF7_ol_peaks <- join_overlap_inner(MCF7_DAR_all, Short_gwas_gr) %>%
# as.data.frame() %>%
# dplyr::filter(gwas =="IHD") %>%
# distinct(names)
# saveRDS(AF,"data/Final_four_data/re_analysis/MCF7_DAR_AF_permtest.RDS")
IHD <-readRDS("data/Final_four_data/re_analysis/MCF7_DAR_IHD_permtest.RDS")
HF <- readRDS("data/Final_four_data/re_analysis/MCF7_DAR_HF_permtest.RDS")
AF <- readRDS("data/Final_four_data/re_analysis/MCF7_DAR_AF_permtest.RDS")
CAD <- readRDS("data/Final_four_data/re_analysis/MCF7_DAR_CAD_permtest.RDS")
# param <- SnowParam(workers = 8)
# AF<- permTest(A= MCF7_DAR_all,
# B= AF_gr,
# ntimes=1000,
# randomize.function=randomizeRegions,
# evaluate.function = numOverlaps,
# genome="hg38",
# count.once= TRUE,
# universe=universe,
# # universe = union_accessible_regions,
# verbose = TRUE,
# BPPARAM = param)
AF$numOverlaps
P-value: 0.64035964035964
Z-score: -0.0163
Number of iterations: 1000
Alternative: less
Evaluation of the original region set: 4
Evaluation function: numOverlaps
Randomization function: randomizeRegions
attr(,"class")
[1] "permTestResultsList"plot(AF)
HF$numOverlaps
P-value: 0.434565434565435
Z-score: 0.3761
Number of iterations: 1000
Alternative: greater
Evaluation of the original region set: 3
Evaluation function: numOverlaps
Randomization function: randomizeRegions
attr(,"class")
[1] "permTestResultsList"plot(HF)
IHD$numOverlaps
P-value: 0.614385614385614
Z-score: -0.7021
Number of iterations: 1000
Alternative: less
Evaluation of the original region set: 0
Evaluation function: numOverlaps
Randomization function: randomizeRegions
attr(,"class")
[1] "permTestResultsList"plot(IHD)
CAD$numOverlaps
P-value: 0.045954045954046
Z-score: 1.8555
Number of iterations: 1000
Alternative: greater
Evaluation of the original region set: 19
Evaluation function: numOverlaps
Randomization function: randomizeRegions
attr(,"class")
[1] "permTestResultsList"plot(CAD)
PT DOX 24hr check
# DOX_24_gr <- DOX_24_sig %>% separate_wider_delim(., cols="genes",names = c("seqnames","start","end"), delim= ".") %>%
# GRanges()
#
# DOX_3_gr <- DOX_3_sig %>% separate_wider_delim(., cols="genes",names = c("seqnames","start","end"), delim= ".") %>%
# GRanges()
# param <- SnowParam(workers = 1)
DOX_AF <- readRDS("data/Final_four_data/re_analysis/perm_test_1000/DOX_AF_24h.RDS")
DOX_HF <- readRDS("data/Final_four_data/re_analysis/perm_test_1000/DOX_HF_24h.RDS")
DOX_IHD <- readRDS("data/Final_four_data/re_analysis/perm_test_1000/DOX_IHD_24h.RDS")
DOX_CAD <- readRDS("data/Final_four_data/re_analysis/perm_test_1000/DOX_CAD_24h.RDS")
DOX_AF_3 <- readRDS("data/Final_four_data/re_analysis/perm_test_1000/DOX_AF_3h.RDS")
DOX_HF_3 <- readRDS("data/Final_four_data/re_analysis/perm_test_1000/DOX_HF_3h.RDS")
DOX_IHD_3 <- readRDS("data/Final_four_data/re_analysis/perm_test_1000/DOX_IHD_3h.RDS")
DOX_CAD_3 <- readRDS("data/Final_four_data/re_analysis/perm_test_1000/DOX_CAD_3h.RDS")
# DOX_CAD<- permTest(A= DOX_24_gr,
# B= CAD_gr,
# ntimes=1000,
# randomize.function=randomizeRegions,
# evaluate.function = numOverlaps,
# genome="hg38",
# count.once= TRUE,
# universe=universe,
# # universe = union_accessible_regions,
# verbose = TRUE,
# BPPARAM = param)
# DOX_AF<- permTest(A= DOX_24_gr,
# B= AF_gr,
# ntimes=1000,
# randomize.function=randomizeRegions,
# evaluate.function = numOverlaps,
# genome="hg38",
# count.once= TRUE,
# universe=universe,
# # universe = union_accessible_regions,
# verbose = TRUE,
# BPPARAM = param)
#
# DOX_HF<- permTest(A= DOX_24_gr,
# B= HF_gr,
# ntimes=1000,
# randomize.function=randomizeRegions,
# evaluate.function = numOverlaps,
# genome="hg38",
# count.once= TRUE,
# universe=universe,
# # universe = union_accessible_regions,
# verbose = TRUE,
# BPPARAM = param)
# DOX_IHD<- permTest(A= DOX_24_gr,
# B= IHD_gr,
# ntimes=1000,
# randomize.function=randomizeRegions,
# evaluate.function = numOverlaps,
# genome="hg38",
# count.once= TRUE,
# universe=universe,
# # universe = union_accessible_regions,
# verbose = TRUE,
# BPPARAM = param)
DOX_AF$numOverlaps
P-value: 0.000999000999000999
Z-score: 9.1355
Number of iterations: 1000
Alternative: greater
Evaluation of the original region set: 34
Evaluation function: numOverlaps
Randomization function: randomizeRegions
attr(,"class")
[1] "permTestResultsList"plot(DOX_AF)
| Version | Author | Date |
|---|---|---|
| 88047ef | reneeisnowhere | 2025-07-28 |
DOX_HF$numOverlaps
P-value: 0.002997002997003
Z-score: 4.1771
Number of iterations: 1000
Alternative: greater
Evaluation of the original region set: 14
Evaluation function: numOverlaps
Randomization function: randomizeRegions
attr(,"class")
[1] "permTestResultsList"plot(DOX_HF)
| Version | Author | Date |
|---|---|---|
| 88047ef | reneeisnowhere | 2025-07-28 |
DOX_IHD$numOverlaps
P-value: 0.0899100899100899
Z-score: 1.9409
Number of iterations: 1000
Alternative: greater
Evaluation of the original region set: 3
Evaluation function: numOverlaps
Randomization function: randomizeRegions
attr(,"class")
[1] "permTestResultsList"plot(DOX_IHD)
| Version | Author | Date |
|---|---|---|
| 88047ef | reneeisnowhere | 2025-07-28 |
DOX_CAD$numOverlaps
P-value: 0.000999000999000999
Z-score: 6.9373
Number of iterations: 1000
Alternative: greater
Evaluation of the original region set: 59
Evaluation function: numOverlaps
Randomization function: randomizeRegions
attr(,"class")
[1] "permTestResultsList"plot(DOX_CAD)
| Version | Author | Date |
|---|---|---|
| 88047ef | reneeisnowhere | 2025-07-28 |
# DOX_CAD_3<- permTest(A= DOX_3_gr,
# B= CAD_gr,
# ntimes=1000,
# randomize.function=randomizeRegions,
# evaluate.function = numOverlaps,
# genome="hg38",
# count.once= TRUE,
# universe=universe,
# # universe = union_accessible_regions,
# verbose = TRUE,
# BPPARAM = param)
# DOX_AF_3<- permTest(A= DOX_3_gr,
# B= AF_gr,
# ntimes=1000,
# randomize.function=randomizeRegions,
# evaluate.function = numOverlaps,
# genome="hg38",
# count.once= TRUE,
# universe=universe,
# # universe = union_accessible_regions,
# verbose = TRUE,
# BPPARAM = param)
# DOX_HF_3<- permTest(A= DOX_3_gr,
# B= HF_gr,
# ntimes=1000,
# randomize.function=randomizeRegions,
# evaluate.function = numOverlaps,
# genome="hg38",
# count.once= TRUE,
# universe=universe,
# # universe = union_accessible_regions,
# verbose = TRUE,
# BPPARAM = param)
# DOX_IHD_3<- permTest(A= DOX_3_gr,
# B= IHD_gr,
# ntimes=1000,
# randomize.function=randomizeRegions,
# evaluate.function = numOverlaps,
# genome="hg38",
# count.once= TRUE,
# universe=universe,
# # universe = union_accessible_regions,
# verbose = TRUE,
# BPPARAM = param)
DOX_AF_3$numOverlaps
P-value: 0.000999000999000999
Z-score: 6.3369
Number of iterations: 1000
Alternative: greater
Evaluation of the original region set: 5
Evaluation function: numOverlaps
Randomization function: randomizeRegions
attr(,"class")
[1] "permTestResultsList"plot(DOX_AF_3)
| Version | Author | Date |
|---|---|---|
| 88047ef | reneeisnowhere | 2025-07-28 |
DOX_HF_3$numOverlaps
P-value: 0.236763236763237
Z-score: 1.38
Number of iterations: 1000
Alternative: greater
Evaluation of the original region set: 1
Evaluation function: numOverlaps
Randomization function: randomizeRegions
attr(,"class")
[1] "permTestResultsList"plot(DOX_HF_3)
| Version | Author | Date |
|---|---|---|
| 88047ef | reneeisnowhere | 2025-07-28 |
DOX_IHD_3$numOverlaps
P-value: 0.93006993006993
Z-score: -0.2661
Number of iterations: 1000
Alternative: less
Evaluation of the original region set: 0
Evaluation function: numOverlaps
Randomization function: randomizeRegions
attr(,"class")
[1] "permTestResultsList"plot(DOX_IHD_3)
| Version | Author | Date |
|---|---|---|
| 88047ef | reneeisnowhere | 2025-07-28 |
DOX_CAD_3$numOverlaps
P-value: 0.467532467532468
Z-score: 0.3003
Number of iterations: 1000
Alternative: greater
Evaluation of the original region set: 2
Evaluation function: numOverlaps
Randomization function: randomizeRegions
attr(,"class")
[1] "permTestResultsList"plot(DOX_CAD_3)
| Version | Author | Date |
|---|---|---|
| 88047ef | reneeisnowhere | 2025-07-28 |
# saveRDS(DOX_AF,"data/Final_four_data/re_analysis/perm_test_100/DOX_AF_24h.RDS")
# saveRDS(DOX_HF,"data/Final_four_data/re_analysis/perm_test_100/DOX_HF_24h.RDS")
# saveRDS(DOX_IHD,"data/Final_four_data/re_analysis/perm_test_100/DOX_IHD_24h.RDS")
# saveRDS(DOX_CAD,"data/Final_four_data/re_analysis/perm_test_100/DOX_CAD_24h.RDS")
# saveRDS(DOX_AF_3,"data/Final_four_data/re_analysis/perm_test_100/DOX_AF_3h.RDS")
# saveRDS(DOX_HF_3,"data/Final_four_data/re_analysis/perm_test_100/DOX_HF_3h.RDS")
# saveRDS(DOX_IHD_3,"data/Final_four_data/re_analysis/perm_test_100/DOX_IHD_3h.RDS")
# saveRDS(DOX_CAD_3,"data/Final_four_data/re_analysis/perm_test_100/DOX_CAD_3h.RDS")
# saveRDS(DOX_AF,"data/Final_four_data/re_analysis/perm_test_1000/DOX_AF_24h.RDS")
# saveRDS(DOX_HF,"data/Final_four_data/re_analysis/perm_test_1000/DOX_HF_24h.RDS")
# saveRDS(DOX_IHD,"data/Final_four_data/re_analysis/perm_test_1000/DOX_IHD_24h.RDS")
# saveRDS(DOX_CAD,"data/Final_four_data/re_analysis/perm_test_1000/DOX_CAD_24h.RDS")
# saveRDS(DOX_AF_3,"data/Final_four_data/re_analysis/perm_test_1000/DOX_AF_3h.RDS")
# saveRDS(DOX_HF_3,"data/Final_four_data/re_analysis/perm_test_1000/DOX_HF_3h.RDS")
# saveRDS(DOX_IHD_3,"data/Final_four_data/re_analysis/perm_test_1000/DOX_IHD_3h.RDS")
# saveRDS(DOX_CAD_3,"data/Final_four_data/re_analysis/perm_test_1000/DOX_CAD_3h.RDS")PT EPI 24hr check
EPI_24_gr <- EPI_24_sig %>% separate_wider_delim(., cols="genes",names = c("seqnames","start","end"), delim= ".") %>%
GRanges()
EPI_3_gr <- EPI_3_sig %>% separate_wider_delim(., cols="genes",names = c("seqnames","start","end"), delim= ".") %>%
GRanges()
EPI_AF <- readRDS("data/Final_four_data/re_analysis/perm_test_1000/EPI_AF_24h.RDS")
EPI_HF <- readRDS("data/Final_four_data/re_analysis/perm_test_1000/EPI_HF_24h.RDS")
EPI_IHD <- readRDS("data/Final_four_data/re_analysis/perm_test_1000/EPI_IHD_24h.RDS")
EPI_CAD <- readRDS("data/Final_four_data/re_analysis/perm_test_1000/EPI_CAD_24h.RDS")
EPI_AF_3 <- readRDS("data/Final_four_data/re_analysis/perm_test_1000/EPI_AF_3h.RDS")
EPI_HF_3 <- readRDS("data/Final_four_data/re_analysis/perm_test_1000/EPI_HF_3h.RDS")
EPI_IHD_3 <- readRDS("data/Final_four_data/re_analysis/perm_test_1000/EPI_IHD_3h.RDS")
EPI_CAD_3 <- readRDS("data/Final_four_data/re_analysis/perm_test_1000/EPI_CAD_3h.RDS")
# EPI_AF_3<- permTest(A= EPI_3_gr,
# B= AF_gr,
# ntimes=1000,
# randomize.function=randomizeRegions,
# evaluate.function = numOverlaps,
# genome="hg38",
# count.once= TRUE,
# universe=universe,
# #
# verbose = TRUE,
# BPPARAM = param)
# EPI_HF_3<- permTest(A= EPI_3_gr,
# B= HF_gr,
# ntimes=1000,
# randomize.function=randomizeRegions,
# evaluate.function = numOverlaps,
# genome="hg38",
# count.once= TRUE,
# universe=universe,
#
# verbose = TRUE,
# BPPARAM = param)
#
#
# EPI_IHD_3<- permTest(A= EPI_3_gr,
# B= IHD_gr,
# ntimes=1000,
# randomize.function=randomizeRegions,
# evaluate.function = numOverlaps,
# genome="hg38",
# count.once= TRUE,
# universe=universe,
#
# verbose = TRUE,
# BPPARAM = param)
#
# EPI_CAD_3<- permTest(A= EPI_3_gr,
# B= CAD_gr,
# ntimes=1000,
# randomize.function=randomizeRegions,
# evaluate.function = numOverlaps,
# genome="hg38",
# count.once= TRUE,
# universe=universe,
#
# verbose = TRUE,
# BPPARAM = param)
EPI_AF_3$numOverlaps
P-value: 0.000999000999000999
Z-score: 7.7892
Number of iterations: 1000
Alternative: greater
Evaluation of the original region set: 14
Evaluation function: numOverlaps
Randomization function: randomizeRegions
attr(,"class")
[1] "permTestResultsList"plot(EPI_AF_3)
| Version | Author | Date |
|---|---|---|
| 88047ef | reneeisnowhere | 2025-07-28 |
EPI_HF_3$numOverlaps
P-value: 0.155844155844156
Z-score: 1.4052
Number of iterations: 1000
Alternative: greater
Evaluation of the original region set: 3
Evaluation function: numOverlaps
Randomization function: randomizeRegions
attr(,"class")
[1] "permTestResultsList"plot(EPI_HF_3)
| Version | Author | Date |
|---|---|---|
| 88047ef | reneeisnowhere | 2025-07-28 |
EPI_IHD_3$numOverlaps
P-value: 0.775224775224775
Z-score: -0.4919
Number of iterations: 1000
Alternative: less
Evaluation of the original region set: 0
Evaluation function: numOverlaps
Randomization function: randomizeRegions
attr(,"class")
[1] "permTestResultsList"plot(EPI_IHD_3)
| Version | Author | Date |
|---|---|---|
| 88047ef | reneeisnowhere | 2025-07-28 |
EPI_CAD_3$numOverlaps
P-value: 0.000999000999000999
Z-score: 4.0831
Number of iterations: 1000
Alternative: greater
Evaluation of the original region set: 18
Evaluation function: numOverlaps
Randomization function: randomizeRegions
attr(,"class")
[1] "permTestResultsList"plot(EPI_CAD_3)
| Version | Author | Date |
|---|---|---|
| 88047ef | reneeisnowhere | 2025-07-28 |
# EPI_AF<- permTest(A= EPI_24_gr,
# B= AF_gr,
# ntimes=1000,
# randomize.function=randomizeRegions,
# evaluate.function = numOverlaps,
# genome="hg38",
# count.once= TRUE,
# universe=universe,
# verbose = TRUE,
# BPPARAM = param)
#
#
# EPI_HF<- permTest(A= EPI_24_gr,
# B= HF_gr,
# ntimes=1000,
# randomize.function=randomizeRegions,
# evaluate.function = numOverlaps,
# genome="hg38",
# count.once= TRUE,
# universe=universe,
# verbose = TRUE,
# BPPARAM = param)
#
#
# EPI_IHD<- permTest(A= EPI_24_gr,
# B= IHD_gr,
# ntimes=1000,
# randomize.function=randomizeRegions,
# evaluate.function = numOverlaps,
# genome="hg38",
# count.once= TRUE,
# universe=universe,
# verbose = TRUE,
# BPPARAM = param)
#
# EPI_CAD <- permTest(A= EPI_24_gr,
# B= CAD_gr,
# ntimes=1000,
# randomize.function=randomizeRegions,
# evaluate.function = numOverlaps,
# genome="hg38",
# count.once= TRUE,
# universe=universe,
# verbose = TRUE,
# BPPARAM = param)
EPI_AF$numOverlaps
P-value: 0.000999000999000999
Z-score: 10.6464
Number of iterations: 1000
Alternative: greater
Evaluation of the original region set: 39
Evaluation function: numOverlaps
Randomization function: randomizeRegions
attr(,"class")
[1] "permTestResultsList"plot(EPI_AF)
| Version | Author | Date |
|---|---|---|
| 88047ef | reneeisnowhere | 2025-07-28 |
EPI_HF$numOverlaps
P-value: 0.000999000999000999
Z-score: 4.3344
Number of iterations: 1000
Alternative: greater
Evaluation of the original region set: 15
Evaluation function: numOverlaps
Randomization function: randomizeRegions
attr(,"class")
[1] "permTestResultsList"plot(EPI_HF)
| Version | Author | Date |
|---|---|---|
| 88047ef | reneeisnowhere | 2025-07-28 |
EPI_IHD$numOverlaps
P-value: 0.0639360639360639
Z-score: 2.1658
Number of iterations: 1000
Alternative: greater
Evaluation of the original region set: 3
Evaluation function: numOverlaps
Randomization function: randomizeRegions
attr(,"class")
[1] "permTestResultsList"plot(EPI_IHD)
| Version | Author | Date |
|---|---|---|
| 88047ef | reneeisnowhere | 2025-07-28 |
EPI_CAD$numOverlaps
P-value: 0.000999000999000999
Z-score: 6.1142
Number of iterations: 1000
Alternative: greater
Evaluation of the original region set: 58
Evaluation function: numOverlaps
Randomization function: randomizeRegions
attr(,"class")
[1] "permTestResultsList"plot(EPI_CAD)
| Version | Author | Date |
|---|---|---|
| 88047ef | reneeisnowhere | 2025-07-28 |
# saveRDS(EPI_AF,"data/Final_four_data/re_analysis/perm_test_100/EPI_AF_24h.RDS")
# saveRDS(EPI_HF,"data/Final_four_data/re_analysis/perm_test_100/EPI_HF_24h.RDS")
# saveRDS(EPI_IHD,"data/Final_four_data/re_analysis/perm_test_100/EPI_IHD_24h.RDS")
# saveRDS(EPI_CAD,"data/Final_four_data/re_analysis/perm_test_100/EPI_CAD_24h.RDS")
#
# saveRDS(EPI_AF_3,"data/Final_four_data/re_analysis/perm_test_100/EPI_AF_3h.RDS")
# saveRDS(EPI_HF_3,"data/Final_four_data/re_analysis/perm_test_100/EPI_HF_3h.RDS")
# saveRDS(EPI_IHD_3,"data/Final_four_data/re_analysis/perm_test_100/EPI_IHD_3h.RDS")
# saveRDS(EPI_CAD_3,"data/Final_four_data/re_analysis/perm_test_100/EPI_CAD_3h.RDS")
# saveRDS(EPI_AF,"data/Final_four_data/re_analysis/perm_test_1000/EPI_AF_24h.RDS")
# saveRDS(EPI_HF,"data/Final_four_data/re_analysis/perm_test_1000/EPI_HF_24h.RDS")
# saveRDS(EPI_IHD,"data/Final_four_data/re_analysis/perm_test_1000/EPI_IHD_24h.RDS")
# saveRDS(EPI_CAD,"data/Final_four_data/re_analysis/perm_test_1000/EPI_CAD_24h.RDS")
#
# saveRDS(EPI_AF_3,"data/Final_four_data/re_analysis/perm_test_1000/EPI_AF_3h.RDS")
# saveRDS(EPI_HF_3,"data/Final_four_data/re_analysis/perm_test_1000/EPI_HF_3h.RDS")
# saveRDS(EPI_IHD_3,"data/Final_four_data/re_analysis/perm_test_1000/EPI_IHD_3h.RDS")
# saveRDS(EPI_CAD_3,"data/Final_four_data/re_analysis/perm_test_1000/EPI_CAD_3h.RDS")PT DNR 24hr check
DNR_24_gr <- DNR_24_sig %>% separate_wider_delim(., cols="genes",names = c("seqnames","start","end"), delim= ".") %>%
GRanges()
DNR_3_gr <- DNR_3_sig %>% separate_wider_delim(., cols="genes",names = c("seqnames","start","end"), delim= ".") %>%
GRanges()
DNR_AF <- readRDS("data/Final_four_data/re_analysis/perm_test_1000/DNR_AF_24h.RDS")
DNR_HF <- readRDS("data/Final_four_data/re_analysis/perm_test_1000/DNR_HF_24h.RDS")
DNR_IHD <- readRDS("data/Final_four_data/re_analysis/perm_test_1000/DNR_IHD_24h.RDS")
DNR_CAD <- readRDS("data/Final_four_data/re_analysis/perm_test_1000/DNR_CAD_24h.RDS")
DNR_AF_3 <- readRDS("data/Final_four_data/re_analysis/perm_test_1000/DNR_AF_3h.RDS")
DNR_HF_3 <- readRDS("data/Final_four_data/re_analysis/perm_test_1000/DNR_HF_3h.RDS")
DNR_IHD_3 <- readRDS("data/Final_four_data/re_analysis/perm_test_1000/DNR_IHD_3h.RDS")
DNR_CAD_3 <- readRDS("data/Final_four_data/re_analysis/perm_test_1000/DNR_CAD_3h.RDS")
# DNR_AF_3<- permTest(A= DNR_3_gr,
# B= AF_gr,
# ntimes=1000,
# randomize.function=randomizeRegions,
# evaluate.function = numOverlaps,
# genome="hg38",
# count.once= TRUE,
# universe=universe,
# #
# verbose = TRUE,
# BPPARAM = param)
# DNR_HF_3<- permTest(A= DNR_3_gr,
# B= HF_gr,
# ntimes=1000,
# randomize.function=randomizeRegions,
# evaluate.function = numOverlaps,
# genome="hg38",
# count.once= TRUE,
# universe=universe,
#
# verbose = TRUE,
# BPPARAM = param)
#
#
# DNR_IHD_3<- permTest(A= DNR_3_gr,
# B= IHD_gr,
# ntimes=1000,
# randomize.function=randomizeRegions,
# evaluate.function = numOverlaps,
# genome="hg38",
# count.once= TRUE,
# universe=universe,
#
# verbose = TRUE,
# BPPARAM = param)
#
# DNR_CAD_3<- permTest(A= DNR_3_gr,
# B= CAD_gr,
# ntimes=1000,
# randomize.function=randomizeRegions,
# evaluate.function = numOverlaps,
# genome="hg38",
# count.once= TRUE,
# universe=universe,
#
# verbose = TRUE,
# BPPARAM = param)
DNR_AF_3$numOverlaps
P-value: 0.000999000999000999
Z-score: 9.0434
Number of iterations: 1000
Alternative: greater
Evaluation of the original region set: 21
Evaluation function: numOverlaps
Randomization function: randomizeRegions
attr(,"class")
[1] "permTestResultsList"plot(DNR_AF_3)
| Version | Author | Date |
|---|---|---|
| 88047ef | reneeisnowhere | 2025-07-28 |
DNR_HF_3$numOverlaps
P-value: 0.035964035964036
Z-score: 2.4989
Number of iterations: 1000
Alternative: greater
Evaluation of the original region set: 6
Evaluation function: numOverlaps
Randomization function: randomizeRegions
attr(,"class")
[1] "permTestResultsList"plot(DNR_HF_3)
| Version | Author | Date |
|---|---|---|
| 88047ef | reneeisnowhere | 2025-07-28 |
DNR_IHD_3$numOverlaps
P-value: 0.0749250749250749
Z-score: 2.36
Number of iterations: 1000
Alternative: greater
Evaluation of the original region set: 2
Evaluation function: numOverlaps
Randomization function: randomizeRegions
attr(,"class")
[1] "permTestResultsList"plot(DNR_IHD_3)
| Version | Author | Date |
|---|---|---|
| 88047ef | reneeisnowhere | 2025-07-28 |
DNR_CAD_3$numOverlaps
P-value: 0.000999000999000999
Z-score: 4.6739
Number of iterations: 1000
Alternative: greater
Evaluation of the original region set: 28
Evaluation function: numOverlaps
Randomization function: randomizeRegions
attr(,"class")
[1] "permTestResultsList"plot(DNR_CAD_3)
| Version | Author | Date |
|---|---|---|
| 88047ef | reneeisnowhere | 2025-07-28 |
# DNR_AF<- permTest(A= DNR_24_gr,
# B= AF_gr,
# ntimes=1000,
# randomize.function=randomizeRegions,
# evaluate.function = numOverlaps,
# genome="hg38",
# count.once= TRUE,
# universe=universe,
# verbose = TRUE,
# BPPARAM = param)
#
#
# DNR_HF<- permTest(A= DNR_24_gr,
# B= HF_gr,
# ntimes=1000,
# randomize.function=randomizeRegions,
# evaluate.function = numOverlaps,
# genome="hg38",
# count.once= TRUE,
# universe=universe,
# verbose = TRUE,
# BPPARAM = param)
#
#
# DNR_IHD<- permTest(A= DNR_24_gr,
# B= IHD_gr,
# ntimes=1000,
# randomize.function=randomizeRegions,
# evaluate.function = numOverlaps,
# genome="hg38",
# count.once= TRUE,
# universe=universe,
# verbose = TRUE,
# BPPARAM = param)
#
# DNR_CAD <- permTest(A= DNR_24_gr,
# B= CAD_gr,
# ntimes=1000,
# randomize.function=randomizeRegions,
# evaluate.function = numOverlaps,
# genome="hg38",
# count.once= TRUE,
# universe=universe,
# verbose = TRUE,
# BPPARAM = param)
#
DNR_AF$numOverlaps
P-value: 0.000999000999000999
Z-score: 9.4608
Number of iterations: 1000
Alternative: greater
Evaluation of the original region set: 41
Evaluation function: numOverlaps
Randomization function: randomizeRegions
attr(,"class")
[1] "permTestResultsList"plot(DNR_AF)
| Version | Author | Date |
|---|---|---|
| 88047ef | reneeisnowhere | 2025-07-28 |
DNR_HF$numOverlaps
P-value: 0.000999000999000999
Z-score: 5.3281
Number of iterations: 1000
Alternative: greater
Evaluation of the original region set: 19
Evaluation function: numOverlaps
Randomization function: randomizeRegions
attr(,"class")
[1] "permTestResultsList"plot(DNR_HF)
| Version | Author | Date |
|---|---|---|
| 88047ef | reneeisnowhere | 2025-07-28 |
DNR_IHD$numOverlaps
P-value: 0.108891108891109
Z-score: 1.6634
Number of iterations: 1000
Alternative: greater
Evaluation of the original region set: 3
Evaluation function: numOverlaps
Randomization function: randomizeRegions
attr(,"class")
[1] "permTestResultsList"plot(DNR_IHD)
| Version | Author | Date |
|---|---|---|
| 88047ef | reneeisnowhere | 2025-07-28 |
DNR_CAD$numOverlaps
P-value: 0.000999000999000999
Z-score: 6.3211
Number of iterations: 1000
Alternative: greater
Evaluation of the original region set: 68
Evaluation function: numOverlaps
Randomization function: randomizeRegions
attr(,"class")
[1] "permTestResultsList"plot(DNR_CAD)
| Version | Author | Date |
|---|---|---|
| 88047ef | reneeisnowhere | 2025-07-28 |
# saveRDS(DNR_AF,"data/Final_four_data/re_analysis/perm_test_100/DNR_AF_24h.RDS")
# saveRDS(DNR_HF,"data/Final_four_data/re_analysis/perm_test_100/DNR_HF_24h.RDS")
# saveRDS(DNR_IHD,"data/Final_four_data/re_analysis/perm_test_100/DNR_IHD_24h.RDS")
# saveRDS(DNR_CAD,"data/Final_four_data/re_analysis/perm_test_100/DNR_CAD_24h.RDS")
#
# saveRDS(DNR_AF_3,"data/Final_four_data/re_analysis/perm_test_100/DNR_AF_3h.RDS")
# saveRDS(DNR_HF_3,"data/Final_four_data/re_analysis/perm_test_100/DNR_HF_3h.RDS")
# saveRDS(DNR_IHD_3,"data/Final_four_data/re_analysis/perm_test_100/DNR_IHD_3h.RDS")
# saveRDS(DNR_CAD_3,"data/Final_four_data/re_analysis/perm_test_100/DNR_CAD_3h.RDS")
# saveRDS(DNR_AF,"data/Final_four_data/re_analysis/perm_test_1000/DNR_AF_24h.RDS")
# saveRDS(DNR_HF,"data/Final_four_data/re_analysis/perm_test_1000/DNR_HF_24h.RDS")
# saveRDS(DNR_IHD,"data/Final_four_data/re_analysis/perm_test_1000/DNR_IHD_24h.RDS")
# saveRDS(DNR_CAD,"data/Final_four_data/re_analysis/perm_test_1000/DNR_CAD_24h.RDS")
#
# saveRDS(DNR_AF_3,"data/Final_four_data/re_analysis/perm_test_1000/DNR_AF_3h.RDS")
# saveRDS(DNR_HF_3,"data/Final_four_data/re_analysis/perm_test_1000/DNR_HF_3h.RDS")
# saveRDS(DNR_IHD_3,"data/Final_four_data/re_analysis/perm_test_1000/DNR_IHD_3h.RDS")
# saveRDS(DNR_CAD_3,"data/Final_four_data/re_analysis/perm_test_1000/DNR_CAD_3h.RDS")PT MTX 24hr check
MTX_24_gr <- MTX_24_sig %>% separate_wider_delim(., cols="genes",names = c("seqnames","start","end"), delim= ".") %>%
GRanges()
MTX_3_gr <- MTX_3_sig %>% separate_wider_delim(., cols="genes",names = c("seqnames","start","end"), delim= ".") %>%
GRanges()
MTX_AF <- readRDS("data/Final_four_data/re_analysis/perm_test_1000/MTX_AF_24h.RDS")
MTX_HF <- readRDS("data/Final_four_data/re_analysis/perm_test_1000/MTX_HF_24h.RDS")
MTX_IHD <- readRDS("data/Final_four_data/re_analysis/perm_test_1000/MTX_IHD_24h.RDS")
MTX_CAD <- readRDS("data/Final_four_data/re_analysis/perm_test_1000/MTX_CAD_24h.RDS")
MTX_AF_3 <- readRDS("data/Final_four_data/re_analysis/perm_test_1000/MTX_AF_3h.RDS")
MTX_HF_3 <- readRDS("data/Final_four_data/re_analysis/perm_test_1000/MTX_HF_3h.RDS")
MTX_IHD_3 <- readRDS("data/Final_four_data/re_analysis/perm_test_1000/MTX_IHD_3h.RDS")
MTX_CAD_3 <- readRDS("data/Final_four_data/re_analysis/perm_test_1000/MTX_CAD_3h.RDS")
# MTX_AF_3<- permTest(A= MTX_3_gr,
# B= AF_gr,
# ntimes=1000,
# randomize.function=randomizeRegions,
# evaluate.function = numOverlaps,
# genome="hg38",
# count.once= TRUE,
# universe=universe,
# #
# verbose = TRUE,
# BPPARAM = param)
# MTX_HF_3<- permTest(A= MTX_3_gr,
# B= HF_gr,
# ntimes=1000,
# randomize.function=randomizeRegions,
# evaluate.function = numOverlaps,
# genome="hg38",
# count.once= TRUE,
# universe=universe,
#
# verbose = TRUE,
# BPPARAM = param)
#
#
# MTX_IHD_3<- permTest(A= MTX_3_gr,
# B= IHD_gr,
# ntimes=1000,
# randomize.function=randomizeRegions,
# evaluate.function = numOverlaps,
# genome="hg38",
# count.once= TRUE,
# universe=universe,
#
# verbose = TRUE,
# BPPARAM = param)
#
# MTX_CAD_3<- permTest(A= MTX_3_gr,
# B= CAD_gr,
# ntimes=1000,
# randomize.function=randomizeRegions,
# evaluate.function = numOverlaps,
# genome="hg38",
# count.once= TRUE,
# universe=universe,
#
# verbose = TRUE,
# BPPARAM = param)
MTX_AF_3$numOverlaps
P-value: 0.877122877122877
Z-score: -0.3634
Number of iterations: 1000
Alternative: less
Evaluation of the original region set: 0
Evaluation function: numOverlaps
Randomization function: randomizeRegions
attr(,"class")
[1] "permTestResultsList"plot(MTX_AF_3)
| Version | Author | Date |
|---|---|---|
| 88047ef | reneeisnowhere | 2025-07-28 |
MTX_HF_3$numOverlaps
P-value: 0.0749250749250749
Z-score: 3.3231
Number of iterations: 1000
Alternative: greater
Evaluation of the original region set: 1
Evaluation function: numOverlaps
Randomization function: randomizeRegions
attr(,"class")
[1] "permTestResultsList"plot(MTX_HF_3)
| Version | Author | Date |
|---|---|---|
| 88047ef | reneeisnowhere | 2025-07-28 |
MTX_IHD_3$numOverlaps
P-value: 0.983016983016983
Z-score: -0.1314
Number of iterations: 1000
Alternative: less
Evaluation of the original region set: 0
Evaluation function: numOverlaps
Randomization function: randomizeRegions
attr(,"class")
[1] "permTestResultsList"plot(MTX_IHD_3)
| Version | Author | Date |
|---|---|---|
| 88047ef | reneeisnowhere | 2025-07-28 |
MTX_CAD_3$numOverlaps
P-value: 0.704295704295704
Z-score: -0.5928
Number of iterations: 1000
Alternative: less
Evaluation of the original region set: 0
Evaluation function: numOverlaps
Randomization function: randomizeRegions
attr(,"class")
[1] "permTestResultsList"plot(MTX_CAD_3)
| Version | Author | Date |
|---|---|---|
| 88047ef | reneeisnowhere | 2025-07-28 |
# MTX_AF<- permTest(A= MTX_24_gr,
# B= AF_gr,
# ntimes=1000,
# randomize.function=randomizeRegions,
# evaluate.function = numOverlaps,
# genome="hg38",
# count.once= TRUE,
# universe=universe,
# verbose = TRUE,
# BPPARAM = param)
#
#
# MTX_HF<- permTest(A= MTX_24_gr,
# B= HF_gr,
# ntimes=1000,
# randomize.function=randomizeRegions,
# evaluate.function = numOverlaps,
# genome="hg38",
# count.once= TRUE,
# universe=universe,
# verbose = TRUE,
# BPPARAM = param)
#
#
# MTX_IHD<- permTest(A= MTX_24_gr,
# B= IHD_gr,
# ntimes=1000,
# randomize.function=randomizeRegions,
# evaluate.function = numOverlaps,
# genome="hg38",
# count.once= TRUE,
# universe=universe,
# verbose = TRUE,
# BPPARAM = param)
#
# MTX_CAD <- permTest(A= MTX_24_gr,
# B= CAD_gr,
# ntimes=1000,
# randomize.function=randomizeRegions,
# evaluate.function = numOverlaps,
# genome="hg38",
# count.once= TRUE,
# universe=universe,
# verbose = TRUE,
# BPPARAM = param)
MTX_AF$numOverlaps
P-value: 0.000999000999000999
Z-score: 8.8214
Number of iterations: 1000
Alternative: greater
Evaluation of the original region set: 20
Evaluation function: numOverlaps
Randomization function: randomizeRegions
attr(,"class")
[1] "permTestResultsList"plot(MTX_AF)
| Version | Author | Date |
|---|---|---|
| 88047ef | reneeisnowhere | 2025-07-28 |
MTX_HF$numOverlaps
P-value: 0.000999000999000999
Z-score: 5.7248
Number of iterations: 1000
Alternative: greater
Evaluation of the original region set: 10
Evaluation function: numOverlaps
Randomization function: randomizeRegions
attr(,"class")
[1] "permTestResultsList"plot(MTX_HF)
| Version | Author | Date |
|---|---|---|
| 88047ef | reneeisnowhere | 2025-07-28 |
MTX_IHD$numOverlaps
P-value: 0.0549450549450549
Z-score: 2.6148
Number of iterations: 1000
Alternative: greater
Evaluation of the original region set: 2
Evaluation function: numOverlaps
Randomization function: randomizeRegions
attr(,"class")
[1] "permTestResultsList"plot(MTX_IHD)
| Version | Author | Date |
|---|---|---|
| 88047ef | reneeisnowhere | 2025-07-28 |
MTX_CAD$numOverlaps
P-value: 0.000999000999000999
Z-score: 5.5747
Number of iterations: 1000
Alternative: greater
Evaluation of the original region set: 28
Evaluation function: numOverlaps
Randomization function: randomizeRegions
attr(,"class")
[1] "permTestResultsList"plot(MTX_CAD)
| Version | Author | Date |
|---|---|---|
| 88047ef | reneeisnowhere | 2025-07-28 |
# saveRDS(MTX_AF,"data/Final_four_data/re_analysis/perm_test_100/MTX_AF_24h.RDS")
# saveRDS(MTX_HF,"data/Final_four_data/re_analysis/perm_test_100/MTX_HF_24h.RDS")
# saveRDS(MTX_IHD,"data/Final_four_data/re_analysis/perm_test_100/MTX_IHD_24h.RDS")
# saveRDS(MTX_CAD,"data/Final_four_data/re_analysis/perm_test_100/MTX_CAD_24h.RDS")
#
# saveRDS(MTX_AF_3,"data/Final_four_data/re_analysis/perm_test_100/MTX_AF_3h.RDS")
# saveRDS(MTX_HF_3,"data/Final_four_data/re_analysis/perm_test_100/MTX_HF_3h.RDS")
# saveRDS(MTX_IHD_3,"data/Final_four_data/re_analysis/perm_test_100/MTX_IHD_3h.RDS")
# saveRDS(MTX_CAD_3,"data/Final_four_data/re_analysis/perm_test_100/MTX_CAD_3h.RDS")
# saveRDS(MTX_AF,"data/Final_four_data/re_analysis/perm_test_1000/MTX_AF_24h.RDS")
# saveRDS(MTX_HF,"data/Final_four_data/re_analysis/perm_test_1000/MTX_HF_24h.RDS")
# saveRDS(MTX_IHD,"data/Final_four_data/re_analysis/perm_test_1000/MTX_IHD_24h.RDS")
# saveRDS(MTX_CAD,"data/Final_four_data/re_analysis/perm_test_1000/MTX_CAD_24h.RDS")
#
# saveRDS(MTX_AF_3,"data/Final_four_data/re_analysis/perm_test_1000/MTX_AF_3h.RDS")
# saveRDS(MTX_HF_3,"data/Final_four_data/re_analysis/perm_test_1000/MTX_HF_3h.RDS")
# saveRDS(MTX_IHD_3,"data/Final_four_data/re_analysis/perm_test_1000/MTX_IHD_3h.RDS")
# saveRDS(MTX_CAD_3,"data/Final_four_data/re_analysis/perm_test_1000/MTX_CAD_3h.RDS")Creating heatmaps
all_results_LFC <- all_results %>% dplyr::select(source,genes,logFC) %>%
pivot_wider(id_cols=genes, values_from = logFC, names_from = source) %>%
dplyr::rename("Peakid"=genes)
ATAC_all_adj.pvals <- readRDS("data/Final_four_data/re_analysis/ATAC_all_adj_pvals.RDS")
AF_heatmap_df <- SNP_overlaps %>%
as.data.frame() %>%
dplyr::filter(gwas=="AF") %>%
group_by(Peakid) %>%
summarise(SNPS=paste(unique(SNPS),collapse = ";"))
HF_heatmap_df <- SNP_overlaps %>%
as.data.frame() %>%
dplyr::filter(gwas=="HF") %>%
group_by(Peakid) %>%
summarise(SNPS=paste(unique(SNPS),collapse = ";"))AF_mat <- AF_heatmap_df %>%
left_join(., all_results_LFC, by=c("Peakid"="Peakid")) %>%
tidyr::unite(., name,Peakid,SNPS) %>%
column_to_rownames("name") %>%
as.matrix()
AF_sig_mat <- AF_heatmap_df %>%
left_join(., ATAC_all_adj.pvals, by=c("Peakid"="genes")) %>%
tidyr::unite(., name,Peakid,SNPS) %>%
column_to_rownames("name") %>%
as.matrix()
simply_AF_lfc <- ComplexHeatmap::Heatmap(AF_mat,
show_row_names = TRUE,
row_names_max_width=
ComplexHeatmap::max_text_width(rownames(AF_mat), gp=gpar(fontsize=14)),
heatmap_legend_param = list(direction = "horizontal"),
column_title = "AF_SNPS",
show_column_names = TRUE,
cluster_rows = FALSE,
cluster_columns = FALSE,
cell_fun = function(j, i, x, y, width, height, fill) {
if (!is.na(AF_sig_mat[i, j]) && AF_sig_mat[i, j] <0.05) {
grid.text("*", x, y, gp = gpar(fontsize = 20)) # Add star if significant
} })
ComplexHeatmap::draw(simply_AF_lfc,
merge_legend = TRUE,
heatmap_legend_side = "left",
annotation_legend_side = "left")
HF_mat <- HF_heatmap_df %>%
left_join(., all_results_LFC, by=c("Peakid"="Peakid")) %>%
tidyr::unite(., name,Peakid,SNPS) %>%
column_to_rownames("name") %>%
as.matrix()
HF_sig_mat <- HF_heatmap_df %>%
left_join(., ATAC_all_adj.pvals, by=c("Peakid"="genes")) %>%
tidyr::unite(., name,Peakid,SNPS) %>%
column_to_rownames("name") %>%
as.matrix()
simply_HF_lfc <- ComplexHeatmap::Heatmap(HF_mat,
show_row_names = TRUE,
row_names_max_width=
ComplexHeatmap::max_text_width(rownames(HF_mat), gp=gpar(fontsize=14)),
heatmap_legend_param = list(direction = "horizontal"),
column_title = "HF_SNPS",
show_column_names = TRUE,
cluster_rows = FALSE,
cluster_columns = FALSE,
cell_fun = function(j, i, x, y, width, height, fill) {
if (!is.na(HF_sig_mat[i, j]) && HF_sig_mat[i, j] <0.05) {
grid.text("*", x, y, gp = gpar(fontsize = 20)) # Add star if significant
} })
ComplexHeatmap::draw(simply_HF_lfc,
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] regioneR_1.38.0
[2] liftOver_1.30.0
[3] Homo.sapiens_1.3.1
[4] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
[5] GO.db_3.20.0
[6] OrganismDbi_1.48.0
[7] gwascat_2.38.0
[8] vargen_0.2.3
[9] devtools_2.4.5
[10] usethis_3.1.0
[11] readxl_1.4.5
[12] smplot2_0.2.5
[13] cowplot_1.1.3
[14] ComplexHeatmap_2.22.0
[15] ggrepel_0.9.6
[16] plyranges_1.26.0
[17] ggsignif_0.6.4
[18] genomation_1.38.0
[19] edgeR_4.4.2
[20] limma_3.62.2
[21] ggpubr_0.6.1
[22] BiocParallel_1.40.2
[23] ggVennDiagram_1.5.4
[24] scales_1.4.0
[25] VennDiagram_1.7.3
[26] futile.logger_1.4.3
[27] gridExtra_2.3
[28] ggfortify_0.4.18
[29] rtracklayer_1.66.0
[30] org.Hs.eg.db_3.20.0
[31] TxDb.Hsapiens.UCSC.hg38.knownGene_3.20.0
[32] GenomicFeatures_1.58.0
[33] AnnotationDbi_1.68.0
[34] Biobase_2.66.0
[35] GenomicRanges_1.58.0
[36] GenomeInfoDb_1.42.3
[37] IRanges_2.40.1
[38] S4Vectors_0.44.0
[39] BiocGenerics_0.52.0
[40] RColorBrewer_1.1-3
[41] broom_1.0.8
[42] kableExtra_1.4.0
[43] lubridate_1.9.4
[44] forcats_1.0.0
[45] stringr_1.5.1
[46] dplyr_1.1.4
[47] purrr_1.0.4
[48] readr_2.1.5
[49] tidyr_1.3.1
[50] tibble_3.3.0
[51] ggplot2_3.5.2
[52] tidyverse_2.0.0
[53] workflowr_1.7.1
loaded via a namespace (and not attached):
[1] fs_1.6.6 matrixStats_1.5.0
[3] bitops_1.0-9 httr_1.4.7
[5] doParallel_1.0.17 profvis_0.4.0
[7] tools_4.4.2 backports_1.5.0
[9] utf8_1.2.6 R6_2.6.1
[11] GetoptLong_1.0.5 urlchecker_1.0.1
[13] withr_3.0.2 prettyunits_1.2.0
[15] cli_3.6.5 textshaping_1.0.1
[17] formatR_1.14 Cairo_1.6-2
[19] labeling_0.4.3 sass_0.4.10
[21] Rsamtools_2.22.0 systemfonts_1.2.3
[23] txdbmaker_1.2.1 foreign_0.8-90
[25] svglite_2.2.1 dichromat_2.0-0.1
[27] sessioninfo_1.2.3 plotrix_3.8-4
[29] BSgenome_1.74.0 pwr_1.3-0
[31] rstudioapi_0.17.1 impute_1.80.0
[33] RSQLite_2.4.1 generics_0.1.4
[35] shape_1.4.6.1 BiocIO_1.16.0
[37] car_3.1-3 Matrix_1.7-3
[39] abind_1.4-8 lifecycle_1.0.4
[41] whisker_0.4.1 yaml_2.3.10
[43] carData_3.0-5 SummarizedExperiment_1.36.0
[45] SparseArray_1.6.2 BiocFileCache_2.14.0
[47] blob_1.2.4 promises_1.3.3
[49] crayon_1.5.3 miniUI_0.1.2
[51] lattice_0.22-7 KEGGREST_1.46.0
[53] magick_2.8.7 pillar_1.11.0
[55] knitr_1.50 rjson_0.2.23
[57] codetools_0.2-20 glue_1.8.0
[59] getPass_0.2-4 data.table_1.17.6
[61] remotes_2.5.0 vctrs_0.6.5
[63] png_0.1-8 cellranger_1.1.0
[65] gtable_0.3.6 cachem_1.1.0
[67] xfun_0.52 S4Arrays_1.6.0
[69] mime_0.13 survival_3.8-3
[71] iterators_1.0.14 statmod_1.5.0
[73] ellipsis_0.3.2 bit64_4.6.0-1
[75] progress_1.2.3 filelock_1.0.3
[77] rprojroot_2.0.4 bslib_0.9.0
[79] KernSmooth_2.23-26 rpart_4.1.24
[81] colorspace_2.1-1 DBI_1.2.3
[83] Hmisc_5.2-3 seqPattern_1.38.0
[85] nnet_7.3-20 tidyselect_1.2.1
[87] processx_3.8.6 bit_4.6.0
[89] compiler_4.4.2 curl_6.4.0
[91] git2r_0.36.2 graph_1.84.1
[93] httr2_1.1.2 htmlTable_2.4.3
[95] xml2_1.3.8 DelayedArray_0.32.0
[97] checkmate_2.3.2 RBGL_1.82.0
[99] callr_3.7.6 rappdirs_0.3.3
[101] digest_0.6.37 rmarkdown_2.29
[103] XVector_0.46.0 htmltools_0.5.8.1
[105] pkgconfig_2.0.3 base64enc_0.1-3
[107] MatrixGenerics_1.18.1 dbplyr_2.5.0
[109] fastmap_1.2.0 rlang_1.1.6
[111] GlobalOptions_0.1.2 htmlwidgets_1.6.4
[113] UCSC.utils_1.2.0 shiny_1.11.1
[115] farver_2.1.2 jquerylib_0.1.4
[117] zoo_1.8-14 jsonlite_2.0.0
[119] VariantAnnotation_1.52.0 RCurl_1.98-1.17
[121] magrittr_2.0.3 Formula_1.2-5
[123] GenomeInfoDbData_1.2.13 patchwork_1.3.1
[125] Rcpp_1.1.0 stringi_1.8.7
[127] zlibbioc_1.52.0 plyr_1.8.9
[129] pkgbuild_1.4.8 parallel_4.4.2
[131] snpStats_1.56.0 Biostrings_2.74.1
[133] splines_4.4.2 hms_1.1.3
[135] circlize_0.4.16 locfit_1.5-9.12
[137] ps_1.9.1 biomaRt_2.62.1
[139] reshape2_1.4.4 pkgload_1.4.0
[141] futile.options_1.0.1 XML_3.99-0.18
[143] evaluate_1.0.4 BiocManager_1.30.26
[145] lambda.r_1.2.4 tzdb_0.5.0
[147] foreach_1.5.2 httpuv_1.6.16
[149] clue_0.3-66 gridBase_0.4-7
[151] xtable_1.8-4 restfulr_0.0.16
[153] rstatix_0.7.2 later_1.4.2
[155] viridisLite_0.4.2 memoise_2.0.1
[157] GenomicAlignments_1.42.0 cluster_2.1.8.1
[159] timechange_0.3.0