Last updated: 2025-05-01
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Knit directory: ATAC_learning/
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| Rmd | 9ce3cdd | reneeisnowhere | 2025-05-01 | updates to webpage |
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| Rmd | 9f3ac8f | reneeisnowhere | 2024-06-25 | after updates |
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| Rmd | 6df7f0c | reneeisnowhere | 2024-03-11 | adjusting INd624h tss plot and adding Frip |
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| Rmd | 2ffc80a | reneeisnowhere | 2024-03-06 | updates to peak calling |
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| Rmd | 4b83ab1 | reneeisnowhere | 2024-03-04 | adding in read progression |
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| Rmd | d61bef5 | reneeisnowhere | 2024-03-04 | updating Peak info |
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| Rmd | 3a404bd | reneeisnowhere | 2024-03-01 | adding basic graphs of reads and reordering others |
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| Rmd | 0a26679 | reneeisnowhere | 2024-02-27 | adding in the the reads data |
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| Rmd | 1a8126f | reneeisnowhere | 2024-02-27 | adding the peak-calling files |
library(tidyverse)
# library(ggsignif)
# library(cowplot)
# library(ggpubr)
# library(scales)
# library(sjmisc)
library(kableExtra)
# library(broom)
# library(biomaRt)
library(RColorBrewer)
# library(gprofiler2)
# library(qvalue)
library(ChIPseeker)
# library("TxDb.Hsapiens.UCSC.hg38.knownGene")
# library("org.Hs.eg.db")
library(ATACseqQC)
library(rtracklayer)
library(TxDb.Hsapiens.UCSC.hg38.knownGene)
library(BSgenome.Hsapiens.UCSC.hg38)
library(MotifDb)
library(ChIPpeakAnno)drug_pal_fac <- c("#8B006D","#DF707E","#F1B72B", "#3386DD","#707031","#41B333")
txdb <- TxDb.Hsapiens.UCSC.hg38.knownGene
loadFile_peakCall <- function(){
file <- choose.files()
file <- readPeakFile(file, header = FALSE)
return(file)
}
prepGRangeObj <- function(seek_object){
seek_object$Peaks = seek_object$V4
seek_object$level = seek_object$V5
seek_object$V4 = seek_object$V5 = NULL
return(seek_object)
}
TSS = getBioRegion(TxDb=txdb, upstream=2000, downstream=2000, by = "gene",
type = "start_site")
# ind4_V24hpeaks <- readRDS("data/ind4_V24hpeaks.RDS")
# ind1_DA24hpeaks <- readRDS("data/ind1_DA24hpeaks.RDS")
# anno_ind4_V24h <- readRDS("data/anno_ind4_V24h.RDS")
# anno_ind1_DA24h <- readRDS("data/anno_ind1_DA24h.RDS")
Ind1_summary <- read.csv("data/Ind1_summary.txt", row.names = 1) %>%
rename(X1="sample",X2="reads",X3="mapped")
Ind2_summary <- read.csv("data/Ind2_summary.txt", row.names = 1)%>%
rename(X1="sample",X2="reads",X3="mapped")
Ind3_summary <- read.csv("data/Ind3_summary.txt", row.names = 1)%>%
rename(X1="sample",X2="reads",X3="mapped")
Ind4_summary <- read.csv("data/Ind4_summary.txt", row.names = 1)%>%
rename(X1="sample",X2="reads",X3="mapped")
Ind5_summary <- read.csv("data/Ind5_summary.txt", row.names = 1)%>%
rename(X1="sample",X2="reads",X3="mapped")
Ind6_summary <- read.csv("data/Ind6_summary.txt", row.names = 1)%>%
rename(X1="sample",X2="reads",X3="mapped")This specific page so far contains the QC analysis after calling peaks using MACS2.
Primary scripts used for ATAC data preprocesing will be linked here in the future:
Currently the steps were are as follows:
Basic Fastqc followed by adapter trimming and Fastqc analysis on the leftover fragments.
Trimmed reads were aligned to the hg38 human genome.
Mitochondrial reads (chrM) were removed
samtools was used to removed non-paired, discordantly paired, and multi-mapped reads from the .bam.
Markduplicates function from Picard was used to mark optical and PCR duplicates, with samtools used to remove these reads using the flag -F 1024.
MACS2 was used to call peaks, with QC of the peak files below.
Initial read summary is found at this LINK
Ind1_frag_files <- read.csv("data/Ind1_fragment_files.txt", row.names = 1)
Ind1_firstfrag_files <- read.csv("data/Ind1_firstfragment_files.txt", row.names = 1)
Ind1_frag_files %>%
mutate(trt=factor(trt, levels=c("DX","E","DA","M","T","V"), labels = c("DOX","EPI","DNR","MTX","TRZ","VEH"))) %>%
dplyr::filter(time =="3h") %>%
ggplot(., aes(y=counts, x=frag_size, group=trt))+
# geom_line(aes(col=trt, alpha = 0.5, linewidth=1 ))+
geom_line(aes(col=trt))+
ggtitle("Individual 1\n3 hour fragment sizes")+
theme_classic()+
facet_wrap(~trt)+
scale_color_manual(values=drug_pal_fac)+
coord_cartesian(ylim=c(0,300000))Ind1_firstfrag_files %>%
mutate(trt=factor(trt, levels=c("DX","E","DA","M","T","V"), labels = c("DOX","EPI","DNR","MTX","TRZ","VEH"))) %>%
dplyr::filter(time =="3h") %>%
ggplot(., aes(y=(counts), x=(frag_size), group=trt))+
# geom_line(aes(col=trt, alpha = 0.5, linewidth=1 ))+
geom_line(aes(col=trt))+
ggtitle("Individual 1\n3 hour fragment sizes BEFORE filtering")+
theme_classic()+
facet_wrap(~trt)+
scale_color_manual(values=drug_pal_fac)+
coord_cartesian(xlim=c(0,1000))#
Ind1_frag_files %>%
mutate(trt=factor(trt, levels=c("DX","E","DA","M","T","V"), labels = c("DOX","EPI","DNR","MTX","TRZ","VEH"))) %>%
dplyr::filter(time =="24h") %>%
ggplot(., aes(y=(counts), x=frag_size, group=trt))+
geom_line(aes(col=trt))+
ggtitle("Individual 1\n24 hour fragment sizes")+
theme_classic()+
facet_wrap(~trt)+
scale_color_manual(values=drug_pal_fac)#+ # coord_cartesian(ylim=c(0,300000))
Ind1_firstfrag_files %>%
mutate(trt=factor(trt, levels=c("DX","E","DA","M","T","V"), labels = c("DOX","EPI","DNR","MTX","TRZ","VEH"))) %>%
dplyr::filter(time =="24h") %>%
ggplot(., aes(y=(counts), x=(frag_size), group=trt))+
# geom_line(aes(col=trt, alpha = 0.5, linewidth=1 ))+
geom_line(aes(col=trt))+
ggtitle("Individual 1\n24 hour fragment sizes BEFORE filtering")+
theme_classic()+
facet_wrap(~trt)+
scale_color_manual(values=drug_pal_fac)+
coord_cartesian(xlim=c(0,1000))# cardiac_muscle_Frip <- read.csv("data/cardiac_muscle_FRIP.csv", row.names = 1)
cardiomyocyte_Frip <- read.csv("data/cardiomyocyte_FRIP.csv", row.names = 1)
left_ventricle_Frip <- read.csv("data/left_ventricle_FRIP.csv", row.names = 1)
embryo_heart_Frip <- read.csv("data/embryo_heart_FRIP.csv", row.names = 1)
# Ind1_summary <- read.csv("data/Ind1_summary.txt", row.names = 1) %>%
# dplyr::rename("sample"=X1,"reads"=X2,"mapped"=X3)
# Frip_1_reads <- Ind1_frip %>%
# rename(X1 ="sample", X2 ="counts_in_peaks") %>%
# separate(sample, into= c(NA,NA,NA,NA,NA,NA, NA,NA,NA,NA,"indv","sample")) %>%
# full_join(Ind1_reads_summary) %>%
# mutate(FRiP_1= counts_in_peaks/dedup_reads*100)
# write.csv(Frip_1_reads,"data/Frip_1_reads.csv")
Frip_1_reads <- read.csv("data/Frip_1_reads.csv", row.names = 1)
all_frip1 <- Frip_1_reads %>%
mutate(sample=gsub("75","1_",sample)) %>%
mutate(sample = gsub("24h","_24h",sample),
sample = gsub("3h","_3h",sample)) %>%
separate(sample, into = c("indv","trt","time")) %>%
mutate(trt=factor(trt, levels=c("DX","E","DA","M","T","V"), labels = c("DOX","EPI","DNR","MTX","TRZ","VEH"))) %>%
mutate(indv=as.numeric(indv)) %>%
left_join(., (cardiac_muscle_Frip %>% mutate(trt=factor(trt, levels=c("DOX","EPI","DNR","MTX","TRZ","VEH"))))) %>%
left_join(., (left_ventricle_Frip %>% mutate(trt=factor(trt, levels=c("DOX","EPI","DNR","MTX","TRZ","VEH"))))) %>%
left_join(., (cardiomyocyte_Frip %>% mutate(trt=factor(trt, levels=c("DOX","EPI","DNR","MTX","TRZ","VEH"))))) %>%
left_join(., (embryo_heart_Frip %>% mutate(trt=factor(trt, levels=c("DOX","EPI","DNR","MTX","TRZ","VEH"))))) %>%
mutate(FRIP_embryo=embryo_counts/dedup_reads *100) %>%
mutate(FRIP_cm=cm_counts/dedup_reads*100) %>%
mutate(FRIP_lv=lv_counts/dedup_reads*100) %>%
mutate(FRIP_adult=c_muscle_counts/dedup_reads*100)
# Ind1_reads_frip <-
# Ind1_summary %>%
# separate(reads,into=c("reads",NA),sep= " ") %>%
# mutate(reads=as.numeric(reads)) %>%
# separate(mapped, into= c("mapped_reads", NA,NA, "percent_mapped_reads",NA)) %>%
# separate(sample, into=c("one","two",NA, "4","sample",NA,"seven","8")) %>%
# mutate(mapped_reads=as.numeric(mapped_reads)) %>%
# mutate(type = if_else(two=="first", "total",
# if_else(two=="noM","nuclear",
# if_else((seven == "fin"& two=="files"), "unique", "dedup"))))%>%
# dplyr::select(sample,type, reads, mapped_reads)%>%
# pivot_longer(cols=reads:mapped_reads, names_to = "read_info", values_to = "reads") %>%
# unite("type",type:read_info, sep="_") %>%
# distinct() %>%
# pivot_wider(id_cols = sample, names_from = "type", values_from = "reads") %>%
# mutate(per_nuclear_mapped = nuclear_mapped_reads/total_mapped_reads*100) %>%
# mutate(dedup_read_pairs=dedup_reads/2) %>%
# dplyr::select(sample,total_reads:nuclear_mapped_reads,per_nuclear_mapped,unique_mapped_reads,dedup_reads,dedup_read_pairs) %>%
# dplyr::select(sample, dedup_reads) %>%
Frip_1_reads %>%
mutate(sample=gsub("75","1_",sample)) %>%
mutate(sample = gsub("24h","_24h",sample),
sample = gsub("3h","_3h",sample)) %>%
separate(sample, into = c("indv","trt","time")) %>%
mutate(time=factor(time, levels = c("3h","24h"))) %>%
mutate(trt=factor(trt, levels=c("DX","E","DA","M","T","V"), labels = c("DOX","EPI","DNR","MTX","TRZ","VEH"))) %>%
ggplot(., aes (x=time, y=FRiP_1, group=trt))+
geom_col(position= "dodge",aes(fill=trt))+
geom_hline(yintercept = 20)+
geom_hline(yintercept = 30,linetype=3)+
facet_wrap(~indv)+
theme_classic()+
ggtitle("FRiP Score across each sample for individual 1")+
scale_fill_manual(values=drug_pal_fac)FRiP_first_run <- read.csv("data/FRiP_first_run.txt", row.names = 1)
FRiP_first_run %>%
mutate(sample=gsub("75","1_",sample)) %>%
mutate(sample=gsub("87","2_",sample)) %>%
mutate(sample=gsub("77","3_",sample)) %>%
mutate(sample=gsub("79","4_",sample)) %>%
mutate(sample=gsub("78","5_",sample)) %>%
mutate(sample=gsub("71","6_",sample)) %>%
mutate(sample = gsub("24h","_24h",sample),
sample = gsub("3h","_3h",sample)) %>%
separate(sample, into = c("indv","trt","time")) %>%
mutate(time=factor(time, levels = c("3h","24h"))) %>%
mutate(trt=factor(trt, levels=c("DX","E","DA","M","T","V"), labels = c("DOX","EPI","DNR","MTX","TRZ","VEH"))) %>%
ggplot(., aes (x=time, y=FRiP, group=trt))+
geom_col(position= "dodge",aes(fill=trt))+
geom_hline(yintercept = 20)+
geom_hline(yintercept = 30,linetype=3)+
facet_wrap(~indv)+
theme_classic()+
ggtitle("FRiP Score across Ind1 sample")+
scale_fill_manual(values=drug_pal_fac)all_frip1 %>% ggplot(., aes (x=time, y=FRIP_cm, group=trt))+
geom_col(position= "dodge",aes(fill=trt))+
geom_hline(yintercept = 20)+
geom_hline(yintercept = 30,linetype=3)+
facet_wrap(~indv)+
theme_classic()+
ggtitle("FRiP Score across Ind1 using cardiomyocyte DNAse peaks")+
scale_fill_manual(values=drug_pal_fac)all_frip1 %>% ggplot(., aes (x=time, y=FRIP_lv, group=trt))+
geom_col(position= "dodge",aes(fill=trt))+
geom_hline(yintercept = 20)+
geom_hline(yintercept = 30,linetype=3)+
facet_wrap(~indv)+
theme_classic()+
ggtitle("FRiP Score across Ind1 using left ventricle DNAse peaks")+
scale_fill_manual(values=drug_pal_fac)all_frip1 %>% ggplot(., aes (x=time, y=FRIP_adult, group=trt))+
geom_col(position= "dodge",aes(fill=trt))+
geom_hline(yintercept = 20)+
geom_hline(yintercept = 30,linetype=3)+
facet_wrap(~indv)+
theme_classic()+
ggtitle("FRiP Score across Ind1 using adult heart DNAse peaks")+
scale_fill_manual(values=drug_pal_fac)all_frip1 %>% ggplot(., aes (x=time, y=FRIP_embryo, group=trt))+
geom_col(position= "dodge",aes(fill=trt))+
geom_hline(yintercept = 20)+
geom_hline(yintercept = 30,linetype=3)+
facet_wrap(~indv)+
theme_classic()+
ggtitle("FRiP Score across Ind1 using embryonic heart DNAse peaks")+
scale_fill_manual(values=drug_pal_fac)Horizontal line is at 20%. Individual 4 at 3 hours is not a good score. Encode recommends >30% (dotted line).
Ind2_frag_files <- read.csv("data/Ind2_fragment_files.txt", row.names = 1)
Ind2_frag_files %>%
mutate(trt=factor(trt, levels=c("DX","E","DA","M","T","V"), labels = c("DOX","EPI","DNR","MTX","TRZ","VEH"))) %>%
dplyr::filter(time =="3h") %>%
ggplot(., aes(y=counts, x=frag_size, group=trt))+
# geom_line(aes(col=trt, alpha = 0.5, linewidth=1 ))+
geom_line(aes(col=trt))+
ggtitle("Individual 2\n3 hour fragment sizes")+
theme_classic()+
facet_wrap(~trt)+
scale_color_manual(values=drug_pal_fac)Ind2_frag_files %>%
mutate(trt=factor(trt, levels=c("DX","E","DA","M","T","V"), labels = c("DOX","EPI","DNR","MTX","TRZ","VEH"))) %>%
dplyr::filter(time =="24h") %>%
ggplot(., aes(y=counts, x=frag_size, group=trt))+
geom_line(aes(col=trt))+
ggtitle("Individual 2\n24 hour fragment sizes")+
theme_classic()+
facet_wrap(~trt)+
scale_color_manual(values=drug_pal_fac)# Ind2_summary <- read.csv("data/Ind2_summary.txt", row.names = 1) %>%
# dplyr::rename("sample"=X1,"reads"=X2,"mapped"=X3)
#
# Ind2_reads_summary <-
# Ind2_summary %>%
# separate(reads,into=c("reads",NA),sep= " ") %>%
# mutate(reads=as.numeric(reads)) %>%
# separate(mapped, into= c("mapped_reads", NA,NA, "percent_mapped_reads",NA)) %>%
# separate(sample, into=c("one","two",NA, "4","sample",NA,"seven","8")) %>%
# mutate(mapped_reads=as.numeric(mapped_reads)) %>%
# mutate(type = if_else(two=="first", "total",
# if_else(two=="noM","nuclear",
# if_else((seven == "fin"& two=="files"), "unique", "dedup"))))%>%
# dplyr::select(sample,type, reads, mapped_reads)%>%
# pivot_longer(cols=reads:mapped_reads, names_to = "read_info", values_to = "reads") %>%
# unite("type",type:read_info, sep="_") %>%
# distinct() %>%
# pivot_wider(id_cols = sample, names_from = "type", values_from = "reads") %>%
# mutate(per_nuclear_mapped = nuclear_mapped_reads/total_mapped_reads*100) %>%
# mutate(dedup_read_pairs=dedup_reads/2) %>%
# dplyr::select(sample,total_reads:nuclear_mapped_reads,per_nuclear_mapped,unique_mapped_reads,dedup_reads,dedup_read_pairs) %>%
# dplyr::select(sample, dedup_reads)
# Ind2_frip <- read_delim("~/ATAC_downloads/Ind1/trimmed/Ind2_frip.txt",
# delim = "\t", escape_double = FALSE,
# col_names = FALSE, trim_ws = TRUE)
#
# Frip_2_reads <- Ind2_frip %>%
# rename(X1 ="sample", X2 ="counts_in_peaks") %>%
# separate(sample, into= c(NA,NA,NA,NA,NA,NA, NA,NA,NA,NA,"indv","sample")) %>%
# full_join(Ind2_reads_summary) %>%
# mutate(FRiP_2= counts_in_peaks/dedup_reads*100)
# write.csv(Frip_2_reads,"data/Frip_2_reads.csv")
Frip_2_reads <- read.csv("data/Frip_2_reads.csv", row.names = 1)
all_frip2 <- Frip_2_reads %>%
mutate(sample=gsub("87","2_",sample)) %>%
mutate(sample = gsub("24h","_24h",sample),
sample = gsub("3h","_3h",sample)) %>%
separate(sample, into = c("indv","trt","time")) %>%
mutate(time=factor(time, levels = c("3h","24h"))) %>%
mutate(trt=factor(trt, levels=c("DX","E","DA","M","T","V"), labels = c("DOX","EPI","DNR","MTX","TRZ","VEH"))) %>%
mutate(indv=as.numeric(indv)) %>%
left_join(., (cardiac_muscle_Frip %>% mutate(trt=factor(trt, levels=c("DOX","EPI","DNR","MTX","TRZ","VEH"))))) %>%
left_join(., (left_ventricle_Frip %>% mutate(trt=factor(trt, levels=c("DOX","EPI","DNR","MTX","TRZ","VEH"))))) %>%
left_join(., (cardiomyocyte_Frip %>% mutate(trt=factor(trt, levels=c("DOX","EPI","DNR","MTX","TRZ","VEH"))))) %>%
left_join(., (embryo_heart_Frip %>% mutate(trt=factor(trt, levels=c("DOX","EPI","DNR","MTX","TRZ","VEH"))))) %>%
mutate(FRIP_embryo=embryo_counts/dedup_reads *100) %>%
mutate(FRIP_cm=cm_counts/dedup_reads*100) %>%
mutate(FRIP_lv=lv_counts/dedup_reads*100) %>%
mutate(FRIP_adult=c_muscle_counts/dedup_reads*100)
Frip_2_reads %>%
mutate(sample=gsub("87","2_",sample)) %>%
mutate(sample = gsub("24h","_24h",sample),
sample = gsub("3h","_3h",sample)) %>%
separate(sample, into = c("indv","trt","time")) %>%
mutate(time=factor(time, levels = c("3h","24h"))) %>%
mutate(trt=factor(trt, levels=c("DX","E","DA","M","T","V"), labels = c("DOX","EPI","DNR","MTX","TRZ","VEH"))) %>%
ggplot(., aes (x=time, y=FRiP_2, group=trt))+
geom_col(position= "dodge",aes(fill=trt))+
geom_hline(yintercept = 20)+
geom_hline(yintercept = 30,linetype=3)+
facet_wrap(~indv)+
theme_classic()+
ggtitle("FRiP Score across each sample for individual 2")+
scale_fill_manual(values=drug_pal_fac)all_frip2 %>% ggplot(., aes (x=time, y=FRIP_cm, group=trt))+
geom_col(position= "dodge",aes(fill=trt))+
geom_hline(yintercept = 20)+
geom_hline(yintercept = 30,linetype=3)+
facet_wrap(~indv)+
theme_classic()+
ggtitle("FRiP Score across Ind2 using cardiomyocyte DNAse peaks")+
scale_fill_manual(values=drug_pal_fac)all_frip2 %>% ggplot(., aes (x=time, y=FRIP_lv, group=trt))+
geom_col(position= "dodge",aes(fill=trt))+
geom_hline(yintercept = 20)+
geom_hline(yintercept = 30,linetype=3)+
facet_wrap(~indv)+
theme_classic()+
ggtitle("FRiP Score across Ind2 using left ventricle DNAse peaks")+
scale_fill_manual(values=drug_pal_fac)all_frip2 %>% ggplot(., aes (x=time, y=FRIP_adult, group=trt))+
geom_col(position= "dodge",aes(fill=trt))+
geom_hline(yintercept = 20)+
geom_hline(yintercept = 30,linetype=3)+
facet_wrap(~indv)+
theme_classic()+
ggtitle("FRiP Score across Ind2 using adult heart DNAse peaks")+
scale_fill_manual(values=drug_pal_fac)all_frip2 %>% ggplot(., aes (x=time, y=FRIP_embryo, group=trt))+
geom_col(position= "dodge",aes(fill=trt))+
geom_hline(yintercept = 20)+
geom_hline(yintercept = 30,linetype=3)+
facet_wrap(~indv)+
theme_classic()+
ggtitle("FRiP Score across Ind2 using embryonic heart DNAse peaks")+
scale_fill_manual(values=drug_pal_fac)Ind3_frag_files <- read.csv("data/Ind3_fragment_files.txt", row.names = 1)
Ind3_frag_files %>%
mutate(trt=factor(trt, levels=c("DX","E","DA","M","T","V"), labels = c("DOX","EPI","DNR","MTX","TRZ","VEH"))) %>%
dplyr::filter(time =="3h") %>%
ggplot(., aes(y=counts, x=frag_size, group=trt))+
# geom_line(aes(col=trt, alpha = 0.5, linewidth=1 ))+
geom_line(aes(col=trt))+
ggtitle("Individual 3\n3 hour fragment sizes")+
theme_classic()+
facet_wrap(~trt)+
scale_color_manual(values=drug_pal_fac)Ind3_frag_files %>%
mutate(trt=factor(trt, levels=c("DX","E","DA","M","T","V"), labels = c("DOX","EPI","DNR","MTX","TRZ","VEH"))) %>%
dplyr::filter(time =="24h") %>%
ggplot(., aes(y=counts, x=frag_size, group=trt))+
geom_line(aes(col=trt))+
ggtitle("Individual 3\n24 hour fragment sizes")+
theme_classic()+
facet_wrap(~trt)+
scale_color_manual(values=drug_pal_fac)# Ind3_summary <- read.csv("data/Ind3_summary.txt", row.names = 1) %>%
# dplyr::rename("sample"=X1,"reads"=X2,"mapped"=X3)
#
# Ind3_reads_summary <-
# Ind3_summary %>%
# separate(reads,into=c("reads",NA),sep= " ") %>%
# mutate(reads=as.numeric(reads)) %>%
# separate(mapped, into= c("mapped_reads", NA,NA, "percent_mapped_reads",NA)) %>%
# separate(sample, into=c("one","two",NA, "4","sample",NA,"seven","8")) %>%
# mutate(mapped_reads=as.numeric(mapped_reads)) %>%
# mutate(type = if_else(two=="first", "total",
# if_else(two=="noM","nuclear",
# if_else((seven == "fin"& two=="files"), "unique", "dedup"))))%>%
# dplyr::select(sample,type, reads, mapped_reads)%>%
# pivot_longer(cols=reads:mapped_reads, names_to = "read_info", values_to = "reads") %>%
# unite("type",type:read_info, sep="_") %>%
# distinct() %>%
# pivot_wider(id_cols = sample, names_from = "type", values_from = "reads") %>%
# mutate(per_nuclear_mapped = nuclear_mapped_reads/total_mapped_reads*100) %>%
# mutate(dedup_read_pairs=dedup_reads/2) %>%
# dplyr::select(sample,total_reads:nuclear_mapped_reads,per_nuclear_mapped,unique_mapped_reads,dedup_reads,dedup_read_pairs) %>%
# dplyr::select(sample, dedup_reads)
# Ind3_frip <- read_delim("~/ATAC_downloads/Ind1/trimmed/Ind3_frip.txt",
# delim = "\t", escape_double = FALSE,
# col_names = FALSE, trim_ws = TRUE)
#
# Frip_3_reads <- Ind3_frip %>%
# rename(X1 ="sample", X2 ="counts_in_peaks") %>%
# separate(sample, into= c(NA,NA,NA,NA,NA,NA, NA,NA,NA,NA,"indv","sample")) %>%
# full_join(Ind3_reads_summary) %>%
# mutate(FRiP_3= counts_in_peaks/dedup_reads*100)
# write.csv(Frip_3_reads,"data/Frip_3_reads.csv")
Frip_3_reads <- read.csv("data/Frip_3_reads.csv", row.names = 1)
all_frip3 <- Frip_3_reads %>%
mutate(sample=gsub("77","3_",sample)) %>%
mutate(sample = gsub("24h","_24h",sample),
sample = gsub("3h","_3h",sample)) %>%
separate(sample, into = c("indv","trt","time")) %>%
mutate(time=factor(time, levels = c("3h","24h"))) %>%
mutate(trt=factor(trt, levels=c("DX","E","DA","M","T","V"), labels = c("DOX","EPI","DNR","MTX","TRZ","VEH"))) %>%
mutate(indv=as.numeric(indv)) %>%
left_join(., (cardiac_muscle_Frip %>% mutate(trt=factor(trt, levels=c("DOX","EPI","DNR","MTX","TRZ","VEH"))))) %>%
left_join(., (left_ventricle_Frip %>% mutate(trt=factor(trt, levels=c("DOX","EPI","DNR","MTX","TRZ","VEH"))))) %>%
left_join(., (cardiomyocyte_Frip %>% mutate(trt=factor(trt, levels=c("DOX","EPI","DNR","MTX","TRZ","VEH"))))) %>%
left_join(., (embryo_heart_Frip %>% mutate(trt=factor(trt, levels=c("DOX","EPI","DNR","MTX","TRZ","VEH"))))) %>%
mutate(FRIP_embryo=embryo_counts/dedup_reads *100) %>%
mutate(FRIP_cm=cm_counts/dedup_reads*100) %>%
mutate(FRIP_lv=lv_counts/dedup_reads*100) %>%
mutate(FRIP_adult=c_muscle_counts/dedup_reads*100)
Frip_3_reads %>%
mutate(sample=gsub("77","3_",sample)) %>%
mutate(sample = gsub("24h","_24h",sample),
sample = gsub("3h","_3h",sample)) %>%
separate(sample, into = c("indv","trt","time")) %>%
mutate(time=factor(time, levels = c("3h","24h"))) %>%
mutate(trt=factor(trt, levels=c("DX","E","DA","M","T","V"), labels = c("DOX","EPI","DNR","MTX","TRZ","VEH"))) %>%
ggplot(., aes (x=time, y=FRiP_3, group=trt))+
geom_col(position= "dodge",aes(fill=trt))+
geom_hline(yintercept = 20)+
geom_hline(yintercept = 30,linetype=3)+
facet_wrap(~indv)+
theme_classic()+
ggtitle("FRiP Score across each sample for individual 3")+
scale_fill_manual(values=drug_pal_fac)all_frip3 %>% ggplot(., aes (x=time, y=FRIP_cm, group=trt))+
geom_col(position= "dodge",aes(fill=trt))+
geom_hline(yintercept = 20)+
geom_hline(yintercept = 30,linetype=3)+
facet_wrap(~indv)+
theme_classic()+
ggtitle("FRiP Score across Ind3 using cardiomyocyte DNAse peaks")+
scale_fill_manual(values=drug_pal_fac)all_frip3 %>% ggplot(., aes (x=time, y=FRIP_lv, group=trt))+
geom_col(position= "dodge",aes(fill=trt))+
geom_hline(yintercept = 20)+
geom_hline(yintercept = 30,linetype=3)+
facet_wrap(~indv)+
theme_classic()+
ggtitle("FRiP Score across Ind3 using left ventricle DNAse peaks")+
scale_fill_manual(values=drug_pal_fac)all_frip3 %>% ggplot(., aes (x=time, y=FRIP_adult, group=trt))+
geom_col(position= "dodge",aes(fill=trt))+
geom_hline(yintercept = 20)+
geom_hline(yintercept = 30,linetype=3)+
facet_wrap(~indv)+
theme_classic()+
ggtitle("FRiP Score across Ind3 using adult heart DNAse peaks")+
scale_fill_manual(values=drug_pal_fac)all_frip3 %>% ggplot(., aes (x=time, y=FRIP_embryo, group=trt))+
geom_col(position= "dodge",aes(fill=trt))+
geom_hline(yintercept = 20)+
geom_hline(yintercept = 30,linetype=3)+
facet_wrap(~indv)+
theme_classic()+
ggtitle("FRiP Score across Ind3 using embryonic heart DNAse peaks")+
scale_fill_manual(values=drug_pal_fac)Ind4_frag_files <- read.csv("data/Ind4_fragment_files.txt", row.names = 1)
Ind4_frag_files %>%
mutate(trt=factor(trt, levels=c("DX","E","DA","M","T","V"), labels = c("DOX","EPI","DNR","MTX","TRZ","VEH"))) %>%
dplyr::filter(time =="3h") %>%
ggplot(., aes(y=counts, x=frag_size, group=trt))+
geom_line(aes(col=trt ))+
ggtitle("Individual 4\n3 hour fragment sizes")+
theme_classic()+
facet_wrap(~trt)+
scale_color_manual(values=drug_pal_fac)Ind4_frag_files %>%
mutate(trt=factor(trt, levels=c("DX","E","DA","M","T","V"), labels = c("DOX","EPI","DNR","MTX","TRZ","VEH"))) %>%
dplyr::filter(time =="24h") %>%
ggplot(., aes(y=counts, x=frag_size, group=trt))+
geom_line(aes(col=trt ))+
ggtitle("Individual 4\n24 hour fragment sizes")+
theme_classic()+
facet_wrap(~trt)+
scale_color_manual(values=drug_pal_fac)# Ind4_summary <- read.csv("data/Ind4_summary.txt", row.names = 1) %>%
# dplyr::rename("sample"=X1,"reads"=X2,"mapped"=X3)
#
# Ind4_reads_summary <-
# Ind4_summary %>%
# separate(reads,into=c("reads",NA),sep= " ") %>%
# mutate(reads=as.numeric(reads)) %>%
# separate(mapped, into= c("mapped_reads", NA,NA, "percent_mapped_reads",NA)) %>%
# separate(sample, into=c("one","two",NA, "4","sample",NA,"seven","8")) %>%
# mutate(mapped_reads=as.numeric(mapped_reads)) %>%
# mutate(type = if_else(two=="first", "total",
# if_else(two=="noM","nuclear",
# if_else((seven == "fin"& two=="files"), "unique", "dedup"))))%>%
# dplyr::select(sample,type, reads, mapped_reads)%>%
# pivot_longer(cols=reads:mapped_reads, names_to = "read_info", values_to = "reads") %>%
# unite("type",type:read_info, sep="_") %>%
# distinct() %>%
# pivot_wider(id_cols = sample, names_from = "type", values_from = "reads") %>%
# mutate(per_nuclear_mapped = nuclear_mapped_reads/total_mapped_reads*100) %>%
# mutate(dedup_read_pairs=dedup_reads/2) %>%
# dplyr::select(sample,total_reads:nuclear_mapped_reads,per_nuclear_mapped,unique_mapped_reads,dedup_reads,dedup_read_pairs) %>%
# dplyr::select(sample, dedup_reads)
# Ind4_frip <- read_delim("~/ATAC_downloads/Ind1/trimmed/Ind4_frip.txt",
# delim = "\t", escape_double = FALSE,
# col_names = FALSE, trim_ws = TRUE)
#
# Frip_4_reads <- Ind4_frip %>%
# rename(X1 ="sample", X2 ="counts_in_peaks") %>%
# separate(sample, into= c(NA,NA,NA,NA,NA,NA, NA,NA,NA,NA,"indv","sample")) %>%
# full_join(Ind4_reads_summary) %>%
# mutate(FRiP_4= counts_in_peaks/dedup_reads*100)
# write.csv(Frip_4_reads,"data/Frip_4_reads.csv")
Frip_4_reads <- read.csv("data/Frip_4_reads.csv", row.names = 1)
all_frip4 <- Frip_4_reads %>%
mutate(sample=gsub("79","4_",sample)) %>%
mutate(sample = gsub("24h","_24h",sample),
sample = gsub("3h","_3h",sample)) %>%
separate(sample, into = c("indv","trt","time")) %>%
mutate(time=factor(time, levels = c("3h","24h"))) %>%
mutate(trt=factor(trt, levels=c("DX","E","DA","M","T","V"), labels = c("DOX","EPI","DNR","MTX","TRZ","VEH"))) %>%
mutate(indv=as.numeric(indv)) %>%
left_join(., (cardiac_muscle_Frip %>% mutate(trt=factor(trt, levels=c("DOX","EPI","DNR","MTX","TRZ","VEH"))))) %>%
left_join(., (left_ventricle_Frip %>% mutate(trt=factor(trt, levels=c("DOX","EPI","DNR","MTX","TRZ","VEH"))))) %>%
left_join(., (cardiomyocyte_Frip %>% mutate(trt=factor(trt, levels=c("DOX","EPI","DNR","MTX","TRZ","VEH"))))) %>%
left_join(., (embryo_heart_Frip %>% mutate(trt=factor(trt, levels=c("DOX","EPI","DNR","MTX","TRZ","VEH"))))) %>%
mutate(FRIP_embryo=embryo_counts/dedup_reads *100) %>%
mutate(FRIP_cm=cm_counts/dedup_reads*100) %>%
mutate(FRIP_lv=lv_counts/dedup_reads*100) %>%
mutate(FRIP_adult=c_muscle_counts/dedup_reads*100)
Frip_4_reads %>%
mutate(sample=gsub("79","4_",sample)) %>%
mutate(sample = gsub("24h","_24h",sample),
sample = gsub("3h","_3h",sample)) %>%
separate(sample, into = c("indv","trt","time")) %>%
mutate(time=factor(time, levels = c("3h","24h"))) %>%
mutate(trt=factor(trt, levels=c("DX","E","DA","M","T","V"), labels = c("DOX","EPI","DNR","MTX","TRZ","VEH"))) %>%
ggplot(., aes (x=time, y=FRiP_4, group=trt))+
geom_col(position= "dodge",aes(fill=trt))+
geom_hline(yintercept = 20)+
geom_hline(yintercept = 30,linetype=3)+
facet_wrap(~indv)+
theme_classic()+
ggtitle("FRiP Score across each sample for individual 4")+
scale_fill_manual(values=drug_pal_fac)all_frip4 %>% ggplot(., aes (x=time, y=FRIP_cm, group=trt))+
geom_col(position= "dodge",aes(fill=trt))+
geom_hline(yintercept = 20)+
geom_hline(yintercept = 30,linetype=3)+
facet_wrap(~indv)+
theme_classic()+
ggtitle("FRiP Score across Ind4 using cardiomyocyte DNAse peaks")+
scale_fill_manual(values=drug_pal_fac)all_frip4 %>% ggplot(., aes (x=time, y=FRIP_lv, group=trt))+
geom_col(position= "dodge",aes(fill=trt))+
geom_hline(yintercept = 20)+
geom_hline(yintercept = 30,linetype=3)+
facet_wrap(~indv)+
theme_classic()+
ggtitle("FRiP Score across Ind4 using left ventricle DNAse peaks")+
scale_fill_manual(values=drug_pal_fac)all_frip4 %>% ggplot(., aes (x=time, y=FRIP_adult, group=trt))+
geom_col(position= "dodge",aes(fill=trt))+
geom_hline(yintercept = 20)+
geom_hline(yintercept = 30,linetype=3)+
facet_wrap(~indv)+
theme_classic()+
ggtitle("FRiP Score across Ind4 using adult heart DNAse peaks")+
scale_fill_manual(values=drug_pal_fac)all_frip4 %>% ggplot(., aes (x=time, y=FRIP_embryo, group=trt))+
geom_col(position= "dodge",aes(fill=trt))+
geom_hline(yintercept = 20)+
geom_hline(yintercept = 30,linetype=3)+
facet_wrap(~indv)+
theme_classic()+
ggtitle("FRiP Score across Ind4 using embryonic heart DNAse peaks")+
scale_fill_manual(values=drug_pal_fac)Ind5_frag_files <- read.csv("data/Ind5_fragment_files.txt", row.names = 1)
Ind5_frag_files %>%
mutate(trt=factor(trt, levels=c("DX","E","DA","M","T","V"), labels = c("DOX","EPI","DNR","MTX","TRZ","VEH"))) %>%
dplyr::filter(time =="3h") %>%
ggplot(., aes(y=counts, x=frag_size, group=trt))+
# geom_line(aes(col=trt, alpha = 0.5, linewidth=1 ))+
geom_line(aes(col=trt))+
ggtitle("Individual 5\n3 hour fragment sizes")+
theme_classic()+
facet_wrap(~trt)+
scale_color_manual(values=drug_pal_fac)Ind5_frag_files %>%
mutate(trt=factor(trt, levels=c("DX","E","DA","M","T","V"), labels = c("DOX","EPI","DNR","MTX","TRZ","VEH"))) %>%
dplyr::filter(time =="24h") %>%
ggplot(., aes(y=counts, x=frag_size, group=trt))+
geom_line(aes(col=trt))+
ggtitle("Individual 5\n24 hour fragment sizes")+
theme_classic()+
facet_wrap(~trt)+
scale_color_manual(values=drug_pal_fac)#
# Ind5_summary <- read.csv("data/Ind5_summary.txt", row.names = 1) %>%
# dplyr::rename("sample"=X1,"reads"=X2,"mapped"=X3)
#
# Ind5_reads_summary <-
# Ind5_summary %>%
# separate(reads,into=c("reads",NA),sep= " ") %>%
# mutate(reads=as.numeric(reads)) %>%
# separate(mapped, into= c("mapped_reads", NA,NA, "percent_mapped_reads",NA)) %>%
# separate(sample, into=c("one","two",NA, "4","sample",NA,"seven","8")) %>%
# mutate(mapped_reads=as.numeric(mapped_reads)) %>%
# mutate(type = if_else(two=="first", "total",
# if_else(two=="noM","nuclear",
# if_else((seven == "fin"& two=="files"), "unique", "dedup"))))%>%
# dplyr::select(sample,type, reads, mapped_reads)%>%
# pivot_longer(cols=reads:mapped_reads, names_to = "read_info", values_to = "reads") %>%
# unite("type",type:read_info, sep="_") %>%
# distinct() %>%
# pivot_wider(id_cols = sample, names_from = "type", values_from = "reads") %>%
# mutate(per_nuclear_mapped = nuclear_mapped_reads/total_mapped_reads*100) %>%
# mutate(dedup_read_pairs=dedup_reads/2) %>%
# dplyr::select(sample,total_reads:nuclear_mapped_reads,per_nuclear_mapped,unique_mapped_reads,dedup_reads,dedup_read_pairs) %>%
# dplyr::select(sample, dedup_reads)
# Ind5_frip <- read_delim("~/ATAC_downloads/Ind1/trimmed/Ind5_frip.txt",
# delim = "\t", escape_double = FALSE,
# col_names = FALSE, trim_ws = TRUE)
#
# Frip_5_reads <- Ind5_frip %>%
# rename(X1 ="sample", X2 ="counts_in_peaks") %>%
# separate(sample, into= c(NA,NA,NA,NA,NA,NA, NA,NA,NA,NA,"indv","sample")) %>%
# full_join(Ind5_reads_summary) %>%
# mutate(FRiP_5= counts_in_peaks/dedup_reads*100)
# write.csv(Frip_5_reads,"data/Frip_5_reads.csv")
Frip_5_reads <- read.csv("data/Frip_5_reads.csv", row.names = 1)
all_frip5 <- Frip_5_reads %>%
mutate(sample=gsub("78","5_",sample)) %>%
mutate(sample = gsub("24h","_24h",sample),
sample = gsub("3h","_3h",sample)) %>%
separate(sample, into = c("indv","trt","time")) %>%
mutate(time=factor(time, levels = c("3h","24h"))) %>%
mutate(trt=factor(trt, levels=c("DX","E","DA","M","T","V"), labels = c("DOX","EPI","DNR","MTX","TRZ","VEH"))) %>%
mutate(indv=as.numeric(indv)) %>%
left_join(., (cardiac_muscle_Frip %>% mutate(trt=factor(trt, levels=c("DOX","EPI","DNR","MTX","TRZ","VEH"))))) %>%
left_join(., (left_ventricle_Frip %>% mutate(trt=factor(trt, levels=c("DOX","EPI","DNR","MTX","TRZ","VEH"))))) %>%
left_join(., (cardiomyocyte_Frip %>% mutate(trt=factor(trt, levels=c("DOX","EPI","DNR","MTX","TRZ","VEH"))))) %>%
left_join(., (embryo_heart_Frip %>% mutate(trt=factor(trt, levels=c("DOX","EPI","DNR","MTX","TRZ","VEH"))))) %>%
mutate(FRIP_embryo=embryo_counts/dedup_reads *100) %>%
mutate(FRIP_cm=cm_counts/dedup_reads*100) %>%
mutate(FRIP_lv=lv_counts/dedup_reads*100) %>%
mutate(FRIP_adult=c_muscle_counts/dedup_reads*100)
Frip_5_reads %>%
mutate(sample=gsub("78","5_",sample)) %>%
mutate(sample = gsub("24h","_24h",sample),
sample = gsub("3h","_3h",sample)) %>%
separate(sample, into = c("indv","trt","time")) %>%
mutate(time=factor(time, levels = c("3h","24h"))) %>%
mutate(trt=factor(trt, levels=c("DX","E","DA","M","T","V"), labels = c("DOX","EPI","DNR","MTX","TRZ","VEH"))) %>%
ggplot(., aes (x=time, y=FRiP_5, group=trt))+
geom_col(position= "dodge",aes(fill=trt))+
geom_hline(yintercept = 20)+
geom_hline(yintercept = 30,linetype=3)+
facet_wrap(~indv)+
theme_classic()+
ggtitle("FRiP Score a across Ind5 for individual 5")+
scale_fill_manual(values=drug_pal_fac)all_frip5 %>% ggplot(., aes (x=time, y=FRIP_cm, group=trt))+
geom_col(position= "dodge",aes(fill=trt))+
geom_hline(yintercept = 20)+
geom_hline(yintercept = 30,linetype=3)+
facet_wrap(~indv)+
theme_classic()+
ggtitle("FRiP Score across Ind5 using cardiomyocyte DNAse peaks")+
scale_fill_manual(values=drug_pal_fac)all_frip5 %>% ggplot(., aes (x=time, y=FRIP_lv, group=trt))+
geom_col(position= "dodge",aes(fill=trt))+
geom_hline(yintercept = 20)+
geom_hline(yintercept = 30,linetype=3)+
facet_wrap(~indv)+
theme_classic()+
ggtitle("FRiP Score across Ind5 using left ventricle DNAse peaks")+
scale_fill_manual(values=drug_pal_fac)all_frip5 %>% ggplot(., aes (x=time, y=FRIP_adult, group=trt))+
geom_col(position= "dodge",aes(fill=trt))+
geom_hline(yintercept = 20)+
geom_hline(yintercept = 30,linetype=3)+
facet_wrap(~indv)+
theme_classic()+
ggtitle("FRiP Score across each sample using adult heart DNAse peaks")+
scale_fill_manual(values=drug_pal_fac)all_frip5 %>% ggplot(., aes (x=time, y=FRIP_embryo, group=trt))+
geom_col(position= "dodge",aes(fill=trt))+
geom_hline(yintercept = 20)+
geom_hline(yintercept = 30,linetype=3)+
facet_wrap(~indv)+
theme_classic()+
ggtitle("FRiP Score across Ind5 using embryonic heart DNAse peaks")+
scale_fill_manual(values=drug_pal_fac)Ind6_frag_files <- read.csv("data/Ind6_fragment_files.txt", row.names = 1)
Ind6_frag_files %>%
dplyr::filter(time =="3h") %>%
mutate(trt=factor(trt, levels=c("DX","E","DA","M","T","V"), labels = c("DOX","EPI","DNR","MTX","TRZ","VEH"))) %>%
ggplot(., aes(y=counts, x=frag_size, group=trt))+
# geom_line(aes(col=trt, alpha = 0.5, linewidth=1 ))+
geom_line(aes(col=trt))+
ggtitle("Individual 6\n3 hour fragment sizes")+
theme_classic()+
facet_wrap(~trt)+
scale_color_manual(values=drug_pal_fac)Ind6_frag_files %>%
dplyr::filter(time =="24h") %>%
mutate(trt=factor(trt, levels=c("DX","E","DA","M","T","V"), labels = c("DOX","EPI","DNR","MTX","TRZ","VEH"))) %>%
ggplot(., aes(y=counts, x=frag_size, group=trt))+
geom_line(aes(col=trt))+
ggtitle("Individual 6\n24 hour fragment sizes")+
theme_classic()+
facet_wrap(~trt)+
scale_color_manual(values=drug_pal_fac)#
# Ind6_summary <- read.csv("data/Ind6_summary.txt", row.names = 1) %>%
# dplyr::rename("sample"=X1,"reads"=X2,"mapped"=X3)
#
# Ind6_reads_summary <-
# Ind6_summary %>%
# separate(reads,into=c("reads",NA),sep= " ") %>%
# mutate(reads=as.numeric(reads)) %>%
# separate(mapped, into= c("mapped_reads", NA,NA, "percent_mapped_reads",NA)) %>%
# separate(sample, into=c("one","two",NA, "4","sample",NA,"seven","8")) %>%
# mutate(mapped_reads=as.numeric(mapped_reads)) %>%
# mutate(type = if_else(two=="first", "total",
# if_else(two=="noM","nuclear",
# if_else((seven == "fin"& two=="files"), "unique", "dedup"))))%>%
# dplyr::select(sample,type, reads, mapped_reads)%>%
# pivot_longer(cols=reads:mapped_reads, names_to = "read_info", values_to = "reads") %>%
# unite("type",type:read_info, sep="_") %>%
# distinct() %>%
# pivot_wider(id_cols = sample, names_from = "type", values_from = "reads") %>%
# mutate(per_nuclear_mapped = nuclear_mapped_reads/total_mapped_reads*100) %>%
# mutate(dedup_read_pairs=dedup_reads/2) %>%
# dplyr::select(sample,total_reads:nuclear_mapped_reads,per_nuclear_mapped,unique_mapped_reads,dedup_reads,dedup_read_pairs) %>%
# dplyr::select(sample, dedup_reads)
#
# Ind6_frip <- read_delim("~/ATAC_downloads/Ind1/trimmed/Ind6_frip.txt",
# delim = "\t", escape_double = FALSE,
# col_names = FALSE, trim_ws = TRUE)
#
# Frip_6_reads <- Ind6_frip %>%
# rename(X1 ="sample", X2 ="counts_in_peaks") %>%
# separate(sample, into= c(NA,NA,NA,NA,NA,NA, NA,NA,NA,NA,"indv","sample")) %>%
# full_join(Ind6_reads_summary) %>%
# mutate(FRiP_6= counts_in_peaks/dedup_reads*100)
# write.csv(Frip_6_reads,"data/Frip_6_reads.csv")
Frip_6_reads <- read.csv("data/Frip_6_reads.csv", row.names = 1)
all_frip6 <- Frip_6_reads %>%
mutate(sample=gsub("71","6_",sample)) %>%
mutate(sample = gsub("24h","_24h",sample),
sample = gsub("3h","_3h",sample)) %>%
separate(sample, into = c("indv","trt","time")) %>%
mutate(time=factor(time, levels = c("3h","24h"))) %>%
mutate(trt=factor(trt, levels=c("DX","E","DA","M","T","V"), labels = c("DOX","EPI","DNR","MTX","TRZ","VEH"))) %>%
mutate(indv=as.numeric(indv)) %>%
left_join(., (cardiac_muscle_Frip %>% mutate(trt=factor(trt, levels=c("DOX","EPI","DNR","MTX","TRZ","VEH"))))) %>%
left_join(., (left_ventricle_Frip %>% mutate(trt=factor(trt, levels=c("DOX","EPI","DNR","MTX","TRZ","VEH"))))) %>%
left_join(., (cardiomyocyte_Frip %>% mutate(trt=factor(trt, levels=c("DOX","EPI","DNR","MTX","TRZ","VEH"))))) %>%
left_join(., (embryo_heart_Frip %>% mutate(trt=factor(trt, levels=c("DOX","EPI","DNR","MTX","TRZ","VEH"))))) %>%
mutate(FRIP_embryo=embryo_counts/dedup_reads *100) %>%
mutate(FRIP_cm=cm_counts/dedup_reads*100) %>%
mutate(FRIP_lv=lv_counts/dedup_reads*100) %>%
mutate(FRIP_adult=c_muscle_counts/dedup_reads*100)
Frip_6_reads %>%
mutate(sample=gsub("71","6_",sample)) %>%
mutate(sample = gsub("24h","_24h",sample),
sample = gsub("3h","_3h",sample)) %>%
separate(sample, into = c("indv","trt","time")) %>%
mutate(time=factor(time, levels = c("3h","24h"))) %>%
mutate(trt=factor(trt, levels=c("DX","E","DA","M","T","V"), labels = c("DOX","EPI","DNR","MTX","TRZ","VEH"))) %>%
ggplot(., aes (x=time, y=FRiP_6, group=trt))+
geom_col(position= "dodge",aes(fill=trt))+
geom_hline(yintercept = 20)+
geom_hline(yintercept = 30,linetype=3)+
facet_wrap(~indv)+
theme_classic()+
ggtitle("FRiP Score across each sample for individual 6")+
scale_fill_manual(values=drug_pal_fac)all_frip6 %>% ggplot(., aes (x=time, y=FRIP_cm, group=trt))+
geom_col(position= "dodge",aes(fill=trt))+
geom_hline(yintercept = 20)+
geom_hline(yintercept = 30,linetype=3)+
facet_wrap(~indv)+
theme_classic()+
ggtitle("FRiP Score across Ind6 using cardiomyocyte DNAse peaks")+
scale_fill_manual(values=drug_pal_fac)all_frip6 %>% ggplot(., aes (x=time, y=FRIP_lv, group=trt))+
geom_col(position= "dodge",aes(fill=trt))+
geom_hline(yintercept = 20)+
geom_hline(yintercept = 30,linetype=3)+
facet_wrap(~indv)+
theme_classic()+
ggtitle("FRiP Score across Ind6 using left ventricle DNAse peaks")+
scale_fill_manual(values=drug_pal_fac)all_frip6 %>% ggplot(., aes (x=time, y=FRIP_adult, group=trt))+
geom_col(position= "dodge",aes(fill=trt))+
geom_hline(yintercept = 20)+
geom_hline(yintercept = 30,linetype=3)+
facet_wrap(~indv)+
theme_classic()+
ggtitle("FRiP Score across Ind6 using adult heart DNAse peaks")+
scale_fill_manual(values=drug_pal_fac)all_frip6 %>% ggplot(., aes (x=time, y=FRIP_embryo, group=trt))+
geom_col(position= "dodge",aes(fill=trt))+
geom_hline(yintercept = 20)+
geom_hline(yintercept = 30,linetype=3)+
facet_wrap(~indv)+
theme_classic()+
ggtitle("FRiP Score across Ind6 using embryonic heart DNAse peaks")+
scale_fill_manual(values=drug_pal_fac)##collecting summary files
# Ind1_peaksummary <- read_table("~/ATAC_downloads/Ind1/trimmed/macs_output/Ind1_peaksummary.txt",
# col_names = FALSE) %>%
# rename(X1 ="counts", X2= "sample")
# Ind2_peaksummary <- read_table("~/ATAC_downloads/Ind2/trimmed/macs_output/Ind2_peaksummary.txt",
# col_names = FALSE)%>%
# rename(X1 ="counts", X2= "sample")
# Ind3_peaksummary <- read_table("~/ATAC_downloads/Ind3/trimmed/macs_output/Ind3_peaksummary.txt",
# col_names = FALSE)%>%
# rename(X1 ="counts", X2= "sample")
# Ind4_peaksummary <- read_table("~/ATAC_downloads/Ind4/trimmed/macs_output/Ind4_peaksummary.txt",
# col_names = FALSE)%>%
# rename(X1 ="counts", X2= "sample")
# Ind5_peaksummary <- read_table("~/ATAC_downloads/Ind5/trimmed/macs_output/Ind5_peaksummary.txt",
# col_names = FALSE)%>%
# rename(X1 ="counts", X2= "sample")
# Ind6_peaksummary <- read_table("~/ATAC_downloads/Ind6/trimmed/macs_output/Ind6_peaksummary.txt",
# col_names = FALSE)%>%
# rename(X1 ="counts", X2= "sample")
# Peaksummary <- rbind(Ind1_peaksummary,Ind2_peaksummary,Ind3_peaksummary,Ind4_peaksummary,Ind5_peaksummary,Ind6_peaksummary)
# #
# write.csv(Peaksummary, "data/first_Peaksummarycounts.csv")
Peaksummary <- read.csv("data/first_Peaksummarycounts.csv",row.names=1)
Peaksummary %>%
dplyr::filter(sample != "total") %>%
separate(sample, into=c(NA,"indv","sample",NA,NA,NA)) %>%
mutate(trt=gsub("[[:digit:]]", "",sample)) %>%
# mutate(trt=substr(trt,-1,2))
mutate(time = if_else(grepl("24h$", sample) ==TRUE, "24_hours","3_hours")) %>%
mutate(trt = case_match(trt,"DAh"~"DNR","DXh"~"DOX","Eh"~"EPI", "Mh" ~ "MTX", "Th" ~ "TRZ", "Vh" ~"VEH",.default = trt)) %>%
mutate(indv = factor(indv, levels = c("Ind1", "Ind2", "Ind3", "Ind4", "Ind5", "Ind6"))) %>%
mutate(time = factor(time, levels = c("3_hours", "24_hours"), labels= c("3 hours","24 hours"))) %>%
mutate(trt = factor(trt, levels = c("DOX","EPI", "DNR", "MTX", "TRZ", "VEH")))%>%
ggplot(., aes(x =trt, y=counts,group=trt))+
geom_boxplot(aes(fill= trt))+
geom_point(aes(col=indv, size =3))+
facet_wrap(~time)+
scale_fill_manual(values=drug_pal_fac)+
scale_color_brewer(palette = "Dark2")+
ggtitle("Peak counts by treatment")+
theme_bw()# plotAnnoBar(anno_ind4_V24h, main = "Genomic Feature Distribution")+ ggtitle("Ind4 VEH 24 hour")
# plotAnnoBar(anno_ind1_DA24h, main = "Genomic Feature Distribution")+ ggtitle("Ind1 DNR 24 hour")
# ind4_V24hpeaks_gr <- prepGRangeObj(ind4_V24hpeaks)
# ind1_DA24hpeaks_gr <- prepGRangeObj((ind1_DA24hpeaks))
# Epi_list <- GRangesList(ind1_DA24hpeaks_gr, ind4_V24hpeaks_gr)
# # ##plotting the TSS average window (making an overlap of each using Epi_list as list holder)
# Epi_list_tagMatrix = lapply(Epi_list, getTagMatrix, windows = TSS)
# plotAvgProf(Epi_list_tagMatrix, xlim=c(-3000, 3000), ylab = "Count Frequency")
#plotPeakProf(Epi_list_tagMatrix, facet = "none", conf = 0.95)
## What I did here: I called all my narrowpeak files
# peakfiles1 <- choose.files()
##these were practice for getting file names and shortening for the for loop below
# testname <- basename(peakfiles1[1])
# str_split_i(testname, "_",3)
##This loop first established a list then (because I already knew the list had 12 files)
## I then imported each of these onto that list. Once I had the list, I stored it as
## an R object,
# Ind1_peaks <- list()
# for (file in 1:12){
# testname <- basename(peakfiles1[file])
# banana_peel <- str_split_i(testname, "_",3)
# Ind1_peaks[[banana_peel]] <- readPeakFile(peakfiles1[file])
# }
# saveRDS(Ind1_peaks, "data/Ind1_peaks_list.RDS")
# I then called annotatePeak on that list object, and stored that as a R object for later retrieval.)
# peakAnnoList_1 <- lapply(Ind1_peaks, annotatePeak, tssRegion =c(-2000,2000), TxDb= txdb)
# saveRDS(peakAnnoList_1, "data/peakAnnoList_1.RDS")
peakAnnoList_1 <- readRDS("data/peakAnnoList_1.RDS")
plotAnnoBar(peakAnnoList_1, main = "Genomic Feature Distribution")# saveRDS(Epi_list_tagMatrix, "data/Ind1_TSS_peaks.RDS")
Ind1_TSS_peaks_plot <- readRDS("data/Ind1_TSS_peaks.RDS")
# Epi_list_tagMatrix = lapply(Ind1_peaks, getTagMatrix, windows = TSS)
plotAvgProf(Ind1_TSS_peaks_plot[c(1,3,5,7,9,11)], xlim=c(-3000, 3000), ylab = "Count Frequency")+ ggtitle("3 hour Individual 1" )>> plotting figure... 2025-05-01 12:51:38 PM # + coord_cartesian(xlim = c(-100,500))
plotAvgProf(Ind1_TSS_peaks_plot[c(2,4,6,8,10,12)], xlim=c(-3000, 3000),ylab = "Count Frequency")+ ggtitle("24 hour Individual 1" )>> plotting figure... 2025-05-01 12:51:39 PM # + coord_cartesian(xlim = c(-100,500))## What I did here: I called all my narrowpeak files
# peakfiles2 <- choose.files()
##This loop first established a list then (because I already knew the list had 12 files)
## I then imported each of these onto that list. Once I had the list, I stored it as
## an R object,
# Ind2_peaks <- list()
# for (file in 1:12){
# testname <- basename(peakfiles2[file])
# banana_peel <- str_split_i(testname, "_",3)
# Ind2_peaks[[banana_peel]] <- readPeakFile(peakfiles2[file])
# }
# saveRDS(Ind2_peaks, "data/Ind2_peaks_list.RDS")
# I then called annotatePeak on that list object, and stored that as a R object for later retrieval.)
Ind2_peaks <- readRDS("data/Ind2_peaks_list.RDS")
# peakAnnoList_2 <- lapply(Ind2_peaks, annotatePeak, tssRegion =c(-2000,2000), TxDb= txdb)
# saveRDS(peakAnnoList_2, "data/peakAnnoList_2.RDS")
peakAnnoList_2 <- readRDS("data/peakAnnoList_2.RDS")
plotAnnoBar(peakAnnoList_2, main = "Genomic Feature Distribution, Individual 2")# Epi_list_tagMatrix = lapply(Ind2_peaks, getTagMatrix, windows = TSS)
# saveRDS(Epi_list_tagMatrix, "data/Ind2_TSS_peaks.RDS")
Ind2_TSS_peaks_plot <- readRDS("data/Ind2_TSS_peaks.RDS")
plotAvgProf(Ind2_TSS_peaks_plot[c(1,3,5,7,9,11)], xlim=c(-3000, 3000), conf = 0.01, ylab = "Count Frequency")+ ggtitle("3 hour Individual 2" )>> plotting figure... 2025-05-01 12:51:55 PM
>> Running bootstrapping for tag matrix... 2025-05-01 12:55:17 PM
>> Running bootstrapping for tag matrix... 2025-05-01 12:58:32 PM
>> Running bootstrapping for tag matrix... 2025-05-01 1:02:38 PM
>> Running bootstrapping for tag matrix... 2025-05-01 1:07:33 PM
>> Running bootstrapping for tag matrix... 2025-05-01 1:12:03 PM
>> Running bootstrapping for tag matrix... 2025-05-01 1:16:21 PM # + coord_cartesian(xlim = c(-100,1000))
plotAvgProf(Ind2_TSS_peaks_plot[c(2,4,6,8,10,12)], xlim=c(-3000, 3000),ylab = "Count Frequency")+ ggtitle("24 hour Individual 2" )#+ coord_cartesian(xlim = c(-100,500))>> plotting figure... 2025-05-01 1:16:22 PM ## What I did here: I called all my narrowpeak files
# peakfiles3 <- choose.files()
##This loop first established a list then (because I already knew the list had 12 files)
## I then imported each of these onto that list. Once I had the list, I stored it as
## an R object,
# Ind3_peaks <- list()
# for (file in 1:12){
# testname <- basename(peakfiles3[file])
# banana_peel <- str_split_i(testname, "_",3)
# Ind3_peaks[[banana_peel]] <- readPeakFile(peakfiles3[file])
# }
# saveRDS(Ind3_peaks, "data/Ind3_peaks_list.RDS")
# I then called annotatePeak on that list object, and stored that as a R object for later retrieval.)
Ind3_peaks <- readRDS("data/Ind3_peaks_list.RDS")
# peakAnnoList_3 <- lapply(Ind3_peaks, annotatePeak, tssRegion =c(-2000,2000), TxDb= txdb)
# saveRDS(peakAnnoList_3, "data/peakAnnoList_3.RDS")
peakAnnoList_3 <- readRDS("data/peakAnnoList_3.RDS")
plotAnnoBar(peakAnnoList_3, main = "Genomic Feature Distribution, Individual 3")# Epi_list_tagMatrix = lapply(Ind3_peaks, getTagMatrix, windows = TSS)
# saveRDS(Epi_list_tagMatrix, "data/Ind3_TSS_peaks.RDS")
Ind3_TSS_peaks_plot <- readRDS("data/Ind3_TSS_peaks.RDS")
plotAvgProf(Ind3_TSS_peaks_plot[c(1,3,5,7,9,11)], xlim=c(-3000, 3000), ylab = "Count Frequency")+ ggtitle("3 hour Individual 3" )>> plotting figure... 2025-05-01 1:16:36 PM plotAvgProf(Ind3_TSS_peaks_plot[c(2,4,6,8,10,12)], xlim=c(-3000, 3000),ylab = "Count Frequency")+ ggtitle("24 hour Individual 3" )>> plotting figure... 2025-05-01 1:16:38 PM ## What I did here: I called all my narrowpeak files
# peakfiles4 <- choose.files()
#
# ##This loop first established a list then (because I already knew the list had 12 files)
# ## I then imported each of these onto that list. Once I had the list, I stored it as
# ## an R object,
# Ind4_peaks <- list()
# for (file in 1:12){
# testname <- basename(peakfiles4[file])
# banana_peel <- str_split_i(testname, "_",3)
# Ind4_peaks[[banana_peel]] <- readPeakFile(peakfiles4[file])
# }
# saveRDS(Ind4_peaks, "data/Ind4_peaks_list.RDS")
# I then called annotatePeak on that list object, and stored that as a R object for later retrieval.)
Ind4_peaks <- readRDS("data/Ind4_peaks_list.RDS")
# peakAnnoList_4 <- lapply(Ind4_peaks, annotatePeak, tssRegion =c(-2000,2000), TxDb= txdb)
# saveRDS(peakAnnoList_4, "data/peakAnnoList_4.RDS")
peakAnnoList_4 <- readRDS("data/peakAnnoList_4.RDS")
plotAnnoBar(peakAnnoList_4, main = "Genomic Feature Distribution, Individual 4")
# Epi_list_tagMatrix = lapply(Ind4_peaks, getTagMatrix, windows = TSS)
# saveRDS(Epi_list_tagMatrix, "data/Ind4_TSS_peaks.RDS")
Ind4_TSS_peaks_plot <- readRDS("data/Ind4_TSS_peaks.RDS")
plotAvgProf(Ind4_TSS_peaks_plot[c(1,3,5,7,9,11)], xlim=c(-3000, 3000), ylab = "Count Frequency")+ ggtitle("3 hour Individual 4" )
plotAvgProf(Ind4_TSS_peaks_plot[c(2,4,6,8,10,12)], xlim=c(-3000, 3000),ylab = "Count Frequency")+ ggtitle("24 hour Individual 4" )## What I did here: I called all my narrowpeak files
# peakfiles4 <- choose.files()
# #
# # ##This loop first established a list then (because I already knew the list had 12 files)
# # ## I then imported each of these onto that list. Once I had the list, I stored it as
# ## an R object,
# Ind4_peaks <- list()
# for (file in 1:12){
# testname <- basename(peakfiles4[file])
# banana_peel <- str_split_i(testname, "_",3)
# Ind4_peaks[[banana_peel]] <- readPeakFile(peakfiles4[file])
# }
# saveRDS(Ind4_peaks, "data/Ind4_peaks_list.RDS")
# I then called annotatePeak on that list object, and stored that as a R object for later retrieval.)
Ind5_peaks <- readRDS("data/Ind5_peaks_list.RDS")
# peakAnnoList_5 <- lapply(Ind5_peaks, annotatePeak, tssRegion =c(-2000,2000), TxDb= txdb)
# saveRDS(peakAnnoList_5, "data/peakAnnoList_5.RDS")
peakAnnoList_5 <- readRDS("data/peakAnnoList_5.RDS")
plotAnnoBar(peakAnnoList_5, main = "Genomic Feature Distribution, Individual 4")
# Epi_list_tagMatrix = lapply(Ind5_peaks, getTagMatrix, windows = TSS)
# saveRDS(Epi_list_tagMatrix, "data/Ind5_TSS_peaks.RDS")
Ind5_TSS_peaks_plot <- readRDS("data/Ind5_TSS_peaks.RDS")
plotAvgProf(Ind5_TSS_peaks_plot[c(1,3,5,7,9,11)], xlim=c(-3000, 3000), ylab = "Count Frequency")+ ggtitle("3 hour Individual 5" )
plotAvgProf(Ind5_TSS_peaks_plot[c(2,4,6,8,10,12)], xlim=c(-3000, 3000),ylab = "Count Frequency")+ ggtitle("24 hour Individual 5" )## What I did here: I called all my narrowpeak files
# peakfiles6 <- choose.files()
#
# ##This loop first established a list then (because I already knew the list had 12 files)
# ## I then imported each of these onto that list. Once I had the list, I stored it as
# ## an R object,
# Ind6_peaks <- list()
# for (file in 1:12){
# testname <- basename(peakfiles6[file])
# banana_peel <- str_split_i(testname, "_",3)
# Ind6_peaks[[banana_peel]] <- readPeakFile(peakfiles6[file])
# }
# saveRDS(Ind6_peaks, "data/Ind6_peaks_list.RDS")
# I then called annotatePeak on that list object, and stored that as a R object for later retrieval.)
Ind6_peaks <- readRDS("data/Ind6_peaks_list.RDS")
# peakAnnoList_6 <- lapply(Ind6_peaks, annotatePeak, tssRegion =c(-2000,2000), TxDb= txdb)
# saveRDS(peakAnnoList_6, "data/peakAnnoList_6.RDS")
peakAnnoList_6 <- readRDS("data/peakAnnoList_6.RDS")
plotAnnoBar(peakAnnoList_6, main = "Genomic Feature Distribution, Individual 6")+ggtitle ("Genomic Feature Distribution, Individual 6")##Epi_list_tagMatrix title was just because I was too lazy to change the name
# Epi_list_tagMatrix = lapply(Ind6_peaks, getTagMatrix, windows = TSS)
# saveRDS(Epi_list_tagMatrix, "data/Ind6_TSS_peaks.RDS")
Ind6_TSS_peaks_plot <- readRDS("data/Ind6_TSS_peaks.RDS")
plotAvgProf(Ind6_TSS_peaks_plot[c(1,3,5,7,9,11)], xlim=c(-3000, 3000), ylab = "Count Frequency")+ ggtitle("3 hour Individual 6" )>> plotting figure... 2025-05-01 1:16:54 PM plotAvgProf(Ind6_TSS_peaks_plot[c(2,4,6,8,10,12)], xlim=c(-3000, 3000),ylab = "Count Frequency")+ ggtitle("24 hour Individual 6" )>> plotting figure... 2025-05-01 1:16:56 PM possibleTag <- list("integer"=c("AM", "AS", "CM", "CP", "FI", "H0", "H1", "H2",
"HI", "IH", "MQ", "NH", "NM", "OP", "PQ", "SM",
"TC", "UQ"),
"character"=c("BC", "BQ", "BZ", "CB", "CC", "CO", "CQ", "CR",
"CS", "CT", "CY", "E2", "FS", "LB", "MC", "MD",
"MI", "OA", "OC", "OQ", "OX", "PG", "PT", "PU",
"Q2", "QT", "QX", "R2", "RG", "RX", "SA", "TS",
"U2"))
library(Rsamtools)
bamTop100 <- scanBam(BamFile(bamfile, yieldSize = 100),
param = ScanBamParam(tag=unlist(possibleTag)))[[1]]$tag
tags <- names(bamTop100)[lengths(bamTop100)>0]
tags
outPath <- "splited"
dir.create(outPath)
library(TxDb.Hsapiens.UCSC.hg38.knownGene)
seqlev <- "chr1"
seqinformation <- seqinfo(TxDb.Hsapiens.UCSC.hg38.knownGene)
# which <- as(seqinformation[seqlev], "GRanges")
gal <- readBamFile(bamFile=bamfile, tag=character(0),
which=GRanges("chr1", IRanges(1, 1e6)),
asMates=TRUE)
shiftedBamfile <- file.path(outPath, "shifted.bam")
gal1 <- shiftGAlignmentsList(gal, outbam=shiftedBamfile)
txs <- transcripts(TxDb.Hsapiens.UCSC.hg38.knownGene)
tsse <- TSSEscore(gal, txs)
tsse$TSSEscore
tsse
summary(tsse$values)allTSSE <- readRDS( "data/all_TSSE_scores.RDS")
allTSSE %>% as.data.frame() %>%
rownames_to_column("sample") %>%
separate(sample, into = c("indv","trt","time"), sep= "_") %>%
mutate(trt= factor(trt, levels = c("DOX","EPI","DNR","MTX","TRZ","VEH"))) %>%
mutate(time = factor(time, levels = c("3","24"),labels = c("3 hours","24 hours"))) %>%
ggplot(., aes(x= time, y= V1, group = indv))+
# geom_col(position= "dodge",aes(fill=trt)) %>%
geom_jitter(aes(col = trt, size = 1.5, alpha = 0.5) , position=position_jitter(0.25))+
geom_hline(yintercept=5, linetype = 3)+
geom_hline(yintercept=7)+
facet_wrap(~indv)+
theme_bw()+
ggtitle("TSS enrichment scores")+
scale_color_manual(values=drug_pal_fac)allTSSE %>% as.data.frame() %>%
rownames_to_column("sample") %>%
separate(sample, into = c("indv","trt","time"), sep= "_") %>%
dplyr::filter(indv!="4") %>%
dplyr::filter(indv!="5") %>%
mutate(trt= factor(trt, levels = c("DOX","EPI","DNR","MTX","TRZ","VEH"))) %>%
mutate(time = factor(time, levels = c("3","24"),labels = c("3 hours","24 hours"))) %>%
ggplot(., aes(x= time, y= V1, group = indv))+
# geom_col(position= "dodge",aes(fill=trt)) %>%
geom_jitter(aes(col = trt, size = 1.5, alpha = 0.5) , position=position_jitter(0.25))+
geom_hline(yintercept=5, linetype = 3)+
geom_hline(yintercept=7)+
facet_wrap(~indv)+
theme_bw()+
ggtitle("TSS enrichment scores")+
scale_color_manual(values=drug_pal_fac)### bring in the Feature counted files
# Ind1_counts <- read.delim("~/ATAC_downloads/Ind1_files.txt", comment.char="#")
# Ind2_counts <- read.delim("~/ATAC_downloads/Ind2_files.txt", comment.char="#")
# Ind3_counts <- read.delim("~/ATAC_downloads/Ind3_files.txt", comment.char="#")
# Ind4_counts <- read.delim("~/ATAC_downloads/Ind4_files.txt", comment.char="#")
# Ind5_counts <- read.delim("~/ATAC_downloads/Ind5_files.txt", comment.char="#")
# Ind6_counts <- read.delim("~/ATAC_downloads/Ind6_files.txt", comment.char="#")
#
# first_run_frag_counts <- Ind1_counts %>%
# full_join(Ind2_counts) %>%
# full_join(Ind3_counts) %>%
# full_join(Ind4_counts) %>%
# full_join(Ind5_counts) %>%
# full_join(Ind6_counts)
#
# names(first_run_frag_counts) = gsub(pattern = "_S.*", replacement = "", x = names(first_run_frag_counts))
#
# names(first_run_frag_counts)=gsub(pattern = "^ind6.trimmed.filt_files.trimmed_", replacement = "", x = names(first_run_frag_counts))
#
# write.csv(first_run_frag_counts,"data/first_run_frag_counts.txt")
first_run_frag_counts <- read.csv("data/first_run_frag_counts.txt", row.names = 1)
Frag_cor <- first_run_frag_counts %>%
dplyr::select(Ind1_75DA24h:Ind6_71V3h) %>%
cor()
# pheatmap::pheatmap(Frag_cor , cluster_rows = TRUE, cluster_cols = TRUE)
filmat_groupmat_col <- data.frame(timeset = colnames(Frag_cor))
counts_corr_mat <-filmat_groupmat_col %>%
mutate(timeset=gsub("75","1_",timeset)) %>%
mutate(timeset=gsub("87","2_",timeset)) %>%
mutate(timeset=gsub("77","3_",timeset)) %>%
mutate(timeset=gsub("79","4_",timeset)) %>%
mutate(timeset=gsub("78","5_",timeset)) %>%
mutate(timeset=gsub("71","6_",timeset)) %>%
mutate(timeset = gsub("24h","_24h",timeset),
timeset = gsub("3h","_3h",timeset)) %>%
separate(timeset, into = c(NA,"indv","trt","time"), sep= "_") %>%
mutate(trt= case_match(trt, 'DX' ~'DOX', 'E'~'EPI', 'DA'~'DNR', 'M'~'MTX', 'T'~'TRZ', 'V'~'VEH',.default = trt)) %>%
mutate(class = if_else(trt == "DNR", "AC", if_else(
trt == "DOX", "AC", if_else(trt == "EPI", "AC", "nAC")
))) %>%
mutate(TOP2i = if_else(trt == "DNR", "yes", if_else(
trt == "DOX", "yes", if_else(trt == "EPI", "yes", if_else(trt == "MTX", "yes", "no"))
)))
mat_colors <- list(
trt= c("#F1B72B","#8B006D","#DF707E","#3386DD","#707031","#41B333"),
indv=c("#1B9E77", "#D95F02" ,"#7570B3", "#E7298A" ,"#66A61E", "#E6AB02"),
time=c("pink", "chocolate4"),
class=c("yellow1","darkorange1"),
TOP2i =c("darkgreen","lightgreen"))
names(mat_colors$trt) <- unique(counts_corr_mat$trt)
names(mat_colors$indv) <- unique(counts_corr_mat$indv)
names(mat_colors$time) <- unique(counts_corr_mat$time)
names(mat_colors$class) <- unique(counts_corr_mat$class)
names(mat_colors$TOP2i) <- unique(counts_corr_mat$TOP2i)
ComplexHeatmap::pheatmap(Frag_cor,
# column_title=(paste0("RNA-seq log"[2]~"cpm correlation")),
annotation_col = counts_corr_mat,
annotation_colors = mat_colors,
heatmap_legend_param = mat_colors,
fontsize=10,
fontsize_row = 8,
angle_col="90",
treeheight_row=25,
fontsize_col = 8,
treeheight_col = 20)all_frip1 %>% dplyr::rename("FRiP_score"="FRiP_1") %>%
rbind(all_frip2%>% dplyr::rename("FRiP_score"="FRiP_2")) %>%
rbind(all_frip3%>% dplyr::rename("FRiP_score"="FRiP_3")) %>%
rbind(all_frip6%>% dplyr::rename("FRiP_score"="FRiP_6")) %>%
mutate(time=factor(time, levels= c("3h","24h"), labels = c("3 hours","24 hours")), indv=factor(indv, levels = c("1","2","3","6"))) %>%
ggplot(., aes(x = trt, y=FRIP_adult))+
geom_boxplot(aes(fill=trt))+
geom_point(aes(col=indv, size =3))+
# geom_hline(yintercept = 20)+
facet_wrap(time~.)+
scale_fill_manual(values=drug_pal_fac)+
scale_color_brewer(palette = "Dark2")+
ggtitle("reads in adult heart DNAse peaks")+
theme_bw()all_frip1 %>% dplyr::rename("FRiP_score"="FRiP_1") %>%
rbind(all_frip2%>% dplyr::rename("FRiP_score"="FRiP_2")) %>%
rbind(all_frip3%>% dplyr::rename("FRiP_score"="FRiP_3")) %>%
rbind(all_frip6%>% dplyr::rename("FRiP_score"="FRiP_6")) %>%
mutate(time=factor(time, levels= c("3h","24h"), labels = c("3 hours","24 hours")), indv=factor(indv, levels = c("1","2","3","6"))) %>%
ggplot(., aes(x = trt, y=FRiP_score))+
geom_boxplot(aes(fill=trt))+
geom_point(aes(col=indv, size =3))+
# geom_hline(yintercept = 20)+
facet_wrap(time~.)+
scale_fill_manual(values=drug_pal_fac)+
scale_color_brewer(palette = "Dark2")+
ggtitle("Fraction of all reads in peaks")+
theme_bw()
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] stats4 stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] ChIPpeakAnno_3.40.0
[2] MotifDb_1.48.0
[3] BSgenome.Hsapiens.UCSC.hg38_1.4.5
[4] BSgenome_1.74.0
[5] BiocIO_1.16.0
[6] Biostrings_2.74.1
[7] XVector_0.46.0
[8] TxDb.Hsapiens.UCSC.hg38.knownGene_3.20.0
[9] GenomicFeatures_1.58.0
[10] AnnotationDbi_1.68.0
[11] Biobase_2.66.0
[12] rtracklayer_1.66.0
[13] GenomicRanges_1.58.0
[14] GenomeInfoDb_1.42.3
[15] IRanges_2.40.1
[16] ATACseqQC_1.30.0
[17] S4Vectors_0.44.0
[18] BiocGenerics_0.52.0
[19] ChIPseeker_1.42.1
[20] RColorBrewer_1.1-3
[21] kableExtra_1.4.0
[22] lubridate_1.9.4
[23] forcats_1.0.0
[24] stringr_1.5.1
[25] dplyr_1.1.4
[26] purrr_1.0.4
[27] readr_2.1.5
[28] tidyr_1.3.1
[29] tibble_3.2.1
[30] ggplot2_3.5.1
[31] tidyverse_2.0.0
[32] workflowr_1.7.1
loaded via a namespace (and not attached):
[1] fs_1.6.5
[2] ProtGenerics_1.38.0
[3] matrixStats_1.5.0
[4] bitops_1.0-9
[5] DirichletMultinomial_1.48.0
[6] enrichplot_1.26.6
[7] TFBSTools_1.44.0
[8] doParallel_1.0.17
[9] httr_1.4.7
[10] InteractionSet_1.34.0
[11] tools_4.4.2
[12] R6_2.6.1
[13] HDF5Array_1.34.0
[14] lazyeval_0.2.2
[15] GetoptLong_1.0.5
[16] rhdf5filters_1.18.1
[17] withr_3.0.2
[18] prettyunits_1.2.0
[19] VennDiagram_1.7.3
[20] cli_3.6.4
[21] formatR_1.14
[22] labeling_0.4.3
[23] sass_0.4.9
[24] randomForest_4.7-1.2
[25] Rsamtools_2.22.0
[26] systemfonts_1.2.1
[27] yulab.utils_0.2.0
[28] DOSE_4.0.0
[29] svglite_2.1.3
[30] R.utils_2.13.0
[31] plotrix_3.8-4
[32] limma_3.62.2
[33] rstudioapi_0.17.1
[34] RSQLite_2.3.9
[35] shape_1.4.6.1
[36] generics_0.1.3
[37] gridGraphics_0.5-1
[38] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
[39] gtools_3.9.5
[40] GO.db_3.20.0
[41] Matrix_1.7-3
[42] futile.logger_1.4.3
[43] abind_1.4-8
[44] R.methodsS3_1.8.2
[45] lifecycle_1.0.4
[46] whisker_0.4.1
[47] yaml_2.3.10
[48] edgeR_4.4.2
[49] SummarizedExperiment_1.36.0
[50] gplots_3.2.0
[51] rhdf5_2.50.2
[52] qvalue_2.38.0
[53] SparseArray_1.6.2
[54] BiocFileCache_2.14.0
[55] grid_4.4.2
[56] blob_1.2.4
[57] promises_1.3.2
[58] crayon_1.5.3
[59] pwalign_1.2.0
[60] ggtangle_0.0.6
[61] lattice_0.22-6
[62] cowplot_1.1.3
[63] annotate_1.84.0
[64] KEGGREST_1.46.0
[65] magick_2.8.5
[66] GenomicScores_2.18.1
[67] ComplexHeatmap_2.22.0
[68] pillar_1.10.1
[69] knitr_1.49
[70] fgsea_1.32.2
[71] rjson_0.2.23
[72] boot_1.3-31
[73] codetools_0.2-20
[74] fastmatch_1.1-6
[75] glue_1.8.0
[76] getPass_0.2-4
[77] ggfun_0.1.8
[78] data.table_1.17.0
[79] vctrs_0.6.5
[80] png_0.1-8
[81] treeio_1.30.0
[82] poweRlaw_1.0.0
[83] gtable_0.3.6
[84] cachem_1.1.0
[85] xfun_0.51
[86] S4Arrays_1.6.0
[87] preseqR_4.0.0
[88] survival_3.8-3
[89] iterators_1.0.14
[90] statmod_1.5.0
[91] nlme_3.1-167
[92] ggtree_3.14.0
[93] bit64_4.6.0-1
[94] progress_1.2.3
[95] filelock_1.0.3
[96] rprojroot_2.0.4
[97] bslib_0.9.0
[98] KernSmooth_2.23-26
[99] splitstackshape_1.4.8
[100] seqLogo_1.72.0
[101] colorspace_2.1-1
[102] DBI_1.2.3
[103] ade4_1.7-23
[104] motifStack_1.50.0
[105] tidyselect_1.2.1
[106] processx_3.8.6
[107] bit_4.6.0
[108] compiler_4.4.2
[109] curl_6.2.1
[110] git2r_0.35.0
[111] httr2_1.1.1
[112] graph_1.84.1
[113] xml2_1.3.7
[114] DelayedArray_0.32.0
[115] scales_1.3.0
[116] caTools_1.18.3
[117] RBGL_1.82.0
[118] callr_3.7.6
[119] rappdirs_0.3.3
[120] digest_0.6.37
[121] rmarkdown_2.29
[122] htmltools_0.5.8.1
[123] pkgconfig_2.0.3
[124] MatrixGenerics_1.18.1
[125] dbplyr_2.5.0
[126] regioneR_1.38.0
[127] fastmap_1.2.0
[128] ensembldb_2.30.0
[129] GlobalOptions_0.1.2
[130] htmlwidgets_1.6.4
[131] rlang_1.1.5
[132] UCSC.utils_1.2.0
[133] farver_2.1.2
[134] jquerylib_0.1.4
[135] jsonlite_1.9.1
[136] BiocParallel_1.40.0
[137] GOSemSim_2.32.0
[138] R.oo_1.27.0
[139] RCurl_1.98-1.16
[140] magrittr_2.0.3
[141] polynom_1.4-1
[142] GenomeInfoDbData_1.2.13
[143] ggplotify_0.1.2
[144] patchwork_1.3.0
[145] Rhdf5lib_1.28.0
[146] munsell_0.5.1
[147] Rcpp_1.0.14
[148] ape_5.8-1
[149] stringi_1.8.4
[150] zlibbioc_1.52.0
[151] MASS_7.3-65
[152] AnnotationHub_3.14.0
[153] plyr_1.8.9
[154] parallel_4.4.2
[155] ggrepel_0.9.6
[156] CNEr_1.42.0
[157] splines_4.4.2
[158] multtest_2.62.0
[159] circlize_0.4.16
[160] hms_1.1.3
[161] locfit_1.5-9.12
[162] ps_1.9.0
[163] igraph_2.1.4
[164] reshape2_1.4.4
[165] biomaRt_2.62.1
[166] TFMPvalue_0.0.9
[167] futile.options_1.0.1
[168] BiocVersion_3.20.0
[169] XML_3.99-0.18
[170] evaluate_1.0.3
[171] universalmotif_1.24.2
[172] lambda.r_1.2.4
[173] BiocManager_1.30.25
[174] foreach_1.5.2
[175] tzdb_0.4.0
[176] httpuv_1.6.15
[177] clue_0.3-66
[178] xtable_1.8-4
[179] restfulr_0.0.15
[180] AnnotationFilter_1.30.0
[181] tidytree_0.4.6
[182] later_1.4.1
[183] viridisLite_0.4.2
[184] aplot_0.2.5
[185] memoise_2.0.1
[186] GenomicAlignments_1.42.0
[187] cluster_2.1.8.1
[188] timechange_0.3.0