Plot for 6c
ERM
2025-01-17
Last updated: 2025-01-17
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 | e179f61 | E. Renee Matthews | 2025-01-17 | updates with comments |
| html | d09c7db | E. Renee Matthews | 2025-01-17 | Build site. |
| Rmd | 20ed2fe | E. Renee Matthews | 2025-01-17 | additional analysis |
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)Notes to self(and anyone else who is reading this!):
This is me applying the same code from my correlation_of_SNPnPeak.rmd
document.
Summary of what I am doing: 1: create a list of peaks within +/-20 kb,
+/-10 kb, and +/- 5 kb of an RNA expressed gene TSS. (3 separate
lists)
2: making a dataframe that has all ATAC 3 hour and 24 hr LFC by peak for
later ease of use. 3: creating lists of gwas SNPs (HF and ARR lists
only) that are either 1bp, 10kb, 20kb, or 50kb in length to determine
impact of the SNP on surrounding peaks.
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) %>% GRanges()
peak_10kb_neargenes <-
Collapsed_new_peaks %>%
dplyr::filter(dist_to_NG<5000&dist_to_NG>-5000) %>%
distinct(Peakid, .keep_all = TRUE) %>%
dplyr::select(Peakid,NCBI_gene,SYMBOL)
peak_20kb_neargenes <-
Collapsed_new_peaks %>%
dplyr::filter(dist_to_NG<10000&dist_to_NG>-10000) %>%
distinct(Peakid, .keep_all = TRUE) %>%
dplyr::select(Peakid,NCBI_gene,SYMBOL)
peak_40kb_neargenes <-
Collapsed_new_peaks %>%
dplyr::filter(dist_to_NG<20000&dist_to_NG>-20000) %>%
distinct(Peakid, .keep_all = TRUE) %>%
dplyr::select(Peakid,NCBI_gene,SYMBOL)
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")
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")
gwas_HF <- readRDS("data/gwas_5_dataframe.RDS")
gwas_ARR <- readRDS("data/gwas_2_dataframe.RDS")
Short_gwas_gr <-
gwas_ARR %>%
distinct(SNPS,.keep_all = TRUE) %>%
dplyr::select(CHR_ID, CHR_POS,SNPS) %>%
mutate(gwas="ARR") %>%
rbind(gwas_HF %>%
distinct(SNPS,.keep_all = TRUE) %>%
dplyr::select(CHR_ID, CHR_POS,SNPS) %>%
mutate(gwas="HF")) %>%
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()
Short_gwas_5k_gr <-
gwas_ARR %>%
distinct(SNPS,.keep_all = TRUE) %>%
dplyr::select(CHR_ID, CHR_POS,SNPS) %>%
mutate(gwas="ARR") %>%
rbind(gwas_HF %>%
distinct(SNPS,.keep_all = TRUE) %>%
dplyr::select(CHR_ID, CHR_POS,SNPS) %>%
mutate(gwas="HF")) %>%
na.omit() %>%
mutate(seqnames=paste0("chr",CHR_ID), CHR_POS=as.numeric(CHR_POS)) %>%
na.omit() %>%
mutate(start=CHR_POS-5000, end=CHR_POS+4999) %>%
GRanges()
Short_gwas_20k_gr <-
gwas_ARR %>%
distinct(SNPS,.keep_all = TRUE) %>%
dplyr::select(CHR_ID, CHR_POS,SNPS) %>%
mutate(gwas="ARR") %>%
rbind(gwas_HF %>%
distinct(SNPS,.keep_all = TRUE) %>%
dplyr::select(CHR_ID, CHR_POS,SNPS) %>%
mutate(gwas="HF")) %>%
na.omit() %>%
mutate(seqnames=paste0("chr",CHR_ID), CHR_POS=as.numeric(CHR_POS)) %>%
na.omit() %>%
mutate(start=(CHR_POS-10000),end=(CHR_POS+9999), width=20000) %>%
distinct() %>%
GRanges()
Short_gwas_50k_gr <-
gwas_ARR %>%
distinct(SNPS,.keep_all = TRUE) %>%
dplyr::select(CHR_ID, CHR_POS,SNPS) %>%
mutate(gwas="ARR") %>%
rbind(gwas_HF %>%
distinct(SNPS,.keep_all = TRUE) %>%
dplyr::select(CHR_ID, CHR_POS,SNPS) %>%
mutate(gwas="HF")) %>%
na.omit() %>%
mutate(seqnames=paste0("chr",CHR_ID), CHR_POS=as.numeric(CHR_POS)) %>%
na.omit() %>%
mutate(start=(CHR_POS-25000),end=(CHR_POS+24999), width=50000) %>%
distinct() %>%
GRanges()
gwas_peak_check <- join_overlap_intersect(Collapsed_new_peaks_gr,Short_gwas_gr) %>%
as.data.frame()
#
gwas_peak_check_10k <- join_overlap_intersect(Collapsed_new_peaks_gr,Short_gwas_5k_gr) %>%
as.data.frame()
gwas_peak_check_20k <- join_overlap_intersect(Collapsed_new_peaks_gr,Short_gwas_20k_gr) %>%
as.data.frame()
gwas_peak_check_50k <- join_overlap_intersect(Collapsed_new_peaks_gr,Short_gwas_50k_gr) %>%
as.data.frame()
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"))Here I am doing the overlapping of the previous ranges of SNPs and the full ATAC peak set. I also later create the data frames from the reheat data, the reheat data using the p<0.005 top genes, the cluster names associated with each peak, and the list of TE/notTE associated with each peak.
ATAC_peaks_gr <- Collapsed_new_peaks %>% GRanges()
Peaks_cutoff <- read_delim("data/Final_four_data/LCPM_matrix_ff.txt",delim = "/") %>% dplyr::select(Peakid)
gwas_short_list <- gwas_peak_check %>% as.data.frame %>% dplyr::filter(Peakid %in%Peaks_cutoff$Peakid)
gwas_10k_list <- gwas_peak_check_10k %>% distinct(SNPS,Peakid)%>% dplyr::filter(Peakid %in%Peaks_cutoff$Peakid)
gwas_20k_list <- gwas_peak_check_20k %>% distinct(SNPS,Peakid)%>% dplyr::filter(Peakid %in%Peaks_cutoff$Peakid)
gwas_50k_list <- gwas_peak_check_50k %>% distinct(SNPS,Peakid)%>% dplyr::filter(Peakid %in%Peaks_cutoff$Peakid)
Reheat_data <- read_excel("data/other_papers/jah36123-sup-0002-tables2.xlsx")
top_reheat <- Reheat_data %>%
dplyr::filter(fisher_pvalue<0.005)
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) Peaks within 5kb +/- RNA TSS, gwas range +/- 10 kb
To break down what I am doing here: I start with the list of peaks that overlap a gwas SNP that has been expanded by 20kb. I then only add the RNA expressed genes associated with the peaks that are within +/- 5 kb of its TSS. I join the median LFC data frames for ATAC and RNA at 3 and 24 hours, the TEstatus, the reheat status and exclude any SNP-Peak combinations that do not have RNA assigned. (This effectively is filtering out peaks outside of the 10kb TSS range That would make the list drop from 2019 to 298 rows)
gwas_df <-gwas_20k_list%>%
as.data.frame() %>%
left_join(., peak_10kb_neargenes, 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"))%>%
left_join(., RNA_median_3_lfc,by =c("NCBI_gene"="ENTREZID", "SYMBOL"="SYMBOL")) %>%
left_join(., RNA_median_24_lfc,by =c("NCBI_gene"="ENTREZID", "SYMBOL"="SYMBOL")) %>%
mutate(reheat=if_else(SYMBOL %in% Reheat_data$gene,"reheat_gene","not_reheat_gene")) %>%
distinct(SNPS,Peakid,.keep_all = TRUE) %>%
tidyr::unite(name,SNPS,SYMBOL,Peakid,sep ="_",remove=FALSE) %>%
left_join(.,(match %>%
group_by(Peakid) %>%
filter(!(TEstatus=="not_TE_peak" & any (TEstatus == "TE_peak"))) %>%
ungroup() %>%
distinct(TEstatus,Peakid,.keep_all = TRUE)),
by = c("Peakid"="Peakid")) %>%
mutate(dist_to_SNP=case_when(Peakid %in% gwas_short_list$Peakid &SNPS %in% gwas_short_list$SNPS~ 0,
Peakid %in% gwas_10k_list$Peakid &SNPS %in% gwas_10k_list$SNPS~ 10,
Peakid %in% gwas_20k_list$Peakid &SNPS %in% gwas_20k_list$SNPS~ 20,
Peakid %in% gwas_50k_list$Peakid &SNPS %in% gwas_50k_list$SNPS ~ 50)) %>%
group_by(SNPS,Peakid) %>%
summarize(name=unique(name),
med_3h_lfc=unique(med_3h_lfc),
med_24h_lfc=unique(med_24h_lfc),
RNA_3h_lfc=unique(RNA_3h_lfc),
RNA_24h_lfc=unique(RNA_24h_lfc),
repClass=paste(unique(repClass),collapse=":"),
TEstatus=paste(unique(TEstatus),collapse=";"),
SYMBOL=paste(unique(SYMBOL),collapse=";"),
reheat=paste(unique(reheat),collapse=";"),
mrc=unique(mrc),
dist_to_SNP=min(dist_to_SNP)) %>%
na.omit(RNA_3h_lfc)
gwas_mat <- gwas_df %>%
ungroup() %>%
dplyr::select(name,med_3h_lfc:RNA_24h_lfc) %>%
column_to_rownames("name") %>%
as.matrix()
gwas_name_mat <- gwas_df %>%
ungroup() %>%
dplyr::select(name,TEstatus,mrc,reheat,dist_to_SNP)
row_anno <- ComplexHeatmap::rowAnnotation(TE_status=gwas_name_mat$TEstatus,reheat_status=gwas_name_mat$reheat,MRC=gwas_name_mat$mrc,direct_overlap=gwas_name_mat$dist_to_SNP,col= list(TE_status=c("TE_peak"="goldenrod",
"TE_peak;not_TE_peak"="goldenrod",
"not_TE_peak;TE_peak"="goldenrod",
"not_TE_peak"="lightblue"), MRC = c("EAR_open" = "#F8766D", "EAR_close" = "#f6483c","ESR_open" = "#7CAE00",
"ESR_close" = "#587b00",
"ESR_opcl"="grey40",
"ESR_C"="grey40",
"ESR_clop"="tan",
"ESR_D"="tan",
"ESR_OC" = "#6a9500",
"LR_open" = "#00BFC4",
"LR_close" = "#008d91",
"NR" = "#C77CFF",
"not_mrc"="black"),
reheat_status=c("reheat_gene"="green","not_reheat_gene"="orange"),
direct_overlap=c("0"="red","10"="pink","20"="tan2","50"="grey8")))
mat2 <- gwas_mat
# rownames(mat2)[1] = paste(c(letters, LETTERS), collapse = "")
simply_map <- ComplexHeatmap::Heatmap(gwas_mat,
left_annotation = row_anno,
show_row_names = TRUE,
# row_names_side = "left",
row_names_max_width= max_text_width(rownames(gwas_mat), gp=gpar(fontsize=8)),
heatmap_legend_param = list(direction = "horizontal"),
show_column_names = TRUE,
cluster_rows = FALSE,
cluster_columns = FALSE)
draw(simply_map, merge_legend = TRUE, heatmap_legend_side = "bottom",
annotation_legend_side = "bottom")
| Version | Author | Date |
|---|---|---|
| d09c7db | E. Renee Matthews | 2025-01-17 |
Peaks within 5kb +/- RNA TSS, gwas range +/- 25 kb
For comparison, I went ahead and did the same this as above, but used the +/- 25 kb expanded SNP range. This left me with 660 ATAC-SNP_RNA sets.
gwas_df <-
gwas_50k_list%>%
as.data.frame() %>%
left_join(., peak_10kb_neargenes, by=c("Peakid"="Peakid")) %>%
left_join(., (ATAC_3_lfc %>%
dplyr::select(peak,med_3h_lfc)),by=c("Peakid"="peak")) %>%
left_join(., (ATAC_24_lfc %>%
dplyr::select(peak,med_24h_lfc)),by=c("Peakid"="peak"))%>%
left_join(., RNA_median_3_lfc,by =c("NCBI_gene"="ENTREZID", "SYMBOL"="SYMBOL")) %>%
left_join(., RNA_median_24_lfc,by =c("NCBI_gene"="ENTREZID", "SYMBOL"="SYMBOL")) %>%
mutate(reheat=if_else(SYMBOL %in% Reheat_data$gene,"reheat_gene","not_reheat_gene")) %>%
distinct(SNPS,Peakid,.keep_all = TRUE) %>%
tidyr::unite(name,SNPS,SYMBOL,Peakid,sep ="_",remove=FALSE) %>%
left_join(.,(match %>%
group_by(Peakid) %>%
filter(!(TEstatus=="not_TE_peak" & any (TEstatus == "TE_peak"))) %>%
ungroup() %>%
distinct(TEstatus,Peakid,.keep_all = TRUE)),
by = c("Peakid"="Peakid")) %>%
mutate(dist_to_SNP=case_when(Peakid %in% gwas_short_list$Peakid &SNPS %in% gwas_short_list$SNPS~ 0,
Peakid %in% gwas_10k_list$Peakid &SNPS %in% gwas_10k_list$SNPS~ 10,
Peakid %in% gwas_20k_list$Peakid &SNPS %in% gwas_20k_list$SNPS~ 20,
Peakid %in% gwas_50k_list$Peakid &SNPS %in% gwas_50k_list$SNPS ~ 50)) %>%
group_by(SNPS,Peakid) %>%
# mutate(Keep=case_when(SNPS))
# group_by(Peakid) %>%
summarize(name=unique(name),
# SNPS=unique(SNPS),
med_3h_lfc=unique(med_3h_lfc),
med_24h_lfc=unique(med_24h_lfc),
# AC_3h_lfc=unique(AC_3h_lfc),
# AC_24h_lfc=unique(AC_24h_lfc),
RNA_3h_lfc=unique(RNA_3h_lfc),
RNA_24h_lfc=unique(RNA_24h_lfc),
repClass=paste(unique(repClass),collapse=":"),
TEstatus=paste(unique(TEstatus),collapse=";"),
SYMBOL=paste(unique(SYMBOL),collapse=";"),
reheat=paste(unique(reheat),collapse=";"),
mrc=unique(mrc),
dist_to_SNP=min(dist_to_SNP)) %>%
na.omit(RNA_3h_lfc)
gwas_mat <- gwas_df %>%
ungroup() %>%
dplyr::select(name,med_3h_lfc:RNA_24h_lfc) %>%
column_to_rownames("name") %>%
as.matrix()
gwas_name_mat <- gwas_df %>%
ungroup() %>%
dplyr::select(name,TEstatus,mrc,reheat,dist_to_SNP)
row_anno <- ComplexHeatmap::rowAnnotation(TE_status=gwas_name_mat$TEstatus,reheat_status=gwas_name_mat$reheat,MRC=gwas_name_mat$mrc,direct_overlap=gwas_name_mat$dist_to_SNP,col= list(TE_status=c("TE_peak"="goldenrod",
"TE_peak;not_TE_peak"="goldenrod",
"not_TE_peak;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_C"="grey40",
"ESR_clop"="tan",
"ESR_D"="tan",
"ESR_OC" = "#6a9500",
"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")))
mat2 <- gwas_mat
# rownames(mat2)[1] = paste(c(letters, LETTERS), collapse = "")
simply_map <- ComplexHeatmap::Heatmap(gwas_mat,
left_annotation = row_anno,
show_row_names = TRUE,
# row_names_side = "left",
row_names_max_width= max_text_width(rownames(gwas_mat), gp=gpar(fontsize=8)),
heatmap_legend_param = list(direction = "horizontal"),
show_column_names = TRUE,
cluster_rows = FALSE,
cluster_columns = FALSE)
draw(simply_map, merge_legend = TRUE, heatmap_legend_side = "bottom",
annotation_legend_side = "bottom")
| Version | Author | Date |
|---|---|---|
| d09c7db | E. Renee Matthews | 2025-01-17 |
Peaks within +/-20 kb RNA TSS ,gwas range +/- 10 kb
warning, 702 rows below
gwas_df <-
gwas_20k_list%>%
as.data.frame() %>%
left_join(., peak_40kb_neargenes, 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"))%>%
left_join(., RNA_median_3_lfc,by =c("NCBI_gene"="ENTREZID", "SYMBOL"="SYMBOL")) %>%
left_join(., RNA_median_24_lfc,by =c("NCBI_gene"="ENTREZID", "SYMBOL"="SYMBOL")) %>%
mutate(reheat=if_else(SYMBOL %in% Reheat_data$gene,"reheat_gene","not_reheat_gene")) %>%
distinct(SNPS,Peakid,.keep_all = TRUE) %>%
tidyr::unite(name,SNPS,SYMBOL,Peakid,sep ="_",remove=FALSE) %>%
left_join(.,(match %>%
group_by(Peakid) %>%
filter(!(TEstatus=="not_TE_peak" & any (TEstatus == "TE_peak"))) %>%
ungroup() %>%
distinct(TEstatus,Peakid,.keep_all = TRUE)),
by = c("Peakid"="Peakid")) %>%
mutate(dist_to_SNP=case_when(Peakid %in% gwas_short_list$Peakid &SNPS %in% gwas_short_list$SNPS~ 0,
Peakid %in% gwas_10k_list$Peakid &SNPS %in% gwas_10k_list$SNPS~ 10,
Peakid %in% gwas_20k_list$Peakid &SNPS %in% gwas_20k_list$SNPS~ 20,
Peakid %in% gwas_50k_list$Peakid &SNPS %in% gwas_50k_list$SNPS ~ 50)) %>%
group_by(SNPS,Peakid) %>%
# mutate(Keep=case_when(SNPS))
# group_by(Peakid) %>%
summarize(name=unique(name),
# SNPS=unique(SNPS),
med_3h_lfc=unique(med_3h_lfc),
med_24h_lfc=unique(med_24h_lfc),
# AC_3h_lfc=unique(AC_3h_lfc),
# AC_24h_lfc=unique(AC_24h_lfc),
RNA_3h_lfc=unique(RNA_3h_lfc),
RNA_24h_lfc=unique(RNA_24h_lfc),
repClass=paste(unique(repClass),collapse=":"),
TEstatus=paste(unique(TEstatus),collapse=";"),
SYMBOL=paste(unique(SYMBOL),collapse=";"),
reheat=paste(unique(reheat),collapse=";"),
mrc=unique(mrc),
dist_to_SNP=min(dist_to_SNP)) %>%
na.omit(RNA_3h_lfc)
gwas_mat <- gwas_df %>%
ungroup() %>%
dplyr::select(name,med_3h_lfc:RNA_24h_lfc) %>%
column_to_rownames("name") %>%
as.matrix()
gwas_name_mat <- gwas_df %>%
ungroup() %>%
dplyr::select(name,TEstatus,mrc,reheat,dist_to_SNP)
row_anno <- ComplexHeatmap::rowAnnotation(TE_status=gwas_name_mat$TEstatus,reheat_status=gwas_name_mat$reheat,MRC=gwas_name_mat$mrc,direct_overlap=gwas_name_mat$dist_to_SNP,col= list(TE_status=c("TE_peak"="goldenrod",
"TE_peak;not_TE_peak"="goldenrod",
"not_TE_peak;TE_peak"="goldenrod",
"not_TE_peak"="lightblue"), MRC = c("EAR_open" = "#F8766D", "EAR_close" = "#f6483c","ESR_open" = "#7CAE00",
"ESR_close" = "#587b00",
"ESR_opcl"="grey40",
"ESR_C"="grey40",
"ESR_clop"="tan",
"ESR_D"="tan",
"ESR_OC" = "#6a9500",
"LR_open" = "#00BFC4",
"LR_close" = "#008d91",
"NR" = "#C77CFF",
"not_mrc"="black"),
reheat_status=c("reheat_gene"="green","not_reheat_gene"="orange"),
direct_overlap=c("0"="red","10"="pink","20"="tan2","50"="grey8")))
mat2 <- gwas_mat
# rownames(mat2)[1] = paste(c(letters, LETTERS), collapse = "")
simply_map <- ComplexHeatmap::Heatmap(gwas_mat,
left_annotation = row_anno,
show_row_names = TRUE,
# row_names_side = "left",
row_names_max_width= max_text_width(rownames(gwas_mat), gp=gpar(fontsize=8)),
heatmap_legend_param = list(direction = "horizontal"),
show_column_names = TRUE,
cluster_rows = FALSE,
cluster_columns = FALSE)
draw(simply_map, merge_legend = TRUE, heatmap_legend_side = "bottom",
annotation_legend_side = "bottom")
| Version | Author | Date |
|---|---|---|
| d09c7db | E. Renee Matthews | 2025-01-17 |
gwas short list only
To make life really easy, or the smallest set that was readable, I used only the peaks that were directly overlapping a SNP, but filtered out peaks that were more than +/-20 kb from an expressed RNA TSS. This gave me 33 ATAC-SNP-RNA rows.
gwas_df_short <-gwas_short_list%>%
as.data.frame() %>%
left_join(., peak_40kb_neargenes, by=c("Peakid"="Peakid")) %>%
left_join(., (ATAC_3_lfc %>%
dplyr::select(peak,med_3h_lfc)),by=c("Peakid"="peak")) %>%
left_join(., (ATAC_24_lfc %>%
dplyr::select(peak,med_24h_lfc)),by=c("Peakid"="peak"))%>%
left_join(., RNA_median_3_lfc,by =c("NCBI_gene"="ENTREZID", "SYMBOL"="SYMBOL")) %>%
left_join(., RNA_median_24_lfc,by =c("NCBI_gene"="ENTREZID", "SYMBOL"="SYMBOL")) %>%
na.omit(RNA_median_24_lfc) %>%
mutate(reheat=if_else(SYMBOL %in% Reheat_data$gene,"reheat_gene","not_reheat_gene")) %>%
distinct(SNPS,Peakid,.keep_all = TRUE) %>%
tidyr::unite(name,SNPS,SYMBOL,Peakid,sep ="_",remove=FALSE) %>%
left_join(.,(match %>%
group_by(Peakid) %>%
filter(!(TEstatus=="not_TE_peak" & any (TEstatus == "TE_peak"))) %>%
ungroup() %>%
distinct(TEstatus,Peakid,.keep_all = TRUE)),
by = c("Peakid"="Peakid")) %>%
mutate(dist_to_SNP=case_when(Peakid %in% gwas_short_list$Peakid &SNPS %in% gwas_short_list$SNPS~ 0,
Peakid %in% gwas_10k_list$Peakid &SNPS %in% gwas_10k_list$SNPS~ 10,
Peakid %in% gwas_20k_list$Peakid &SNPS %in% gwas_20k_list$SNPS~ 20,
Peakid %in% gwas_50k_list$Peakid &SNPS %in% gwas_50k_list$SNPS ~ 50)) %>%
group_by(SNPS,Peakid) %>%
# mutate(Keep=case_when(SNPS))
# group_by(Peakid) %>%
summarize(name=unique(name),
# SNPS=unique(SNPS),
med_3h_lfc=unique(med_3h_lfc),
med_24h_lfc=unique(med_24h_lfc),
# AC_3h_lfc=unique(AC_3h_lfc),
# AC_24h_lfc=unique(AC_24h_lfc),
RNA_3h_lfc=unique(RNA_3h_lfc),
RNA_24h_lfc=unique(RNA_24h_lfc),
repClass=paste(unique(repClass),collapse=":"),
TEstatus=paste(unique(TEstatus),collapse=";"),
SYMBOL=paste(unique(SYMBOL),collapse=";"),
reheat=paste(unique(reheat),collapse=";"),
mrc=unique(mrc),
dist_to_SNP=min(dist_to_SNP)) #%>%
gwas_mat_short <- gwas_df_short %>%
ungroup() %>%
dplyr::select(name,med_3h_lfc:RNA_24h_lfc) %>%
column_to_rownames("name") %>%
as.matrix()
gwas_name_mat_short <- gwas_df_short %>%
ungroup() %>%
dplyr::select(name,TEstatus,mrc,reheat,dist_to_SNP)
row_anno_short <- ComplexHeatmap::rowAnnotation(TE_status=gwas_name_mat_short$TEstatus,reheat_status=gwas_name_mat_short$reheat,MRC=gwas_name_mat_short$mrc,direct_overlap=gwas_name_mat_short$dist_to_SNP,col= list(TE_status=c("TE_peak"="goldenrod",
"TE_peak;not_TE_peak"="goldenrod",
"not_TE_peak;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_C"="grey40",
"ESR_clop"="tan",
"ESR_D"="tan",
"ESR_OC" = "#6a9500",
"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")))
mat2_short <- gwas_mat_short
# rownames(mat2)[1] = paste(c(letters, LETTERS), collapse = "")
simply_map_short <- ComplexHeatmap::Heatmap(gwas_mat_short,
left_annotation = row_anno_short,
show_row_names = TRUE,
# row_names_side = "left",
row_names_max_width= max_text_width(rownames(gwas_mat_short), gp=gpar(fontsize=8)),
heatmap_legend_param = list(direction = "horizontal"),
show_column_names = TRUE,
cluster_rows = FALSE,
cluster_columns = FALSE)
draw(simply_map_short, merge_legend = TRUE, heatmap_legend_side = "bottom",
annotation_legend_side = "bottom")
| Version | Author | Date |
|---|---|---|
| d09c7db | E. Renee Matthews | 2025-01-17 |
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] readxl_1.4.3
[2] smplot2_0.2.4
[3] cowplot_1.1.3
[4] ComplexHeatmap_2.22.0
[5] ggrepel_0.9.6
[6] plyranges_1.26.0
[7] ggsignif_0.6.4
[8] genomation_1.38.0
[9] edgeR_4.4.1
[10] limma_3.62.1
[11] ggpubr_0.6.0
[12] BiocParallel_1.40.0
[13] ggVennDiagram_1.5.2
[14] scales_1.3.0
[15] VennDiagram_1.7.3
[16] futile.logger_1.4.3
[17] gridExtra_2.3
[18] ggfortify_0.4.17
[19] rtracklayer_1.66.0
[20] org.Hs.eg.db_3.20.0
[21] TxDb.Hsapiens.UCSC.hg38.knownGene_3.20.0
[22] GenomicFeatures_1.58.0
[23] AnnotationDbi_1.68.0
[24] Biobase_2.66.0
[25] GenomicRanges_1.58.0
[26] GenomeInfoDb_1.42.1
[27] IRanges_2.40.1
[28] S4Vectors_0.44.0
[29] BiocGenerics_0.52.0
[30] RColorBrewer_1.1-3
[31] broom_1.0.7
[32] kableExtra_1.4.0
[33] lubridate_1.9.4
[34] forcats_1.0.0
[35] stringr_1.5.1
[36] dplyr_1.1.4
[37] purrr_1.0.2
[38] readr_2.1.5
[39] tidyr_1.3.1
[40] tibble_3.2.1
[41] ggplot2_3.5.1
[42] tidyverse_2.0.0
[43] 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_4.1.23 XML_3.99-0.17
[7] lifecycle_1.0.4 rstatix_0.7.2
[9] doParallel_1.0.17 rprojroot_2.0.4
[11] vroom_1.6.5 processx_3.8.4
[13] lattice_0.22-6 backports_1.5.0
[15] magrittr_2.0.3 Hmisc_5.2-1
[17] sass_0.4.9 rmarkdown_2.29
[19] jquerylib_0.1.4 yaml_2.3.10
[21] plotrix_3.8-4 httpuv_1.6.15
[23] DBI_1.2.3 abind_1.4-8
[25] zlibbioc_1.52.0 RCurl_1.98-1.16
[27] nnet_7.3-19 git2r_0.35.0
[29] circlize_0.4.16 GenomeInfoDbData_1.2.13
[31] svglite_2.1.3 codetools_0.2-20
[33] DelayedArray_0.32.0 xml2_1.3.6
[35] tidyselect_1.2.1 shape_1.4.6.1
[37] farver_2.1.2 UCSC.utils_1.2.0
[39] base64enc_0.1-3 matrixStats_1.4.1
[41] GenomicAlignments_1.42.0 jsonlite_1.8.9
[43] GetoptLong_1.0.5 Formula_1.2-5
[45] iterators_1.0.14 systemfonts_1.1.0
[47] foreach_1.5.2 tools_4.4.2
[49] Rcpp_1.0.13-1 glue_1.8.0
[51] SparseArray_1.6.0 xfun_0.49
[53] MatrixGenerics_1.18.0 withr_3.0.2
[55] formatR_1.14 fastmap_1.2.0
[57] callr_3.7.6 digest_0.6.37
[59] timechange_0.3.0 R6_2.5.1
[61] seqPattern_1.38.0 colorspace_2.1-1
[63] RSQLite_2.3.9 generics_0.1.3
[65] data.table_1.16.4 htmlwidgets_1.6.4
[67] httr_1.4.7 S4Arrays_1.6.0
[69] whisker_0.4.1 pkgconfig_2.0.3
[71] gtable_0.3.6 blob_1.2.4
[73] impute_1.80.0 XVector_0.46.0
[75] htmltools_0.5.8.1 carData_3.0-5
[77] pwr_1.3-0 clue_0.3-66
[79] png_0.1-8 knitr_1.49
[81] lambda.r_1.2.4 rstudioapi_0.17.1
[83] tzdb_0.4.0 reshape2_1.4.4
[85] rjson_0.2.23 checkmate_2.3.2
[87] curl_6.0.1 zoo_1.8-12
[89] cachem_1.1.0 GlobalOptions_0.1.2
[91] KernSmooth_2.23-24 parallel_4.4.2
[93] foreign_0.8-87 restfulr_0.0.15
[95] pillar_1.10.0 vctrs_0.6.5
[97] promises_1.3.2 car_3.1-3
[99] cluster_2.1.8 htmlTable_2.4.3
[101] evaluate_1.0.1 magick_2.8.5
[103] cli_3.6.3 locfit_1.5-9.10
[105] compiler_4.4.2 futile.options_1.0.1
[107] Rsamtools_2.22.0 rlang_1.1.4
[109] crayon_1.5.3 ps_1.8.1
[111] getPass_0.2-4 plyr_1.8.9
[113] fs_1.6.5 stringi_1.8.4
[115] viridisLite_0.4.2 gridBase_0.4-7
[117] munsell_0.5.1 Biostrings_2.74.1
[119] Matrix_1.7-1 BSgenome_1.74.0
[121] patchwork_1.3.0 hms_1.1.3
[123] bit64_4.5.2 KEGGREST_1.46.0
[125] statmod_1.5.0 SummarizedExperiment_1.36.0
[127] memoise_2.0.1 bslib_0.8.0
[129] bit_4.5.0.1