Figure 6
Renee Matthews
2025-02-25
Last updated: 2025-05-01
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Knit directory: ATAC_learning/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| html | 99ea869 | E. Renee Matthews | 2025-02-26 | Build site. |
| Rmd | 3af930f | E. Renee Matthews | 2025-02-26 | wflow_publish("analysis/Figure_6.Rmd") |
Figure 6:
knitr::include_graphics("assets/Figure\ 6.png", error=FALSE)
knitr::include_graphics("docs/assets/Figure\ 6.png",error = FALSE)
library(tidyverse)
library(kableExtra)
library(broom)
library(RColorBrewer)
library("TxDb.Hsapiens.UCSC.hg38.knownGene")
library("org.Hs.eg.db")
library(rtracklayer)
library(ggfortify)
library(readr)
library(BiocGenerics)
library(gridExtra)
library(VennDiagram)
library(scales)
library(ggVennDiagram)
library(BiocParallel)
library(ggpubr)
library(edgeR)
library(genomation)
library(ggsignif)
library(plyranges)
library(ggrepel)
library(ComplexHeatmap)
library(cowplot)
library(smplot2)
library(readxl)
library(devtools)
library(vargen)Collapsed_new_peaks <- read_delim("data/Final_four_data/collapsed_new_peaks.txt", delim = "\t", col_names = TRUE)
Collapsed_new_peaks_gr <- Collapsed_new_peaks %>% dplyr::select(chr:Peakid) %>% dplyr::filter(chr!="chrY") %>%
GRanges() %>%
keepStandardChromosomes(pruning.mode = "coarse")
RNA_median_3_lfc <- readRDS("data/other_papers/RNA_median_3_lfc.RDS")
RNA_median_24_lfc <- readRDS("data/other_papers/RNA_median_24_lfc.RDS")
RNA_LFC <- RNA_median_3_lfc %>%
left_join(RNA_median_24_lfc, by = c("ENTREZID"="ENTREZID","SYMBOL"="SYMBOL"))
ATAC_24_lfc <- read_csv("data/Final_four_data/median_24_lfc.csv")
ATAC_3_lfc <- read_csv("data/Final_four_data/median_3_lfc.csv")
ATAC_LFC <- Collapsed_new_peaks %>%
dplyr::select(Peakid) %>%
left_join(.,(ATAC_3_lfc %>% dplyr::select(peak, med_3h_lfc)), by=c("Peakid"="peak")) %>%
left_join(.,(ATAC_24_lfc %>% dplyr::select(peak, med_24h_lfc)), by=c("Peakid"="peak"))
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) %>%
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) %>%
summarize(Chr=unique(Chr),
Start=unique(Start),
End=unique(End),
SYMBOL=paste(unique(SYMBOL),collapse=";"),
Gene=paste(Gene,collapse=";"),
SOURCE=paste(SOURCE,collapse=";")
) %>%
GRanges() %>% keepStandardChromosomes(pruning.mode = "coarse") %>% as.data.frame
schneider_gr <-Schneider_all_SNPS_df%>%
dplyr::select(seqnames,start,end,RSID:SOURCE) %>%
distinct() %>%
GRanges()
schneider_10k_gr <- Schneider_all_SNPS_df%>%
dplyr::select(seqnames,start,end,RSID:SOURCE) %>%
mutate(start=(start-5000),end=(end+4999), width=10000) %>%
distinct() %>%
GRanges()
schneider_20k_gr <- Schneider_all_SNPS_df%>%
dplyr::select(seqnames,start,end,RSID:SOURCE) %>%
mutate(start=(start-10000),end=(end+9999), width=20000) %>%
distinct() %>%
GRanges()
schneider_50k_gr <- Schneider_all_SNPS_df%>%
dplyr::select(seqnames,start,end,RSID:SOURCE) %>%
mutate(start=(start-20000),end=(end+24999), width=50000) %>%
distinct() %>%
GRanges()
SNP_peak_check <- join_overlap_intersect(Collapsed_new_peaks_gr,schneider_gr) %>%
as.data.frame()
SNP_peak_check_10k <- join_overlap_intersect(Collapsed_new_peaks_gr,schneider_10k_gr) %>%
as.data.frame()
SNP_peak_check_20k <- join_overlap_intersect(Collapsed_new_peaks_gr,schneider_20k_gr) %>%
as.data.frame()
SNP_peak_check_50k <- join_overlap_intersect(Collapsed_new_peaks_gr,schneider_50k_gr) %>%
as.data.frame()
point_only <- SNP_peak_check
SNP_10k_only <- SNP_peak_check_10k
SNP_20k_only <- SNP_peak_check_20k
SNP_50k_only <- SNP_peak_check_50k
ATAC_peaks_gr <- Collapsed_new_peaks %>% GRanges()
Peaks_cutoff <- read_delim("data/Final_four_data/LCPM_matrix_ff.txt",delim = "/") %>%
dplyr::select(Peakid)
schneider_short_list <- point_only %>% as.data.frame %>% dplyr::filter(Peakid %in%Peaks_cutoff$Peakid)
schneider_10k_list <- SNP_10k_only %>% distinct(RSID,Peakid)%>% dplyr::filter(Peakid %in%Peaks_cutoff$Peakid)
schneider_20k_list <- SNP_20k_only %>% distinct(RSID,Peakid)%>% dplyr::filter(Peakid %in%Peaks_cutoff$Peakid)
schneider_50k_list <- SNP_50k_only %>% distinct(RSID,Peakid)%>% dplyr::filter(Peakid %in%Peaks_cutoff$Peakid)
Nine_te_df <- readRDS("data/Final_four_data/Nine_group_TE_df.RDS")
###needed to change TE status to at least 1 bp overlap
match <- Nine_te_df %>%
mutate(TEstatus=if_else(!is.na(per_ol),"TE_peak","not_TE_peak")) %>%
distinct(Peakid,TEstatus,mrc,.keep_all = TRUE) Figure 6A:
hm_lfc_df <- SNP_peak_check_20k %>%
left_join(., Nine_te_df, by = c("Peakid"="Peakid") ) %>%
dplyr::filter(mrc != "NR") %>%
dplyr::filter(mrc !="not_mrc") %>%
mutate(dist_to_SNP=case_when(
Peakid %in% schneider_short_list$Peakid &RSID %in% schneider_short_list$RSID~ 0,
Peakid %in% schneider_10k_list$Peakid &RSID %in% schneider_10k_list$RSID~ 10,
Peakid %in% schneider_20k_list$Peakid &RSID %in% schneider_20k_list$RSID~ 20,
Peakid %in% schneider_50k_list$Peakid &RSID %in% schneider_50k_list$RSID ~ 50)) %>%
left_join(ATAC_LFC) %>%
tidyr::unite(name,Peakid,RSID,SYMBOL, sep = "_", remove = FALSE) %>%
group_by(Peakid, RSID) %>%
summarize(name=unique(name),
med_3h_lfc=unique(med_3h_lfc),
med_24h_lfc=unique(med_24h_lfc),
repClass=paste(unique(repClass),collapse=":"),
TEstatus=paste(unique(TEstatus),collapse=";"),
SYMBOL=paste(unique(SYMBOL),collapse=";"),
mrc=unique(mrc),
dist_to_SNP=min(dist_to_SNP)) %>%
arrange(., Peakid)
hm_lfc_mat <-hm_lfc_df %>%
dplyr::select(Peakid,RSID, med_3h_lfc, med_24h_lfc) %>%
tidyr::unite(name, Peakid, RSID, sep = "_") %>%
column_to_rownames("name") %>%
as.matrix()
hm_name_mat <-hm_lfc_df %>%
dplyr::select (Peakid, RSID, TEstatus, mrc,dist_to_SNP) %>%
tidyr::unite(name, Peakid, RSID, sep = "_")
col_fun <- circlize::colorRamp2(c(-4, 0, 4), c("blue", "white", "red"))
row_anno_lfc <- ComplexHeatmap::rowAnnotation(
TE_status=hm_name_mat$TEstatus,
reheat_status=hm_name_mat$reheat,
MRC=hm_name_mat$mrc,
direct_overlap=hm_name_mat$dist_to_SNP,
col= list(
TE_status=c("TE_peak"="goldenrod",
"not_TE_peak"="lightblue"),
MRC = c("EAR_open" = "#F8766D",
"EAR_close" = "#f6483c",
"ESR_open" = "#7CAE00",
"ESR_close" = "#587b00",
"ESR_opcl"="grey40",
"ESR_clop"="tan",
"LR_open" = "#00BFC4",
"LR_close" = "#008d91",
"NR" = "#C77CFF",
"not_mrc"="black"),
reheat_status=c("reheat_gene"="green",
"not_reheat_gene"="orange"),
direct_overlap=c("0"="red",
"10"="pink",
"20"="tan2",
"50"="grey8")))
simply_map_lfc <- ComplexHeatmap::Heatmap(hm_lfc_mat,
col = col_fun,
left_annotation = row_anno_lfc,
show_row_names = TRUE,
row_names_max_width= max_text_width(rownames(hm_lfc_mat), gp=gpar(fontsize=16)),
heatmap_legend_param = list(direction = "horizontal"),
show_column_names = TRUE,
cluster_rows = FALSE,
cluster_columns = FALSE)
draw(simply_map_lfc,
merge_legend = TRUE,
heatmap_legend_side = "bottom",
annotation_legend_side = "bottom")
| Version | Author | Date |
|---|---|---|
| 99ea869 | E. Renee Matthews | 2025-02-26 |
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()
ParkSNP_10k_gr <- ParkSNP_table %>%
mutate(begin=as.numeric(begin),term=as.numeric(term)) %>%
mutate(start=begin-5000, end=term+5000) %>%
GRanges()
ParkSNP_20k_gr <- ParkSNP_table %>%
mutate(begin=as.numeric(begin),term=as.numeric(term)) %>%
mutate(start=begin-10000, end=term+10000) %>%
GRanges()
ParkSNP_gr_check <- join_overlap_intersect(Collapsed_new_peaks_gr,ParkSNP_gr) %>%
as.data.frame()
ParkSNP_gr_10k_check <- join_overlap_intersect(Collapsed_new_peaks_gr,ParkSNP_10k_gr) %>%
as.data.frame()
ParkSNP_gr_20k_check <- join_overlap_intersect(Collapsed_new_peaks_gr,ParkSNP_20k_gr) %>%
as.data.frame()ParkSNP_gr_check <- join_overlap_intersect(Collapsed_new_peaks_gr,ParkSNP_gr) %>%
as.data.frame()
Park_df <-ParkSNP_gr_20k_check%>%
as.data.frame() %>%
dplyr::select(Peakid, X.Uploaded_variation) %>%
dplyr::rename("SNPS"=X.Uploaded_variation) %>%
left_join(., Nine_te_df, by=("Peakid"="Peakid")) %>%
dplyr::select(Peakid, SNPS,mrc,TEstatus) %>%
# left_join(., (Collapsed_new_peaks %>%
# dplyr::select(Peakid,NCBI_gene,SYMBOL)), by=c("Peakid"="Peakid"))
left_join(., (ATAC_3_lfc %>%
dplyr::select(peak,med_3h_lfc)),by=c("Peakid"="peak")) %>%
left_join(., (ATAC_24_lfc %>%
dplyr::select(peak,med_24h_lfc)),by=c("Peakid"="peak"))%>%
dplyr::filter(mrc !="NR") %>%
dplyr::filter(mrc !="not_mrc") %>%
mutate(dist_to_SNP=case_when(
Peakid %in% ParkSNP_gr_check$Peakid &SNPS %in% ParkSNP_gr_check$X.Uploaded_variation~ 0,
Peakid %in% ParkSNP_gr_10k_check$Peakid &SNPS %in% ParkSNP_gr_10k_check$X.Uploaded_variation~ 10,
Peakid %in% ParkSNP_gr_20k_check$Peakid &SNPS %in% ParkSNP_gr_20k_check$X.Uploaded_variation~ 20)) %>%
tidyr::unite(name,Peakid,SNPS, sep = "_", remove = FALSE) %>%
group_by(Peakid) %>%
summarize(name=unique(name),
med_3h_lfc=unique(med_3h_lfc),
med_24h_lfc=unique(med_24h_lfc),
TEstatus=paste(unique(TEstatus),collapse=";"),
mrc=unique(mrc),
dist_to_SNP=min(dist_to_SNP)) %>%
arrange(., Peakid)
new_park_mat <- Park_df%>%
ungroup() %>%
dplyr::select(name,med_3h_lfc, med_24h_lfc) %>%
column_to_rownames("name") %>%
as.matrix()
new_park_name_mat <- Park_df %>%
ungroup() %>%
dplyr::select(name,TEstatus,mrc,dist_to_SNP)
row_anno_park <-
rowAnnotation(
TE_status=new_park_name_mat$TEstatus,
# gwas_status=new_park_name_mat$GWAS,
MRC=new_park_name_mat$mrc,
direct_overlap=new_park_name_mat$dist_to_SNP,
col= list(TE_status=c("TE_peak"="goldenrod",
"not_TE_peak"="lightblue"),
MRC = c("EAR_open" = "#F8766D",
"EAR_close" = "#f6483c",
"ESR_open" = "#7CAE00",
"ESR_close" = "#587b00",
"ESR_opcl"="grey40",
"ESR_clop"="tan",
"LR_open" = "#00BFC4",
"LR_close" = "#008d91",
"NR" = "#C77CFF",
"not_mrc"="black"),
# gwas_status=c("AF"="green",
# "HF"="orange",
# "AF;HF"="purple3"),
direct_overlap=c("0"="red",
"10"="pink",
"20"="tan2",
"50"="grey8")))
col_fun <- circlize::colorRamp2(c(-4, 0, 4), c("blue", "white", "red"))
simply_map_park <- ComplexHeatmap::Heatmap(new_park_mat,
col=col_fun,
left_annotation = row_anno_park,
row_names_max_width = max_text_width(rownames(new_park_mat),
gp=gpar(fontsize=16)),
heatmap_legend_param = list(direction = "horizontal"),
show_column_names = TRUE,
cluster_rows = FALSE,
cluster_columns = FALSE)
draw(simply_map_park, merge_legend = TRUE, heatmap_legend_side = "bottom",
annotation_legend_side = "bottom")
| Version | Author | Date |
|---|---|---|
| 99ea869 | E. Renee Matthews | 2025-02-26 |
Figure 6B: Chromatin accessibility within 5 kb of rs10753081
drug_pal <- c("#8B006D","#DF707E","#F1B72B", "#3386DD","#707031","#41B333")
# K27_counts <- readRDS("data/Final_four_data/All_Raodahpeaks.RDS")
ATAC_counts <- readRDS("data/Final_four_data/ATAC_filtered_raw_counts_allsamples.RDS") %>%
cpm(., log = TRUE) %>%
as.data.frame() %>%
rename_with(.,~gsub(pattern = "Ind1_75", replacement = "D_",.)) %>%
rename_with(.,~gsub(pattern = "Ind2_87", replacement = "A_",.)) %>%
rename_with(.,~gsub(pattern = "Ind3_77", replacement = "B_",.)) %>%
rename_with(.,~gsub(pattern = "Ind6_71", replacement = "C_",.)) %>%
rename_with(.,~gsub( "DX" ,'DOX',.)) %>%
rename_with(.,~gsub( "DA" ,'DNR',.)) %>%
rename_with(.,~gsub( "E" ,'EPI',.)) %>%
rename_with(.,~gsub( "T" ,'TRZ',.)) %>%
rename_with(.,~gsub( "M" ,'MTX',.)) %>%
rename_with(.,~gsub( "V" ,'VEH',.)) %>%
rename_with(.,~gsub("24h","_24h",.)) %>%
rename_with(.,~gsub("3h","_3h",.)) %>%
rownames_to_column("Peakid")
RNA_counts <- readRDS("data/other_papers/cpmcount.RDS") %>%
dplyr::rename_with(.,~gsub(pattern="Da",replacement="DNR",.)) %>%
dplyr::rename_with(.,~gsub(pattern="Do",replacement="DOX",.)) %>%
dplyr::rename_with(.,~gsub(pattern="Ep",replacement="EPI",.)) %>%
dplyr::rename_with(.,~gsub(pattern="Mi",replacement="MTX",.)) %>%
dplyr::rename_with(.,~gsub(pattern="Tr",replacement="TRZ",.)) %>%
dplyr::rename_with(.,~gsub(pattern="Ve",replacement="VEH",.)) %>%
rownames_to_column("ENTREZID")
df_gene_schneider <- data.frame(SYMBOL=c("PRDX6","DARS2"))
df_gene_schneider <- df_gene_schneider %>%
left_join(., (RNA_median_24_lfc %>% dplyr::select(ENTREZID,SYMBOL)), by = c ("SYMBOL"="SYMBOL")) %>%
left_join(., (hm_lfc_df %>% dplyr::select(RSID,Peakid,SYMBOL) %>% separate_longer_delim(SYMBOL,delim=";")),by = c("SYMBOL"="SYMBOL")) %>% distinct(SYMBOL,.keep_all = TRUE)
ATAC_counts %>%
dplyr::filter(Peakid %in% df_gene_schneider$Peakid) %>%
pivot_longer(cols = !Peakid, names_to = "sample", values_to = "counts") %>%
left_join(., df_gene_schneider, by =c("Peakid"="Peakid")) %>%
separate("sample", into = c("ind","trt","time")) %>%
mutate(time=factor(time, levels = c("3h","24h"))) %>%
mutate(trt=factor(trt, levels= c("DOX","EPI","DNR","MTX","TRZ","VEH"))) %>%
ggplot(., aes (x = time, y=counts))+
geom_boxplot(aes(fill=trt))+
facet_wrap(~Peakid+SYMBOL,scales="free_y")+
ggtitle(" ATAC accessibility")+
scale_fill_manual(values = drug_pal)+
theme_bw()+
ylab("log2 cpm ATAC")
| Version | Author | Date |
|---|---|---|
| 99ea869 | E. Renee Matthews | 2025-02-26 |
Figure 6C: Gene expression of rs10753081 heart eGene
RNA_counts %>%
dplyr::filter(ENTREZID %in% df_gene_schneider$ENTREZID) %>%
pivot_longer(cols = !ENTREZID, names_to = "sample", values_to = "counts") %>%
# mutate(ENTREZID=as.numeric(ENTREZID)) %>%
left_join(., df_gene_schneider, by =c("ENTREZID"="ENTREZID")) %>%
# left_join(., df_gene_schneider, by =c("ENTREZID"="ENTREZID.x")) %>%
separate("sample", into = c("trt","ind","time")) %>%
mutate(time=factor(time, levels = c("3h","24h"))) %>%
mutate(trt=factor(trt, levels= c("DOX","EPI","DNR","MTX","TRZ","VEH"))) %>%
ggplot(., aes (x = time, y=counts))+
geom_boxplot(aes(fill=trt))+
facet_wrap(~SYMBOL, scales="free_y")+
scale_fill_manual(values = drug_pal)+
ggtitle("RNA Log 2 cpm of expressed gene")+
theme_bw()+
ylab("log2 cpm RNA")
| Version | Author | Date |
|---|---|---|
| 99ea869 | E. Renee Matthews | 2025-02-26 |
Figure 6D: Chromatin accessibility at rs117299725
Park_rsid <- data.frame("RSID"=c("rs117299725"),"Peakid"=c("chr9.76808694.76808955"))
ATAC_counts %>%
dplyr::filter(Peakid %in% Park_rsid$Peakid) %>%
# mutate(Peakid = row.names(.)) %>%
pivot_longer(cols = !Peakid, names_to = "sample", values_to = "counts") %>%
left_join(., Park_rsid, by =c("Peakid"="Peakid")) %>%
separate("sample", into = c("ind","trt","time")) %>%
mutate(time=factor(time, levels = c("3h","24h"))) %>%
mutate(trt=factor(trt, levels= c("DOX","EPI","DNR","MTX","TRZ","VEH"))) %>%
ggplot(., aes (x = time, y=counts))+
geom_boxplot(aes(fill=trt))+
facet_wrap(~Peakid,scales="free_y")+
ggtitle(" ATAC accessibility")+
scale_fill_manual(values = drug_pal)+
theme_bw()+
ylab("log2 cpm ATAC")
| Version | Author | Date |
|---|---|---|
| 99ea869 | E. Renee Matthews | 2025-02-26 |
sessionInfo()R version 4.4.2 (2024-10-31 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 26100)
Matrix products: default
locale:
[1] LC_COLLATE=English_United States.utf8
[2] LC_CTYPE=English_United States.utf8
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C
[5] LC_TIME=English_United States.utf8
time zone: America/Chicago
tzcode source: internal
attached base packages:
[1] grid stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] vargen_0.2.3
[2] devtools_2.4.5
[3] usethis_3.1.0
[4] readxl_1.4.5
[5] smplot2_0.2.5
[6] cowplot_1.1.3
[7] ComplexHeatmap_2.22.0
[8] ggrepel_0.9.6
[9] plyranges_1.26.0
[10] ggsignif_0.6.4
[11] genomation_1.38.0
[12] edgeR_4.4.2
[13] limma_3.62.2
[14] ggpubr_0.6.0
[15] BiocParallel_1.40.0
[16] ggVennDiagram_1.5.2
[17] scales_1.3.0
[18] VennDiagram_1.7.3
[19] futile.logger_1.4.3
[20] gridExtra_2.3
[21] ggfortify_0.4.17
[22] rtracklayer_1.66.0
[23] org.Hs.eg.db_3.20.0
[24] TxDb.Hsapiens.UCSC.hg38.knownGene_3.20.0
[25] GenomicFeatures_1.58.0
[26] AnnotationDbi_1.68.0
[27] Biobase_2.66.0
[28] GenomicRanges_1.58.0
[29] GenomeInfoDb_1.42.3
[30] IRanges_2.40.1
[31] S4Vectors_0.44.0
[32] BiocGenerics_0.52.0
[33] RColorBrewer_1.1-3
[34] broom_1.0.7
[35] kableExtra_1.4.0
[36] lubridate_1.9.4
[37] forcats_1.0.0
[38] stringr_1.5.1
[39] dplyr_1.1.4
[40] purrr_1.0.4
[41] readr_2.1.5
[42] tidyr_1.3.1
[43] tibble_3.2.1
[44] ggplot2_3.5.1
[45] tidyverse_2.0.0
[46] workflowr_1.7.1
loaded via a namespace (and not attached):
[1] later_1.4.1 BiocIO_1.16.0
[3] bitops_1.0-9 cellranger_1.1.0
[5] rpart_4.1.24 XML_3.99-0.18
[7] lifecycle_1.0.4 rstatix_0.7.2
[9] doParallel_1.0.17 rprojroot_2.0.4
[11] vroom_1.6.5 processx_3.8.6
[13] lattice_0.22-6 backports_1.5.0
[15] magrittr_2.0.3 Hmisc_5.2-2
[17] sass_0.4.9 rmarkdown_2.29
[19] remotes_2.5.0 jquerylib_0.1.4
[21] yaml_2.3.10 plotrix_3.8-4
[23] httpuv_1.6.15 sessioninfo_1.2.3
[25] pkgbuild_1.4.6 DBI_1.2.3
[27] pkgload_1.4.0 abind_1.4-8
[29] zlibbioc_1.52.0 RCurl_1.98-1.16
[31] nnet_7.3-20 git2r_0.35.0
[33] circlize_0.4.16 GenomeInfoDbData_1.2.13
[35] svglite_2.1.3 codetools_0.2-20
[37] DelayedArray_0.32.0 xml2_1.3.7
[39] tidyselect_1.2.1 shape_1.4.6.1
[41] farver_2.1.2 UCSC.utils_1.2.0
[43] base64enc_0.1-3 matrixStats_1.5.0
[45] GenomicAlignments_1.42.0 jsonlite_1.9.1
[47] GetoptLong_1.0.5 ellipsis_0.3.2
[49] Formula_1.2-5 iterators_1.0.14
[51] systemfonts_1.2.1 foreach_1.5.2
[53] tools_4.4.2 Rcpp_1.0.14
[55] glue_1.8.0 SparseArray_1.6.2
[57] xfun_0.51 MatrixGenerics_1.18.1
[59] withr_3.0.2 formatR_1.14
[61] fastmap_1.2.0 callr_3.7.6
[63] digest_0.6.37 mime_0.12
[65] timechange_0.3.0 R6_2.6.1
[67] seqPattern_1.38.0 colorspace_2.1-1
[69] RSQLite_2.3.9 generics_0.1.3
[71] data.table_1.17.0 htmlwidgets_1.6.4
[73] httr_1.4.7 S4Arrays_1.6.0
[75] whisker_0.4.1 pkgconfig_2.0.3
[77] gtable_0.3.6 blob_1.2.4
[79] impute_1.80.0 XVector_0.46.0
[81] htmltools_0.5.8.1 carData_3.0-5
[83] profvis_0.4.0 pwr_1.3-0
[85] clue_0.3-66 png_0.1-8
[87] knitr_1.49 lambda.r_1.2.4
[89] rstudioapi_0.17.1 tzdb_0.4.0
[91] reshape2_1.4.4 rjson_0.2.23
[93] checkmate_2.3.2 curl_6.2.1
[95] zoo_1.8-13 cachem_1.1.0
[97] GlobalOptions_0.1.2 KernSmooth_2.23-26
[99] miniUI_0.1.1.1 parallel_4.4.2
[101] foreign_0.8-88 restfulr_0.0.15
[103] pillar_1.10.1 vctrs_0.6.5
[105] urlchecker_1.0.1 promises_1.3.2
[107] car_3.1-3 xtable_1.8-4
[109] cluster_2.1.8.1 htmlTable_2.4.3
[111] evaluate_1.0.3 magick_2.8.5
[113] cli_3.6.4 locfit_1.5-9.12
[115] compiler_4.4.2 futile.options_1.0.1
[117] Rsamtools_2.22.0 rlang_1.1.5
[119] crayon_1.5.3 labeling_0.4.3
[121] ps_1.9.0 getPass_0.2-4
[123] plyr_1.8.9 fs_1.6.5
[125] stringi_1.8.4 viridisLite_0.4.2
[127] gridBase_0.4-7 munsell_0.5.1
[129] Biostrings_2.74.1 Matrix_1.7-3
[131] BSgenome_1.74.0 patchwork_1.3.0
[133] hms_1.1.3 bit64_4.6.0-1
[135] shiny_1.10.0 KEGGREST_1.46.0
[137] statmod_1.5.0 SummarizedExperiment_1.36.0
[139] memoise_2.0.1 bslib_0.9.0
[141] bit_4.6.0