Figure 3
ERM
2025-05-01
Last updated: 2025-05-01
Checks: 7 0
Knit directory: ATAC_learning/
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| html | 18684b9 | reneeisnowhere | 2025-04-02 | Build site. |
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| html | df8f6ef | E. Renee Matthews | 2025-02-24 | Build site. |
| Rmd | 24b7203 | E. Renee Matthews | 2025-02-24 | taking out a leftover data frame, adding in image |
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| Rmd | 8c12c80 | E. Renee Matthews | 2025-02-24 | first commit |
packages
library(tidyverse)
library(cowplot)
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(Cormotif)
library(BiocParallel)
library(ggpubr)
library(devtools)
library(JASPAR2022)
library(TFBSTools)
library(MotifDb)
library(BSgenome.Hsapiens.UCSC.hg38)
library(data.table)Data loading
EAR_close_xstreme <-
readRDS("data/Final_four_data/xstreme/EAR_close_xstreme.RDS")%>%
slice_head(n = length(.$ID)-3)
EAR_open_xstreme <-
readRDS("data/Final_four_data/xstreme/EAR_open_xstreme.RDS")%>%
slice_head(n = length(.$ID)-3)
ESR_open_xstreme <-
readRDS("data/Final_four_data/xstreme/ESR_open_xstreme.RDS")%>%
slice_head(n = length(.$ID)-3)
ESR_close_xstreme <-
readRDS("data/Final_four_data/xstreme/ESR_close_xstreme.RDS")%>%
slice_head(n = length(.$ID)-3)
ESR_OC_xstreme <-
readRDS("data/Final_four_data/xstreme/ESR_OC_xstreme.RDS")%>%
slice_head(n = length(.$ID)-3)
ESR_opcl_xstreme <-
readRDS("data/Final_four_data/xstreme/xstreme_ESR_opcl200.RDS")%>%
slice_head(n = length(.$ID)-3)
ESR_clop_xstreme <-
readRDS("data/Final_four_data/xstreme/xstreme_ESR_clop200.RDS")%>%
slice_head(n = length(.$ID)-3)
LR_close_xstreme <-
readRDS("data/Final_four_data/xstreme/LR_close_xstreme.RDS")%>%
slice_head(n = length(.$ID)-3)
LR_open_xstreme <-
readRDS("data/Final_four_data/xstreme/LR_open_10h_xstreme.RDS")%>%
slice_head(n = length(.$ID)-3)
LR_open_10h_xstreme <-
readRDS("data/Final_four_data/xstreme/LR_open_10h_xstreme.RDS")%>%
slice_head(n = length(.$ID)-3)
EAR_close_200xstreme <-
readRDS("data/Final_four_data/xstreme/xstreme_EAR_close200.RDS")%>%
slice_head(n = length(.$ID)-3)
EAR_open_200xstreme <-
readRDS("data/Final_four_data/xstreme/xstreme_EAR_open200.RDS")%>%
slice_head(n = length(.$ID)-3)
ESR_open_200xstreme <-
readRDS("data/Final_four_data/xstreme/xstreme_ESR_open200.RDS")%>%
slice_head(n = length(.$ID)-3)
ESR_close_200xstreme <-
readRDS("data/Final_four_data/xstreme/xstreme_ESR_close200.RDS")%>%
slice_head(n = length(.$ID)-3)
ESR_OC_xstreme <-
readRDS("data/Final_four_data/xstreme/xstreme_LR_open200.RDS")%>%
slice_head(n = length(.$ID)-3)
LR_close_200xstreme <-
readRDS("data/Final_four_data/xstreme/xstreme_LR_close200.RDS")%>%
slice_head(n = length(.$ID)-3)
LR_open_200xstreme <-
readRDS("data/Final_four_data/xstreme/xstreme_LR_open200.RDS")%>%
slice_head(n = length(.$ID)-3)
#### full sequence sea out
sea_EAR_open <- readRDS("data/Final_four_data/xstreme/sea_EAR_open.RDS")%>%
slice_head(n = length(.$ID)-3)
sea_EAR_close <- readRDS("data/Final_four_data/xstreme/sea_EAR_close.RDS")%>%
slice_head(n = length(.$ID)-3)
sea_ESR_close <- readRDS("data/Final_four_data/xstreme/sea_ESR_close.RDS")
sea_ESR_open <- readRDS("data/Final_four_data/xstreme/sea_ESR_open.RDS")#%>%
# slice_head(n = length(.$ID)-3)
sea_ESR_OC <- readRDS("data/Final_four_data/xstreme/sea_ESR_OC.RDS")%>%
slice_head(n = length(.$ID)-3)
sea_LR_open <- readRDS("data/Final_four_data/xstreme/sea_LR_open_10h.RDS")%>%
slice_head(n = length(.$ID)-3)
sea_LR_close <- readRDS("data/Final_four_data/xstreme/sea_LR_close.RDS")%>%
slice_head(n = length(.$ID)-3)
sea_LR_open <- readRDS("data/Final_four_data/xstreme/sea_LR_open_10h.RDS")%>%
slice_head(n = length(.$ID)-3)
sea_EAR_open_200 <- readRDS("data/Final_four_data/xstreme/sea_EAR_open_200.RDS")%>%
slice_head(n = length(.$ID)-3)
sea_EAR_close_200 <- readRDS("data/Final_four_data/xstreme/sea_EAR_close_200.RDS")%>%
slice_head(n = length(.$ID)-3)
sea_ESR_close_200 <- readRDS("data/Final_four_data/xstreme/sea_ESR_close_200.RDS")%>%
slice_head(n = length(.$ID)-3)
sea_ESR_open_200 <- readRDS("data/Final_four_data/xstreme/sea_ESR_open_200.RDS")%>%
slice_head(n = length(.$ID)-3)
sea_ESR_opcl_200 <- readRDS("data/Final_four_data/xstreme/sea_ESR_opcl_200.RDS")%>%
slice_head(n = length(.$ID)-3)
sea_ESR_clop_200 <- readRDS("data/Final_four_data/xstreme/sea_ESR_clop_200.RDS")%>%
slice_head(n = length(.$ID)-3)
sea_LR_open_200 <- readRDS("data/Final_four_data/xstreme/sea_LR_open_200.RDS")%>%
slice_head(n = length(.$ID)-3)
sea_LR_close_200 <- readRDS("data/Final_four_data/xstreme/sea_LR_close_200.RDS")%>%
slice_head(n = length(.$ID)-3)
sea_EAR_open_200_p2 <- readRDS("data/Final_four_data/xstreme/sea_EAR_open_200_p2.RDS")%>%
slice_head(n = length(.$ID)-3)
sea_EAR_close_200_p2 <- readRDS("data/Final_four_data/xstreme/sea_EAR_close_200_p2.RDS")%>%
slice_head(n = length(.$ID)-3)
sea_ESR_close_200_p2 <- readRDS("data/Final_four_data/xstreme/sea_ESR_close_200_p2.RDS")%>%
slice_head(n = length(.$ID)-3)
sea_ESR_open_200_p2 <- readRDS("data/Final_four_data/xstreme/sea_ESR_open_200_p2.RDS")%>%
slice_head(n = length(.$ID)-3)
sea_ESR_opcl_200_p2 <- readRDS("data/Final_four_data/xstreme/sea_ESR_opcl_200_p2.RDS")%>%
slice_head(n = length(.$ID)-3)
sea_ESR_clop_200_p2 <- readRDS("data/Final_four_data/xstreme/sea_ESR_clop_200_p2.RDS")%>%
slice_head(n = length(.$ID)-3)
sea_LR_open_200_p2 <- readRDS("data/Final_four_data/xstreme/sea_LR_open_200_p2.RDS")%>%
slice_head(n = length(.$ID)-3)
sea_LR_close_200_p2 <- readRDS("data/Final_four_data/xstreme/sea_LR_close_200_p2.RDS")%>%
slice_head(n = length(.$ID)-3)Figure 3:
knitr::include_graphics("assets/Figure\ 3.png", error=FALSE)
knitr::include_graphics("docs/assets/Figure\ 3.png",error = FALSE)
mrc_palette <- c(
"EAR_open" = "#F8766D",
"EAR_close" = "#f6483c",
"ESR_open" = "#7CAE00",
"ESR_close" = "#587b00",
"ESR_C"="grey40",
"ESR_opcl"="grey40",
"ESR_D"="tan",
"ESR_clop"="tan",
"ESR_OC" = "#6a9500",
"LR_open" = "#00BFC4",
"LR_close" = "#008d91",
"NR" = "#C77CFF"
)
spd_EARo_200<-EAR_open_200xstreme%>%
mutate(CONSENSUS=gsub('[[:digit:]]+-', '', CONSENSUS)) %>%
dplyr::filter(EVALUE<0.05) %>%
left_join(., (sea_EAR_open_200_p2 %>%
anti_join(.,sea_EAR_open_200, by = c("ID"="ID","ALT_ID"="ALT_ID")) %>%
rbind(sea_EAR_open_200) %>%
dplyr::select(DB:LOG_QVALUE)), by= c( "ALT_ID"="ALT_ID", "CONSENSUS"="CONSENSUS","ID"="ID"))%>%
separate(SIM_MOTIF, into= c("SIM_MOTIF", "NAME"), sep= " ") %>%
mutate(motif_name= gsub("[()]","",NAME), mrc="EAR_open") %>%
mutate(motif_name=
if_else(is.na(motif_name)&str_detect(SIM_MOTIF,"^M"),ALT_ID,
if_else(is.na(motif_name),ID,motif_name)))
#### breaks
spd_EARc_200 <- EAR_close_200xstreme%>%
mutate(CONSENSUS=gsub('[[:digit:]]+-', '', CONSENSUS)) %>%
dplyr::filter(EVALUE<0.05) %>%
left_join(., (sea_EAR_close_200_p2 %>%
anti_join(.,sea_EAR_close_200, by = c("ID"="ID","ALT_ID"="ALT_ID")) %>%
rbind(sea_EAR_close_200) %>%
dplyr::select(DB:LOG_QVALUE)), by= c( "ALT_ID"="ALT_ID", "CONSENSUS"="CONSENSUS","ID"="ID"))%>%
separate(SIM_MOTIF, into= c("SIM_MOTIF", "NAME"), sep= " ") %>%
mutate(motif_name= gsub("[()]","",NAME), mrc="EAR_close") %>%
mutate(motif_name=
if_else(is.na(motif_name)&str_detect(SIM_MOTIF,"^M"),ALT_ID,
if_else(is.na(motif_name),ID,motif_name)))
spd_ESRo_200 <-ESR_open_200xstreme%>%
mutate(CONSENSUS=gsub('[[:digit:]]+-', '', CONSENSUS)) %>%
dplyr::filter(EVALUE<0.05) %>%
left_join(., (sea_ESR_open_200_p2 %>%
anti_join(.,sea_ESR_open_200, by = c("ID"="ID","ALT_ID"="ALT_ID")) %>%
rbind(sea_ESR_open_200) %>%
dplyr::select(DB:LOG_QVALUE)), by= c( "ALT_ID"="ALT_ID", "CONSENSUS"="CONSENSUS","ID"="ID"))%>%
separate(SIM_MOTIF, into= c("SIM_MOTIF", "NAME"), sep= " ") %>%
mutate(motif_name= gsub("[()]","",NAME), mrc="ESR_open") %>%
mutate(motif_name=
if_else(is.na(motif_name)&str_detect(SIM_MOTIF,"^M"),ALT_ID,
if_else(is.na(motif_name),ID,motif_name)))spd_ESRc_200 <- ESR_close_200xstreme%>%
mutate(CONSENSUS=gsub('[[:digit:]]+-', '', CONSENSUS)) %>%
dplyr::filter(EVALUE<0.05) %>%
left_join(., (sea_ESR_close_200_p2 %>%
anti_join(.,sea_ESR_close_200, by = c("ID"="ID","ALT_ID"="ALT_ID")) %>%
rbind(sea_ESR_close_200) %>%
dplyr::select(DB:LOG_QVALUE)), by= c( "ALT_ID"="ALT_ID", "CONSENSUS"="CONSENSUS","ID"="ID"))%>%
separate(SIM_MOTIF, into= c("SIM_MOTIF", "NAME"), sep= " ") %>%
mutate(motif_name= gsub("[()]","",NAME), mrc="ESR_close") %>%
mutate(motif_name=
if_else(is.na(motif_name)&str_detect(SIM_MOTIF,"^M"),ALT_ID,
if_else(is.na(motif_name),ID,motif_name)))
spd_ESRopcl_200<-ESR_opcl_xstreme%>%
mutate(CONSENSUS=gsub('[[:digit:]]+-', '', CONSENSUS)) %>%
dplyr::filter(EVALUE<0.05) %>%
left_join(., (sea_ESR_opcl_200_p2 %>%
anti_join(.,sea_ESR_opcl_200, by = c("ID"="ID","ALT_ID"="ALT_ID")) %>%
rbind(sea_ESR_opcl_200) %>%
dplyr::select(DB:LOG_QVALUE)), by= c( "ALT_ID"="ALT_ID", "CONSENSUS"="CONSENSUS","ID"="ID"))%>%
separate(SIM_MOTIF, into= c("SIM_MOTIF", "NAME"), sep= " ") %>%
mutate(motif_name= gsub("[()]","",NAME), mrc="ESR_opcl") %>%
mutate(motif_name=
if_else(is.na(motif_name)&str_detect(SIM_MOTIF,"^M"),ALT_ID,
if_else(is.na(motif_name),ID,motif_name)))
#### break for clop!
spd_ESRclop_200<-ESR_clop_xstreme%>%
mutate(CONSENSUS=gsub('[[:digit:]]+-', '', CONSENSUS)) %>%
dplyr::filter(EVALUE<0.05) %>%
left_join(., (sea_ESR_clop_200_p2 %>%
anti_join(.,sea_ESR_clop_200, by = c("ID"="ID","ALT_ID"="ALT_ID")) %>%
rbind(sea_ESR_clop_200) %>%
dplyr::select(DB:LOG_QVALUE)), by= c( "ALT_ID"="ALT_ID", "CONSENSUS"="CONSENSUS","ID"="ID"))%>%
separate(SIM_MOTIF, into= c("SIM_MOTIF", "NAME"), sep= " ") %>%
mutate(motif_name= gsub("[()]","",NAME), mrc="ESR_clop") %>%
mutate(motif_name=
if_else(is.na(motif_name)&str_detect(SIM_MOTIF,"^M"),ALT_ID,
if_else(is.na(motif_name),ID,motif_name)))
spd_LRo_200 <-LR_open_200xstreme%>%
mutate(CONSENSUS=gsub('[[:digit:]]+-', '', CONSENSUS)) %>%
dplyr::filter(EVALUE<0.05) %>%
left_join(., (sea_LR_open_200_p2 %>%
anti_join(.,sea_LR_open_200, by = c("ID"="ID","ALT_ID"="ALT_ID")) %>%
rbind(sea_LR_open_200) %>%
dplyr::select(DB:LOG_QVALUE)), by= c( "ALT_ID"="ALT_ID", "CONSENSUS"="CONSENSUS","ID"="ID"))%>%
separate(SIM_MOTIF, into= c("SIM_MOTIF", "NAME"), sep= " ") %>%
mutate(motif_name= gsub("[()]","",NAME), mrc="LR_open") %>%
mutate(motif_name=
if_else(is.na(motif_name)&str_detect(SIM_MOTIF,"^M"),ALT_ID, if_else(is.na(motif_name),ID,motif_name)))
spd_LRc_200 <- LR_close_200xstreme%>%
mutate(CONSENSUS=gsub('[[:digit:]]+-', '', CONSENSUS)) %>%
dplyr::filter(EVALUE<0.05) %>%
left_join(., (sea_LR_close_200_p2 %>%
anti_join(.,sea_LR_close_200, by = c("ID"="ID","ALT_ID"="ALT_ID")) %>%
rbind(sea_LR_close_200) %>%
dplyr::select(DB:LOG_QVALUE)), by= c( "ALT_ID"="ALT_ID", "CONSENSUS"="CONSENSUS","ID"="ID"))%>%
separate(SIM_MOTIF, into= c("SIM_MOTIF", "NAME"), sep= " ") %>%
mutate(motif_name= gsub("[()]","",NAME), mrc="LR_close") %>%
mutate(motif_name=
if_else(is.na(motif_name)&str_detect(SIM_MOTIF,"^M"),ALT_ID,
if_else(is.na(motif_name),ID,motif_name)))
spec_dataframe_200 <- spd_EARo_200 %>%
rbind(spd_EARc_200) %>%
rbind(spd_ESRo_200) %>%
rbind(spd_ESRc_200) %>%
rbind(spd_ESRopcl_200) %>%
rbind(spd_ESRclop_200) %>%
rbind(spd_LRo_200) %>%
rbind(spd_LRc_200)
# spec_dataframe_200 <- readRDS("data/Final_four_data/spec_dataframe_200.RDS")EAR open
ER_rat <- 1.25
mrc_type <- "EAR_open"
spec_dataframe_200%>%
dplyr::filter(mrc==mrc_type) %>%
dplyr::filter(ENR_RATIO>ER_rat) %>%
dplyr::select(RANK,CLUSTER, SITES,SIM_MOTIF,ALT_ID, ID,EVALUE.x, TP:'FP%', motif_name)%>%
arrange(.,EVALUE.x) %>%
mutate(log10Evalue= log(EVALUE.x, base = 10)*(-1)) %>%
mutate(motif_name=if_else(motif_name=="ELK3",paste(motif_name, RANK, sep="_"), if_else(str_starts(motif_name,"^Z*"),paste(motif_name, RANK, sep="_"), if_else(motif_name=="Nrf1",paste(motif_name, RANK,sep="_"),motif_name))))%>%
distinct(CLUSTER,.keep_all = TRUE) %>%
slice_head(n=5) %>%
ggplot(., aes (y= reorder(motif_name,log10Evalue))) +
geom_col(aes(x=log10Evalue),fill=mrc_palette[mrc_type]) +
geom_point(aes(x=`TP%`/1.5), size =4)+
# geom_line(aes(x=`TP%`,y= motif_name, group=log10Evalue))+
scale_x_continuous(expand=c (0,.25),sec.axis = sec_axis(transform= ~.*1.5,name="Percent of peaks with motif"))+
# geom_text(aes())
theme_classic()+
ylab("Enriched TF motif")+
ggtitle(paste( mrc_type,"response peaks(200bp)Enrichment ratio:",ER_rat," merged clusters"))
| Version | Author | Date |
|---|---|---|
| d4442e0 | E. Renee Matthews | 2025-02-24 |
EAR_close
ER_rat <- 1.25
mrc_type <- "EAR_close"
spec_dataframe_200 %>%
dplyr::filter(mrc==mrc_type) %>%
# dplyr::filter(ENR_RATIO>ER_rat) %>%
dplyr::select(RANK,CLUSTER, SITES,SIM_MOTIF,ALT_ID, ID,EVALUE.x, TP:'FP%', motif_name)%>%
# arrange(.,EVALUE.x) %>%
mutate(log10Evalue= log(EVALUE.x, base = 10)*(-1)) %>%
distinct(CLUSTER,.keep_all = TRUE) %>%
arrange(.,EVALUE.x) %>%
slice_head(n=5) %>%
ggplot(., aes (y= reorder(motif_name,log10Evalue))) +
geom_col(aes(x=log10Evalue),fill=mrc_palette[mrc_type]) +
geom_point(aes(x=`TP%`/1.25), size =4)+
# geom_line(aes(x=`TP%`,y= motif_name, group=log10Evalue))+
scale_x_continuous(expand=c (0,.25),sec.axis = sec_axis(transform= ~.*1.25,name="Percent of peaks with motif"))+
# geom_text(aes())
theme_classic()+
ylab("Enriched TF motif")+
ggtitle(paste( mrc_type,"response peaks (200bp) Enrichment ratio: not applied modified merged clusters"))
| Version | Author | Date |
|---|---|---|
| d4442e0 | E. Renee Matthews | 2025-02-24 |
ESR_open
ER_rat <- 1.25
mrc_type <- "ESR_open"
spec_dataframe_200 %>%
dplyr::filter(mrc=="ESR_open") %>%
dplyr::filter(ENR_RATIO>ER_rat) %>%
dplyr::select(RANK,CLUSTER, SITES,SIM_MOTIF,ALT_ID, ID,EVALUE.x, TP:'FP%', motif_name)%>%
arrange(.,EVALUE.x) %>%
mutate(log10Evalue= log(EVALUE.x, base = 10)*(-1)) %>%
mutate(motif_name=
if_else(str_starts(motif_name,"JUND"),paste(motif_name, RANK, sep="_"), if_else(str_starts(motif_name,"^ZN*"),paste(motif_name, RANK, sep="_"), if_else(motif_name=="ZSCAN4", paste(motif_name, RANK,sep="_"),if_else(motif_name=="KLF9",paste(motif_name,RANK,sep="_"), motif_name)))))%>%
distinct(CLUSTER,.keep_all = TRUE) %>%
slice_head(n=5) %>%
ggplot(., aes (y= reorder(motif_name,log10Evalue))) +
geom_col(aes(x=log10Evalue),fill=mrc_palette["ESR_open"]) +
geom_point(aes(x=`TP%`*4.7), size =4)+
# geom_line(aes(x=`TP%`,y= motif_name, group=log10Evalue))+
scale_x_continuous(expand=c (0,.25),sec.axis = sec_axis(transform= ~./4.7,name="Percent of peaks with motif"))+
# geom_text(aes())
theme_classic()+
ylab("Enriched TF motif")+
ggtitle(paste(mrc_type,"response peaks 200bp Enrichment ratio:",ER_rat," merged motif clusters"))
| Version | Author | Date |
|---|---|---|
| d4442e0 | E. Renee Matthews | 2025-02-24 |
ESR_close
ER_rat <- 1.25
mrc_type <- "ESR_close"
spec_dataframe_200 %>%
dplyr::filter(mrc==mrc_type) %>%
dplyr::filter(ENR_RATIO>(ER_rat+.1)) %>%
dplyr::select(RANK,CLUSTER, SITES,SIM_MOTIF,ALT_ID, ID,EVALUE.x, TP:'FP%', motif_name)%>%
arrange(.,EVALUE.x) %>%
mutate(log10Evalue= log(EVALUE.x, base = 10)*(-1)) %>%
mutate(motif_name=if_else(motif_name=="KLF9",paste(motif_name, RANK, sep="_"), if_else(motif_name=="^ZN*",paste(motif_name, RANK, sep="_"), if_else(motif_name=="ZSCAN4",paste(motif_name, RANK,sep="_"),motif_name))))%>%
distinct(CLUSTER,.keep_all = TRUE) %>%
slice_head(n=5) %>%
ggplot(., aes (y= reorder(motif_name,log10Evalue))) +
geom_col(aes(x=log10Evalue),fill=mrc_palette[mrc_type]) +
geom_point(aes(x=`TP%`/0.5), size =4)+
# geom_line(aes(x=`TP%`,y= motif_name, group=log10Evalue))+
scale_x_continuous(expand=c (0,.25),sec.axis = sec_axis(transform= ~.*0.5,name="Percent of peaks with motif"))+
# geom_text(aes())
theme_classic()+
ylab("Enriched TF motif")+
ggtitle(paste( mrc_type,"response peaks 200bp Enrichment ratio:",ER_rat+.1," merged motif clusters"))
| Version | Author | Date |
|---|---|---|
| d4442e0 | E. Renee Matthews | 2025-02-24 |
ESR_opcl {C}
ER_rat <- 1.25
mrc_type <- "ESR_opcl"
spec_dataframe_200 %>%
dplyr::filter(mrc==mrc_type) %>%
dplyr::filter(ENR_RATIO>(ER_rat)) %>%
dplyr::select(RANK,CLUSTER, SITES,SIM_MOTIF,ALT_ID, ID,EVALUE.x, TP:'FP%', motif_name)%>%
arrange(.,EVALUE.x) %>%
mutate(log10Evalue= log(EVALUE.x, base = 10)*(-1)) %>%
mutate(motif_name=if_else(motif_name=="KLF9",paste(motif_name, RANK, sep="_"), if_else(motif_name=="^ZN*",paste(motif_name, RANK, sep="_"), if_else(motif_name=="ZSCAN4",paste(motif_name, RANK,sep="_"),motif_name))))%>%
distinct(CLUSTER,.keep_all = TRUE) %>%
slice_head(n=5) %>%
ggplot(., aes (y= reorder(motif_name,log10Evalue))) +
geom_col(aes(x=log10Evalue),fill=mrc_palette[mrc_type]) +
geom_point(aes(x=`TP%`/3), size =4)+
# geom_line(aes(x=`TP%`,y= motif_name, group=log10Evalue))+
scale_x_continuous(expand=c (0,.25),sec.axis = sec_axis(transform= ~.*3,name="Percent of peaks with motif"))+
# geom_text(aes())
theme_classic()+
ylab("Enriched TF motif")+
ggtitle(paste( mrc_type,"response peaks 200bp Enrichment ratio:",ER_rat," merged motif clusters"))
| Version | Author | Date |
|---|---|---|
| d4442e0 | E. Renee Matthews | 2025-02-24 |
ESR_clop {D}
ER_rat <- 1.25
mrc_type <- "ESR_clop"
spec_dataframe_200 %>%
dplyr::filter(mrc==mrc_type) %>%
dplyr::filter(ENR_RATIO>(ER_rat)) %>%
dplyr::select(RANK,CLUSTER, SITES,SIM_MOTIF,ALT_ID, ID,EVALUE.x, TP:'FP%', motif_name)%>%
arrange(.,EVALUE.x) %>%
mutate(log10Evalue= log(EVALUE.x, base = 10)*(-1)) %>%
mutate(motif_name=if_else(motif_name=="KLF9",paste(motif_name, RANK, sep="_"), if_else(motif_name=="^ZN*",paste(motif_name, RANK, sep="_"), if_else(motif_name=="ZSCAN4",paste(motif_name, RANK,sep="_"),motif_name))))%>%
distinct(CLUSTER,.keep_all = TRUE) %>%
slice_head(n=5) %>%
ggplot(., aes (y= reorder(motif_name,log10Evalue))) +
geom_col(aes(x=log10Evalue),fill=mrc_palette[mrc_type]) +
geom_point(aes(x=`TP%`*2.3), size =4)+
# geom_line(aes(x=`TP%`,y= motif_name, group=log10Evalue))+
scale_x_continuous(expand=c (0,.25),sec.axis = sec_axis(transform= ~./2.3,name="Percent of peaks with motif"))+
# geom_text(aes())
theme_classic()+
ylab("Enriched TF motif")+
ggtitle(paste( mrc_type,"response peaks 200bp Enrichment ratio:",ER_rat," merged motif clusters"))
| Version | Author | Date |
|---|---|---|
| d4442e0 | E. Renee Matthews | 2025-02-24 |
LR_open
ER_rat <- 1.25
mrc_type <- "LR_open"
spec_dataframe_200 %>%
dplyr::filter(mrc==mrc_type) %>%
dplyr::filter(ENR_RATIO>ER_rat) %>%
dplyr::select(RANK,CLUSTER, SITES,SIM_MOTIF,ALT_ID, ID,EVALUE.x, TP:'FP%', motif_name)%>%
arrange(.,EVALUE.x) %>%
mutate(log10Evalue= log(EVALUE.x, base = 10)*(-1)) %>%
mutate(motif_name=
if_else(str_starts(motif_name,"BATF"),paste(motif_name, RANK, sep="_"), if_else(str_starts(motif_name,"^FO*"),paste(motif_name, RANK, sep="_"), if_else(motif_name=="ZSCAN4", paste(motif_name, RANK,sep="_"),if_else(motif_name=="KLF9",paste(motif_name,RANK,sep="_"), motif_name)))))%>%
distinct(CLUSTER,.keep_all = TRUE) %>%
slice_head(n=5) %>%
ggplot(., aes (y= reorder(motif_name,log10Evalue))) +
geom_col(aes(x=log10Evalue),fill=mrc_palette[mrc_type]) +
geom_point(aes(x=`TP%`*5), size =4)+
# geom_line(aes(x=`TP%`,y= motif_name, group=log10Evalue))+
scale_x_continuous(expand=c (0,.25),sec.axis = sec_axis(transform= ~./5,name="Percent of peaks with motif"))+
# geom_text(aes())
theme_classic()+
ylab("Enriched TF motif")+
ggtitle(paste( mrc_type,"response peaks 200 bp Enrichment ratio:",ER_rat," merged motif clusters"))
| Version | Author | Date |
|---|---|---|
| d4442e0 | E. Renee Matthews | 2025-02-24 |
LR_close
ER_rat <- 1.25
mrc_type <- "LR_close"
spec_dataframe_200 %>%
dplyr::filter(mrc==mrc_type) %>%
dplyr::filter(ENR_RATIO>ER_rat) %>%
dplyr::select(RANK,CLUSTER, SITES,SIM_MOTIF,ALT_ID, ID,EVALUE.x, TP:'FP%', motif_name)%>%
arrange(.,EVALUE.x) %>%
mutate(log10Evalue= log(EVALUE.x, base = 10)*(-1)) %>%
mutate(motif_name=if_else(motif_name=="KLF9",paste(motif_name, RANK, sep="_"), if_else(str_starts(motif_name,"^R*"),paste(motif_name, RANK, sep="_"), if_else(motif_name=="PKNOX2",paste(motif_name, RANK,sep="_"),motif_name))))%>%
distinct(CLUSTER,.keep_all = TRUE) %>%
slice_head(n=5) %>%
ggplot(., aes (y= reorder(motif_name,log10Evalue))) +
geom_col(aes(x=log10Evalue),fill=mrc_palette[mrc_type]) +
geom_point(aes(x=`TP%`/.4), size =4)+
# geom_line(aes(x=`TP%`,y= motif_name, group=log10Evalue))+
scale_x_continuous(expand=c (0,.25),sec.axis = sec_axis(transform= ~.*.4,name="Percent of peaks with motif"))+
# geom_text(aes())
theme_classic()+
ylab("Enriched TF motif")+
ggtitle(paste( mrc_type,"response peaks 200bp Enrichment ratio:",ER_rat," merged motif clusters"))
| Version | Author | Date |
|---|---|---|
| d4442e0 | E. Renee Matthews | 2025-02-24 |
# spec_dataframe %>%
# dplyr::filter(mrc==mrc_type) %>%
# dplyr::filter(ENR_RATIO>ER_rat) #%>%
# dplyr::select(RANK,CLUSTER, SITES,SIM_MOTIF,ALT_ID, ID,EVALUE, TP:'FP%', motif_name)How I printed out the consensus motifs for graphing
library(universalmotif)
library(ggseqlogo)
library(motifmatchr)
library(gridExtra)
meme_motifs_EAR_open <- read_meme("C:/Users/renee/ATAC_folder/ATAC_meme_data/200bp/EAR_open_200xstreme/xstreme.txt")
meme_motifs_ESR_open <- read_meme("C:/Users/renee/ATAC_folder/ATAC_meme_data/200bp/ESR_open_200xstreme/xstreme.txt")
meme_motifs_LR_open <- read_meme("C:/Users/renee/ATAC_folder/ATAC_meme_data/200bp/LR_open_200xstreme/xstreme.txt")
meme_motifs_EAR_close <- read_meme("C:/Users/renee/ATAC_folder/ATAC_meme_data/200bp/EAR_close_200xstreme/xstreme.txt")
meme_motifs_ESR_close <- read_meme("C:/Users/renee/ATAC_folder/ATAC_meme_data/200bp/ESR_close_200xstreme/xstreme.txt")
meme_motifs_LR_close <- read_meme("C:/Users/renee/ATAC_folder/ATAC_meme_data/200bp/LR_close_200xstreme/xstreme.txt")
meme_motifs_ESR_opcl <- read_meme("C:/Users/renee/ATAC_folder/ATAC_meme_data/200bp/ESR_C_200xstreme/xstreme.txt")
meme_motifs_ESR_clop <- read_meme("C:/Users/renee/ATAC_folder/ATAC_meme_data/200bp/ESR_D_200xstreme/xstreme.txt")group_of_memes <- list(EAR_open=meme_motifs_EAR_open,
ESR_open=meme_motifs_ESR_open,
LR_open=meme_motifs_LR_open,
EAR_close=meme_motifs_EAR_close,
ESR_close=meme_motifs_ESR_close,
LR_close=meme_motifs_LR_close,
ESR_opcl=meme_motifs_ESR_opcl,
ESR_clop=meme_motifs_ESR_clop)
name_plot <- c("meme_motifs_EAR_open",
"meme_motifs_ESR_open",
"meme_motifs_LR_open",
"meme_motifs_EAR_close",
"meme_motifs_ESR_close",
"meme_motifs_LR_close",
"meme_motifs_ESR_opcl",
"meme_motifs_ESR_clop")
counter <- 0
for (meme_frame in group_of_memes ){
counter <- counter +1
motif_info <- lapply(meme_frame, function(motif) {
data.frame(
motif_name = ifelse(is.null(motif@name), NA, motif@name),
alt_name = ifelse(is.null(motif@altname), NA, motif@altname) # Handle missing altname
# Store the position weight matrix (PWM) in a list
)
})
# Combine the individual motif data frames into one long data frame
motif_df <- bind_rows(motif_info)
plots <- lapply(1:nrow(motif_df), function(i) {
pwm <-meme_frame[[i]]@motif # Extract PWM for each motif
name <- motif_df$motif_name[i]
alt_name <- motif_df$alt_name[i]
# Check if the PWM is valid and can be plotted
if (!is.null(pwm) && is.matrix(pwm)) {
ggseqlogo::ggseqlogo(pwm, method="bits") +
ggtitle(paste("Motif:", name, "\n| Altname:", alt_name)) +
theme_minimal()
} else {
message("Invalid PWM for motif:", name)
NULL
}
})
savefile <- paste0("data/Final_four_data/",name_plot[counter],".pdf")
plots_per_page <- 16
multi_page_grobs <- marrangeGrob(plots, nrow = 4, ncol = 4)
ggsave(savefile, multi_page_grobs)
grid.draw(multi_page_grobs)
}
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] data.table_1.17.0
[2] BSgenome.Hsapiens.UCSC.hg38_1.4.5
[3] BSgenome_1.74.0
[4] BiocIO_1.16.0
[5] MotifDb_1.48.0
[6] Biostrings_2.74.1
[7] XVector_0.46.0
[8] TFBSTools_1.44.0
[9] JASPAR2022_0.99.8
[10] BiocFileCache_2.14.0
[11] dbplyr_2.5.0
[12] devtools_2.4.5
[13] usethis_3.1.0
[14] ggpubr_0.6.0
[15] BiocParallel_1.40.0
[16] Cormotif_1.52.0
[17] affy_1.84.0
[18] scales_1.3.0
[19] VennDiagram_1.7.3
[20] futile.logger_1.4.3
[21] gridExtra_2.3
[22] ggfortify_0.4.17
[23] edgeR_4.4.2
[24] limma_3.62.2
[25] rtracklayer_1.66.0
[26] org.Hs.eg.db_3.20.0
[27] TxDb.Hsapiens.UCSC.hg38.knownGene_3.20.0
[28] GenomicFeatures_1.58.0
[29] AnnotationDbi_1.68.0
[30] Biobase_2.66.0
[31] GenomicRanges_1.58.0
[32] GenomeInfoDb_1.42.3
[33] IRanges_2.40.1
[34] S4Vectors_0.44.0
[35] BiocGenerics_0.52.0
[36] ChIPseeker_1.42.1
[37] RColorBrewer_1.1-3
[38] broom_1.0.7
[39] kableExtra_1.4.0
[40] cowplot_1.1.3
[41] lubridate_1.9.4
[42] forcats_1.0.0
[43] stringr_1.5.1
[44] dplyr_1.1.4
[45] purrr_1.0.4
[46] readr_2.1.5
[47] tidyr_1.3.1
[48] tibble_3.2.1
[49] ggplot2_3.5.1
[50] tidyverse_2.0.0
[51] workflowr_1.7.1
loaded via a namespace (and not attached):
[1] fs_1.6.5
[2] matrixStats_1.5.0
[3] bitops_1.0-9
[4] DirichletMultinomial_1.48.0
[5] enrichplot_1.26.6
[6] httr_1.4.7
[7] profvis_0.4.0
[8] tools_4.4.2
[9] backports_1.5.0
[10] R6_2.6.1
[11] lazyeval_0.2.2
[12] urlchecker_1.0.1
[13] withr_3.0.2
[14] preprocessCore_1.68.0
[15] cli_3.6.4
[16] formatR_1.14
[17] labeling_0.4.3
[18] sass_0.4.9
[19] Rsamtools_2.22.0
[20] systemfonts_1.2.1
[21] yulab.utils_0.2.0
[22] DOSE_4.0.0
[23] svglite_2.1.3
[24] R.utils_2.13.0
[25] sessioninfo_1.2.3
[26] plotrix_3.8-4
[27] rstudioapi_0.17.1
[28] RSQLite_2.3.9
[29] generics_0.1.3
[30] gridGraphics_0.5-1
[31] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
[32] gtools_3.9.5
[33] car_3.1-3
[34] GO.db_3.20.0
[35] Matrix_1.7-3
[36] abind_1.4-8
[37] R.methodsS3_1.8.2
[38] lifecycle_1.0.4
[39] whisker_0.4.1
[40] yaml_2.3.10
[41] carData_3.0-5
[42] SummarizedExperiment_1.36.0
[43] gplots_3.2.0
[44] qvalue_2.38.0
[45] SparseArray_1.6.2
[46] blob_1.2.4
[47] promises_1.3.2
[48] pwalign_1.2.0
[49] crayon_1.5.3
[50] miniUI_0.1.1.1
[51] ggtangle_0.0.6
[52] lattice_0.22-6
[53] annotate_1.84.0
[54] KEGGREST_1.46.0
[55] pillar_1.10.1
[56] knitr_1.49
[57] fgsea_1.32.2
[58] rjson_0.2.23
[59] boot_1.3-31
[60] codetools_0.2-20
[61] fastmatch_1.1-6
[62] glue_1.8.0
[63] getPass_0.2-4
[64] ggfun_0.1.8
[65] remotes_2.5.0
[66] vctrs_0.6.5
[67] png_0.1-8
[68] treeio_1.30.0
[69] poweRlaw_1.0.0
[70] gtable_0.3.6
[71] cachem_1.1.0
[72] xfun_0.51
[73] S4Arrays_1.6.0
[74] mime_0.12
[75] statmod_1.5.0
[76] ellipsis_0.3.2
[77] nlme_3.1-167
[78] ggtree_3.14.0
[79] bit64_4.6.0-1
[80] filelock_1.0.3
[81] rprojroot_2.0.4
[82] bslib_0.9.0
[83] affyio_1.76.0
[84] KernSmooth_2.23-26
[85] splitstackshape_1.4.8
[86] seqLogo_1.72.0
[87] colorspace_2.1-1
[88] DBI_1.2.3
[89] tidyselect_1.2.1
[90] processx_3.8.6
[91] bit_4.6.0
[92] compiler_4.4.2
[93] curl_6.2.1
[94] git2r_0.35.0
[95] xml2_1.3.7
[96] DelayedArray_0.32.0
[97] caTools_1.18.3
[98] callr_3.7.6
[99] digest_0.6.37
[100] rmarkdown_2.29
[101] htmltools_0.5.8.1
[102] pkgconfig_2.0.3
[103] MatrixGenerics_1.18.1
[104] fastmap_1.2.0
[105] rlang_1.1.5
[106] htmlwidgets_1.6.4
[107] UCSC.utils_1.2.0
[108] shiny_1.10.0
[109] farver_2.1.2
[110] jquerylib_0.1.4
[111] jsonlite_1.9.1
[112] GOSemSim_2.32.0
[113] R.oo_1.27.0
[114] RCurl_1.98-1.16
[115] magrittr_2.0.3
[116] Formula_1.2-5
[117] GenomeInfoDbData_1.2.13
[118] ggplotify_0.1.2
[119] patchwork_1.3.0
[120] munsell_0.5.1
[121] Rcpp_1.0.14
[122] ape_5.8-1
[123] stringi_1.8.4
[124] zlibbioc_1.52.0
[125] plyr_1.8.9
[126] pkgbuild_1.4.6
[127] parallel_4.4.2
[128] ggrepel_0.9.6
[129] CNEr_1.42.0
[130] splines_4.4.2
[131] hms_1.1.3
[132] locfit_1.5-9.12
[133] ps_1.9.0
[134] igraph_2.1.4
[135] ggsignif_0.6.4
[136] reshape2_1.4.4
[137] pkgload_1.4.0
[138] TFMPvalue_0.0.9
[139] futile.options_1.0.1
[140] XML_3.99-0.18
[141] evaluate_1.0.3
[142] lambda.r_1.2.4
[143] BiocManager_1.30.25
[144] tzdb_0.4.0
[145] httpuv_1.6.15
[146] xtable_1.8-4
[147] restfulr_0.0.15
[148] tidytree_0.4.6
[149] rstatix_0.7.2
[150] later_1.4.1
[151] viridisLite_0.4.2
[152] aplot_0.2.5
[153] memoise_2.0.1
[154] GenomicAlignments_1.42.0
[155] timechange_0.3.0