Plot for 6b
ERM
2025-01-24
Last updated: 2025-01-24
Checks: 7 0
Knit directory: ATAC_learning/
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Unstaged changes:
Modified: ATAC_learning.Rproj
Modified: analysis/Enhancer_files_ff.Rmd
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Modified: analysis/final_plot_attempt.Rmd
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 8a21094 | E. Renee Matthews | 2025-01-24 | new final figure updates |
| html | bdb9ba0 | E. Renee Matthews | 2025-01-21 | Build site. |
| Rmd | b3248cc | E. Renee Matthews | 2025-01-21 | adding in protein data |
| html | 949b32b | E. Renee Matthews | 2025-01-21 | Build site. |
| Rmd | 1652dee | E. Renee Matthews | 2025-01-21 | updated |
| html | a505a0a | E. Renee Matthews | 2025-01-17 | Build site. |
| Rmd | 4a1d2bf | E. Renee Matthews | 2025-01-17 | updates to plots |
| html | 17a22e9 | E. Renee Matthews | 2025-01-17 | Build site. |
| Rmd | 06d6816 | E. Renee Matthews | 2025-01-17 | fix |
| html | 29becd1 | E. Renee Matthews | 2025-01-14 | Build site. |
| Rmd | 98d5af7 | E. Renee Matthews | 2025-01-14 | adding in short heatmap |
| html | 3267bc8 | reneeisnowhere | 2024-12-11 | Build site. |
| Rmd | e85df93 | reneeisnowhere | 2024-12-11 | adding in neargene changes |
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| Rmd | d73fc08 | reneeisnowhere | 2024-12-10 | adding in the webpage |
| Rmd | b13ed21 | reneeisnowhere | 2024-12-09 | first update |
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)# Collapsed_H3k27ac_NG <- read_delim("data/Final_four_data/H3K27ac_files/Collapsed_H3k27ac_NG.txt",delim = "\t",col_names = TRUE)
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")
overlap_df_ggplot <- readRDS("data/Final_four_data/LFC_ATAC_K27ac.RDS")
AC_median_3_lfc <- read_csv("data/Final_four_data/AC_median_3_lfc.csv")
AC_median_24_lfc <- read_csv("data/Final_four_data/AC_median_24_lfc.csv")
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")
lt1<- readRDS("data/Final_four_data/updated_RNA_gene_lookuptable")
checklist <- lt1 %>%
separate_longer_delim(.,col= ENTREZID, delim= ":") %>%
separate_longer_delim(.,col= SYMBOL, delim= ":") %>%
dplyr::select(ENTREZID,SYMBOL) %>%
mutate(ENTREZID=as.numeric(ENTREZID)) %>%
distinct()
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) %>%
# dplyr::filter(SOURCE=="Ensembl") %>%
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() %>% as.data.frame
# schneider_closest_output <- readRDS("data/other_papers/Schneider_closestgene_SNP_file.RDS")
# left_join(., checklist, by=c("ENTREZID"="ENTREZID"))
# dplyr::select(RSID)
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()
# new_SNP_peak_check_10k <- readRDS("data/Final_four_data/new_SNP_peak_check_10k.RDS")
###pulled for ensemble closest gene associated
# overlapSNP <- data.frame(RSID=new_SNP_peak_check_10k$RSID)
# write_delim(overlapSNP,"data/other_papers/overlapSNP.txt", delim = "\t")
overlap_SNP_gene_ensembl <- read_delim("data/other_papers/overlap_SNP_gene_ensembl.txt",
delim = "\t", escape_double = FALSE,
trim_ws = TRUE)
SNP_closest_genes <- overlap_SNP_gene_ensembl %>%
dplyr::select(Existing_variation,SYMBOL,HGNC_ID) %>%
distinct(Existing_variation,SYMBOL,.keep_all = TRUE) %>%
group_by(Existing_variation) %>%
mutate(SYMBOL=if_else(SYMBOL=="-","none",SYMBOL)) %>%
summarize(SYMBOL=paste(unique(SYMBOL),collapse=";")) %>%
mutate(SYMBOL=str_remove(SYMBOL,";none")) %>%
dplyr::rename("RSID"=Existing_variation)%>%
mutate(RSID=str_remove(RSID,",COS[^.]*$"))
# #Schneider_all_SNPS_df %>%
# #dplyr::select(RSID:Gene)
### for collecting peak LFC data that overlaps with SNPs
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"))
#### adding in protein LFC data from Omar and Sayan's paper, unpublished data, pending acceptance.
# Proteomics_data <- read_excel("data/other_papers/Proteomics_data.xlsx",
# skip = 1)
# Dox_prot <- Proteomics_data %>%
# dplyr::select(Protein, logFC,P.Value) %>%
# left_join(., idmapping_2025_01_21ENTREZID, by =c("Protein"="From"))%>%
# dplyr::rename("ENTREZID"=To) %>%
# left_join(., idmapping_2025_01_SYMBOL, by=c("Protein"="From")) %>%
# dplyr::rename("SYMBOL"=To)
# saveRDS(Dox_prot,"data/other_papers/Dox_proteome_paper.RDS")
Dox_prot <- readRDS("data/other_papers/Dox_proteome_paper.RDS")
proto_list <- Dox_prot %>%
group_by(SYMBOL,logFC) %>%
summarize(ENTREZID=paste(unique(ENTREZID),collapse=";"),
Protein=paste(unique(Protein),collapse=";"),
P.Value=min(P.Value))# new_SNP_peak_check <- readRDS("data/Final_four_data/new_SNP_peak_check.RDS")
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
# new_SNP_pc_gr <- new_SNP_peak_check_10k
# new_SNP_peak_check_10k# %>%
# dplyr::filter(ENTREZID.x !=ENTREZID.y)
# schneider_gr %>% write_bed(.,"data/Final_four_data/meme_bed/Schnieder_SNPs.bed")
ATAC_peaks_gr <- Collapsed_new_peaks %>% GRanges()
# point_only <- join_overlap_intersect(schneider_gr,ATAC_peaks_gr)
# expand_schneider <- join_overlap_intersect(ATAC_peaks_gr,schneider_10k_gr)
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)
# ATAC_RNA_direct <- ATAC_LFC %>%
# dplyr::filter(Peakid %in% schneider_short_list$Peakid) %>%
# left_join(., schneider_short_list %>% dplyr::select(Peakid:SYMBOL)) %>%
###now we are taking out the "-" and separating them for RNA 3hr and 24 hour matches
# separate_longer_delim(SYMBOL,delim=";") %>%
# dplyr::filter(SYMBOL!="-") %>%
# left_join(., RNA_median_3_lfc,by =c("SYMBOL"="SYMBOL")) %>%
#
# left_join(., RNA_median_24_lfc,by =c("ENTREZID"="ENTREZID", "SYMBOL"="SYMBOL"))
# ATAC_RNA_direct %>%
# ggplot(., aes(med_3h_lfc,RNA_3h_lfc)) +
# geom_point()+
# sm_statCorr(corr_method = 'pearson')+
# ggtitle("3 hour using direct overlap")
# ATAC_RNA_direct %>%
# ggplot(., aes(med_24h_lfc,RNA_24h_lfc)) +
# geom_point()+
# sm_statCorr(corr_method = 'pearson')+
# ggtitle("24 hour using direct overlap")
#
ATAC_RNA_10kb <- ATAC_LFC %>%
dplyr::filter(Peakid %in% SNP_10k_only$Peakid) %>%
left_join(., SNP_10k_only %>% dplyr::select(Peakid:SYMBOL)) %>%
###now we are taking out the "-" and separating them for RNA 3hr and 24 hour matches
separate_longer_delim(SYMBOL,delim=";") %>%
dplyr::filter(SYMBOL!="-") %>%
left_join(., RNA_median_3_lfc,by =c("SYMBOL"="SYMBOL")) %>%
left_join(., RNA_median_24_lfc,by =c("ENTREZID"="ENTREZID", "SYMBOL"="SYMBOL"))
#
# ATAC_RNA_10kb %>%
# ggplot(., aes(med_3h_lfc,RNA_3h_lfc)) +
# geom_point()+
# sm_statCorr(corr_method = 'pearson')+
# ggtitle(" 3 hour correlation Using +/- 5kb")
#
# ATAC_RNA_10kb %>%
# # dplyr::filter(Peakid %in% peak_50kb_neargenes$Peakid) %>%
# ggplot(., aes(med_24h_lfc,RNA_24h_lfc)) +
# geom_point()+
# sm_statCorr(corr_method = 'pearson')+
# ggtitle(" 24 hour correlation Using +/- 5kb")
#
# ATAC_RNA_10kb %>%
# dplyr::filter(Peakid %in% peak_10kb_neargenes$Peakid) %>%
# ggplot(., aes(med_3h_lfc,RNA_3h_lfc)) +
# geom_point()+
# sm_statCorr(corr_method = 'pearson')+
# ggtitle(" 3 hour correlation Using +/- 5kb\nfiltering peaks that are 5kb +/- away from TSS")
#
# ATAC_RNA_10kb %>%
# dplyr::filter(Peakid %in% peak_10kb_neargenes$Peakid) %>%
# ggplot(., aes(med_24h_lfc,RNA_24h_lfc)) +
# geom_point()+
# sm_statCorr(corr_method = 'pearson')+
# ggtitle(" 24 hour correlation Using +/- 5kb\nfiltering peaks that are 5kb +/- away from TSS")ATAC_RNA_20kb <- ATAC_LFC %>%
dplyr::filter(Peakid %in% SNP_20k_only$Peakid) %>%
left_join(., SNP_20k_only %>% dplyr::select(Peakid:SYMBOL)) %>%
###now we are taking out the "-" and separating them for RNA 3hr and 24 hour matches
separate_longer_delim(SYMBOL,delim=";") %>%
dplyr::filter(SYMBOL!="-") %>%
left_join(., RNA_median_3_lfc,by =c("SYMBOL"="SYMBOL")) %>%
left_join(., RNA_median_24_lfc,by =c("ENTREZID"="ENTREZID", "SYMBOL"="SYMBOL")) %>%
na.omit()
# ATAC_RNA_20kb %>%
# ggplot(., aes(med_3h_lfc,RNA_3h_lfc)) +
# geom_point()+
# sm_statCorr(corr_method = 'pearson')+
# ggtitle(" 3 hour correlation Using +/- 10kb")
# ATAC_RNA_20kb%>%
# ggplot(., aes(med_24h_lfc,RNA_24h_lfc)) +
# geom_point()+
# sm_statCorr(corr_method = 'pearson')+
# ggtitle(" 24 hour correlation Using +/- 10kb")
ATAC_RNA_50kb <- ATAC_LFC %>%
dplyr::filter(Peakid %in% SNP_20k_only$Peakid) %>%
left_join(., SNP_20k_only %>% dplyr::select(Peakid:SYMBOL)) %>%
###now we are taking out the "-" and separating them for RNA 3hr and 24 hour matches
separate_longer_delim(SYMBOL,delim=";") %>%
dplyr::filter(SYMBOL!="-") %>%
left_join(., RNA_median_3_lfc,by =c("SYMBOL"="SYMBOL")) %>%
left_join(., RNA_median_24_lfc,by =c("ENTREZID"="ENTREZID", "SYMBOL"="SYMBOL")) %>%
na.omit()
# ATAC_RNA_50kb %>%
# ggplot(., aes(med_3h_lfc,RNA_3h_lfc)) +
# geom_point()+
# sm_statCorr(corr_method = 'pearson')+
# ggtitle(" 3 hour correlation Using +/- 25 kb")
#
# ATAC_RNA_50kb %>%
# ggplot(., aes(med_24h_lfc,RNA_24h_lfc)) +
# geom_point()+
# sm_statCorr(corr_method = 'pearson')+
# ggtitle(" 24 hour correlation Using +/- 25 kb")
#
#
# ATAC_RNA_20kb %>%
# dplyr::filter(Peakid %in% peak_10kb_neargenes$Peakid)Reheat_data <- readxl::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)
# Knowles_dox_eQTL <- readRDS("data/Knowles_5.RDS")
# Knowles_mar_eQTL <- readRDS("data/Knowles_4.RDS")
# dox_eQTL_gr <- Knowles_dox_eQTL %>%
# distinct(RSID,.keep_all = TRUE) %>%
# mutate(CHR_ID=(gsub("chr","",chr))) %>%
# dplyr::rename("SNPS"=RSID,"CHR_POS"=pos) %>%
# dplyr::select(CHR_ID, CHR_POS,SNPS) %>%
# mutate(CHR_ID=as.numeric(CHR_ID), CHR_POS=as.numeric(CHR_POS)) %>%
# na.omit() %>%
# mutate(gwas="eQTL") %>%
# mutate(start=CHR_POS, end=CHR_POS, chr=paste0("chr",CHR_ID)) %>%
# GRanges()
# mar_eQTL_gr <- Knowles_mar_eQTL %>%
# distinct(RSID,.keep_all = TRUE) %>%
# mutate(CHR_ID=(gsub("chr","",chr))) %>%
# dplyr::rename("SNPS"=RSID,"CHR_POS"=pos) %>%
# dplyr::select(CHR_ID, CHR_POS,SNPS) %>%
# mutate(gwas="beQTL") %>%
# mutate(CHR_ID=as.numeric(CHR_ID), CHR_POS=as.numeric(CHR_POS)) %>%
# na.omit() %>%
# mutate(start=CHR_POS, end=CHR_POS, chr=paste0("chr",CHR_ID)) %>%
# GRanges()
# mar_eqtl_SNPS <-
# join_overlap_intersect(ATAC_peaks_gr,mar_eQTL_gr) %>%
# as.data.frame()
#
# dox_eqtl_SNPs <-
# join_overlap_intersect(ATAC_peaks_gr,dox_eQTL_gr) %>%
# as.data.frame()
#
# test <- dox_eqtl_SNPs %>%
# dplyr::filter(Peakid %in% new_SNP_peak_check_10k$Peakid)
# join_overlap_intersect((new_SNP_peak_check_10k %>% GRanges() ),mar_eQTL_gr) %>%
# as.data.frame() Peaks within 5kb +/- RNA TSS
schneider_df <-
schneider_50k_list%>%
as.data.frame() %>%
left_join(., peak_10kb_neargenes, by=c("Peakid"="Peakid")) %>%
left_join(., (ATAC_3_lfc %>%
dplyr::select(peak,med_3h_lfc)),by=c("Peakid"="peak")) %>%
left_join(., (ATAC_24_lfc %>%
dplyr::select(peak,med_24h_lfc)),by=c("Peakid"="peak"))%>%
left_join(., RNA_median_3_lfc,by =c("NCBI_gene"="ENTREZID", "SYMBOL"="SYMBOL")) %>%
left_join(., RNA_median_24_lfc,by =c("NCBI_gene"="ENTREZID", "SYMBOL"="SYMBOL")) %>%
mutate(reheat=if_else(SYMBOL %in% Reheat_data$gene,"reheat_gene","not_reheat_gene")) %>%
distinct(RSID,Peakid,.keep_all = TRUE) %>%
tidyr::unite(name,RSID,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% 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)) %>%
group_by(RSID,Peakid) %>%
# mutate(Keep=case_when(RSID))
# group_by(Peakid) %>%
summarize(name=unique(name),
# RSID=unique(RSID),
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)) #%>%
# mutate(point_ol=if_else(RSID %in% point_only$RSID,"yes","no")) %>%
# mutate(eQTL=if_else(RSID %in% dox_eqtl_SNPs$SNPS,"yes",if_else(Peakid %in% mar_eqtl_SNPS$Peakid,"yes","no")))
schneider_mat <- schneider_df %>%
ungroup() %>%
dplyr::select(name,med_3h_lfc:RNA_24h_lfc) %>%
column_to_rownames("name") %>%
as.matrix()
schneider_name_mat <- schneider_df %>%
ungroup() %>%
dplyr::select(name,TEstatus,mrc,reheat,dist_to_SNP)
row_anno <- ComplexHeatmap::rowAnnotation(TE_status=schneider_name_mat$TEstatus,reheat_status=schneider_name_mat$reheat,MRC=schneider_name_mat$mrc,direct_overlap=schneider_name_mat$dist_to_SNP,col= list(TE_status=c("TE_peak"="goldenrod",
"TE_peak;not_TE_peak"="goldenrod",
"not_TE_peak;TE_peak"="goldenrod",
"not_TE_peak"="lightblue"), MRC = c("EAR_open" = "#F8766D", "EAR_close" = "#f6483c","ESR_open" = "#7CAE00",
"ESR_close" = "#587b00",
"ESR_opcl"="grey40",
"ESR_C"="grey40",
"ESR_clop"="tan",
"ESR_D"="tan",
"ESR_OC" = "#6a9500",
"LR_open" = "#00BFC4",
"LR_close" = "#008d91",
"NR" = "#C77CFF",
"not_mrc"="black"),
reheat_status=c("reheat_gene"="green","not_reheat_gene"="orange"),
direct_overlap=c("0"="red","10"="pink","20"="tan2","50"="grey8")))
mat2 <- schneider_mat
# rownames(mat2)[1] = paste(c(letters, LETTERS), collapse = "")
simply_map <- ComplexHeatmap::Heatmap(schneider_mat,
left_annotation = row_anno,
show_row_names = TRUE,
# row_names_side = "left",
row_names_max_width= max_text_width(rownames(schneider_mat), gp=gpar(fontsize=8)),
heatmap_legend_param = list(direction = "horizontal"),
show_column_names = TRUE,
cluster_rows = FALSE,
cluster_columns = FALSE)
draw(simply_map, merge_legend = TRUE, heatmap_legend_side = "bottom",
annotation_legend_side = "bottom")
Peaks within 10 kb +/- of RNA TSS
schneider_df <-
schneider_50k_list%>%
as.data.frame() %>%
left_join(., peak_20kb_neargenes, by=c("Peakid"="Peakid")) %>%
left_join(., (ATAC_3_lfc %>%
dplyr::select(peak,med_3h_lfc)),by=c("Peakid"="peak")) %>%
left_join(., (ATAC_24_lfc %>%
dplyr::select(peak,med_24h_lfc)),by=c("Peakid"="peak"))%>%
left_join(., RNA_median_3_lfc,by =c("NCBI_gene"="ENTREZID", "SYMBOL"="SYMBOL")) %>%
left_join(., RNA_median_24_lfc,by =c("NCBI_gene"="ENTREZID", "SYMBOL"="SYMBOL")) %>%
mutate(reheat=if_else(SYMBOL %in% Reheat_data$gene,"reheat_gene","not_reheat_gene")) %>%
distinct(RSID,Peakid,.keep_all = TRUE) %>%
tidyr::unite(name,RSID,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% 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)) %>%
group_by(RSID,Peakid) %>%
# mutate(Keep=case_when(RSID))
# group_by(Peakid) %>%
summarize(name=unique(name),
# RSID=unique(RSID),
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)) #%>%
# mutate(point_ol=if_else(RSID %in% point_only$RSID,"yes","no")) %>%
# mutate(eQTL=if_else(RSID %in% dox_eqtl_SNPs$SNPS,"yes",if_else(Peakid %in% mar_eqtl_SNPS$Peakid,"yes","no")))
schneider_mat <- schneider_df %>%
ungroup() %>%
dplyr::select(name,med_3h_lfc:RNA_24h_lfc) %>%
column_to_rownames("name") %>%
as.matrix()
schneider_name_mat <- schneider_df %>%
ungroup() %>%
dplyr::select(name,TEstatus,mrc,reheat,dist_to_SNP)
row_anno <- ComplexHeatmap::rowAnnotation(TE_status=schneider_name_mat$TEstatus,reheat_status=schneider_name_mat$reheat,MRC=schneider_name_mat$mrc,direct_overlap=schneider_name_mat$dist_to_SNP,col= list(TE_status=c("TE_peak"="goldenrod",
"TE_peak;not_TE_peak"="goldenrod",
"not_TE_peak;TE_peak"="goldenrod",
"not_TE_peak"="lightblue"), MRC = c("EAR_open" = "#F8766D", "EAR_close" = "#f6483c","ESR_open" = "#7CAE00",
"ESR_close" = "#587b00",
"ESR_opcl"="grey40",
"ESR_C"="grey40",
"ESR_clop"="tan",
"ESR_D"="tan",
"ESR_OC" = "#6a9500",
"LR_open" = "#00BFC4",
"LR_close" = "#008d91",
"NR" = "#C77CFF",
"not_mrc"="black"),
reheat_status=c("reheat_gene"="green","not_reheat_gene"="orange"),
direct_overlap=c("0"="red","10"="pink","20"="tan2","50"="grey8")))
mat2 <- schneider_mat
# rownames(mat2)[1] = paste(c(letters, LETTERS), collapse = "")
simply_map <- ComplexHeatmap::Heatmap(schneider_mat,
left_annotation = row_anno,
show_row_names = TRUE,
# row_names_side = "left",
row_names_max_width= max_text_width(rownames(schneider_mat), gp=gpar(fontsize=8)),
heatmap_legend_param = list(direction = "horizontal"),
show_column_names = TRUE,
cluster_rows = FALSE,
cluster_columns = FALSE)
draw(simply_map, merge_legend = TRUE, heatmap_legend_side = "bottom",
annotation_legend_side = "bottom")
Peaks within +/-20 kb RNA TSS
schneider_df <-
schneider_50k_list%>%
as.data.frame() %>%
left_join(., peak_40kb_neargenes, by=c("Peakid"="Peakid")) %>%
left_join(., (ATAC_3_lfc %>%
dplyr::select(peak,med_3h_lfc)),by=c("Peakid"="peak")) %>%
left_join(., (ATAC_24_lfc %>%
dplyr::select(peak,med_24h_lfc)),by=c("Peakid"="peak"))%>%
left_join(., RNA_median_3_lfc,by =c("NCBI_gene"="ENTREZID", "SYMBOL"="SYMBOL")) %>%
left_join(., RNA_median_24_lfc,by =c("NCBI_gene"="ENTREZID", "SYMBOL"="SYMBOL")) %>%
mutate(reheat=if_else(SYMBOL %in% Reheat_data$gene,"reheat_gene","not_reheat_gene")) %>%
distinct(RSID,Peakid,.keep_all = TRUE) %>%
tidyr::unite(name,RSID,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% 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)) %>%
group_by(RSID,Peakid) %>%
# mutate(Keep=case_when(RSID))
# group_by(Peakid) %>%
summarize(name=unique(name),
# RSID=unique(RSID),
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)) #%>%
# mutate(point_ol=if_else(RSID %in% point_only$RSID,"yes","no")) %>%
# mutate(eQTL=if_else(RSID %in% dox_eqtl_SNPs$SNPS,"yes",if_else(Peakid %in% mar_eqtl_SNPS$Peakid,"yes","no")))
schneider_mat <- schneider_df %>%
ungroup() %>%
dplyr::select(name,med_3h_lfc:RNA_24h_lfc) %>%
column_to_rownames("name") %>%
as.matrix()
schneider_name_mat <- schneider_df %>%
ungroup() %>%
dplyr::select(name,TEstatus,mrc,reheat,dist_to_SNP)
row_anno <- ComplexHeatmap::rowAnnotation(TE_status=schneider_name_mat$TEstatus,reheat_status=schneider_name_mat$reheat,MRC=schneider_name_mat$mrc,direct_overlap=schneider_name_mat$dist_to_SNP,col= list(TE_status=c("TE_peak"="goldenrod",
"TE_peak;not_TE_peak"="goldenrod",
"not_TE_peak;TE_peak"="goldenrod",
"not_TE_peak"="lightblue"), MRC = c("EAR_open" = "#F8766D", "EAR_close" = "#f6483c","ESR_open" = "#7CAE00",
"ESR_close" = "#587b00",
"ESR_opcl"="grey40",
"ESR_C"="grey40",
"ESR_clop"="tan",
"ESR_D"="tan",
"ESR_OC" = "#6a9500",
"LR_open" = "#00BFC4",
"LR_close" = "#008d91",
"NR" = "#C77CFF",
"not_mrc"="black"),
reheat_status=c("reheat_gene"="green","not_reheat_gene"="orange"),
direct_overlap=c("0"="red","10"="pink","20"="tan2","50"="grey8")))
mat2 <- schneider_mat
# rownames(mat2)[1] = paste(c(letters, LETTERS), collapse = "")
simply_map <- ComplexHeatmap::Heatmap(schneider_mat,
left_annotation = row_anno,
show_row_names = TRUE,
# row_names_side = "left",
row_names_max_width= max_text_width(rownames(schneider_mat), gp=gpar(fontsize=8)),
heatmap_legend_param = list(direction = "horizontal"),
show_column_names = TRUE,
cluster_rows = FALSE,
cluster_columns = FALSE)
draw(simply_map, merge_legend = TRUE, heatmap_legend_side = "bottom",
annotation_legend_side = "bottom")
Final +/-5kb peak-TSS of expressed RNA, +/-25 kb of SNP
schneider_df_short <-schneider_20k_list%>%
as.data.frame() %>%
left_join(., peak_10kb_neargenes, by=c("Peakid"="Peakid")) %>%
left_join(., (ATAC_3_lfc %>%
dplyr::select(peak,med_3h_lfc)),by=c("Peakid"="peak")) %>%
left_join(., (ATAC_24_lfc %>%
dplyr::select(peak,med_24h_lfc)),by=c("Peakid"="peak"))%>%
left_join(., RNA_median_3_lfc,by =c("NCBI_gene"="ENTREZID", "SYMBOL"="SYMBOL")) %>%
left_join(., RNA_median_24_lfc,by =c("NCBI_gene"="ENTREZID", "SYMBOL"="SYMBOL")) %>%
na.omit(RNA_median_24_lfc) %>%
# mutate(reheat=if_else(SYMBOL %in% Reheat_data$gene,"reheat_gene","not_reheat_gene")) %>%
distinct(RSID,Peakid,.keep_all = TRUE) %>%
tidyr::unite(name,RSID,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% 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)) %>%
group_by(RSID,Peakid) %>%
# mutate(Keep=case_when(RSID))
# group_by(Peakid) %>%
summarize(name=unique(name),
# RSID=unique(RSID),
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)) %>%
arrange(., Peakid) %>%
left_join(., proto_list, by=c("SYMBOL"="SYMBOL"))
schneider_mat_short <- schneider_df_short %>%
ungroup() %>%
dplyr::select(name,med_3h_lfc:RNA_24h_lfc,logFC) %>%
column_to_rownames("name") %>%
as.matrix()
schneider_name_mat_short <- schneider_df_short %>%
ungroup() %>%
dplyr::select(name,TEstatus,mrc,dist_to_SNP)
row_anno_short <- ComplexHeatmap::rowAnnotation(TE_status=schneider_name_mat_short$TEstatus,reheat_status=schneider_name_mat_short$reheat,MRC=schneider_name_mat_short$mrc,direct_overlap=schneider_name_mat_short$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 <- schneider_mat_short
# rownames(mat2)[1] = paste(c(letters, LETTERS), collapse = "")
simply_map_short <- ComplexHeatmap::Heatmap(schneider_mat_short,
left_annotation = row_anno_short,
show_row_names = TRUE,
# row_names_side = "left",
row_names_max_width= max_text_width(rownames(schneider_mat_short), gp=gpar(fontsize=16)),
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")
# saveRDS(schneider_df_short,"data/Final_four_data/schneider_df_short.RDS")Genes of interest
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/x4_filtered.RDS")
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","ANKRD4", "SLC9C2", "TNFSF4", "DARS2", "KLHL20"))
# ,ENTREZID= c(9585,26051,284525,7292,55157,27252))
df_gene_schneider_RNA <- df_gene_schneider %>%
left_join(., (RNA_median_24_lfc %>% dplyr::select(ENTREZID,SYMBOL)), by = c ("SYMBOL"="SYMBOL")) %>%
left_join(., (schneider_df_short %>% dplyr::select(RSID,Peakid,SYMBOL)),by = c("SYMBOL"="SYMBOL")) %>% distinct(SYMBOL,.keep_all = TRUE)
df_gene_schneider_ATAC <- df_gene_schneider %>%
left_join(., (RNA_median_24_lfc %>% dplyr::select(ENTREZID,SYMBOL)), by = c ("SYMBOL"="SYMBOL")) %>%
left_join(., (schneider_df_short %>% dplyr::select(RSID,Peakid,SYMBOL)),by = c("SYMBOL"="SYMBOL")) %>% distinct(Peakid,SYMBOL,.keep_all = TRUE)RNA_counts %>%
dplyr::filter(ENTREZID %in% df_gene_schneider_RNA$ENTREZID) %>%
pivot_longer(cols = !ENTREZID, names_to = "sample", values_to = "counts") %>%
# mutate(ENTREZID=as.numeric(ENTREZID)) %>%
left_join(., df_gene_schneider_RNA, by =c("ENTREZID"="ENTREZID")) %>%
# left_join(., df_gene_schneider, by =c("ENTREZID"="ENTREZID.x")) %>%
separate("sample", into = c("trt","ind","time")) %>%
mutate(time=factor(time, levels = c("3h","24h"))) %>%
mutate(trt=factor(trt, levels= c("DOX","EPI","DNR","MTX","TRZ","VEH"))) %>%
ggplot(., aes (x = time, y=counts))+
geom_boxplot(aes(fill=trt))+
facet_wrap(~SYMBOL, scales="free_y")+
scale_fill_manual(values = drug_pal)+
ggtitle("RNA LFC of expressed gene")+
theme_bw()+
ylab("log2 cpm RNA")
ATAC_counts %>%
cpm(., log = TRUE) %>%
as.data.frame() %>%
rename_with(.,~gsub(pattern = "Ind1_75", replacement = "1_",.)) %>%
rename_with(.,~gsub(pattern = "Ind2_87", replacement = "2_",.)) %>%
rename_with(.,~gsub(pattern = "Ind3_77", replacement = "3_",.)) %>%
rename_with(.,~gsub(pattern = "Ind6_71", replacement = "6_",.)) %>%
rename_with(.,~gsub( "DX" ,'DOX',.)) %>%
rename_with(.,~gsub( "DA" ,'DNR',.)) %>%
rename_with(.,~gsub( "E" ,'EPI',.)) %>%
rename_with(.,~gsub( "T" ,'TRZ',.)) %>%
rename_with(.,~gsub( "M" ,'MTX',.)) %>%
rename_with(.,~gsub( "V" ,'VEH',.)) %>%
rename_with(.,~gsub("24h","_24h",.)) %>%
rename_with(.,~gsub("3h","_3h",.)) %>%
dplyr::filter(row.names(.) %in% df_gene_schneider_ATAC$Peakid) %>%
mutate(Peakid = row.names(.)) %>%
pivot_longer(cols = !Peakid, names_to = "sample", values_to = "counts") %>%
left_join(., df_gene_schneider_ATAC, 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")
#### ATAC-seq by peaks
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),
# 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)) %>%
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 = "_")
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,
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")
hm_lfc_df_50 <- SNP_peak_check_50k %>%
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),
# 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)) %>%
arrange(., Peakid)
hm_lfc_mat_50 <-hm_lfc_df_50 %>%
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_50 <-hm_lfc_df_50 %>%
dplyr::select (Peakid, RSID, TEstatus, mrc,dist_to_SNP) %>%
tidyr::unite(name, Peakid, RSID, sep = "_")
row_anno_lfc_50 <- ComplexHeatmap::rowAnnotation(
TE_status=hm_name_mat_50$TEstatus,
reheat_status=hm_name_mat_50$reheat,
MRC=hm_name_mat_50$mrc,
direct_overlap=hm_name_mat_50$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_50 <- ComplexHeatmap::Heatmap(hm_lfc_mat_50,
left_annotation = row_anno_lfc_50,
show_row_names = TRUE,
row_names_max_width= max_text_width(rownames(hm_lfc_mat_50), gp=gpar(fontsize=16)),
heatmap_legend_param = list(direction = "horizontal"),
show_column_names = TRUE,
cluster_rows = FALSE,
cluster_columns = FALSE)
draw(simply_map_lfc_50,
merge_legend = TRUE,
heatmap_legend_side = "bottom",
annotation_legend_side = "bottom")
# SNP_peak_check_50k %>%
# left_join(., Nine_te_df, by = c("Peakid"="Peakid") ) %>%
# # dplyr::filter(mrc != "NR") %>%
# dplyr::filter(mrc !="not_mrc") %>%
# distinct(RSID)
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 labeling_0.4.3
[111] ps_1.8.1 getPass_0.2-4
[113] plyr_1.8.9 fs_1.6.5
[115] stringi_1.8.4 viridisLite_0.4.2
[117] gridBase_0.4-7 munsell_0.5.1
[119] Biostrings_2.74.1 Matrix_1.7-1
[121] BSgenome_1.74.0 patchwork_1.3.0
[123] hms_1.1.3 bit64_4.5.2
[125] KEGGREST_1.46.0 statmod_1.5.0
[127] SummarizedExperiment_1.36.0 memoise_2.0.1
[129] bslib_0.8.0 bit_4.5.0.1