QC of ATAC sequencing data

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Last updated: 2025-08-06

Checks: 7 0

Knit directory: ATAC_learning/

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---

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Repository version: 4c8601d

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Unstaged changes:
    Modified:   ATAC_learning.Rproj
    Modified:   analysis/AC_shared_analysis.Rmd
    Modified:   analysis/AF_HF_SNPs.Rmd
    Modified:   analysis/Cardiotox_SNPs.Rmd
    Modified:   analysis/Cormotif_analysis.Rmd
    Modified:   analysis/DEG_analysis.Rmd
    Modified:   analysis/GO_analysis_DAR_paper.Rmd
    Modified:   analysis/H3K27ac_integration.Rmd
    Modified:   analysis/Jaspar_motif.Rmd
    Modified:   analysis/Jaspar_motif_ff.Rmd
    Modified:   analysis/SNP_TAD_peaks.Rmd
    Modified:   analysis/TE_analysis_norm.Rmd
    Modified:   analysis/final_four_analysis.Rmd
    Modified:   analysis/index.Rmd

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

---

These are the previous versions of the repository in which changes were made to the R Markdown (analysis/QC_on_reads_DAR.Rmd) and HTML (docs/QC_on_reads_DAR.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File	Version	Author	Date	Message
Rmd	4c8601d	reneeisnowhere	2025-08-06	first commit
html	83dcfe1	reneeisnowhere	2025-08-06	Build site.
Rmd	f583703	reneeisnowhere	2025-08-06	first commit

---

library(tidyverse)
library(RColorBrewer)
library(ChIPseeker)
library(cowplot)
library(data.table)

drug_pal <- c("#8B006D","#DF707E","#F1B72B", "#3386DD","#707031","#41B333")

Loading data

IndD_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)

IndA_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)

IndB_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)

IndC_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 <- IndD_summary %>% rbind(IndA_summary) %>% rbind(IndB_summary) %>% rbind(IndC_summary)

FF_bound <- Final_four_counts %>% 
  mutate(indv=case_when(indv == "Ind1" ~ "Ind_D",
                        indv =="Ind2" ~ "Ind_A",
                        indv =="Ind3" ~ "Ind_B",
                        indv =="Ind6" ~ "Ind_C",
                        TRUE~ indv)) %>% 
  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))

Past versions of "Plotting reads-1.png"

Version	Author	Date
83dcfe1	reneeisnowhere	2025-08-06

Individual D fragment files:

IndD_frag_files <- read.csv("data/Ind1_fragment_files.txt", row.names = 1)
 
IndD_firstfrag_files <- read.csv("data/Ind1_firstfragment_files.txt", row.names = 1)
IndD_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 D\n3 hour fragment sizes")+
  theme_classic()+
  facet_wrap(~trt)+
  scale_color_manual(values=drug_pal)+
  coord_cartesian(ylim=c(0,300000))

Past versions of fragfiles1-1.png

Version	Author	Date
83dcfe1	reneeisnowhere	2025-08-06

# IndD_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 D\n3 hour fragment sizes BEFORE filtering")+
#   theme_classic()+
#   facet_wrap(~trt)+
#   scale_color_manual(values=drug_pal)+
#   coord_cartesian(xlim=c(0,1000))
 

IndD_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 D\n24 hour fragment sizes")+
  theme_classic()+
  facet_wrap(~trt)+
  scale_color_manual(values=drug_pal)

Past versions of fragfiles1-2.png

Version	Author	Date
83dcfe1	reneeisnowhere	2025-08-06

# IndD_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 D\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 D

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 D")+
   scale_fill_manual(values=drug_pal)

Past versions of Frip1-1.png

Version	Author	Date
83dcfe1	reneeisnowhere	2025-08-06

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 IndD using cardiomyocyte DNAse peaks")+
   scale_fill_manual(values=drug_pal)

Past versions of Frip1-2.png

Version	Author	Date
83dcfe1	reneeisnowhere	2025-08-06

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 IndD using left ventricle DNAse peaks")+
   scale_fill_manual(values=drug_pal)

Past versions of Frip1-3.png

Version	Author	Date
83dcfe1	reneeisnowhere	2025-08-06

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 IndD using adult heart DNAse peaks")+
   scale_fill_manual(values=drug_pal)

Past versions of Frip1-4.png

Version	Author	Date
83dcfe1	reneeisnowhere	2025-08-06

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 IndD using embryonic heart DNAse peaks")+
   scale_fill_manual(values=drug_pal)

Past versions of Frip1-5.png

Version	Author	Date
83dcfe1	reneeisnowhere	2025-08-06

Horizontal line is at 20%. Encode recommends >30% (dotted line).

Individual A fragment files:

IndA_frag_files <- read.csv("data/Ind2_fragment_files.txt", row.names = 1)
IndA_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 A\n3 hour fragment sizes")+
  theme_classic()+
   facet_wrap(~trt)+
  scale_color_manual(values=drug_pal)

Past versions of fragfiles2-1.png

Version	Author	Date
83dcfe1	reneeisnowhere	2025-08-06

IndA_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 A\n24 hour fragment sizes")+
  theme_classic()+
  facet_wrap(~trt)+
  scale_color_manual(values=drug_pal)

Past versions of fragfiles2-2.png

Version	Author	Date
83dcfe1	reneeisnowhere	2025-08-06

FRiP Individual A

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 A")+
   scale_fill_manual(values=drug_pal)

Past versions of Frip2-1.png

Version	Author	Date
83dcfe1	reneeisnowhere	2025-08-06

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 IndA using cardiomyocyte DNAse peaks")+
   scale_fill_manual(values=drug_pal)

Past versions of Frip2-2.png

Version	Author	Date
83dcfe1	reneeisnowhere	2025-08-06

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 IndA  using left ventricle DNAse peaks")+
   scale_fill_manual(values=drug_pal)

Past versions of Frip2-3.png

Version	Author	Date
83dcfe1	reneeisnowhere	2025-08-06

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 IndA  using adult heart DNAse peaks")+
   scale_fill_manual(values=drug_pal)

Past versions of Frip2-4.png

Version	Author	Date
83dcfe1	reneeisnowhere	2025-08-06

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 IndA using embryonic heart DNAse peaks")+
   scale_fill_manual(values=drug_pal)

Past versions of Frip2-5.png

Version	Author	Date
83dcfe1	reneeisnowhere	2025-08-06

Individual B fragment files:

Ind_B_frag_files <- read.csv("data/Ind3_fragment_files.txt", row.names = 1)
Ind_B_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 B\n3 hour fragment sizes")+
  theme_classic()+
   facet_wrap(~trt)+
  scale_color_manual(values=drug_pal)

Past versions of fragfiles3-1.png

Version	Author	Date
83dcfe1	reneeisnowhere	2025-08-06

Ind_B_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 B\n24 hour fragment sizes")+
  theme_classic()+
  facet_wrap(~trt)+
  scale_color_manual(values=drug_pal)

Past versions of fragfiles3-2.png

Version	Author	Date
83dcfe1	reneeisnowhere	2025-08-06

FRiP Individual B

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 B")+
   scale_fill_manual(values=drug_pal)

Past versions of Frip3-1.png

Version	Author	Date
83dcfe1	reneeisnowhere	2025-08-06

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 Ind_B using cardiomyocyte DNAse peaks")+
   scale_fill_manual(values=drug_pal)

Past versions of Frip3-2.png

Version	Author	Date
83dcfe1	reneeisnowhere	2025-08-06

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 Ind_B using left ventricle DNAse peaks")+
   scale_fill_manual(values=drug_pal)

Past versions of Frip3-3.png

Version	Author	Date
83dcfe1	reneeisnowhere	2025-08-06

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 Ind_B using adult heart DNAse peaks")+
   scale_fill_manual(values=drug_pal)

Past versions of Frip3-4.png

Version	Author	Date
83dcfe1	reneeisnowhere	2025-08-06

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 Ind_B using embryonic heart DNAse peaks")+
   scale_fill_manual(values=drug_pal)

Past versions of Frip3-5.png

Version	Author	Date
83dcfe1	reneeisnowhere	2025-08-06

Individual C fragment files:

Ind_C_frag_files <- read.csv("data/Ind6_fragment_files.txt", row.names = 1)
Ind_C_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 C\n3 hour fragment sizes")+
  theme_classic()+
   facet_wrap(~trt)+
  scale_color_manual(values=drug_pal)

Past versions of fragfiles6-1.png

Version	Author	Date
83dcfe1	reneeisnowhere	2025-08-06

Ind_C_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 C\n24 hour fragment sizes")+
  theme_classic()+
  facet_wrap(~trt)+
  scale_color_manual(values=drug_pal)

Past versions of fragfiles6-2.png

Version	Author	Date
83dcfe1	reneeisnowhere	2025-08-06

FRiP Individual C

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 C")+
   scale_fill_manual(values=drug_pal)

Past versions of Frip6-1.png

Version	Author	Date
83dcfe1	reneeisnowhere	2025-08-06

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 Ind_C using cardiomyocyte DNAse peaks")+
   scale_fill_manual(values=drug_pal)

Past versions of Frip6-2.png

Version	Author	Date
83dcfe1	reneeisnowhere	2025-08-06

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 Ind_C using left ventricle DNAse peaks")+
   scale_fill_manual(values=drug_pal)

Past versions of Frip6-3.png

Version	Author	Date
83dcfe1	reneeisnowhere	2025-08-06

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 Ind_C using adult heart DNAse peaks")+
   scale_fill_manual(values=drug_pal)

Past versions of Frip6-4.png

Version	Author	Date
83dcfe1	reneeisnowhere	2025-08-06

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 Ind_C using embryonic heart DNAse peaks")+
   scale_fill_manual(values=drug_pal)

Past versions of Frip6-5.png

Version	Author	Date
83dcfe1	reneeisnowhere	2025-08-06

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"), labels = c("Ind D", "Ind A", "Ind B", "Ind4", "Ind5", "Ind C"))) %>%
  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()

Past versions of peaksummary-1.png

Version	Author	Date
83dcfe1	reneeisnowhere	2025-08-06

Ind_D Peaks

Making the TSS average window code

# ind4_V24hpeaks_gr <- prepGRangeObj(ind4_V24hpeaks)
# Ind_D_DA24hpeaks_gr <- prepGRangeObj((Ind_D_DA24hpeaks))
# Epi_list <- GRangesList(Ind_D_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, 
# Ind_D_peaks <- list()
# for (file in 1:12){
#     testname <- basename(peakfiles1[file])
#   banana_peel <- str_split_i(testname, "_",3)
#  Ind_D_peaks[[banana_peel]] <- readPeakFile(peakfiles1[file])
# }
# saveRDS(Ind_D_peaks, "data/Ind_D_peaks_list.RDS")
# I then called annotatePeak on that list object, and stored that as a R object for later retrieval.)
# peakAnnoList_1 <- lapply(Ind_D_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)+ggtitle("Genomic Feature Distribution for indvidual D")

Past versions of "Ind_D inital TSS setup-1.png"

Version	Author	Date
83dcfe1	reneeisnowhere	2025-08-06

# saveRDS(Epi_list_tagMatrix, "data/Ind_D_TSS_peaks.RDS")

Ind_D_TSS_peaks_plot <- readRDS("data/Ind1_TSS_peaks.RDS")

# Epi_list_tagMatrix = lapply(Ind_D_peaks, getTagMatrix, windows = TSS)

plotAvgProf(Ind_D_TSS_peaks_plot[c(1,3,5,7,9,11)], xlim=c(-3000, 3000), ylab = "Count Frequency")+ ggtitle("3 hour Individual D" )

>> plotting figure...            2025-08-06 1:16:58 PM 

Past versions of "Ind_D inital TSS setup-2.png"

Version	Author	Date
83dcfe1	reneeisnowhere	2025-08-06

# + coord_cartesian(xlim = c(-100,500))

plotAvgProf(Ind_D_TSS_peaks_plot[c(2,4,6,8,10,12)], xlim=c(-3000, 3000),ylab = "Count Frequency")+ ggtitle("24 hour Individual D" )

>> plotting figure...            2025-08-06 1:16:59 PM 

Past versions of "Ind_D inital TSS setup-3.png"

Version	Author	Date
83dcfe1	reneeisnowhere	2025-08-06

# + coord_cartesian(xlim = c(-100,500))

Ind_A 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, 
# Ind_A_peaks <- list()
# for (file in 1:12){
#     testname <- basename(peakfiles2[file])
#   banana_peel <- str_split_i(testname, "_",3)
#  Ind_A_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.)

Ind_A_peaks <- readRDS("data/Ind2_peaks_list.RDS")
# peakAnnoList_2 <- lapply(Ind_A_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)+ggtitle("Genomic Feature Distribution, Individual A")

Past versions of "Ind_A inital TSS setup-1.png"

Version	Author	Date
83dcfe1	reneeisnowhere	2025-08-06

# Epi_list_tagMatrix = lapply(Ind_A_peaks, getTagMatrix, windows = TSS)
# saveRDS(Epi_list_tagMatrix, "data/Ind2_TSS_peaks.RDS")

Ind_A_TSS_peaks_plot <- readRDS("data/Ind2_TSS_peaks.RDS")



plotAvgProf(Ind_A_TSS_peaks_plot[c(1,3,5,7,9,11)], xlim=c(-3000, 3000), ylab = "Count Frequency")+ ggtitle("3 hour Individual A" )

>> plotting figure...            2025-08-06 1:17:14 PM 

Past versions of "Ind_A inital TSS setup-2.png"

Version	Author	Date
83dcfe1	reneeisnowhere	2025-08-06

# + coord_cartesian(xlim = c(-100,1000))

plotAvgProf(Ind_A_TSS_peaks_plot[c(2,4,6,8,10,12)], xlim=c(-3000, 3000),ylab = "Count Frequency")+ ggtitle("24 hour Individual A" )#+ coord_cartesian(xlim = c(-100,500))

>> plotting figure...            2025-08-06 1:17:15 PM 

Past versions of "Ind_A inital TSS setup-3.png"

Version	Author	Date
83dcfe1	reneeisnowhere	2025-08-06

Ind_B 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.)

Ind_B_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)+ggtitle("Genomic Feature Distribution, Individual B")

Past versions of "Ind_B inital TSS setup-1.png"

Version	Author	Date
83dcfe1	reneeisnowhere	2025-08-06

# Epi_list_tagMatrix = lapply(Ind3_peaks, getTagMatrix, windows = TSS)
# saveRDS(Epi_list_tagMatrix, "data/Ind3_TSS_peaks.RDS")

Ind_B_TSS_peaks_plot <- readRDS("data/Ind3_TSS_peaks.RDS")



plotAvgProf(Ind_B_TSS_peaks_plot[c(1,3,5,7,9,11)], xlim=c(-3000, 3000), ylab = "Count Frequency")+ ggtitle("3 hour Individual B" )

>> plotting figure...            2025-08-06 1:17:26 PM 

Past versions of "Ind_B inital TSS setup-2.png"

Version	Author	Date
83dcfe1	reneeisnowhere	2025-08-06

plotAvgProf(Ind_B_TSS_peaks_plot[c(2,4,6,8,10,12)], xlim=c(-3000, 3000),ylab = "Count Frequency")+ ggtitle("24 hour Individual B" )

>> plotting figure...            2025-08-06 1:17:27 PM 

Past versions of "Ind_B inital TSS setup-3.png"

Version	Author	Date
83dcfe1	reneeisnowhere	2025-08-06

Ind_C 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.)

Ind_C_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 C")+ggtitle ("Genomic Feature Distribution, Individual C")

Past versions of "Ind_C inital TSS setup-1.png"

Version	Author	Date
83dcfe1	reneeisnowhere	2025-08-06

##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")

Ind_C_TSS_peaks_plot <- readRDS("data/Ind6_TSS_peaks.RDS")


plotAvgProf(Ind_C_TSS_peaks_plot[c(1,3,5,7,9,11)], xlim=c(-3000, 3000), ylab = "Count Frequency")+ ggtitle("3 hour Individual C" )

>> plotting figure...            2025-08-06 1:17:41 PM 

Past versions of "Ind_C inital TSS setup-2.png"

Version	Author	Date
83dcfe1	reneeisnowhere	2025-08-06

plotAvgProf(Ind_C_TSS_peaks_plot[c(2,4,6,8,10,12)], xlim=c(-3000, 3000),ylab = "Count Frequency")+ ggtitle("24 hour Individual C" )

>> plotting figure...            2025-08-06 1:17:42 PM 

Past versions of "Ind_C inital TSS setup-3.png"

Version	Author	Date
83dcfe1	reneeisnowhere	2025-08-06

### 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))

Past versions of "TSS enrichment part 2-1.png"

Version	Author	Date
83dcfe1	reneeisnowhere	2025-08-06

#### 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")


Ind_D_TSS_peaks_plot <- readRDS("data/Ind1_TSS_peaks.RDS")
Ind_A_TSS_peaks_plot <- readRDS("data/Ind2_TSS_peaks.RDS")
Ind_B_TSS_peaks_plot <- readRDS("data/Ind3_TSS_peaks.RDS")
Ind_C_TSS_peaks_plot <- readRDS("data/Ind6_TSS_peaks.RDS")

a1<- plotAvgProf(Ind_D_TSS_peaks_plot[c(1,3,5,7,9,11)], xlim=c(-3000, 3000), ylab = "Count Frequency")+ ggtitle("3 hour Individual D" )+coord_cartesian(xlim=c(-2000,2000))

>> plotting figure...            2025-08-06 1:18:14 PM 

b1 <- plotAvgProf(Ind_A_TSS_peaks_plot[c(1,3,5,7,9,11)], xlim=c(-3000, 3000), ylab = "Count Frequency")+ ggtitle("3 hour Individual A" )+coord_cartesian(xlim=c(-2000,2000))

>> plotting figure...            2025-08-06 1:18:15 PM 

c1 <- plotAvgProf(Ind_B_TSS_peaks_plot[c(1,3,5,7,9,11)], xlim=c(-3000, 3000), ylab = "Count Frequency")+ ggtitle("3 hour Individual B" )+coord_cartesian(xlim=c(-2000,2000))

>> plotting figure...            2025-08-06 1:18:16 PM 

d1 <- plotAvgProf(Ind_C_TSS_peaks_plot[c(1,3,5,7,9,11)], xlim=c(-3000, 3000), ylab = "Count Frequency")+ ggtitle("3 hour Individual C" )+coord_cartesian(xlim=c(-2000,2000))

>> plotting figure...            2025-08-06 1:18:17 PM 

a2 <- plotAvgProf(Ind_D_TSS_peaks_plot[c(2,4,6,8,10,12)], xlim=c(-3000, 3000),ylab = "Count Frequency")+ ggtitle("24 hour Individual D" )+coord_cartesian(xlim=c(-2000,2000))

>> plotting figure...            2025-08-06 1:18:18 PM 

b2 <- plotAvgProf(Ind_A_TSS_peaks_plot[c(2,4,6,8,10,12)], xlim=c(-3000, 3000),ylab = "Count Frequency")+ ggtitle("24 hour Individual A" )+coord_cartesian(xlim=c(-2000,2000))

>> plotting figure...            2025-08-06 1:18:19 PM 

c2 <- plotAvgProf(Ind_B_TSS_peaks_plot[c(2,4,6,8,10,12)], xlim=c(-3000, 3000),ylab = "Count Frequency")+ ggtitle("24 hour Individual B" )+coord_cartesian(xlim=c(-2000,2000))

>> plotting figure...            2025-08-06 1:18:20 PM 

d2 <- plotAvgProf(Ind_C_TSS_peaks_plot[c(2,4,6,8,10,12)], xlim=c(-3000, 3000),ylab = "Count Frequency")+ ggtitle("24 hour Individual C" )+coord_cartesian(xlim=c(-2000,2000))

>> plotting figure...            2025-08-06 1:18:21 PM 

plot_grid(a1,a2, b1,b2,c1,c2,d1,d2, axis="l",align = "hv",nrow=4, ncol=2)

Past versions of "TSS peaks plot-1.png"

Version	Author	Date
83dcfe1	reneeisnowhere	2025-08-06

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

Session information

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] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] data.table_1.17.6  cowplot_1.1.3      ChIPseeker_1.42.1  RColorBrewer_1.1-3
 [5] lubridate_1.9.4    forcats_1.0.0      stringr_1.5.1      dplyr_1.1.4       
 [9] purrr_1.0.4        readr_2.1.5        tidyr_1.3.1        tibble_3.3.0      
[13] ggplot2_3.5.2      tidyverse_2.0.0    workflowr_1.7.1   

loaded via a namespace (and not attached):
  [1] rstudioapi_0.17.1                      
  [2] jsonlite_2.0.0                         
  [3] magrittr_2.0.3                         
  [4] ggtangle_0.0.7                         
  [5] GenomicFeatures_1.58.0                 
  [6] farver_2.1.2                           
  [7] rmarkdown_2.29                         
  [8] fs_1.6.6                               
  [9] BiocIO_1.16.0                          
 [10] zlibbioc_1.52.0                        
 [11] vctrs_0.6.5                            
 [12] memoise_2.0.1                          
 [13] Rsamtools_2.22.0                       
 [14] RCurl_1.98-1.17                        
 [15] ggtree_3.14.0                          
 [16] htmltools_0.5.8.1                      
 [17] S4Arrays_1.6.0                         
 [18] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
 [19] plotrix_3.8-4                          
 [20] curl_6.4.0                             
 [21] SparseArray_1.6.2                      
 [22] gridGraphics_0.5-1                     
 [23] sass_0.4.10                            
 [24] KernSmooth_2.23-26                     
 [25] bslib_0.9.0                            
 [26] plyr_1.8.9                             
 [27] cachem_1.1.0                           
 [28] GenomicAlignments_1.42.0               
 [29] igraph_2.1.4                           
 [30] whisker_0.4.1                          
 [31] lifecycle_1.0.4                        
 [32] pkgconfig_2.0.3                        
 [33] Matrix_1.7-3                           
 [34] R6_2.6.1                               
 [35] fastmap_1.2.0                          
 [36] GenomeInfoDbData_1.2.13                
 [37] MatrixGenerics_1.18.1                  
 [38] digest_0.6.37                          
 [39] aplot_0.2.8                            
 [40] enrichplot_1.26.6                      
 [41] colorspace_2.1-1                       
 [42] patchwork_1.3.1                        
 [43] AnnotationDbi_1.68.0                   
 [44] S4Vectors_0.44.0                       
 [45] ps_1.9.1                               
 [46] rprojroot_2.0.4                        
 [47] GenomicRanges_1.58.0                   
 [48] RSQLite_2.4.1                          
 [49] labeling_0.4.3                         
 [50] timechange_0.3.0                       
 [51] httr_1.4.7                             
 [52] abind_1.4-8                            
 [53] compiler_4.4.2                         
 [54] bit64_4.6.0-1                          
 [55] withr_3.0.2                            
 [56] BiocParallel_1.40.2                    
 [57] DBI_1.2.3                              
 [58] gplots_3.2.0                           
 [59] R.utils_2.13.0                         
 [60] DelayedArray_0.32.0                    
 [61] rjson_0.2.23                           
 [62] caTools_1.18.3                         
 [63] gtools_3.9.5                           
 [64] tools_4.4.2                            
 [65] ape_5.8-1                              
 [66] httpuv_1.6.16                          
 [67] R.oo_1.27.1                            
 [68] glue_1.8.0                             
 [69] restfulr_0.0.16                        
 [70] callr_3.7.6                            
 [71] nlme_3.1-168                           
 [72] GOSemSim_2.32.0                        
 [73] promises_1.3.3                         
 [74] grid_4.4.2                             
 [75] getPass_0.2-4                          
 [76] reshape2_1.4.4                         
 [77] fgsea_1.32.4                           
 [78] generics_0.1.4                         
 [79] gtable_0.3.6                           
 [80] tzdb_0.5.0                             
 [81] R.methodsS3_1.8.2                      
 [82] hms_1.1.3                              
 [83] XVector_0.46.0                         
 [84] BiocGenerics_0.52.0                    
 [85] ggrepel_0.9.6                          
 [86] pillar_1.11.0                          
 [87] yulab.utils_0.2.0                      
 [88] later_1.4.2                            
 [89] splines_4.4.2                          
 [90] treeio_1.30.0                          
 [91] lattice_0.22-7                         
 [92] rtracklayer_1.66.0                     
 [93] bit_4.6.0                              
 [94] tidyselect_1.2.1                       
 [95] GO.db_3.20.0                           
 [96] Biostrings_2.74.1                      
 [97] knitr_1.50                             
 [98] git2r_0.36.2                           
 [99] IRanges_2.40.1                         
[100] SummarizedExperiment_1.36.0            
[101] stats4_4.4.2                           
[102] xfun_0.52                              
[103] Biobase_2.66.0                         
[104] matrixStats_1.5.0                      
[105] stringi_1.8.7                          
[106] UCSC.utils_1.2.0                       
[107] lazyeval_0.2.2                         
[108] ggfun_0.1.9                            
[109] yaml_2.3.10                            
[110] boot_1.3-31                            
[111] evaluate_1.0.4                         
[112] codetools_0.2-20                       
[113] qvalue_2.38.0                          
[114] ggplotify_0.1.2                        
[115] cli_3.6.5                              
[116] processx_3.8.6                         
[117] jquerylib_0.1.4                        
[118] dichromat_2.0-0.1                      
[119] Rcpp_1.1.0                             
[120] GenomeInfoDb_1.42.3                    
[121] png_0.1-8                              
[122] XML_3.99-0.18                          
[123] parallel_4.4.2                         
[124] blob_1.2.4                             
[125] DOSE_4.0.1                             
[126] bitops_1.0-9                           
[127] tidytree_0.4.6                         
[128] scales_1.4.0                           
[129] crayon_1.5.3                           
[130] rlang_1.1.6                            
[131] fastmatch_1.1-6                        
[132] KEGGREST_1.46.0