QC_on_reads
Renee Matthews
2025-05-04
Last updated: 2025-05-07
Checks: 7 0
Knit directory: ATAC_learning/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 0c95219 | reneeisnowhere | 2025-05-07 | updates |
library(tidyverse)
library(kableExtra)
# library(broom)
library(RColorBrewer)
library(ChIPseeker)
library("TxDb.Hsapiens.UCSC.hg38.knownGene")
library("org.Hs.eg.db")
library(rtracklayer)
# library(edgeR)
library(ggfortify)
# library(limma)
library(readr)
library(BiocGenerics)
library(gridExtra)
library(VennDiagram)
library(scales)
library(BiocParallel)
library(ggpubr)
library(devtools)
library(eulerr)
# library(genomation)
library(ggsignif)
library(plyranges)
library(ggrepel)
library(ComplexHeatmap)
library(cowplot)
library(smplot2)
library(data.table)
library(ATACseqQC)drug_pal <- c("#8B006D","#DF707E","#F1B72B", "#3386DD","#707031","#41B333")Loading data
Ind1_summary <- read.csv("data/Ind1_summary.txt", row.names = 1) %>%
dplyr::rename("sample"=X1,"reads"=X2,"mapped"=X3)%>%
mutate(sample=gsub(pattern = "flagstat_first/trimmed_","total_",sample)) %>%
mutate(sample= gsub(pattern= "flagstat_noM/trimmed_","nuclear_",sample)) %>%
mutate(sample=gsub(pattern = "filt_files/trimmed_","other_",sample)) %>%
mutate(sample = gsub("24h","_24h",sample),
sample = gsub("3h","_3h",sample)) %>%
separate(sample, into=c("read_type","indv","trt","time",NA,"neat")) %>%
mutate(read_type=if_else(read_type=="other",neat,read_type)) %>%
mutate(trt= gsub('75DX','DOX',trt),
trt= gsub('75E','EPI', trt),
trt=gsub('75DA','DNR',trt),
trt=gsub('75M','MTX',trt),
trt=gsub('75T','TRZ',trt),
trt=gsub('75V','VEH',trt)) %>%
separate(reads,into=c("reads",NA),sep= " ") %>%
mutate(reads=as.numeric(reads)) %>%
separate(mapped, into= c("mapped_reads", NA,NA, "percent_mapped_reads",NA)) %>%
mutate(mapped_reads=as.numeric(mapped_reads)) %>%
tidyr::unite("sample",indv:time,sep = "_",remove = FALSE) %>%
dplyr::select(sample, indv:time, read_type,reads, mapped_reads)
Ind2_summary <- read.csv("data/Ind2_summary.txt", row.names = 1)%>%
dplyr::rename("sample"=X1,"reads"=X2,"mapped"=X3)%>%
mutate(sample=gsub(pattern = "flagstat_first/trimmed_","total_",sample)) %>%
mutate(sample= gsub(pattern= "flagstat_noM/trimmed_","nuclear_",sample)) %>%
mutate(sample=gsub(pattern = "filt_files/trimmed_","other_",sample)) %>%
mutate(sample = gsub("24h","_24h",sample),
sample = gsub("3h","_3h",sample)) %>%
separate(sample, into=c("read_type","indv","trt","time",NA,"neat")) %>%
mutate(read_type=if_else(read_type=="other",neat,read_type)) %>%
mutate(trt= gsub('87DX','DOX',trt),
trt= gsub('87E','EPI', trt),
trt=gsub('87DA','DNR',trt),
trt=gsub('87M','MTX',trt),
trt=gsub('87T','TRZ',trt),
trt=gsub('87V','VEH',trt)) %>%
separate(reads,into=c("reads",NA),sep= " ") %>%
mutate(reads=as.numeric(reads)) %>%
separate(mapped, into= c("mapped_reads", NA,NA, "percent_mapped_reads",NA)) %>%
mutate(mapped_reads=as.numeric(mapped_reads)) %>%
tidyr::unite("sample",indv:time,sep = "_",remove = FALSE) %>%
dplyr::select(sample, indv:time, read_type,reads, mapped_reads)
Ind3_summary <- read.csv("data/Ind3_summary.txt", row.names = 1)%>%
dplyr::rename("sample"=X1,"reads"=X2,"mapped"=X3)%>%
mutate(sample=gsub(pattern = "flagstat_first/trimmed_","total_",sample)) %>%
mutate(sample= gsub(pattern= "flagstat_noM/trimmed_","nuclear_",sample)) %>%
mutate(sample=gsub(pattern = "filt_files/trimmed_","other_",sample)) %>%
mutate(sample = gsub("24h","_24h",sample),
sample = gsub("3h","_3h",sample)) %>%
separate(sample, into=c("read_type","indv","trt","time",NA,"neat")) %>%
mutate(read_type=if_else(read_type=="other",neat,read_type)) %>%
mutate(trt= gsub('77DX','DOX',trt),
trt= gsub('77E','EPI', trt),
trt=gsub('77DA','DNR',trt),
trt=gsub('77M','MTX',trt),
trt=gsub('77T','TRZ',trt),
trt=gsub('77V','VEH',trt)) %>%
separate(reads,into=c("reads",NA),sep= " ") %>%
mutate(reads=as.numeric(reads)) %>%
separate(mapped, into= c("mapped_reads", NA,NA, "percent_mapped_reads",NA)) %>%
mutate(mapped_reads=as.numeric(mapped_reads)) %>%
tidyr::unite("sample",indv:time,sep = "_",remove = FALSE) %>%
dplyr::select(sample, indv:time, read_type,reads, mapped_reads)
Ind6_summary <- read.csv("data/Ind6_summary.txt", row.names = 1)%>%
dplyr::rename("sample"=X1,"reads"=X2,"mapped"=X3)%>%
mutate(sample=gsub(pattern = "flagstat_first/trimmed_","total_",sample)) %>%
mutate(sample= gsub(pattern= "flagstat_noM/trimmed_","nuclear_",sample)) %>%
mutate(sample=gsub(pattern = "filt_files/trimmed_","other_",sample)) %>%
mutate(sample = gsub("24h","_24h",sample),
sample = gsub("3h","_3h",sample)) %>%
separate(sample, into=c("read_type","indv","trt","time",NA,"neat")) %>%
mutate(read_type=if_else(read_type=="other",neat,read_type)) %>%
mutate(trt= gsub('71DX','DOX',trt),
trt= gsub('71E','EPI', trt),
trt=gsub('71DA','DNR',trt),
trt=gsub('71M','MTX',trt),
trt=gsub('71T','TRZ',trt),
trt=gsub('71V','VEH',trt)) %>%
separate(reads,into=c("reads",NA),sep= " ") %>%
mutate(reads=as.numeric(reads)) %>%
separate(mapped, into= c("mapped_reads", NA,NA, "percent_mapped_reads",NA)) %>%
mutate(mapped_reads=as.numeric(mapped_reads)) %>%
tidyr::unite("sample",indv:time,sep = "_",remove = FALSE) %>%
dplyr::select(sample, indv:time, read_type,reads, mapped_reads)
Final_four_counts <- Ind1_summary %>% rbind(Ind2_summary) %>% rbind(Ind3_summary) %>% rbind(Ind6_summary)
FF_bound <- Final_four_counts %>%
mutate(time=factor(time, levels =c("3h","24h"))) %>%
pivot_longer(cols = reads:mapped_reads, names_to="count_type", values_to = "read_num") %>%
mutate(read_type=case_when(read_type=="fin"~"unique",.default = read_type)) %>%
tidyr::unite("Total_reads",read_type:count_type,sep = "_", remove = TRUE) %>%
tidyr::unite("Total_reads_time",Total_reads:time,sep = "_", remove = FALSE) %>%
mutate(Total_reads=factor(Total_reads, levels = c("total_reads", "total_mapped_reads","nuclear_reads","nuclear_mapped_reads","unique_reads", "unique_mapped_reads", "nodup_reads", "nodup_mapped_reads"))) %>%
dplyr::filter(Total_reads != "nuclear_reads") %>%
dplyr::filter(Total_reads != "nodup_reads") %>%
dplyr::filter(Total_reads != "unique_reads") %>%
dplyr::mutate(trt=factor(trt, levels = c("DOX", "EPI","DNR", "MTX","TRZ","VEH")))FF_bound %>%
ggplot(., aes(x=Total_reads, y=read_num, col=indv, group=interaction(time,Total_reads_time)))+
geom_boxplot(aes(fill=trt)) +
geom_point(aes(col=indv))+
theme_bw()+
facet_wrap(~trt+time,nrow = 3, ncol = 6 )+
scale_fill_manual(values=drug_pal)+
theme(strip.text =
element_text(face = "bold",
hjust = 0,
size = 8),
strip.background =
element_rect(fill = "white",
linetype = "solid",
color = "black",
linewidth = 1),
panel.spacing = unit(1, 'points'),
axis.text.x=
element_text(angle = 90,
vjust = 0.5,
hjust=1))
Individual 1 fragment files:
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))+
ggtitle("Individual 1\n3 hour fragment sizes")+
theme_classic()+
facet_wrap(~trt)+
scale_color_manual(values=drug_pal)+
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))+
ggtitle("Individual 1\n3 hour fragment sizes BEFORE filtering")+
theme_classic()+
facet_wrap(~trt)+
scale_color_manual(values=drug_pal)+
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)
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))+
ggtitle("Individual 1\n24 hour fragment sizes BEFORE filtering")+
theme_classic()+
facet_wrap(~trt)+
scale_color_manual(values=drug_pal)+
coord_cartesian(xlim=c(0,1000))
#### FRiP Individual 1
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)
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)
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)
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)
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)
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)
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)
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)
Horizontal line is at 20%. Individual 4 at 3 hours is not a good score. Encode recommends >30% (dotted line).
Individual 2 fragment files:
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)
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)
FRiP Individual 2
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)
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)
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)
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)
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)
Individual 3 fragment files:
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)
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)
FRiP Individual 3
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)
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)
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)
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)
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)
### Individual 6 fragment files:
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)
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)
#### FRiP Individual 6
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)
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)
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)
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)
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)
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")))%>%
dplyr::filter(indv != "Ind4") %>%
dplyr::filter(indv != "Ind5") %>%
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)+
scale_color_brewer(palette = "Dark2")+
ggtitle("Peak counts by treatment")+
theme_bw()
Ind1 Peaks
Making the TSS average window code
# 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... 2025-05-07 4:45:27 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... 2025-05-07 4:45:28 PM 
# + coord_cartesian(xlim = c(-100,500))Ind2 Peaks
## 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... 2025-05-07 4:45:43 PM
>> Running bootstrapping for tag matrix... 2025-05-07 4:49:05 PM
>> Running bootstrapping for tag matrix... 2025-05-07 4:52:19 PM
>> Running bootstrapping for tag matrix... 2025-05-07 4:55:36 PM
>> Running bootstrapping for tag matrix... 2025-05-07 4:59:13 PM
>> Running bootstrapping for tag matrix... 2025-05-07 5:02:37 PM
>> Running bootstrapping for tag matrix... 2025-05-07 5:05:50 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... 2025-05-07 5:05:51 PM 
Ind3 Peaks
## 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... 2025-05-07 5:06:01 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... 2025-05-07 5:06:02 PM 
Ind6 Peaks
## 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... 2025-05-07 5:06:14 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... 2025-05-07 5:06:15 PM 
### for code to calculate the TSSE scores, reference the file "code/TSSE.R"### for code to calculate the TSSE scores, reference the file "code/TSSE.R"
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"))) %>%
dplyr::filter(indv !=4 &indv !=5) %>%
ggplot(., aes(x= time, y= V1, group = indv))+
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, col = "blue")+
facet_wrap(~indv)+
theme_bw()+
ggtitle("TSS enrichment scores")+
scale_color_manual(values=drug_pal)+
theme(strip.text = element_text(face = "bold", hjust = .5, size = 8),
strip.background = element_rect(fill = "white", linetype = "solid",
color = "black", linewidth = 1))
#### These Ind1 TSS_peaks were created on the "analysis/Peak_calling.Rmd file example code:
# Import in peak 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.)
# peakAnnoList_3 <- lapply(Ind3_peaks, annotatePeak, tssRegion =c(-2000,2000), TxDb= txdb)
# saveRDS(peakAnnoList_3, "data/peakAnnoList_3.RDS")
# Epi_list_tagMatrix = lapply(Ind3_peaks, getTagMatrix, windows = TSS)
# saveRDS(Epi_list_tagMatrix, "data/Ind3_TSS_peaks.RDS")
Ind1_TSS_peaks_plot <- readRDS("data/Ind1_TSS_peaks.RDS")
Ind2_TSS_peaks_plot <- readRDS("data/Ind2_TSS_peaks.RDS")
Ind3_TSS_peaks_plot <- readRDS("data/Ind3_TSS_peaks.RDS")
Ind6_TSS_peaks_plot <- readRDS("data/Ind6_TSS_peaks.RDS")
a1<- plotAvgProf(Ind1_TSS_peaks_plot[c(1,3,5,7,9,11)], xlim=c(-3000, 3000), ylab = "Count Frequency")+ ggtitle("3 hour Individual 1" )+coord_cartesian(xlim=c(-2000,2000))>> plotting figure... 2025-05-07 5:06:50 PM b1 <- plotAvgProf(Ind2_TSS_peaks_plot[c(1,3,5,7,9,11)], xlim=c(-3000, 3000), ylab = "Count Frequency")+ ggtitle("3 hour Individual 2" )+coord_cartesian(xlim=c(-2000,2000))>> plotting figure... 2025-05-07 5:06:51 PM c1 <- plotAvgProf(Ind3_TSS_peaks_plot[c(1,3,5,7,9,11)], xlim=c(-3000, 3000), ylab = "Count Frequency")+ ggtitle("3 hour Individual 3" )+coord_cartesian(xlim=c(-2000,2000))>> plotting figure... 2025-05-07 5:06:52 PM d1 <- plotAvgProf(Ind6_TSS_peaks_plot[c(1,3,5,7,9,11)], xlim=c(-3000, 3000), ylab = "Count Frequency")+ ggtitle("3 hour Individual 6" )+coord_cartesian(xlim=c(-2000,2000))>> plotting figure... 2025-05-07 5:06:53 PM a2 <- plotAvgProf(Ind1_TSS_peaks_plot[c(2,4,6,8,10,12)], xlim=c(-3000, 3000),ylab = "Count Frequency")+ ggtitle("24 hour Individual 1" )+coord_cartesian(xlim=c(-2000,2000))>> plotting figure... 2025-05-07 5:06:54 PM b2 <- 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(-2000,2000))>> plotting figure... 2025-05-07 5:06:55 PM c2 <- plotAvgProf(Ind3_TSS_peaks_plot[c(2,4,6,8,10,12)], xlim=c(-3000, 3000),ylab = "Count Frequency")+ ggtitle("24 hour Individual 3" )+coord_cartesian(xlim=c(-2000,2000))>> plotting figure... 2025-05-07 5:06:56 PM d2 <- plotAvgProf(Ind6_TSS_peaks_plot[c(2,4,6,8,10,12)], xlim=c(-3000, 3000),ylab = "Count Frequency")+ ggtitle("24 hour Individual 6" )+coord_cartesian(xlim=c(-2000,2000))>> plotting figure... 2025-05-07 5:06:57 PM plot_grid(a1,a2, b1,b2,c1,c2,d1,d2, axis="l",align = "hv",nrow=4, ncol=2)
Creating the masterlist of high confidence regions (masterpeaks)
To create this peak set, -First I moved all .narrowPeak files into
the same folder and ran
bedtools multiinter -i ./* >log.file.txt to create an
intersection of all peaks. I then was only interested in segments that
had a count of more than 4 (intersection existed in at least 4 of the
data sets) in all files. I filtered column #4 of the
log.file.txt output by
awk -F"\t" '$4 > 4 {print $1"\t"$2"\t"$3 }' log.file.txt > all_filt_peaks.bed
and printed the results of anything >4 in bed format to the
all_filt_peaks.bed file. Upon further reading of bedtools
documents, I realized the number of “peaks” was actually fragments of
peaks that were intersected among all files. This was not the final
output I wanted so I intersected these high counted segments back with
the very first initial mergedPeaks.bed file using
bedtools intersect -a mergedPeaks.bed -b all_filt_peaks.bed -wa -u > merged_filtered_peaks.bed.
using the -wa -u flags allowed me to filter the first
mergedPeaks file, keeping only those high confidence peaks that
overlapped the all_filt_peaks.bed and only reporting the
unique calls.
Additionally, I filtered out the blacklisted regions using the
following code:
intersectBed -v -a merged_filtered_peaks.bed -b Blacklist/hg38.blacklist.bed.gz > final_bl_filt_peaks.bed
My final high confidence peaks file contains 172,418 peaks. I used this
file to collect PE-read counts using
featureCounts -p -a high_conf_peaks.saf -F SAF -o all_four_filtered_counts.txt ind1/trimmed/filt_files/*nodup.bam ind2/trimmed/filt_files/*nodup.bam ind3/trimmed/filt_files/*nodup.bam ind6/trimmed/filt_files/*nodup.bam
sessionInfo()R version 4.4.2 (2024-10-31 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 26100)
Matrix products: default
locale:
[1] LC_COLLATE=English_United States.utf8
[2] LC_CTYPE=English_United States.utf8
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C
[5] LC_TIME=English_United States.utf8
time zone: America/Chicago
tzcode source: internal
attached base packages:
[1] grid stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] ATACseqQC_1.30.0
[2] data.table_1.17.0
[3] smplot2_0.2.5
[4] cowplot_1.1.3
[5] ComplexHeatmap_2.22.0
[6] ggrepel_0.9.6
[7] plyranges_1.26.0
[8] ggsignif_0.6.4
[9] eulerr_7.0.2
[10] devtools_2.4.5
[11] usethis_3.1.0
[12] ggpubr_0.6.0
[13] BiocParallel_1.40.0
[14] scales_1.3.0
[15] VennDiagram_1.7.3
[16] futile.logger_1.4.3
[17] gridExtra_2.3
[18] ggfortify_0.4.17
[19] rtracklayer_1.66.0
[20] org.Hs.eg.db_3.20.0
[21] TxDb.Hsapiens.UCSC.hg38.knownGene_3.20.0
[22] GenomicFeatures_1.58.0
[23] AnnotationDbi_1.68.0
[24] Biobase_2.66.0
[25] GenomicRanges_1.58.0
[26] GenomeInfoDb_1.42.3
[27] IRanges_2.40.1
[28] S4Vectors_0.44.0
[29] BiocGenerics_0.52.0
[30] ChIPseeker_1.42.1
[31] RColorBrewer_1.1-3
[32] kableExtra_1.4.0
[33] lubridate_1.9.4
[34] forcats_1.0.0
[35] stringr_1.5.1
[36] dplyr_1.1.4
[37] purrr_1.0.4
[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] R.methodsS3_1.8.2
[2] progress_1.2.3
[3] urlchecker_1.0.1
[4] poweRlaw_1.0.0
[5] nnet_7.3-20
[6] Biostrings_2.74.1
[7] HDF5Array_1.34.0
[8] vctrs_0.6.5
[9] ggtangle_0.0.6
[10] ChIPpeakAnno_3.40.0
[11] digest_0.6.37
[12] png_0.1-8
[13] shape_1.4.6.1
[14] git2r_0.35.0
[15] MASS_7.3-65
[16] reshape2_1.4.4
[17] httpuv_1.6.15
[18] foreach_1.5.2
[19] qvalue_2.38.0
[20] withr_3.0.2
[21] xfun_0.51
[22] ggfun_0.1.8
[23] ellipsis_0.3.2
[24] survival_3.8-3
[25] memoise_2.0.1
[26] profvis_0.4.0
[27] systemfonts_1.2.1
[28] tidytree_0.4.6
[29] zoo_1.8-13
[30] GlobalOptions_0.1.2
[31] gtools_3.9.5
[32] R.oo_1.27.0
[33] Formula_1.2-5
[34] prettyunits_1.2.0
[35] KEGGREST_1.46.0
[36] promises_1.3.2
[37] httr_1.4.7
[38] rstatix_0.7.2
[39] restfulr_0.0.15
[40] rhdf5filters_1.18.1
[41] ps_1.9.0
[42] rhdf5_2.50.2
[43] rstudioapi_0.17.1
[44] UCSC.utils_1.2.0
[45] miniUI_0.1.1.1
[46] generics_0.1.3
[47] DOSE_4.0.0
[48] base64enc_0.1-3
[49] processx_3.8.6
[50] curl_6.2.1
[51] zlibbioc_1.52.0
[52] randomForest_4.7-1.2
[53] GenomeInfoDbData_1.2.13
[54] SparseArray_1.6.2
[55] RBGL_1.82.0
[56] ade4_1.7-23
[57] xtable_1.8-4
[58] doParallel_1.0.17
[59] evaluate_1.0.3
[60] S4Arrays_1.6.0
[61] BiocFileCache_2.14.0
[62] hms_1.1.3
[63] colorspace_2.1-1
[64] filelock_1.0.3
[65] polynom_1.4-1
[66] magrittr_2.0.3
[67] later_1.4.1
[68] ggtree_3.14.0
[69] lattice_0.22-6
[70] getPass_0.2-4
[71] XML_3.99-0.18
[72] matrixStats_1.5.0
[73] Hmisc_5.2-2
[74] pillar_1.10.1
[75] nlme_3.1-167
[76] iterators_1.0.14
[77] pwalign_1.2.0
[78] caTools_1.18.3
[79] compiler_4.4.2
[80] stringi_1.8.4
[81] SummarizedExperiment_1.36.0
[82] GenomicAlignments_1.42.0
[83] plyr_1.8.9
[84] crayon_1.5.3
[85] abind_1.4-8
[86] BiocIO_1.16.0
[87] gridGraphics_0.5-1
[88] locfit_1.5-9.12
[89] bit_4.6.0
[90] fastmatch_1.1-6
[91] whisker_0.4.1
[92] codetools_0.2-20
[93] bslib_0.9.0
[94] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
[95] GetoptLong_1.0.5
[96] multtest_2.62.0
[97] mime_0.12
[98] splines_4.4.2
[99] circlize_0.4.16
[100] Rcpp_1.0.14
[101] dbplyr_2.5.0
[102] knitr_1.49
[103] blob_1.2.4
[104] seqLogo_1.72.0
[105] BiocVersion_3.20.0
[106] clue_0.3-66
[107] AnnotationFilter_1.30.0
[108] fs_1.6.5
[109] checkmate_2.3.2
[110] pkgbuild_1.4.6
[111] ggplotify_0.1.2
[112] Matrix_1.7-3
[113] callr_3.7.6
[114] statmod_1.5.0
[115] tzdb_0.4.0
[116] svglite_2.1.3
[117] pkgconfig_2.0.3
[118] tools_4.4.2
[119] cachem_1.1.0
[120] RSQLite_2.3.9
[121] viridisLite_0.4.2
[122] DBI_1.2.3
[123] fastmap_1.2.0
[124] rmarkdown_2.29
[125] Rsamtools_2.22.0
[126] AnnotationHub_3.14.0
[127] broom_1.0.7
[128] sass_0.4.9
[129] patchwork_1.3.0
[130] BiocManager_1.30.25
[131] graph_1.84.1
[132] carData_3.0-5
[133] rpart_4.1.24
[134] farver_2.1.2
[135] yaml_2.3.10
[136] MatrixGenerics_1.18.1
[137] foreign_0.8-88
[138] cli_3.6.4
[139] lifecycle_1.0.4
[140] lambda.r_1.2.4
[141] sessioninfo_1.2.3
[142] backports_1.5.0
[143] annotate_1.84.0
[144] timechange_0.3.0
[145] gtable_0.3.6
[146] rjson_0.2.23
[147] parallel_4.4.2
[148] ape_5.8-1
[149] limma_3.62.2
[150] jsonlite_1.9.1
[151] edgeR_4.4.2
[152] TFBSTools_1.44.0
[153] bitops_1.0-9
[154] bit64_4.6.0-1
[155] pwr_1.3-0
[156] yulab.utils_0.2.0
[157] CNEr_1.42.0
[158] futile.options_1.0.1
[159] jquerylib_0.1.4
[160] GOSemSim_2.32.0
[161] R.utils_2.13.0
[162] lazyeval_0.2.2
[163] shiny_1.10.0
[164] htmltools_0.5.8.1
[165] enrichplot_1.26.6
[166] GO.db_3.20.0
[167] rappdirs_0.3.3
[168] formatR_1.14
[169] ensembldb_2.30.0
[170] glue_1.8.0
[171] TFMPvalue_0.0.9
[172] GenomicScores_2.18.1
[173] httr2_1.1.1
[174] XVector_0.46.0
[175] RCurl_1.98-1.16
[176] InteractionSet_1.34.0
[177] rprojroot_2.0.4
[178] treeio_1.30.0
[179] BSgenome_1.74.0
[180] motifStack_1.50.0
[181] boot_1.3-31
[182] preseqR_4.0.0
[183] universalmotif_1.24.2
[184] igraph_2.1.4
[185] R6_2.6.1
[186] gplots_3.2.0
[187] labeling_0.4.3
[188] cluster_2.1.8.1
[189] pkgload_1.4.0
[190] Rhdf5lib_1.28.0
[191] regioneR_1.38.0
[192] aplot_0.2.5
[193] DirichletMultinomial_1.48.0
[194] DelayedArray_0.32.0
[195] tidyselect_1.2.1
[196] plotrix_3.8-4
[197] ProtGenerics_1.38.0
[198] htmlTable_2.4.3
[199] xml2_1.3.7
[200] car_3.1-3
[201] munsell_0.5.1
[202] KernSmooth_2.23-26
[203] htmlwidgets_1.6.4
[204] fgsea_1.32.2
[205] biomaRt_2.62.1
[206] rlang_1.1.5
[207] remotes_2.5.0