Combine_data
ERM
2024-11-14
Last updated: 2024-11-14
Checks: 7 0
Knit directory: ATAC_learning/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 0cdaa94 | reneeisnowhere | 2024-11-14 | adding in updates |
| Rmd | 84c2d63 | reneeisnowhere | 2024-11-14 | adding in commits |
| html | c959920 | reneeisnowhere | 2024-10-28 | Build site. |
| Rmd | c3af560 | reneeisnowhere | 2024-10-28 | updates to peaks/rna stuff |
| html | 90fa7d6 | reneeisnowhere | 2024-10-28 | Build site. |
| Rmd | 0c8593f | reneeisnowhere | 2024-10-28 | updates to new SNP associaitions |
| html | ad854f2 | reneeisnowhere | 2024-10-24 | Build site. |
| Rmd | d949388 | reneeisnowhere | 2024-10-24 | wflow_publish("analysis/final_plot_attempt.Rmd") |
| html | 6074022 | reneeisnowhere | 2024-10-17 | Build site. |
| Rmd | 00d7d3d | reneeisnowhere | 2024-10-17 | updates |
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)# toplistall_RNA <- readRDS("data/other_papers/toplistall_RNA.RDS")
# toplistall_RNA <- toplistall_RNA %>%
# mutate(logFC = logFC*(-1))
# toplist_ATAC <- readRDS("data/Final_four_data/toplist_ff.RDS")
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)
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")
joined_LFC_df <- overlap_df_ggplot %>%
left_join(.,(Collapsed_new_peaks %>%
dplyr::select(Peakid,dist_to_NG, NCBI_gene:SYMBOL)),
by=c("peakid"="Peakid")) %>%
left_join(., RNA_median_3_lfc ,
# %>%
# dplyr::select(SYMBOL,RNA_3h_lfc)),
by=c("SYMBOL"="SYMBOL", "NCBI_gene"="ENTREZID")) %>%
left_join(., RNA_median_24_lfc,# %>%
# dplyr::select(SYMBOL,RNA_24h_lfc)),
by=c("SYMBOL"="SYMBOL", "NCBI_gene"="ENTREZID"))
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_closest_output <- readRDS("data/other_papers/Schneider_closestgene_SNP_file.RDS") %>%
# left_join(., checklist, by=c("ENTREZID"="ENTREZID"))
# schneider_gr <- schneider_closest_output %>%
# dplyr::select(chrom,start,stop,RSID,ENTREZID) %>%
# left_join(., (checklist %>% mutate(ENTREZID=as.numeric(ENTREZID))) , by=c("ENTREZID"="ENTREZID")) %>%
# GRanges()
#
# schneider_10k_gr <- schneider_closest_output %>%
# mutate(start=(start-5000),stop=(stop+4999), width=10000) %>%
# dplyr::select(chrom,start,stop,RSID,ENTREZID) %>%
# left_join(., checklist , by=c("ENTREZID"="ENTREZID")) %>%
# GRanges()
# SNP_peak_check <- join_overlap_intersect(TSS_assigned_NG_gr,schneider_gr) %>%
# as.data.frame()
#
# SNP_peak_check_10k <- join_overlap_intersect(TSS_assigned_NG_gr,schneider_10k_gr) %>%
# as.data.frame()
new_SNP_peak_check_10k <- readRDS("data/Final_four_data/new_SNP_peak_check_10k.RDS")
new_SNP_peak_check <- readRDS("data/Final_four_data/new_SNP_peak_check.RDS")
point_only <- new_SNP_peak_check
# new_SNP_pc_gr <- new_SNP_peak_check_10k
new_SNP_peak_check_10k %>% dplyr::filter(ENTREZID.x !=ENTREZID.y) seqnames start end width strand Peakid NG_chr
1 chr8 141160879 141162083 1205 * chr8.141160879.141162083 chr8
NG_start NG_end.x NG_TSS ENTREZID.x SYMBOL.x dist_to_NG Variation
1 141117277 141195808 141117278 22898 DENND3 -43602 rs428300
RSID ENTREZID.y SYMBOL.y TSS_chr TSS_start TSS_end NG_end.y
1 rs428300 57210 SLC45A4 chr8 141207165 141207166 141308305
dist_SNP_NG_TSS
1 43909# 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)
library(readxl)
Reheat_data <- read_excel("data/other_papers/jah36123-sup-0002-tables2.xlsx")
top_reheat <- Reheat_data %>%
dplyr::filter(fisher_pvalue<0.005)
schneider_short_list <- point_only %>% as.data.frame
#
# peakAnnoList_ff_motif <- readRDS("data/Final_four_data/peakAnnoList_ff_motif.RDS")
#
# background_peaks <- as.data.frame(peakAnnoList_ff_motif$background)
# EAR_df <- as.data.frame(peakAnnoList_ff_motif$EAR)
# ESR_df <- as.data.frame(peakAnnoList_ff_motif$ESR)
# LR_df <- as.data.frame(peakAnnoList_ff_motif$LR)
# NR_df <- as.data.frame(peakAnnoList_ff_motif$NR)
# open_3med <- ATAC_3_lfc %>%
# dplyr::filter(med_3h_lfc > 0)
#
# close_3med <- ATAC_3_lfc %>%
# dplyr::filter(med_3h_lfc < 0)
#
# open_24med <- ATAC_24_lfc %>%
# dplyr::filter(med_24h_lfc > 0)
#
# close_24med <- ATAC_24_lfc %>%
# dplyr::filter(med_24h_lfc < 0)
#
# medA <- ATAC_3_lfc %>%
# left_join(ATAC_24_lfc, by=c("peak"="peak")) %>%
# dplyr::filter(med_3h_lfc > 0 & med_24h_lfc>0)
#
# medB <- ATAC_3_lfc %>%
# left_join(ATAC_24_lfc, by=c("peak"="peak")) %>%
# dplyr::filter(med_3h_lfc < 0 & med_24h_lfc < 0)
#
# medC <- ATAC_3_lfc %>%
# left_join(ATAC_24_lfc, by=c("peak"="peak")) %>%
# dplyr::filter(med_3h_lfc > 0& med_24h_lfc <0)
#
#
# medD <- ATAC_3_lfc %>%
# left_join(ATAC_24_lfc, by=c("peak"="peak"))%>%
# dplyr::filter(med_3h_lfc < 0 & med_24h_lfc > 0)
Nine_te_df <- readRDS("data/Final_four_data/Nine_group_TE_df.RDS")
match <- Nine_te_df %>% distinct(Peakid,TEstatus,mrc,.keep_all = TRUE)
# NR <- NR_df %>% dplyr::select(Peakid)
# EAR_open <- EAR_df %>% dplyr::select(Peakid) %>% dplyr::filter(Peakid %in% open_3med$peak)
# EAR_close <-EAR_df %>% dplyr::select(Peakid) %>% dplyr::filter(Peakid %in% close_3med$peak)
# ESR_open <- ESR_df %>% dplyr::select(Peakid) %>% dplyr::filter(Peakid %in% medA$peak)
# ESR_close <- ESR_df %>% dplyr::select(Peakid) %>% dplyr::filter(Peakid %in% medB$peak)
# LR_open <- LR_df %>% dplyr::select(Peakid) %>% dplyr::filter(Peakid %in% open_24med$peak)
# LR_close <- LR_df %>% dplyr::select(Peakid) %>% dplyr::filter(Peakid %in% open_24med$peak)
# ESR_opcl <- ESR_df %>% dplyr::select(Peakid) %>% dplyr::filter(Peakid %in% medC$peak)
# ESR_clop <- ESR_df %>% dplyr::select(Peakid) %>% dplyr::filter(Peakid %in% medD$peak)
# expand_schneider %>% as.data.frame() %>%
# dplyr::select(Peakid,RSID,NCBI_gene:SYMBOL.y) %>% dplyr::filter(grepl("rs425*", RSID))
Knowles_dox_eQTL <- readRDS("data/Knowles_5.RDS")
Knowles_mar_eQTL <- readRDS("data/Knowles_4.RDS")
dox_eQTL_gr <- Knowles_dox_eQTL %>%
distinct(RSID,.keep_all = TRUE) %>%
mutate(CHR_ID=(gsub("chr","",chr))) %>%
dplyr::rename("SNPS"=RSID,"CHR_POS"=pos) %>%
dplyr::select(CHR_ID, CHR_POS,SNPS) %>%
mutate(CHR_ID=as.numeric(CHR_ID), CHR_POS=as.numeric(CHR_POS)) %>%
na.omit() %>%
mutate(gwas="eQTL") %>%
mutate(start=CHR_POS, end=CHR_POS, chr=paste0("chr",CHR_ID)) %>%
GRanges()
mar_eQTL_gr <- Knowles_mar_eQTL %>%
distinct(RSID,.keep_all = TRUE) %>%
mutate(CHR_ID=(gsub("chr","",chr))) %>%
dplyr::rename("SNPS"=RSID,"CHR_POS"=pos) %>%
dplyr::select(CHR_ID, CHR_POS,SNPS) %>%
mutate(gwas="beQTL") %>%
mutate(CHR_ID=as.numeric(CHR_ID), CHR_POS=as.numeric(CHR_POS)) %>%
na.omit() %>%
mutate(start=CHR_POS, end=CHR_POS, chr=paste0("chr",CHR_ID)) %>%
GRanges()
mar_eqtl_SNPS <-
join_overlap_intersect(ATAC_peaks_gr,mar_eQTL_gr) %>%
as.data.frame()
dox_eqtl_SNPs <-
join_overlap_intersect(ATAC_peaks_gr,dox_eQTL_gr) %>%
as.data.frame()
test <- dox_eqtl_SNPs %>%
dplyr::filter(Peakid %in% new_SNP_peak_check_10k$Peakid)
join_overlap_intersect((new_SNP_peak_check_10k %>% GRanges() ),mar_eQTL_gr) %>%
as.data.frame() [1] seqnames start end width
[5] strand Peakid NG_chr NG_start
[9] NG_end.x NG_TSS ENTREZID.x SYMBOL.x
[13] dist_to_NG Variation RSID ENTREZID.y
[17] SYMBOL.y TSS_chr TSS_start TSS_end
[21] NG_end.y dist_SNP_NG_TSS CHR_ID CHR_POS
[25] SNPS gwas
<0 rows> (or 0-length row.names)schneider_df <-
new_SNP_peak_check_10k%>% as.data.frame() %>%
dplyr::select(Peakid,RSID,ENTREZID.x:dist_to_NG,ENTREZID.y:SYMBOL.y, dist_SNP_NG_TSS) %>%
mutate(SYMBOL=if_else(is.na(SYMBOL.y),SYMBOL.x,SYMBOL.y)) %>%
mutate(ENTREZID=if_else(is.na(ENTREZID.y),ENTREZID.x,ENTREZID.y))%>%
dplyr::select(Peakid, RSID, ENTREZID, SYMBOL) %>%
distinct(Peakid,RSID,.keep_all = TRUE) %>%
# left_join(., joined_LFC_df,by = c("Peakid"="peakid", "ENTREZID"="NCBI_gene","SYMBOL"="SYMBOL")) #%>%
# dplyr::select(Peakid:Geneid, AC_3h_lfc, AC_24h_lfc) %>%
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"))%>%
### peaks left out here were chopped due to low counts (row_means<0) %>%
mutate(ENTREZID= if_else(RSID=="rs36022097","8970",ENTREZID),
SYMBOL=if_else(RSID=="rs36022097","H2BC11",SYMBOL)) %>%
dplyr::filter(!is.na(med_24h_lfc)) %>%
left_join(., RNA_median_3_lfc,by =c("ENTREZID"="ENTREZID")) %>%
left_join(., RNA_median_24_lfc,by =c("ENTREZID"="ENTREZID", "SYMBOL.y"="SYMBOL")) %>%
mutate(reheat=if_else(SYMBOL.x %in% Reheat_data$gene,"reheat_gene","not_reheat_gene")) %>%
dplyr::filter(!is.na(med_3h_lfc)) %>%
distinct(RSID,.keep_all = TRUE) %>%
dplyr::select(RSID,Peakid,med_3h_lfc,med_24h_lfc,,RNA_3h_lfc,RNA_24h_lfc, ENTREZID,SYMBOL.x,reheat) %>% #AC_3h_lfc,AC_24h_lfc) %>%
tidyr::unite(name,RSID,SYMBOL.x,Peakid,sep ="_",remove=FALSE) %>%
left_join(.,(match %>%
group_by(Peakid) %>%
filter(!(TEstatus=="not_TE_peak" & any (TEstatus == "TE_peak"))) %>%
ungroup() %>%
distinct(TEstatus,Peakid,.keep_all = TRUE)),
by = c("Peakid"="Peakid")) %>%
group_by(Peakid) %>%
summarize(name=unique(name),
RSID=unique(RSID),
med_3h_lfc=unique(med_3h_lfc),
med_24h_lfc=unique(med_24h_lfc),
# AC_3h_lfc=unique(AC_3h_lfc),
# AC_24h_lfc=unique(AC_24h_lfc),
RNA_3h_lfc=unique(RNA_3h_lfc),
RNA_24h_lfc=unique(RNA_24h_lfc),
repClass=paste(unique(repClass),collapse=":"),
TEstatus=paste(unique(TEstatus),collapse=";"),
SYMBOL.x=paste(unique(SYMBOL.x),collapse=";"),
reheat=paste(unique(reheat),collapse=";"),
mrc=unique(mrc)) %>%
mutate(point_ol=if_else(RSID %in% point_only$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,point_ol)
row_anno <- ComplexHeatmap::rowAnnotation(TE_status=schneider_name_mat$TEstatus,reheat_status=schneider_name_mat$reheat,MRC=schneider_name_mat$mrc,direct_overlap=schneider_name_mat$point_ol,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("yes"="red","no"="grey8"))
ComplexHeatmap::Heatmap(schneider_mat,
left_annotation = row_anno,
show_row_names = TRUE,
show_column_names = TRUE,cluster_rows = FALSE,cluster_columns = FALSE)
#### GWAS SNP overlap log2cpm
TTN
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/Counts_RNA_ERMatthews.RDS")
# overlap_atac_ac_peaks <- readRDS( "data/Final_four_data/overlapping_ac_atac_peaks.RDS")
TNT_peak <- data.frame(peak="chr2.178547784.178549172", RNA="TTN", ENTREZID=7273)
AS1 <- 100506866
RNA_counts %>%
column_to_rownames("ENTREZID") %>%
cpm(., log = TRUE) %>%
as.data.frame() %>%
dplyr::filter(row.names(.) %in% TNT_peak$ENTREZID) %>%
mutate(ENTREZID = row.names(.)) %>%
pivot_longer(cols = !ENTREZID, names_to = "sample", values_to = "counts") %>%
separate("sample", into = c("trt","ind","time")) %>%
mutate(time=factor(time, levels = c("3h","24h"))) %>%
mutate(trt=factor(trt, levels= c("DOX","EPI","DNR","MTX","TRZ","VEH"))) %>%
ggplot(., aes (x = time, y=counts))+
geom_boxplot(aes(fill=trt))+
ggtitle("Titin (TTN) RNA expression")+
scale_fill_manual(values = drug_pal)+
theme_bw()+
ylab("log2 cpm RNA")
| Version | Author | Date |
|---|---|---|
| 6074022 | reneeisnowhere | 2024-10-17 |
RNA_counts %>%
column_to_rownames("ENTREZID") %>%
cpm(., log = TRUE) %>%
as.data.frame() %>%
dplyr::filter(row.names(.) %in% AS1) %>%
mutate(ENTREZID = row.names(.)) %>%
pivot_longer(cols = !ENTREZID, names_to = "sample", values_to = "counts") %>%
separate("sample", into = c("trt","ind","time")) %>%
mutate(time=factor(time, levels = c("3h","24h"))) %>%
mutate(trt=factor(trt, levels= c("DOX","EPI","DNR","MTX","TRZ","VEH"))) %>%
ggplot(., aes (x = time, y=counts))+
geom_boxplot(aes(fill=trt))+
ggtitle("Titin-AS1 (TTN-AS1) RNA expression")+
scale_fill_manual(values = drug_pal)+
theme_bw()+
ylab("log2 cpm RNA")
ATAC_counts %>%
cpm(., log = TRUE) %>%
as.data.frame() %>%
rename_with(.,~gsub(pattern = "Ind1_75", replacement = "1_",.)) %>%
rename_with(.,~gsub(pattern = "Ind2_87", replacement = "2_",.)) %>%
rename_with(.,~gsub(pattern = "Ind3_77", replacement = "3_",.)) %>%
rename_with(.,~gsub(pattern = "Ind6_71", replacement = "6_",.)) %>%
rename_with(.,~gsub( "DX" ,'DOX',.)) %>%
rename_with(.,~gsub( "DA" ,'DNR',.)) %>%
rename_with(.,~gsub( "E" ,'EPI',.)) %>%
rename_with(.,~gsub( "T" ,'TRZ',.)) %>%
rename_with(.,~gsub( "M" ,'MTX',.)) %>%
rename_with(.,~gsub( "V" ,'VEH',.)) %>%
rename_with(.,~gsub("24h","_24h",.)) %>%
rename_with(.,~gsub("3h","_3h",.)) %>%
dplyr::filter(row.names(.) %in% TNT_peak$peak) %>%
mutate(Peakid = row.names(.)) %>%
pivot_longer(cols = !Peakid, names_to = "sample", values_to = "counts") %>%
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))+
ggtitle("Titin (TTN) ATAC accessibility")+
scale_fill_manual(values = drug_pal)+
theme_bw()+
ylab("log2 cpm ATAC")
| Version | Author | Date |
|---|---|---|
| ad854f2 | reneeisnowhere | 2024-10-24 |
IGSFB9
IGSF9B_peak <- data.frame(peak="chr11.133816825.133817041", RNA="IGSFB9", ENTREZID=22997)
RNA_counts %>%
column_to_rownames("ENTREZID") %>%
cpm(., log = TRUE) %>%
as.data.frame() %>%
dplyr::filter(row.names(.) %in% IGSF9B_peak$ENTREZID) %>%
mutate(ENTREZID = row.names(.)) %>%
pivot_longer(cols = !ENTREZID, names_to = "sample", values_to = "counts") %>%
separate("sample", into = c("trt","ind","time")) %>%
mutate(time=factor(time, levels = c("3h","24h"))) %>%
mutate(trt=factor(trt, levels= c("DOX","EPI","DNR","MTX","TRZ","VEH"))) %>%
ggplot(., aes (x = time, y=counts))+
geom_boxplot(aes(fill=trt))+
ggtitle("IGSF9B RNA expression")+
scale_fill_manual(values = drug_pal)+
theme_bw()+
ylab("log2 cpm RNA")
| Version | Author | Date |
|---|---|---|
| ad854f2 | reneeisnowhere | 2024-10-24 |
ATAC_counts %>%
cpm(., log = TRUE) %>%
as.data.frame() %>%
rename_with(.,~gsub(pattern = "Ind1_75", replacement = "1_",.)) %>%
rename_with(.,~gsub(pattern = "Ind2_87", replacement = "2_",.)) %>%
rename_with(.,~gsub(pattern = "Ind3_77", replacement = "3_",.)) %>%
rename_with(.,~gsub(pattern = "Ind6_71", replacement = "6_",.)) %>%
rename_with(.,~gsub( "DX" ,'DOX',.)) %>%
rename_with(.,~gsub( "DA" ,'DNR',.)) %>%
rename_with(.,~gsub( "E" ,'EPI',.)) %>%
rename_with(.,~gsub( "T" ,'TRZ',.)) %>%
rename_with(.,~gsub( "M" ,'MTX',.)) %>%
rename_with(.,~gsub( "V" ,'VEH',.)) %>%
rename_with(.,~gsub("24h","_24h",.)) %>%
rename_with(.,~gsub("3h","_3h",.)) %>%
dplyr::filter(row.names(.) %in% IGSF9B_peak$peak) %>%
mutate(Peakid = row.names(.)) %>%
pivot_longer(cols = !Peakid, names_to = "sample", values_to = "counts") %>%
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))+
ggtitle("IGSF9B ATAC accessibility")+
scale_fill_manual(values = drug_pal)+
theme_bw()+
ylab("log2 cpm ATAC")
schneider_df# A tibble: 43 × 14
# Groups: Peakid [35]
Peakid name RSID med_3h_lfc med_24h_lfc RNA_3h_lfc RNA_24h_lfc repClass
<chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <chr>
1 chr1.1734… rs10… rs10… 0.0209 1.28 0.0781 0.841 LINE
2 chr1.1963… rs75… rs75… -0.0841 1.40 0.0279 0.301 Other
3 chr1.1964… rs75… rs75… -0.282 -0.320 0.0279 0.301 NA
4 chr10.167… rs70… rs70… -0.386 -1.43 0.0107 -0.0511 NA
5 chr10.167… rs70… rs70… -0.386 -1.43 0.0107 -0.0511 NA
6 chr10.762… rs70… rs70… 0.268 3.24 0.126 0.202 SINE
7 chr11.124… rs71… rs71… 0.162 0.574 0.904 2.48 NA
8 chr11.124… rs50… rs50… 0.162 0.574 0.904 2.48 NA
9 chr11.133… rs10… rs10… -0.513 0.888 -0.0506 0.198 LTR
10 chr11.364… rs10… rs10… -0.524 0.0904 -0.194 -0.102 SINE
# ℹ 33 more rows
# ℹ 6 more variables: TEstatus <chr>, SYMBOL.x <chr>, reheat <chr>, mrc <chr>,
# point_ol <chr>, eQTL <chr>rs12051934 ALPK2
ALPK2_peak <- data.frame(peak="chr18.58336709.58336869", RNA="ALPK2", ENTREZID=115701)
RNA_counts %>%
column_to_rownames("ENTREZID") %>%
cpm(., log = TRUE) %>%
as.data.frame() %>%
dplyr::filter(row.names(.) %in% ALPK2_peak$ENTREZID) %>%
mutate(ENTREZID = row.names(.)) %>%
pivot_longer(cols = !ENTREZID, names_to = "sample", values_to = "counts") %>%
separate("sample", into = c("trt","ind","time")) %>%
mutate(time=factor(time, levels = c("3h","24h"))) %>%
mutate(trt=factor(trt, levels= c("DOX","EPI","DNR","MTX","TRZ","VEH"))) %>%
ggplot(., aes (x = time, y=counts))+
geom_boxplot(aes(fill=trt))+
ggtitle("ALPK2 RNA expression")+
scale_fill_manual(values = drug_pal)+
theme_bw()+
ylab("log2 cpm RNA")
| Version | Author | Date |
|---|---|---|
| c959920 | reneeisnowhere | 2024-10-28 |
ATAC_counts %>%
cpm(., log = TRUE) %>%
as.data.frame() %>%
rename_with(.,~gsub(pattern = "Ind1_75", replacement = "1_",.)) %>%
rename_with(.,~gsub(pattern = "Ind2_87", replacement = "2_",.)) %>%
rename_with(.,~gsub(pattern = "Ind3_77", replacement = "3_",.)) %>%
rename_with(.,~gsub(pattern = "Ind6_71", replacement = "6_",.)) %>%
rename_with(.,~gsub( "DX" ,'DOX',.)) %>%
rename_with(.,~gsub( "DA" ,'DNR',.)) %>%
rename_with(.,~gsub( "E" ,'EPI',.)) %>%
rename_with(.,~gsub( "T" ,'TRZ',.)) %>%
rename_with(.,~gsub( "M" ,'MTX',.)) %>%
rename_with(.,~gsub( "V" ,'VEH',.)) %>%
rename_with(.,~gsub("24h","_24h",.)) %>%
rename_with(.,~gsub("3h","_3h",.)) %>%
dplyr::filter(row.names(.) %in% ALPK2_peak$peak) %>%
mutate(Peakid = row.names(.)) %>%
pivot_longer(cols = !Peakid, names_to = "sample", values_to = "counts") %>%
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))+
ggtitle("ALPK2 ATAC accessibility")+
scale_fill_manual(values = drug_pal)+
theme_bw()+
ylab("log2 cpm ATAC")
| Version | Author | Date |
|---|---|---|
| c959920 | reneeisnowhere | 2024-10-28 |
rs4916358 PRDX6
schneider_df %>% dplyr::filter(SYMBOL.x=="PRDX6")# A tibble: 1 × 14
# Groups: Peakid [1]
Peakid name RSID med_3h_lfc med_24h_lfc RNA_3h_lfc RNA_24h_lfc repClass
<chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <chr>
1 chr1.17347… rs10… rs10… 0.0209 1.28 0.0781 0.841 LINE
# ℹ 6 more variables: TEstatus <chr>, SYMBOL.x <chr>, reheat <chr>, mrc <chr>,
# point_ol <chr>, eQTL <chr>PRDX6_peak <- data.frame(peak="chr1.173473597.173473889", RNA="PRDX6", ENTREZID=9588)
RNA_counts %>%
column_to_rownames("ENTREZID") %>%
cpm(., log = TRUE) %>%
as.data.frame() %>%
dplyr::filter(row.names(.) %in% PRDX6_peak$ENTREZID) %>%
mutate(ENTREZID = row.names(.)) %>%
pivot_longer(cols = !ENTREZID, names_to = "sample", values_to = "counts") %>%
separate("sample", into = c("trt","ind","time")) %>%
mutate(time=factor(time, levels = c("3h","24h"))) %>%
mutate(trt=factor(trt, levels= c("DOX","EPI","DNR","MTX","TRZ","VEH"))) %>%
ggplot(., aes (x = time, y=counts))+
geom_boxplot(aes(fill=trt))+
ggtitle(paste(PRDX6_peak$RNA," RNA expression"))+
scale_fill_manual(values = drug_pal)+
theme_bw()+
ylab("log2 cpm RNA")
| Version | Author | Date |
|---|---|---|
| ad854f2 | reneeisnowhere | 2024-10-24 |
plotpanelATAC <- 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",.))
plotpanelATAC %>%
dplyr::filter(row.names(.) %in% PRDX6_peak$peak) %>%
mutate(Peakid = row.names(.)) %>%
pivot_longer(cols = !Peakid, names_to = "sample", values_to = "counts") %>%
separate("sample", into = c("ind","trt","time")) %>%
mutate(time=factor(time, levels = c("3h","24h"))) %>%
mutate(trt=factor(trt, levels= c("DOX","EPI","DNR","MTX","TRZ","VEH"))) %>%
ggplot(., aes (x = time, y=counts))+
geom_boxplot(aes(fill=trt))+
ggtitle(paste(PRDX6_peak$RNA," gene and \n",PRDX6_peak$peak," ATAC accessibility"))+
scale_fill_manual(values = drug_pal)+
theme_bw()+
ylab("log2 cpm ATAC")
MSANTD2-AS1 rs7111513
schneider_df %>% dplyr::filter(RSID=="rs7111513")# A tibble: 1 × 14
# Groups: Peakid [1]
Peakid name RSID med_3h_lfc med_24h_lfc RNA_3h_lfc RNA_24h_lfc repClass
<chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <chr>
1 chr11.1248… rs71… rs71… 0.162 0.574 0.904 2.48 NA
# ℹ 6 more variables: TEstatus <chr>, SYMBOL.x <chr>, reheat <chr>, mrc <chr>,
# point_ol <chr>, eQTL <chr>MSANTD2_AS1_peak <- data.frame(peak="chr11.124863016.124863456",RNA="MSANTD2-AS1", ENTREZID=100507283)
RNA_counts %>%
column_to_rownames("ENTREZID") %>%
cpm(., log = TRUE) %>%
as.data.frame() %>%
dplyr::filter(row.names(.) %in% MSANTD2_AS1_peak$ENTREZID) %>%
mutate(ENTREZID = row.names(.)) %>%
pivot_longer(cols = !ENTREZID, names_to = "sample", values_to = "counts") %>%
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))+
ggtitle(paste(MSANTD2_AS1_peak$RNA," RNA expression"))+
scale_fill_manual(values = drug_pal)+
theme_bw()+
ylab("log2 cpm RNA")
| Version | Author | Date |
|---|---|---|
| c959920 | reneeisnowhere | 2024-10-28 |
plotpanelATAC <- ATAC_counts %>%
cpm(., log = TRUE) %>%
as.data.frame() %>%
rename_with(.,~gsub(pattern = "Ind1_75", replacement = "1_",.)) %>%
rename_with(.,~gsub(pattern = "Ind2_87", replacement = "2_",.)) %>%
rename_with(.,~gsub(pattern = "Ind3_77", replacement = "3_",.)) %>%
rename_with(.,~gsub(pattern = "Ind6_71", replacement = "6_",.)) %>%
rename_with(.,~gsub( "DX" ,'DOX',.)) %>%
rename_with(.,~gsub( "DA" ,'DNR',.)) %>%
rename_with(.,~gsub( "E" ,'EPI',.)) %>%
rename_with(.,~gsub( "T" ,'TRZ',.)) %>%
rename_with(.,~gsub( "M" ,'MTX',.)) %>%
rename_with(.,~gsub( "V" ,'VEH',.)) %>%
rename_with(.,~gsub("24h","_24h",.)) %>%
rename_with(.,~gsub("3h","_3h",.))
plotpanelATAC %>%
dplyr::filter(row.names(.) %in% MSANTD2_AS1_peak$peak) %>%
mutate(Peakid = row.names(.)) %>%
pivot_longer(cols = !Peakid, names_to = "sample", values_to = "counts") %>%
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))+
ggtitle(paste(MSANTD2_AS1_peak$RNA," gene and \n",MSANTD2_AS1_peak$peak," ATAC accessibility"))+
scale_fill_manual(values = drug_pal)+
theme_bw()+
ylab("log2 cpm ATAC")
| Version | Author | Date |
|---|---|---|
| c959920 | reneeisnowhere | 2024-10-28 |
MSANTD2-AS1 rs7111513
schneider_df %>% dplyr::filter(RSID=="rs5017048")# A tibble: 1 × 14
# Groups: Peakid [1]
Peakid name RSID med_3h_lfc med_24h_lfc RNA_3h_lfc RNA_24h_lfc repClass
<chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <chr>
1 chr11.1248… rs50… rs50… 0.162 0.574 0.904 2.48 NA
# ℹ 6 more variables: TEstatus <chr>, SYMBOL.x <chr>, reheat <chr>, mrc <chr>,
# point_ol <chr>, eQTL <chr>MSANTD2_AS1_peak <- data.frame(peak="chr11.124863016.124863456", RNA="MSANTD2-AS1", ENTREZID=
100507283)
RNA_counts %>%
column_to_rownames("ENTREZID") %>%
cpm(., log = TRUE) %>%
as.data.frame() %>%
dplyr::filter(row.names(.) %in% MSANTD2_AS1_peak$ENTREZID) %>%
mutate(ENTREZID = row.names(.)) %>%
pivot_longer(cols = !ENTREZID, names_to = "sample", values_to = "counts") %>%
separate("sample", into = c("trt","ind","time")) %>%
mutate(time=factor(time, levels = c("3h","24h"))) %>%
mutate(trt=factor(trt, levels= c("DOX","EPI","DNR","MTX","TRZ","VEH"))) %>%
ggplot(., aes (x = time, y=counts))+
geom_boxplot(aes(fill=trt))+
ggtitle(paste(MSANTD2_AS1_peak$RNA," RNA expression"))+
scale_fill_manual(values = drug_pal)+
theme_bw()+
ylab("log2 cpm RNA")
| Version | Author | Date |
|---|---|---|
| c959920 | reneeisnowhere | 2024-10-28 |
plotpanelATAC <- 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",.))
plotpanelATAC %>%
dplyr::filter(row.names(.) %in% MSANTD2_AS1_peak$peak) %>%
mutate(Peakid = row.names(.)) %>%
pivot_longer(cols = !Peakid, names_to = "sample", values_to = "counts") %>%
separate("sample", into = c("ind","trt","time")) %>%
mutate(time=factor(time, levels = c("3h","24h"))) %>%
mutate(trt=factor(trt, levels= c("DOX","EPI","DNR","MTX","TRZ","VEH"))) %>%
ggplot(., aes (x = time, y=counts))+
geom_boxplot(aes(fill=trt))+
ggtitle(paste(MSANTD2_AS1_peak$RNA," gene and \n",MSANTD2_AS1_peak$peak," ATAC accessibility"))+
scale_fill_manual(values = drug_pal)+
theme_bw()+
ylab("log2 cpm ATAC")
| Version | Author | Date |
|---|---|---|
| c959920 | reneeisnowhere | 2024-10-28 |
SLC8A1 rs6746423
schneider_df %>% dplyr::filter(RSID=="rs6746423")# A tibble: 1 × 14
# Groups: Peakid [1]
Peakid name RSID med_3h_lfc med_24h_lfc RNA_3h_lfc RNA_24h_lfc repClass
<chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <chr>
1 chr2.41038… rs67… rs67… 0.0766 1.64 -0.0446 -2.96 SINE
# ℹ 6 more variables: TEstatus <chr>, SYMBOL.x <chr>, reheat <chr>, mrc <chr>,
# point_ol <chr>, eQTL <chr>SLC8A1_peak <- data.frame(peak="chr2.41038390.41038934", RNA="SLC8A1", ENTREZID=6546)
RNA_counts %>%
column_to_rownames("ENTREZID") %>%
cpm(., log = TRUE) %>%
as.data.frame() %>%
dplyr::filter(row.names(.) %in% SLC8A1_peak$ENTREZID) %>%
mutate(ENTREZID = row.names(.)) %>%
pivot_longer(cols = !ENTREZID, names_to = "sample", values_to = "counts") %>%
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))+
ggtitle(paste(SLC8A1_peak$RNA," RNA expression"))+
scale_fill_manual(values = drug_pal)+
theme_bw()+
ylab("log2 cpm RNA")
| Version | Author | Date |
|---|---|---|
| c959920 | reneeisnowhere | 2024-10-28 |
plotpanelATAC <- ATAC_counts %>%
cpm(., log = TRUE) %>%
as.data.frame() %>%
rename_with(.,~gsub(pattern = "Ind1_75", replacement = "1_",.)) %>%
rename_with(.,~gsub(pattern = "Ind2_87", replacement = "2_",.)) %>%
rename_with(.,~gsub(pattern = "Ind3_77", replacement = "3_",.)) %>%
rename_with(.,~gsub(pattern = "Ind6_71", replacement = "6_",.)) %>%
rename_with(.,~gsub( "DX" ,'DOX',.)) %>%
rename_with(.,~gsub( "DA" ,'DNR',.)) %>%
rename_with(.,~gsub( "E" ,'EPI',.)) %>%
rename_with(.,~gsub( "T" ,'TRZ',.)) %>%
rename_with(.,~gsub( "M" ,'MTX',.)) %>%
rename_with(.,~gsub( "V" ,'VEH',.)) %>%
rename_with(.,~gsub("24h","_24h",.)) %>%
rename_with(.,~gsub("3h","_3h",.))
plotpanelATAC %>%
dplyr::filter(row.names(.) %in% SLC8A1_peak$peak) %>%
mutate(Peakid = row.names(.)) %>%
pivot_longer(cols = !Peakid, names_to = "sample", values_to = "counts") %>%
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))+
ggtitle(paste(SLC8A1_peak$RNA," gene and \n",SLC8A1_peak$peak," ATAC accessibility"))+
scale_fill_manual(values = drug_pal)+
theme_bw()+
ylab("log2 cpm ATAC")
| Version | Author | Date |
|---|---|---|
| c959920 | reneeisnowhere | 2024-10-28 |
CDPF1 rs4253763; rs11090819
schneider_df %>% dplyr::filter(RSID=="rs4253763")# A tibble: 1 × 14
# Groups: Peakid [1]
Peakid name RSID med_3h_lfc med_24h_lfc RNA_3h_lfc RNA_24h_lfc repClass
<chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <chr>
1 chr22.4622… rs42… rs42… 0.837 1.31 -0.251 -0.900 Other
# ℹ 6 more variables: TEstatus <chr>, SYMBOL.x <chr>, reheat <chr>, mrc <chr>,
# point_ol <chr>, eQTL <chr>CDPF1_peak <- data.frame(peak="chr22.46225398.46226192", RNA="CDPF1", ENTREZID=
150383)
RNA_counts %>%
column_to_rownames("ENTREZID") %>%
cpm(., log = TRUE) %>%
as.data.frame() %>%
dplyr::filter(row.names(.) %in% CDPF1_peak$ENTREZID) %>%
mutate(ENTREZID = row.names(.)) %>%
pivot_longer(cols = !ENTREZID, names_to = "sample", values_to = "counts") %>%
separate("sample", into = c("trt","ind","time")) %>%
mutate(time=factor(time, levels = c("3h","24h"))) %>%
mutate(trt=factor(trt, levels= c("DOX","EPI","DNR","MTX","TRZ","VEH"))) %>%
ggplot(., aes (x = time, y=counts))+
geom_boxplot(aes(fill=trt))+
ggtitle(paste(CDPF1_peak$RNA," RNA expression"))+
scale_fill_manual(values = drug_pal)+
theme_bw()+
ylab("log2 cpm RNA")
| Version | Author | Date |
|---|---|---|
| c959920 | reneeisnowhere | 2024-10-28 |
plotpanelATAC <- 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",.))
plotpanelATAC %>%
dplyr::filter(row.names(.) %in% CDPF1_peak$peak) %>%
mutate(Peakid = row.names(.)) %>%
pivot_longer(cols = !Peakid, names_to = "sample", values_to = "counts") %>%
separate("sample", into = c("ind","trt","time")) %>%
mutate(time=factor(time, levels = c("3h","24h"))) %>%
mutate(trt=factor(trt, levels= c("DOX","EPI","DNR","MTX","TRZ","VEH"))) %>%
ggplot(., aes (x = time, y=counts))+
geom_boxplot(aes(fill=trt))+
ggtitle(paste(CDPF1_peak$RNA," gene and \n",CDPF1_peak$peak," ATAC accessibility"))+
scale_fill_manual(values = drug_pal)+
theme_bw()+
ylab("log2 cpm ATAC")
| Version | Author | Date |
|---|---|---|
| c959920 | reneeisnowhere | 2024-10-28 |
GIN1 rs6860588; rs3776874; rs11952361
schneider_df %>% dplyr::filter(RSID=="rs11952361")# A tibble: 1 × 14
# Groups: Peakid [1]
Peakid name RSID med_3h_lfc med_24h_lfc RNA_3h_lfc RNA_24h_lfc repClass
<chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <chr>
1 chr5.10294… rs11… rs11… -0.645 -1.27 -0.598 -0.506 LTR
# ℹ 6 more variables: TEstatus <chr>, SYMBOL.x <chr>, reheat <chr>, mrc <chr>,
# point_ol <chr>, eQTL <chr>GIN1_peak <- data.frame(peak="chr5.102947036.102947471", RNA="GIN1", ENTREZID= 54826)
RNA_counts %>%
column_to_rownames("ENTREZID") %>%
cpm(., log = TRUE) %>%
as.data.frame() %>%
dplyr::filter(row.names(.) %in% GIN1_peak$ENTREZID) %>%
mutate(ENTREZID = row.names(.)) %>%
pivot_longer(cols = !ENTREZID, names_to = "sample", values_to = "counts") %>%
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))+
ggtitle(paste(GIN1_peak$RNA," RNA expression"))+
scale_fill_manual(values = drug_pal)+
theme_bw()+
ylab("log2 cpm RNA")
| Version | Author | Date |
|---|---|---|
| c959920 | reneeisnowhere | 2024-10-28 |
plotpanelATAC <- ATAC_counts %>%
cpm(., log = TRUE) %>%
as.data.frame() %>%
rename_with(.,~gsub(pattern = "Ind1_75", replacement = "1_",.)) %>%
rename_with(.,~gsub(pattern = "Ind2_87", replacement = "2_",.)) %>%
rename_with(.,~gsub(pattern = "Ind3_77", replacement = "3_",.)) %>%
rename_with(.,~gsub(pattern = "Ind6_71", replacement = "6_",.)) %>%
rename_with(.,~gsub( "DX" ,'DOX',.)) %>%
rename_with(.,~gsub( "DA" ,'DNR',.)) %>%
rename_with(.,~gsub( "E" ,'EPI',.)) %>%
rename_with(.,~gsub( "T" ,'TRZ',.)) %>%
rename_with(.,~gsub( "M" ,'MTX',.)) %>%
rename_with(.,~gsub( "V" ,'VEH',.)) %>%
rename_with(.,~gsub("24h","_24h",.)) %>%
rename_with(.,~gsub("3h","_3h",.))
plotpanelATAC %>%
dplyr::filter(row.names(.) %in% GIN1_peak$peak) %>%
mutate(Peakid = row.names(.)) %>%
pivot_longer(cols = !Peakid, names_to = "sample", values_to = "counts") %>%
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))+
ggtitle(paste(GIN1_peak$RNA," gene and \n",GIN1_peak$peak," ATAC accessibility"))+
scale_fill_manual(values = drug_pal)+
theme_bw()+
ylab("log2 cpm ATAC")
| Version | Author | Date |
|---|---|---|
| c959920 | reneeisnowhere | 2024-10-28 |
OGFRL1 rs1204328
schneider_df %>% dplyr::filter(RSID=="rs1204328")# A tibble: 1 × 14
# Groups: Peakid [1]
Peakid name RSID med_3h_lfc med_24h_lfc RNA_3h_lfc RNA_24h_lfc repClass
<chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <chr>
1 chr6.71712… rs12… rs12… 0.224 -0.436 0.233 1.10 LTR
# ℹ 6 more variables: TEstatus <chr>, SYMBOL.x <chr>, reheat <chr>, mrc <chr>,
# point_ol <chr>, eQTL <chr>OGFRL1_peak <- data.frame(peak="chr6.71712533.71713167", RNA="OGFRL1", ENTREZID= 79627)
RNA_counts %>%
column_to_rownames("ENTREZID") %>%
cpm(., log = TRUE) %>%
as.data.frame() %>%
dplyr::filter(row.names(.) %in% OGFRL1_peak$ENTREZID) %>%
mutate(ENTREZID = row.names(.)) %>%
pivot_longer(cols = !ENTREZID, names_to = "sample", values_to = "counts") %>%
separate("sample", into = c("trt","ind","time")) %>%
mutate(time=factor(time, levels = c("3h","24h"))) %>%
mutate(trt=factor(trt, levels= c("DOX","EPI","DNR","MTX","TRZ","VEH"))) %>%
ggplot(., aes (x = time, y=counts))+
geom_boxplot(aes(fill=trt))+
ggtitle(paste(OGFRL1_peak$RNA," RNA expression"))+
scale_fill_manual(values = drug_pal)+
theme_bw()+
ylab("log2 cpm RNA")
| Version | Author | Date |
|---|---|---|
| c959920 | reneeisnowhere | 2024-10-28 |
plotpanelATAC <- 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",.))
plotpanelATAC %>%
dplyr::filter(row.names(.) %in% OGFRL1_peak$peak) %>%
mutate(Peakid = row.names(.)) %>%
pivot_longer(cols = !Peakid, names_to = "sample", values_to = "counts") %>%
separate("sample", into = c("ind","trt","time")) %>%
mutate(time=factor(time, levels = c("3h","24h"))) %>%
mutate(trt=factor(trt, levels= c("DOX","EPI","DNR","MTX","TRZ","VEH"))) %>%
ggplot(., aes (x = time, y=counts))+
geom_boxplot(aes(fill=trt))+
ggtitle(paste(OGFRL1_peak$RNA," gene and \n",OGFRL1_peak$peak," ATAC accessibility"))+
scale_fill_manual(values = drug_pal)+
theme_bw()+
ylab("log2 cpm ATAC")
| Version | Author | Date |
|---|---|---|
| c959920 | reneeisnowhere | 2024-10-28 |
ATP6V1B2 rs12548222
schneider_df %>% dplyr::filter(RSID=="rs12548222")# A tibble: 1 × 14
# Groups: Peakid [1]
Peakid name RSID med_3h_lfc med_24h_lfc RNA_3h_lfc RNA_24h_lfc repClass
<chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <chr>
1 chr8.20115… rs12… rs12… -0.679 0.828 0.0935 0.470 NA
# ℹ 6 more variables: TEstatus <chr>, SYMBOL.x <chr>, reheat <chr>, mrc <chr>,
# point_ol <chr>, eQTL <chr>ATP6V1B2_peak <- data.frame(peak="chr8.20115130.20115538", RNA="ATP6V1B2", ENTREZID= 526)
RNA_counts %>%
column_to_rownames("ENTREZID") %>%
cpm(., log = TRUE) %>%
as.data.frame() %>%
dplyr::filter(row.names(.) %in% ATP6V1B2_peak$ENTREZID) %>%
mutate(ENTREZID = row.names(.)) %>%
pivot_longer(cols = !ENTREZID, names_to = "sample", values_to = "counts") %>%
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))+
ggtitle(paste(ATP6V1B2_peak$RNA," RNA expression"))+
scale_fill_manual(values = drug_pal)+
theme_bw()+
ylab("log2 cpm RNA")
| Version | Author | Date |
|---|---|---|
| c959920 | reneeisnowhere | 2024-10-28 |
plotpanelATAC <- ATAC_counts %>%
cpm(., log = TRUE) %>%
as.data.frame() %>%
rename_with(.,~gsub(pattern = "Ind1_75", replacement = "1_",.)) %>%
rename_with(.,~gsub(pattern = "Ind2_87", replacement = "2_",.)) %>%
rename_with(.,~gsub(pattern = "Ind3_77", replacement = "3_",.)) %>%
rename_with(.,~gsub(pattern = "Ind6_71", replacement = "6_",.)) %>%
rename_with(.,~gsub( "DX" ,'DOX',.)) %>%
rename_with(.,~gsub( "DA" ,'DNR',.)) %>%
rename_with(.,~gsub( "E" ,'EPI',.)) %>%
rename_with(.,~gsub( "T" ,'TRZ',.)) %>%
rename_with(.,~gsub( "M" ,'MTX',.)) %>%
rename_with(.,~gsub( "V" ,'VEH',.)) %>%
rename_with(.,~gsub("24h","_24h",.)) %>%
rename_with(.,~gsub("3h","_3h",.))
plotpanelATAC %>%
dplyr::filter(row.names(.) %in% ATP6V1B2_peak$peak) %>%
mutate(Peakid = row.names(.)) %>%
pivot_longer(cols = !Peakid, names_to = "sample", values_to = "counts") %>%
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))+
ggtitle(paste(ATP6V1B2_peak$RNA," gene and \n",ATP6V1B2_peak$peak," ATAC accessibility"))+
scale_fill_manual(values = drug_pal)+
theme_bw()+
ylab("log2 cpm ATAC")
| Version | Author | Date |
|---|---|---|
| c959920 | reneeisnowhere | 2024-10-28 |
ARFGEF1 rs7820882;rs11779485
schneider_df %>% dplyr::filter(RSID=="rs7820882")# A tibble: 1 × 14
# Groups: Peakid [1]
Peakid name RSID med_3h_lfc med_24h_lfc RNA_3h_lfc RNA_24h_lfc repClass
<chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <chr>
1 chr8.67481… rs78… rs78… -0.695 -0.744 -0.405 0.279 Other
# ℹ 6 more variables: TEstatus <chr>, SYMBOL.x <chr>, reheat <chr>, mrc <chr>,
# point_ol <chr>, eQTL <chr>ARFGEF1_peak <- data.frame(peak="chr8.67481465.67482210", RNA="ARFGEF1", ENTREZID= 10565)
RNA_counts %>%
column_to_rownames("ENTREZID") %>%
cpm(., log = TRUE) %>%
as.data.frame() %>%
dplyr::filter(row.names(.) %in% ARFGEF1_peak$ENTREZID) %>%
mutate(ENTREZID = row.names(.)) %>%
pivot_longer(cols = !ENTREZID, names_to = "sample", values_to = "counts") %>%
separate("sample", into = c("trt","ind","time")) %>%
mutate(time=factor(time, levels = c("3h","24h"))) %>%
mutate(trt=factor(trt, levels= c("DOX","EPI","DNR","MTX","TRZ","VEH"))) %>%
ggplot(., aes (x = time, y=counts))+
geom_boxplot(aes(fill=trt))+
ggtitle(paste(ARFGEF1_peak$RNA," RNA expression"))+
scale_fill_manual(values = drug_pal)+
theme_bw()+
ylab("log2 cpm RNA")
| Version | Author | Date |
|---|---|---|
| c959920 | reneeisnowhere | 2024-10-28 |
plotpanelATAC <- 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",.))
plotpanelATAC %>%
dplyr::filter(row.names(.) %in% ARFGEF1_peak$peak) %>%
mutate(Peakid = row.names(.)) %>%
pivot_longer(cols = !Peakid, names_to = "sample", values_to = "counts") %>%
separate("sample", into = c("ind","trt","time")) %>%
mutate(time=factor(time, levels = c("3h","24h"))) %>%
mutate(trt=factor(trt, levels= c("DOX","EPI","DNR","MTX","TRZ","VEH"))) %>%
ggplot(., aes (x = time, y=counts))+
geom_boxplot(aes(fill=trt))+
ggtitle(paste(ARFGEF1_peak$RNA," gene and \n",ARFGEF1_peak$peak," ATAC accessibility"))+
scale_fill_manual(values = drug_pal)+
theme_bw()+
ylab("log2 cpm ATAC")
| Version | Author | Date |
|---|---|---|
| c959920 | reneeisnowhere | 2024-10-28 |
ARFGEF1 rs4737838
schneider_df %>% dplyr::filter(RSID=="rs4737838")# A tibble: 1 × 14
# Groups: Peakid [1]
Peakid name RSID med_3h_lfc med_24h_lfc RNA_3h_lfc RNA_24h_lfc repClass
<chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <chr>
1 chr8.67503… rs47… rs47… -1.05 -0.827 -0.405 0.279 SINE
# ℹ 6 more variables: TEstatus <chr>, SYMBOL.x <chr>, reheat <chr>, mrc <chr>,
# point_ol <chr>, eQTL <chr>ARFGEF1_peak <- data.frame(peak="chr8.67503991.67504420", RNA="ARFGEF1", ENTREZID= 10565)
RNA_counts %>%
column_to_rownames("ENTREZID") %>%
cpm(., log = TRUE) %>%
as.data.frame() %>%
dplyr::filter(row.names(.) %in% ARFGEF1_peak$ENTREZID) %>%
mutate(ENTREZID = row.names(.)) %>%
pivot_longer(cols = !ENTREZID, names_to = "sample", values_to = "counts") %>%
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))+
ggtitle(paste(ARFGEF1_peak$RNA," RNA expression"))+
scale_fill_manual(values = drug_pal)+
theme_bw()+
ylab("log2 cpm RNA")
| Version | Author | Date |
|---|---|---|
| c959920 | reneeisnowhere | 2024-10-28 |
plotpanelATAC <- ATAC_counts %>%
cpm(., log = TRUE) %>%
as.data.frame() %>%
rename_with(.,~gsub(pattern = "Ind1_75", replacement = "1_",.)) %>%
rename_with(.,~gsub(pattern = "Ind2_87", replacement = "2_",.)) %>%
rename_with(.,~gsub(pattern = "Ind3_77", replacement = "3_",.)) %>%
rename_with(.,~gsub(pattern = "Ind6_71", replacement = "6_",.)) %>%
rename_with(.,~gsub( "DX" ,'DOX',.)) %>%
rename_with(.,~gsub( "DA" ,'DNR',.)) %>%
rename_with(.,~gsub( "E" ,'EPI',.)) %>%
rename_with(.,~gsub( "T" ,'TRZ',.)) %>%
rename_with(.,~gsub( "M" ,'MTX',.)) %>%
rename_with(.,~gsub( "V" ,'VEH',.)) %>%
rename_with(.,~gsub("24h","_24h",.)) %>%
rename_with(.,~gsub("3h","_3h",.))
plotpanelATAC %>%
dplyr::filter(row.names(.) %in% ARFGEF1_peak$peak) %>%
mutate(Peakid = row.names(.)) %>%
pivot_longer(cols = !Peakid, names_to = "sample", values_to = "counts") %>%
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))+
ggtitle(paste(ARFGEF1_peak$RNA," gene and \n",ARFGEF1_peak$peak," ATAC accessibility"))+
scale_fill_manual(values = drug_pal)+
theme_bw()+
ylab("log2 cpm ATAC")
| Version | Author | Date |
|---|---|---|
| c959920 | reneeisnowhere | 2024-10-28 |
sessionInfo()R version 4.4.1 (2024-06-14 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 10 x64 (build 19045)
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_2.20.0
[5] ggrepel_0.9.6
[6] plyranges_1.24.0
[7] ggsignif_0.6.4
[8] genomation_1.36.0
[9] edgeR_4.2.1
[10] limma_3.60.4
[11] ggpubr_0.6.0
[12] BiocParallel_1.38.0
[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_1.64.0
[20] org.Hs.eg.db_3.19.1
[21] TxDb.Hsapiens.UCSC.hg38.knownGene_3.18.0
[22] GenomicFeatures_1.56.0
[23] AnnotationDbi_1.66.0
[24] Biobase_2.64.0
[25] GenomicRanges_1.56.1
[26] GenomeInfoDb_1.40.1
[27] IRanges_2.38.1
[28] S4Vectors_0.42.1
[29] BiocGenerics_0.50.0
[30] RColorBrewer_1.1-3
[31] broom_1.0.7
[32] kableExtra_1.4.0
[33] lubridate_1.9.3
[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_1.3.2 BiocIO_1.14.0
[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_5.2-0
[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_1.50.0 RCurl_1.98-1.16
[27] nnet_7.3-19 git2r_0.35.0
[29] circlize_0.4.16 GenomeInfoDbData_1.2.12
[31] svglite_2.1.3 codetools_0.2-20
[33] DelayedArray_0.30.1 xml2_1.3.6
[35] tidyselect_1.2.1 shape_1.4.6.1
[37] farver_2.1.2 UCSC.utils_1.0.0
[39] base64enc_0.1-3 matrixStats_1.4.1
[41] GenomicAlignments_1.40.0 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_4.4.1
[49] Rcpp_1.0.13-1 glue_1.8.0
[51] SparseArray_1.4.8 xfun_0.49
[53] MatrixGenerics_1.16.0 withr_3.0.2
[55] formatR_1.14 fastmap_1.2.0
[57] fansi_1.0.6 callr_3.7.6
[59] digest_0.6.37 timechange_0.3.0
[61] R6_2.5.1 seqPattern_1.36.0
[63] colorspace_2.1-1 RSQLite_2.3.7
[65] utf8_1.2.4 generics_0.1.3
[67] data.table_1.16.2 htmlwidgets_1.6.4
[69] httr_1.4.7 S4Arrays_1.4.1
[71] whisker_0.4.1 pkgconfig_2.0.3
[73] gtable_0.3.6 blob_1.2.4
[75] impute_1.78.0 XVector_0.44.0
[77] htmltools_0.5.8.1 carData_3.0-5
[79] pwr_1.3-0 clue_0.3-66
[81] png_0.1-8 knitr_1.49
[83] lambda.r_1.2.4 rstudioapi_0.17.1
[85] tzdb_0.4.0 reshape2_1.4.4
[87] rjson_0.2.23 checkmate_2.3.2
[89] curl_6.0.1 zoo_1.8-12
[91] cachem_1.1.0 GlobalOptions_0.1.2
[93] KernSmooth_2.23-24 parallel_4.4.1
[95] foreign_0.8-87 restfulr_0.0.15
[97] pillar_1.9.0 vctrs_0.6.5
[99] promises_1.3.0 car_3.1-3
[101] cluster_2.1.6 htmlTable_2.4.3
[103] evaluate_1.0.1 magick_2.8.5
[105] cli_3.6.3 locfit_1.5-9.10
[107] compiler_4.4.1 futile.options_1.0.1
[109] Rsamtools_2.20.0 rlang_1.1.4
[111] crayon_1.5.3 labeling_0.4.3
[113] ps_1.8.1 getPass_0.2-4
[115] plyr_1.8.9 fs_1.6.5
[117] stringi_1.8.4 viridisLite_0.4.2
[119] gridBase_0.4-7 munsell_0.5.1
[121] Biostrings_2.72.1 Matrix_1.7-1
[123] BSgenome_1.72.0 patchwork_1.3.0
[125] hms_1.1.3 bit64_4.5.2
[127] KEGGREST_1.44.1 statmod_1.5.0
[129] SummarizedExperiment_1.34.0 memoise_2.0.1
[131] bslib_0.8.0 bit_4.5.0