Examining motif clusters
Renee Matthews
2025-08-25
Last updated: 2025-09-03
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Knit directory: DXR_continue/
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library(tidyverse)
library(readr)
library(edgeR)
library(ComplexHeatmap)
library(data.table)
library(dplyr)
library(stringr)
library(ggplot2)
library(viridis)
library(DT)
library(kableExtra)
library(genomation)
library(GenomicRanges)
library(ggpubr) ## For customizing figures
library(corrplot) ## For correlation plot
library(ggpmisc)
library(gcplyr)
library(Rsubread)
library(limma)
library(ggrastr)
library(cowplot)
library(smplot2)
library(ggVennDiagram)
library(ggsignif)
library(BiocParallel)
library(TxDb.Hsapiens.UCSC.hg38.knownGene)
library(ChIPseeker)# sampleinfo <- read_delim("data/sample_info.tsv", delim = "\t")
H3K27ac_set1 <- read_delim("data/motif_lists/H3K27ac_set1.txt",delim="\t")
H3K27ac_set2 <- read_delim("data/motif_lists/H3K27ac_set2.txt",delim="\t")
H3K27ac_set3 <- read_delim("data/motif_lists/H3K27ac_set3.txt",delim="\t")
H3K27me3_set1 <- read_delim("data/motif_lists/H3K27me3_set1.txt",delim="\t")
H3K27me3_set2 <- read_delim("data/motif_lists/H3K27me3_set2.txt",delim="\t")
H3K36me3_set1 <- read_delim("data/motif_lists/H3K36me3_set1.txt",delim="\t")
H3K36me3_set2 <- read_delim("data/motif_lists/H3K36me3_set2.txt",delim="\t")
H3K9me3_set1 <- read_delim("data/motif_lists/H3K9me3_set1.txt",delim="\t")
H3K9me3_set2 <- read_delim("data/motif_lists/H3K9me3_set2.txt",delim="\t")
H3K9me3_set3 <- read_delim("data/motif_lists/H3K9me3_set3.txt",delim="\t")
### loading filtered raw counts for visualization
H3K27ac_filt_raw_counts <- read_delim("data/DER_data/H3K27ac_filtered_raw_counts.txt",delim = "\t") %>% column_to_rownames("Peakid")
H3K27me3_filt_raw_counts <- read_delim("data/DER_data/H3K27me3_filtered_raw_counts.txt",delim = "\t")%>% column_to_rownames("Peakid")
H3K36me3_filt_raw_counts <- read_delim("data/DER_data/H3K36me3_filtered_raw_counts.txt",delim = "\t")%>% column_to_rownames("Peakid")
H3K9me3_filt_raw_counts <- read_delim( "data/DER_data/H3K9me3_filtered_raw_counts.txt",delim = "\t")%>% column_to_rownames("Peakid")
all_H3K27ac_regions <- H3K27ac_filt_raw_counts %>% rownames_to_column("Peakid") %>% distinct(Peakid)
all_H3K27me3_regions <- H3K27me3_filt_raw_counts %>% rownames_to_column("Peakid") %>% distinct(Peakid)
all_H3K36me3_regions <- H3K36me3_filt_raw_counts %>% rownames_to_column("Peakid") %>% distinct(Peakid)
all_H3K9me3_regions <- H3K9me3_filt_raw_counts %>% rownames_to_column("Peakid") %>% distinct(Peakid)
H3K27ac_filt_lcpm <- cpm(H3K27ac_filt_raw_counts, log = TRUE)
H3K27me3_filt_lcpm <- cpm(H3K27me3_filt_raw_counts, log = TRUE)
H3K36me3_filt_lcpm <- cpm(H3K36me3_filt_raw_counts, log = TRUE)
H3K9me3_filt_lcpm <- cpm(H3K9me3_filt_raw_counts, log = TRUE)
H3K9me3_toplist <- readRDS( "data/DER_data/H3K9me3_toplist_nooutlier.RDS")
H3K36me3_toplist <- readRDS( "data/DER_data/H3K36me3_toplist_nooutlier.RDS")
H3K27me3_toplist <- readRDS( "data/DER_data/H3K27me3_toplist.RDS")
H3K27ac_toplist <- readRDS( "data/DER_data/H3K27ac_toplist.RDS")
H3K27ac_toptable_list <- bind_rows(H3K27ac_toplist, .id = "group")
H3K27me3_toptable_list <- bind_rows(H3K27me3_toplist, .id = "group")
H3K36me3_toptable_list <- bind_rows(H3K36me3_toplist, .id = "group")
H3K9me3_toptable_list <- bind_rows(H3K9me3_toplist, .id = "group")H3K27ac motifs samples
Set 1
examp_H3K27ac_1 <-#slice_sample(H3K27ac_set1,n = 3, replace=FALSE)
c("chr1:77923740-77924468","chr20:25484622-25485357","chr1:229148017-229149226")
examp_H3K27ac_2 <-#slice_sample(H3K27ac_set2,n = 3, replace=FALSE)
c("chr5:85451856-85453893","chr10:114271262-114272616", "chr3:63343167-63344341" )
examp_H3K27ac_3 <- #slice_sample(H3K27ac_set3,n = 3, replace=FALSE)
c("chr8:8664034-8666371","chr3:141152796-141154087","chr21:43701994-43704465")
H3K27ac_filt_lcpm %>%
as.data.frame() %>%
rownames_to_column("Peakid") %>%
dplyr::filter(Peakid %in% examp_H3K27ac_1) %>%
###pivot and add additional information from dataframe
pivot_longer(., cols = !Peakid, names_to = "sample", values_to = "lcpm" ) %>%
separate_wider_delim(., cols=sample, delim="_", names=c("ind","tx","time")) %>%
mutate(ind=factor(ind, levels=c("Ind1", "Ind2", "Ind3", "Ind4","Ind5")),
tx=factor(tx,levels = c("DOX","VEH")),
time=factor(time, levels=c("24T","24R","144R")),
group_graph= paste0(tx, "_", time),
group=paste0("H3K27ac_",time),
group_graph = factor(group_graph, levels = c(
"DOX_24T", "VEH_24T",
"DOX_24R", "VEH_24R",
"DOX_144R", "VEH_144R"))) %>%
ggplot(., aes(x=group_graph, y=lcpm)) +
geom_boxplot(aes(fill=tx))+
facet_wrap(~Peakid, scales="free")+
theme_bw()+
theme(axis.text.x=element_text(angle = 90))+
ggtitle("set 1 H3K27ac samples")
Set 2
H3K27ac_filt_lcpm %>%
as.data.frame() %>%
dplyr::filter(row.names(H3K27ac_filt_lcpm) %in% examp_H3K27ac_2) %>%
###pivot and add additional information from dataframe
rownames_to_column("Peakid") %>%
pivot_longer(., cols = !Peakid, names_to = "sample", values_to = "lcpm" ) %>%
separate_wider_delim(., cols=sample, delim="_", names=c("ind","tx","time")) %>%
mutate(ind=factor(ind, levels=c("Ind1", "Ind2", "Ind3", "Ind4","Ind5")),
tx=factor(tx,levels = c("DOX","VEH")),
time=factor(time, levels=c("24T","24R","144R")),
group_graph= paste0(tx, "_", time),
group=paste0("H3K27ac_",time),
group_graph = factor(group_graph, levels = c(
"DOX_24T", "VEH_24T",
"DOX_24R", "VEH_24R",
"DOX_144R", "VEH_144R"))) %>%
ggplot(., aes(x=group_graph, y=lcpm)) +
geom_boxplot(aes(fill=tx))+
facet_wrap(~Peakid, scales="free")+
theme_bw()+
theme(axis.text.x=element_text(angle = 90))+
ggtitle("set 2 H3K27ac samples")
Set 3
H3K27ac_filt_lcpm %>%
as.data.frame() %>%
dplyr::filter(row.names(H3K27ac_filt_lcpm) %in% examp_H3K27ac_3) %>%
###pivot and add additional information from dataframe
rownames_to_column("Peakid") %>%
pivot_longer(., cols = !Peakid, names_to = "sample", values_to = "lcpm" ) %>%
separate_wider_delim(., cols=sample, delim="_", names=c("ind","tx","time")) %>%
mutate(ind=factor(ind, levels=c("Ind1", "Ind2", "Ind3", "Ind4","Ind5")),
tx=factor(tx,levels = c("DOX","VEH")),
time=factor(time, levels=c("24T","24R","144R")),
group_graph= paste0(tx, "_", time),
group=paste0("H3K27ac_",time),
group_graph = factor(group_graph, levels = c(
"DOX_24T", "VEH_24T",
"DOX_24R", "VEH_24R",
"DOX_144R", "VEH_144R"))) %>%
ggplot(., aes(x=group_graph, y=lcpm)) +
geom_boxplot(aes(fill=tx))+
facet_wrap(~Peakid, scales="free")+
theme_bw()+
theme(axis.text.x=element_text(angle = 90))+
ggtitle("set 3 H3K27ac samples")
Genomic regions
txdb <- TxDb.Hsapiens.UCSC.hg38.knownGene
all_H3K27ac_regions_gr <- all_H3K27ac_regions %>%
tidyr::separate(., col = "Peakid", into=c("seqnames","range"), sep = ":", remove=FALSE) %>%
tidyr::separate(., col = "range", into=c("start","end"), sep = "-") %>%
GRanges()
H3K27ac_set1_gr <- H3K27ac_set1 %>%
tidyr::separate(., col = ".", into=c("seqnames","range"), sep = ":", remove=FALSE) %>%
tidyr::separate(., col = "range", into=c("start","end"), sep = "-") %>%
dplyr::rename("Peakid"=".") %>%
GRanges()
H3K27ac_set2_gr <- H3K27ac_set2 %>%
tidyr::separate(., col = ".", into=c("seqnames","range"), sep = ":", remove=FALSE) %>%
tidyr::separate(., col = "range", into=c("start","end"), sep = "-") %>%
dplyr::rename("Peakid"=".") %>%
GRanges()
H3K27ac_set3_gr <- H3K27ac_set3 %>%
tidyr::separate(., col = ".", into=c("seqnames","range"), sep = ":", remove=FALSE) %>%
tidyr::separate(., col = "range", into=c("start","end"), sep = "-") %>%
dplyr::rename("Peakid"=".") %>%
GRanges()
H3K27ac_list <- list("Set_1"=H3K27ac_set1_gr,"Set_2"=H3K27ac_set2_gr,"Set_3"=H3K27ac_set3_gr,"all_H3K27ac"=all_H3K27ac_regions_gr)
# peakAnnoList_H3K27ac <- lapply(H3K27ac_list, annotatePeak, tssRegion =c(-2000,2000), TxDb=txdb)
# saveRDS(peakAnnoList_H3K27ac, "data/motif_lists/H3K27ac_annotated_peaks.RDS")
peakAnnoList_H3K27ac <- readRDS("data/motif_lists/H3K27ac_annotated_peaks.RDS")
plotAnnoBar(peakAnnoList_H3K27ac[c(4,1,2,3)])+
ggtitle("H3K27ac Feature Distribution")
# pie_plots_H3K27ac <- map(peakAnnoList_H3K27ac, ~ plotAnnoPie(.x))
for(nm in names(peakAnnoList_H3K27ac)) {
plotAnnoPie(peakAnnoList_H3K27ac[[nm]])
title(main = nm) # base R title
}
LFC H3K27ac
Set1
# H3K27ac_toptable_list %>%
# dplyr::filter(genes %in% H3K27ac_set1$.) %>%
# dplyr::select(group,genes,logFC) %>%
# mutate(group=factor(group, levels = c("H3K27ac_24T","H3K27ac_24R","H3K27ac_144R"))) %>%
# ggplot(., aes(x=group, y=logFC))+
# geom_boxplot(aes(fill=group))+
# geom_signif(comparisons = list(c("H3K27ac_24T", "H3K27ac_144R"),
# c("H3K27ac_24T","H3K27ac_24R"),
# c("H3K27ac_24R", "H3K27ac_144R")),
# step_increase = 0.1,
# map_signif_level = FALSE,
# test = "wilcox.test")+
# theme_bw()+
# ggtitle("LFC across time for H3K27ac set1")
H3K27ac_toptable_list %>%
dplyr::filter(genes %in% H3K27ac_set1$.) %>%
dplyr::select(group,genes,logFC) %>%
mutate(group=factor(group, levels = c("H3K27ac_24T","H3K27ac_24R","H3K27ac_144R")),
logFC=abs(logFC)) %>%
ggplot(., aes(x=group, y=logFC))+
geom_boxplot(aes(fill=group))+
geom_signif(comparisons = list(c("H3K27ac_24T", "H3K27ac_144R"),
c("H3K27ac_24T","H3K27ac_24R"),
c("H3K27ac_24R", "H3K27ac_144R")),
step_increase = 0.1,
map_signif_level = FALSE,
test = "wilcox.test")+
theme_bw()+
ggtitle("Absolute LFC across time for H3K27ac set1")
set1_27ac <- H3K27ac_toptable_list %>%
dplyr::filter(genes %in% H3K27ac_set1$.) %>%
dplyr::select(group,genes,logFC) %>%
mutate(group=factor(group, levels = c("H3K27ac_24T","H3K27ac_24R","H3K27ac_144R")),
logFC=abs(logFC)) %>%
group_by(group) %>%
summarize(med_Set1=median(logFC),.groups="drop")set2
# H3K27ac_toptable_list %>%
# dplyr::filter(genes %in% H3K27ac_set2$.) %>%
# dplyr::select(group,genes,logFC) %>%
# mutate(group=factor(group, levels = c("H3K27ac_24T","H3K27ac_24R","H3K27ac_144R"))) %>%
# ggplot(., aes(x=group, y=logFC))+
# geom_boxplot(aes(fill=group))+
# geom_signif(comparisons = list(c("H3K27ac_24T", "H3K27ac_144R"),
# c("H3K27ac_24T","H3K27ac_24R"),
# c("H3K27ac_24R", "H3K27ac_144R")),
# step_increase = 0.1,
# map_signif_level = FALSE,
# test = "wilcox.test")+
# theme_bw()+
# ggtitle("LFC across time for H3K27ac set2")
H3K27ac_toptable_list %>%
dplyr::filter(genes %in% H3K27ac_set2$.) %>%
dplyr::select(group,genes,logFC) %>%
mutate(group=factor(group, levels = c("H3K27ac_24T","H3K27ac_24R","H3K27ac_144R")),
logFC=abs(logFC)) %>%
ggplot(., aes(x=group, y=logFC))+
geom_boxplot(aes(fill=group))+
geom_signif(comparisons = list(c("H3K27ac_24T", "H3K27ac_144R"),
c("H3K27ac_24T","H3K27ac_24R"),
c("H3K27ac_24R", "H3K27ac_144R")),
step_increase = 0.1,
map_signif_level = FALSE,
test = "wilcox.test")+
theme_bw()+
ggtitle("Absolute LFC across time for H3K27ac set2")
set2_27ac <- H3K27ac_toptable_list %>%
dplyr::filter(genes %in% H3K27ac_set2$.) %>%
dplyr::select(group,genes,logFC) %>%
mutate(group=factor(group, levels = c("H3K27ac_24T","H3K27ac_24R","H3K27ac_144R")),
logFC=abs(logFC)) %>%
group_by(group) %>%
summarize(med_Set2=median(logFC),.groups="drop")set3
# H3K27ac_toptable_list %>%
# dplyr::filter(genes %in% H3K27ac_set3$.) %>%
# dplyr::select(group,genes,logFC) %>%
# mutate(group=factor(group, levels = c("H3K27ac_24T","H3K27ac_24R","H3K27ac_144R"))) %>%
# ggplot(., aes(x=group, y=logFC))+
# geom_boxplot(aes(fill=group))+
# geom_signif(comparisons = list(c("H3K27ac_24T", "H3K27ac_144R"),
# c("H3K27ac_24T","H3K27ac_24R"),
# c("H3K27ac_24R", "H3K27ac_144R")),
# step_increase = 0.1,
# map_signif_level = FALSE,
# test = "wilcox.test")+
# theme_bw()+
# ggtitle("LFC across time for H3K27ac set3")
H3K27ac_toptable_list %>%
dplyr::filter(genes %in% H3K27ac_set3$.) %>%
dplyr::select(group,genes,logFC) %>%
mutate(group=factor(group, levels = c("H3K27ac_24T","H3K27ac_24R","H3K27ac_144R")),
logFC=abs(logFC)) %>%
ggplot(., aes(x=group, y=logFC))+
geom_boxplot(aes(fill=group))+
geom_signif(comparisons = list(c("H3K27ac_24T", "H3K27ac_144R"),
c("H3K27ac_24T","H3K27ac_24R"),
c("H3K27ac_24R", "H3K27ac_144R")),
step_increase = 0.1,
map_signif_level = FALSE,
test = "wilcox.test")+
theme_bw()+
ggtitle("Absolute LFC across time for H3K27ac set3")
H3K27ac_toptable_list %>%
dplyr::filter(genes %in% H3K27ac_set3$.) %>%
dplyr::select(group,genes,logFC) %>%
mutate(group=factor(group, levels = c("H3K27ac_24T","H3K27ac_24R","H3K27ac_144R")),
logFC=abs(logFC)) %>%
group_by(group) %>%
summarize(med_Set3=median(logFC),.groups="drop") %>%
left_join(set1_27ac, by="group") %>%
left_join(set2_27ac, by="group") %>%
pivot_longer(cols=!group, values_to = "median_LFC", names_to = "set") %>%
ggplot(., aes(x=group, y=median_LFC, group=set, color=set))+
geom_point()+
geom_line(aes(alpha = 0.3))+
theme_bw()+
ggtitle("LFC across time for H3K27ac sets")
Toptable top 100
This is the calculation of Top 100 in each category, then united
Sets_H3K27ac <- H3K27ac_toptable_list %>%
mutate(cluster=case_when(genes %in% H3K27ac_set1$. ~ "Set1",
genes %in% H3K27ac_set2$. ~ "Set2",
genes %in% H3K27ac_set3$. ~ "Set3",
TRUE ~ "not_assigned"))
Sets_H3K27ac %>%
dplyr::filter(cluster != "not_assigned") %>%
group_by(group) %>%
slice_min(.,order_by=adj.P.Val,n = 100) %>%
ungroup() %>%
group_by(cluster) %>% tally()
H3K27ac_toptable_list %>%
group_by(group) %>%
slice_min(.,order_by=adj.P.Val,n = 100) %>%
ungroup() %>%
distinct(genes)
### Emma's way
Sets_H3K27ac %>%
dplyr::filter(cluster != "not_assigned") %>%
group_by(group, genes) %>%
summarise("min.adj.P"= min(adj.P.Val), .groups="drop", cluster=unique(cluster)) %>%
arrange(cluster, min.adj.P) %>%
group_by(cluster) %>%
slice_head(n = 100) %>%
distinct (genes)H3K27me3 motifs samples
Set 1
# slice_sample(H3K27me3_set2,n = 3, replace=FALSE)
examp_H3K27me3_1 <-#slice_sample(H3K27me3_set1,n = 3, replace=FALSE)
c("chr17:8878023-8878923", "chr2:23436468-23436797","chr10:132830096-132830712")
examp_H3K27me3_2 <-#slice_sample(H3K27me3_set2,n = 3, replace=FALSE)
c("chr5:141004122-141004627" , "chr6:101195726-101195968", "chr3:139117155-139117784")
H3K27me3_filt_lcpm %>%
as.data.frame() %>%
dplyr::filter(row.names(H3K27me3_filt_lcpm) %in% examp_H3K27me3_1) %>%
###pivot and add additional information from dataframe
rownames_to_column("Peakid") %>%
pivot_longer(., cols = !Peakid, names_to = "sample", values_to = "lcpm" ) %>%
separate_wider_delim(., cols=sample, delim="_", names=c("ind","tx","time")) %>%
mutate(ind=factor(ind, levels=c("Ind1", "Ind2", "Ind3", "Ind4","Ind5")),
tx=factor(tx,levels = c("DOX","VEH")),
time=factor(time, levels=c("24T","24R","144R")),
group_graph= paste0(tx, "_", time),
group=paste0("H3K27me3_",time),
group_graph = factor(group_graph, levels = c(
"DOX_24T", "VEH_24T",
"DOX_24R", "VEH_24R",
"DOX_144R", "VEH_144R"))) %>%
ggplot(., aes(x=group_graph, y=lcpm)) +
geom_boxplot(aes(fill=tx))+
facet_wrap(~Peakid, scales="free")+
theme_bw()+
theme(axis.text.x=element_text(angle = 90))+
ggtitle("set 1 H3K27me3 samples")
Set 2
H3K27me3_filt_lcpm %>%
as.data.frame() %>%
dplyr::filter(row.names(H3K27me3_filt_lcpm) %in% examp_H3K27me3_2) %>%
###pivot and add additional information from dataframe
rownames_to_column("Peakid") %>%
pivot_longer(., cols = !Peakid, names_to = "sample", values_to = "lcpm" ) %>%
separate_wider_delim(., cols=sample, delim="_", names=c("ind","tx","time")) %>%
mutate(ind=factor(ind, levels=c("Ind1", "Ind2", "Ind3", "Ind4","Ind5")),
tx=factor(tx,levels = c("DOX","VEH")),
time=factor(time, levels=c("24T","24R","144R")),
group_graph= paste0(tx, "_", time),
group=paste0("H3K27me3_",time),
group_graph = factor(group_graph, levels = c(
"DOX_24T", "VEH_24T",
"DOX_24R", "VEH_24R",
"DOX_144R", "VEH_144R"))) %>%
ggplot(., aes(x=group_graph, y=lcpm)) +
geom_boxplot(aes(fill=tx))+
facet_wrap(~Peakid, scales="free")+
theme_bw()+
theme(axis.text.x=element_text(angle = 90))+
ggtitle("set 2 H3K27me3 samples")
all_H3K27me3_regions_gr <- all_H3K27ac_regions %>%
tidyr::separate(., col = "Peakid", into=c("seqnames","range"), sep = ":", remove=FALSE) %>%
tidyr::separate(., col = "range", into=c("start","end"), sep = "-") %>%
GRanges()
H3K27me3_set1_gr <- H3K27me3_set1 %>%
tidyr::separate(., col = ".", into=c("seqnames","range"), sep = ":", remove=FALSE) %>%
tidyr::separate(., col = "range", into=c("start","end"), sep = "-") %>%
dplyr::rename("Peakid"=".") %>%
GRanges()
H3K27me3_set2_gr <- H3K27me3_set2 %>%
tidyr::separate(., col = ".", into=c("seqnames","range"), sep = ":", remove=FALSE) %>%
tidyr::separate(., col = "range", into=c("start","end"), sep = "-") %>%
dplyr::rename("Peakid"=".") %>%
GRanges()
H3K27me3_list <- list("Set_1"=H3K27me3_set1_gr,"Set_2"=H3K27me3_set2_gr,"all_H3K27me3_regions"=all_H3K27me3_regions_gr)
# peakAnnoList_H3K27me3 <- lapply(H3K27me3_list, annotatePeak, tssRegion =c(-2000,2000), TxDb=txdb)
# saveRDS(peakAnnoList_H3K27me3, "data/motif_lists/H3K27me3_annotated_peaks.RDS")
peakAnnoList_H3K27me3 <- readRDS("data/motif_lists/H3K27me3_annotated_peaks.RDS")
plotAnnoBar(peakAnnoList_H3K27me3[c(3,1,2)])+
ggtitle("H3K27me3 Feature Distribution")
# pie_plots_H3K27me3 <- map(peakAnnoList_H3K27me3, ~ plotAnnoPie(.x))
for(nm in names(peakAnnoList_H3K27me3)) {
plotAnnoPie(peakAnnoList_H3K27me3[[nm]])
title(main = nm) # base R title
}
LFC H3K27me3
Set1
# H3K27me3_toptable_list %>%
# dplyr::filter(genes %in% H3K27me3_set1$.) %>%
# dplyr::select(group,genes,logFC) %>%
# mutate(group=factor(group, levels = c("H3K27me3_24T","H3K27me3_24R","H3K27me3_144R"))) %>%
# ggplot(., aes(x=group, y=logFC))+
# geom_boxplot(aes(fill=group))+
# geom_signif(comparisons = list(c("H3K27me3_24T", "H3K27me3_144R"),
# c("H3K27me3_24T","H3K27me3_24R"),
# c("H3K27me3_24R", "H3K27me3_144R")),
# step_increase = 0.1,
# map_signif_level = FALSE,
# test = "wilcox.test")+
# theme_bw()+
# ggtitle("LFC across time for H3K27me3 set1")
H3K27me3_toptable_list %>%
dplyr::filter(genes %in% H3K27me3_set1$.) %>%
dplyr::select(group,genes,logFC) %>%
mutate(group=factor(group, levels = c("H3K27me3_24T","H3K27me3_24R","H3K27me3_144R")),
logFC=abs(logFC)) %>%
ggplot(., aes(x=group, y=logFC))+
geom_boxplot(aes(fill=group))+
geom_signif(comparisons = list(c("H3K27me3_24T", "H3K27me3_144R"),
c("H3K27me3_24T","H3K27me3_24R"),
c("H3K27me3_24R", "H3K27me3_144R")),
step_increase = 0.1,
map_signif_level = FALSE,
test = "wilcox.test")+
theme_bw()+
ggtitle("Absolute LFC across time for H3K27me3 set1")
set1_27me3 <- H3K27me3_toptable_list %>%
dplyr::filter(genes %in% H3K27me3_set1$.) %>%
dplyr::select(group,genes,logFC) %>%
mutate(group=factor(group, levels = c("H3K27me3_24T","H3K27me3_24R","H3K27me3_144R")),
logFC=abs(logFC)) %>%
group_by(group) %>%
summarize(med_Set1=median(logFC),.groups="drop")set2
# H3K27me3_toptable_list %>%
# dplyr::filter(genes %in% H3K27me3_set2$.) %>%
# dplyr::select(group,genes,logFC) %>%
# mutate(group=factor(group, levels = c("H3K27me3_24T","H3K27me3_24R","H3K27me3_144R"))) %>%
# ggplot(., aes(x=group, y=logFC))+
# geom_boxplot(aes(fill=group))+
# geom_signif(comparisons = list(c("H3K27me3_24T", "H3K27me3_144R"),
# c("H3K27me3_24T","H3K27me3_24R"),
# c("H3K27me3_24R", "H3K27me3_144R")),
# step_increase = 0.1,
# map_signif_level = FALSE,
# test = "wilcox.test")+
# theme_bw()+
# ggtitle("LFC across time for H3K27me3 set2")
H3K27me3_toptable_list %>%
dplyr::filter(genes %in% H3K27me3_set2$.) %>%
dplyr::select(group,genes,logFC) %>%
mutate(group=factor(group, levels = c("H3K27me3_24T","H3K27me3_24R","H3K27me3_144R")),
logFC=abs(logFC)) %>%
ggplot(., aes(x=group, y=logFC))+
geom_boxplot(aes(fill=group))+
geom_signif(comparisons = list(c("H3K27me3_24T", "H3K27me3_144R"),
c("H3K27me3_24T","H3K27me3_24R"),
c("H3K27me3_24R", "H3K27me3_144R")),
step_increase = 0.1,
map_signif_level = FALSE,
test = "wilcox.test")+
theme_bw()+
ggtitle("Absolute LFC across time for H3K27me3 set2")
H3K27me3_toptable_list %>%
dplyr::filter(genes %in% H3K27me3_set2$.) %>%
dplyr::select(group,genes,logFC) %>%
mutate(group=factor(group, levels = c("H3K27me3_24T","H3K27me3_24R","H3K27me3_144R")),
logFC=abs(logFC)) %>%
group_by(group) %>%
summarize(med_Set2=median(logFC),.groups="drop") %>%
left_join(set1_27me3,by = "group") %>%
pivot_longer(cols=!group, values_to = "median_LFC", names_to = "set") %>%
ggplot(., aes(x=group, y=median_LFC, group=set, color=set))+
geom_point()+
geom_line(aes(alpha = 0.3))+
theme_bw()+
ggtitle("LFC across time for H3K27me3 sets")
H3K36me3 motifs samples
Set 1
# slice_sample(H3K36me3_set1,n = 3, replace=FALSE)
examp_H3K36me3_1 <-#slice_sample(H3K36me3_set1,n = 3, replace=FALSE)
c("chr6:106975625-106975872","chr17:17259340-17260414","chr14:91506581-91506995")
examp_H3K36me3_2 <-#slice_sample(H3K36me3_set2,n = 3, replace=FALSE)
# c("chr2:191013033-191013351","chr5:66597066-66597500","chr16:22232346-22233214")
c("chr17:3656403-3665271","chr6:118573949-118574870","chr1:164799555-164800172")
H3K36me3_filt_lcpm %>%
as.data.frame() %>%
dplyr::filter(row.names(H3K36me3_filt_lcpm) %in% examp_H3K36me3_1) %>%
###pivot and add additional information from dataframe
rownames_to_column("Peakid") %>%
pivot_longer(., cols = !Peakid, names_to = "sample", values_to = "lcpm" ) %>%
separate_wider_delim(., cols=sample, delim="_", names=c("ind","tx","time")) %>%
mutate(ind=factor(ind, levels=c("Ind1", "Ind2", "Ind3", "Ind4","Ind5")),
tx=factor(tx,levels = c("DOX","VEH")),
time=factor(time, levels=c("24T","24R","144R")),
group_graph= paste0(tx, "_", time),
group=paste0("H3K36me3_",time),
group_graph = factor(group_graph, levels = c(
"DOX_24T", "VEH_24T",
"DOX_24R", "VEH_24R",
"DOX_144R", "VEH_144R"))) %>%
ggplot(., aes(x=group_graph, y=lcpm)) +
geom_boxplot(aes(fill=tx))+
facet_wrap(~Peakid, scales="free")+
theme_bw()+
theme(axis.text.x=element_text(angle = 90))+
ggtitle("set 1 H3K36me3 samples")
Set 2
H3K36me3_filt_lcpm %>%
as.data.frame() %>%
dplyr::filter(row.names(H3K36me3_filt_lcpm) %in% examp_H3K36me3_2) %>%
###pivot and add additional information from dataframe
rownames_to_column("Peakid") %>%
pivot_longer(., cols = !Peakid, names_to = "sample", values_to = "lcpm" ) %>%
separate_wider_delim(., cols=sample, delim="_", names=c("ind","tx","time")) %>%
mutate(ind=factor(ind, levels=c("Ind1", "Ind2", "Ind3", "Ind4","Ind5")),
tx=factor(tx,levels = c("DOX","VEH")),
time=factor(time, levels=c("24T","24R","144R")),
group_graph= paste0(tx, "_", time),
group=paste0("H3K36me3_",time),
group_graph = factor(group_graph, levels = c(
"DOX_24T", "VEH_24T",
"DOX_24R", "VEH_24R",
"DOX_144R", "VEH_144R"))) %>%
ggplot(., aes(x=group_graph, y=lcpm)) +
geom_boxplot(aes(fill=tx))+
facet_wrap(~Peakid, scales="free")+
theme_bw()+
theme(axis.text.x=element_text(angle = 90))+
ggtitle("set 2 H3K36me3 samples")
all_H3K36me3_regions_gr <- all_H3K36me3_regions %>%
tidyr::separate(., col = "Peakid", into=c("seqnames","range"), sep = ":", remove=FALSE) %>%
tidyr::separate(., col = "range", into=c("start","end"), sep = "-") %>%
GRanges()
H3K36me3_set1_gr <- H3K36me3_set1 %>%
tidyr::separate(., col = ".", into=c("seqnames","range"), sep = ":", remove=FALSE) %>%
tidyr::separate(., col = "range", into=c("start","end"), sep = "-") %>%
dplyr::rename("Peakid"=".") %>%
GRanges()
H3K36me3_set2_gr <- H3K36me3_set2 %>%
tidyr::separate(., col = ".", into=c("seqnames","range"), sep = ":", remove=FALSE) %>%
tidyr::separate(., col = "range", into=c("start","end"), sep = "-") %>%
dplyr::rename("Peakid"=".") %>%
GRanges()
H3K36me3_list <- list("Set_1"=H3K36me3_set1_gr,"Set_2"=H3K36me3_set2_gr, "all_H3K36me3_regions"=all_H3K36me3_regions_gr)
# peakAnnoList_H3K36me3 <- lapply(H3K36me3_list, annotatePeak, tssRegion =c(-2000,2000), TxDb=txdb)
# #
# saveRDS(peakAnnoList_H3K36me3, "data/motif_lists/H3K36me3_annotated_peaks.RDS")
peakAnnoList_H3K36me3 <- readRDS("data/motif_lists/H3K36me3_annotated_peaks.RDS")
plotAnnoBar(peakAnnoList_H3K36me3[c(3,1,2)])+
ggtitle("H3K36me3 Feature Distribution")
# pie_plots_H3K36me3 <- map(peakAnnoList_H3K36me3, ~ plotAnnoPie(.x))
for(nm in names(peakAnnoList_H3K36me3)) {
plotAnnoPie(peakAnnoList_H3K36me3[[nm]])
title(main = nm) # base R title
}
LFC H3K36me3
Set1
# H3K36me3_toptable_list %>%
# dplyr::filter(genes %in% H3K36me3_set1$.) %>%
# dplyr::select(group,genes,logFC) %>%
# mutate(group=factor(group, levels = c("H3K36me3_24T","H3K36me3_24R","H3K36me3_144R"))) %>%
# ggplot(., aes(x=group, y=logFC))+
# geom_boxplot(aes(fill=group))+
# geom_signif(comparisons = list(c("H3K36me3_24T", "H3K36me3_144R"),
# c("H3K36me3_24T","H3K36me3_24R"),
# c("H3K36me3_24R", "H3K36me3_144R")),
# step_increase = 0.1,
# map_signif_level = FALSE,
# test = "wilcox.test")+
# theme_bw()+
# ggtitle("LFC across time for H3K36me3 set1")
H3K36me3_toptable_list %>%
dplyr::filter(genes %in% H3K36me3_set1$.) %>%
dplyr::select(group,genes,logFC) %>%
mutate(group=factor(group, levels = c("H3K36me3_24T","H3K36me3_24R","H3K36me3_144R")),
logFC=abs(logFC)) %>%
ggplot(., aes(x=group, y=logFC))+
geom_boxplot(aes(fill=group))+
geom_signif(comparisons = list(c("H3K36me3_24T", "H3K36me3_144R"),
c("H3K36me3_24T","H3K36me3_24R"),
c("H3K36me3_24R", "H3K36me3_144R")),
step_increase = 0.1,
map_signif_level = FALSE,
test = "wilcox.test")+
theme_bw()+
ggtitle("Absolute LFC across time for H3K36me3 set1")
set1_36me3 <- H3K36me3_toptable_list %>%
dplyr::filter(genes %in% H3K36me3_set1$.) %>%
dplyr::select(group,genes,logFC) %>%
mutate(group=factor(group, levels = c("H3K36me3_24T","H3K36me3_24R","H3K36me3_144R")),
logFC=abs(logFC)) %>%
group_by(group) %>%
summarize(med_Set1=median(logFC),.groups="drop") set2
#
# H3K36me3_toptable_list %>%
# dplyr::filter(genes %in% H3K36me3_set2$.) %>%
# dplyr::select(group,genes,logFC) %>%
# mutate(group=factor(group, levels = c("H3K36me3_24T","H3K36me3_24R","H3K36me3_144R"))) %>%
# ggplot(., aes(x=group, y=logFC))+
# geom_boxplot(aes(fill=group))+
# geom_signif(comparisons = list(c("H3K36me3_24T", "H3K36me3_144R"),
# c("H3K36me3_24T","H3K36me3_24R"),
# c("H3K36me3_24R", "H3K36me3_144R")),
# step_increase = 0.1,
# map_signif_level = FALSE,
# test = "wilcox.test")+
# theme_bw()+
# ggtitle("LFC across time for H3K36me3 set2")
H3K36me3_toptable_list %>%
dplyr::filter(genes %in% H3K36me3_set2$.) %>%
dplyr::select(group,genes,logFC) %>%
mutate(group=factor(group, levels = c("H3K36me3_24T","H3K36me3_24R","H3K36me3_144R")),
logFC=abs(logFC)) %>%
ggplot(., aes(x=group, y=logFC))+
geom_boxplot(aes(fill=group))+
geom_signif(comparisons = list(c("H3K36me3_24T", "H3K36me3_144R"),
c("H3K36me3_24T","H3K36me3_24R"),
c("H3K36me3_24R", "H3K36me3_144R")),
step_increase = 0.1,
map_signif_level = FALSE,
test = "wilcox.test")+
theme_bw()+
ggtitle("Absolute LFC across time for H3K36me3 set2")
H3K36me3_toptable_list %>%
dplyr::filter(genes %in% H3K36me3_set2$.) %>%
dplyr::select(group,genes,logFC) %>%
mutate(group=factor(group, levels = c("H3K36me3_24T","H3K36me3_24R","H3K36me3_144R")),
logFC=abs(logFC)) %>%
group_by(group) %>%
summarize(med_Set2=median(logFC),.groups="drop") %>%
left_join(set1_36me3,by = "group") %>%
pivot_longer(cols=!group, values_to = "median_LFC", names_to = "set") %>%
ggplot(., aes(x=group, y=median_LFC, group=set, color=set))+
geom_point()+
geom_line(aes(alpha = 0.3))+
theme_bw()+
ggtitle("LFC across time for H3K36me3 sets")
H3K9me3 motifs samples
Set 1
# slice_sample(H3K9me3_set3,n = 3, replace=FALSE)
examp_H3K9me3_1 <-#slice_sample(H3K9me3_set1,n = 3, replace=FALSE)
c("chr7:101406339-101407813","chr6:147512995-147514238","chr10:66432792-66433011")
examp_H3K9me3_2 <-#slice_sample(H3K9me3_set2,n = 3, replace=FALSE)
c("chr2:120985518-120985865" , "chr7:926998-927207","chr12:108895126-108895841")
examp_H3K9me3_3 <- #slice_sample(H3K9me3_set3,n = 3, replace=FALSE)
c("chr6:44276324-44278629","chr7:906806-907939" ,"chr20:63682374-63682947")
H3K9me3_filt_lcpm %>%
as.data.frame() %>%
dplyr::filter(row.names(H3K9me3_filt_lcpm) %in% examp_H3K9me3_1) %>%
###pivot and add additional information from dataframe
rownames_to_column("Peakid") %>%
pivot_longer(., cols = !Peakid, names_to = "sample", values_to = "lcpm" ) %>%
separate_wider_delim(., cols=sample, delim="_", names=c("ind","tx","time")) %>%
mutate(ind=factor(ind, levels=c("Ind1", "Ind2", "Ind3", "Ind4","Ind5")),
tx=factor(tx,levels = c("DOX","VEH")),
time=factor(time, levels=c("24T","24R","144R")),
group_graph= paste0(tx, "_", time),
group=paste0("H3K9me3_",time),
group_graph = factor(group_graph, levels = c(
"DOX_24T", "VEH_24T",
"DOX_24R", "VEH_24R",
"DOX_144R", "VEH_144R"))) %>%
ggplot(., aes(x=group_graph, y=lcpm)) +
geom_boxplot(aes(fill=tx))+
facet_wrap(~Peakid, scales="free")+
theme_bw()+
theme(axis.text.x=element_text(angle = 90))+
ggtitle("set 1 H3K9me3 samples")
Set 2
H3K9me3_filt_lcpm %>%
as.data.frame() %>%
dplyr::filter(row.names(H3K9me3_filt_lcpm) %in% examp_H3K9me3_2) %>%
###pivot and add additional information from dataframe
rownames_to_column("Peakid") %>%
pivot_longer(., cols = !Peakid, names_to = "sample", values_to = "lcpm" ) %>%
separate_wider_delim(., cols=sample, delim="_", names=c("ind","tx","time")) %>%
mutate(ind=factor(ind, levels=c("Ind1", "Ind2", "Ind3", "Ind4","Ind5")),
tx=factor(tx,levels = c("DOX","VEH")),
time=factor(time, levels=c("24T","24R","144R")),
group_graph= paste0(tx, "_", time),
group=paste0("H3K9me3_",time),
group_graph = factor(group_graph, levels = c(
"DOX_24T", "VEH_24T",
"DOX_24R", "VEH_24R",
"DOX_144R", "VEH_144R"))) %>%
ggplot(., aes(x=group_graph, y=lcpm)) +
geom_boxplot(aes(fill=tx))+
facet_wrap(~Peakid, scales="free")+
theme_bw()+
theme(axis.text.x=element_text(angle = 90))+
ggtitle("set 2 H3K9me3 samples")
Set 3
H3K9me3_filt_lcpm %>%
as.data.frame() %>%
dplyr::filter(row.names(H3K9me3_filt_lcpm) %in% examp_H3K9me3_3) %>%
###pivot and add additional information from dataframe
rownames_to_column("Peakid") %>%
pivot_longer(., cols = !Peakid, names_to = "sample", values_to = "lcpm" ) %>%
separate_wider_delim(., cols=sample, delim="_", names=c("ind","tx","time")) %>%
mutate(ind=factor(ind, levels=c("Ind1", "Ind2", "Ind3", "Ind4","Ind5")),
tx=factor(tx,levels = c("DOX","VEH")),
time=factor(time, levels=c("24T","24R","144R")),
group_graph= paste0(tx, "_", time),
group=paste0("H3K9me3_",time),
group_graph = factor(group_graph, levels = c(
"DOX_24T", "VEH_24T",
"DOX_24R", "VEH_24R",
"DOX_144R", "VEH_144R"))) %>%
ggplot(., aes(x=group_graph, y=lcpm)) +
geom_boxplot(aes(fill=tx))+
facet_wrap(~Peakid, scales="free")+
theme_bw()+
theme(axis.text.x=element_text(angle = 90))+
ggtitle("set 3 H3K9me3 samples")
all_H3K9me3_regions_gr <- all_H3K9me3_regions %>%
tidyr::separate(., col = "Peakid", into=c("seqnames","range"), sep = ":", remove=FALSE) %>%
tidyr::separate(., col = "range", into=c("start","end"), sep = "-") %>%
GRanges()
H3K9me3_set1_gr <- H3K9me3_set1 %>%
tidyr::separate(., col = ".", into=c("seqnames","range"), sep = ":", remove=FALSE) %>%
tidyr::separate(., col = "range", into=c("start","end"), sep = "-") %>%
dplyr::rename("Peakid"=".") %>%
GRanges()
H3K9me3_set2_gr <- H3K9me3_set2 %>%
tidyr::separate(., col = ".", into=c("seqnames","range"), sep = ":", remove=FALSE) %>%
tidyr::separate(., col = "range", into=c("start","end"), sep = "-") %>%
dplyr::rename("Peakid"=".") %>%
GRanges()
H3K9me3_set3_gr <- H3K9me3_set3 %>%
tidyr::separate(., col = ".", into=c("seqnames","range"), sep = ":", remove=FALSE) %>%
tidyr::separate(., col = "range", into=c("start","end"), sep = "-") %>%
dplyr::rename("Peakid"=".") %>%
GRanges()
H3K9me3_list <- list("Set_1"=H3K9me3_set1_gr,"Set_2"=H3K9me3_set2_gr,"Set_3"=H3K9me3_set3_gr,"all_H3K9me3_regions"=all_H3K9me3_regions_gr)
# peakAnnoList_H3K9me3 <- lapply(H3K9me3_list, annotatePeak, tssRegion =c(-2000,2000), TxDb=txdb)
# #
# saveRDS(peakAnnoList_H3K9me3, "data/motif_lists/H3K9me3_annotated_peaks.RDS")
peakAnnoList_H3K9me3 <- readRDS("data/motif_lists/H3K9me3_annotated_peaks.RDS")
plotAnnoBar(peakAnnoList_H3K9me3[c(4,1,2,3)])+
ggtitle("H3K9me3 Feature Distribution")
# pie_plots_H3K9me3 <- map(peakAnnoList_H3K9me3, ~ plotAnnoPie(.x))
for(nm in names(peakAnnoList_H3K9me3)) {
plotAnnoPie(peakAnnoList_H3K9me3[[nm]])
title(main = nm) # base R title
}
LFC H3K9me3
Set1
# H3K9me3_toptable_list %>%
# dplyr::filter(genes %in% H3K9me3_set1$.) %>%
# dplyr::select(group,genes,logFC) %>%
# mutate(group=factor(group, levels = c("H3K9me3_24T","H3K9me3_24R","H3K9me3_144R"))) %>%
# ggplot(., aes(x=group, y=logFC))+
# geom_boxplot(aes(fill=group))+
# geom_signif(comparisons = list(c("H3K9me3_24T", "H3K9me3_144R"),
# c("H3K9me3_24T","H3K9me3_24R"),
# c("H3K9me3_24R", "H3K9me3_144R")),
# step_increase = 0.1,
# map_signif_level = FALSE,
# test = "wilcox.test")+
# theme_bw()+
# ggtitle("LFC across time for H3K9me3 set1")
H3K9me3_toptable_list %>%
dplyr::filter(genes %in% H3K9me3_set1$.) %>%
dplyr::select(group,genes,logFC) %>%
mutate(group=factor(group, levels = c("H3K9me3_24T","H3K9me3_24R","H3K9me3_144R")),
logFC=abs(logFC)) %>%
ggplot(., aes(x=group, y=logFC))+
geom_boxplot(aes(fill=group))+
geom_signif(comparisons = list(c("H3K9me3_24T", "H3K9me3_144R"),
c("H3K9me3_24T","H3K9me3_24R"),
c("H3K9me3_24R", "H3K9me3_144R")),
step_increase = 0.1,
map_signif_level = FALSE,
test = "wilcox.test")+
theme_bw()+
ggtitle("Absolute LFC across time for H3K9me3 set1")
set1_9me3 <- H3K9me3_toptable_list %>%
dplyr::filter(genes %in% H3K9me3_set1$.) %>%
dplyr::select(group,genes,logFC) %>%
mutate(group=factor(group, levels = c("H3K9me3_24T","H3K9me3_24R","H3K9me3_144R")),
logFC=abs(logFC)) %>%
group_by(group) %>%
summarize(med_Set1=median(logFC),.groups="drop")set2
# H3K9me3_toptable_list %>%
# dplyr::filter(genes %in% H3K9me3_set2$.) %>%
# dplyr::select(group,genes,logFC) %>%
# mutate(group=factor(group, levels = c("H3K9me3_24T","H3K9me3_24R","H3K9me3_144R"))) %>%
# ggplot(., aes(x=group, y=logFC))+
# geom_boxplot(aes(fill=group))+
# geom_signif(comparisons = list(c("H3K9me3_24T", "H3K9me3_144R"),
# c("H3K9me3_24T","H3K9me3_24R"),
# c("H3K9me3_24R", "H3K9me3_144R")),
# step_increase = 0.1,
# map_signif_level = FALSE,
# test = "wilcox.test")+
# theme_bw()+
# ggtitle("LFC across time for H3K9me3 set2")
H3K9me3_toptable_list %>%
dplyr::filter(genes %in% H3K9me3_set2$.) %>%
dplyr::select(group,genes,logFC) %>%
mutate(group=factor(group, levels = c("H3K9me3_24T","H3K9me3_24R","H3K9me3_144R")),
logFC=abs(logFC)) %>%
ggplot(., aes(x=group, y=logFC))+
geom_boxplot(aes(fill=group))+
geom_signif(comparisons = list(c("H3K9me3_24T", "H3K9me3_144R"),
c("H3K9me3_24T","H3K9me3_24R"),
c("H3K9me3_24R", "H3K9me3_144R")),
step_increase = 0.1,
map_signif_level = FALSE,
test = "wilcox.test")+
theme_bw()+
ggtitle("Absolute LFC across time for H3K9me3 set2")
set2_9me3 <- H3K9me3_toptable_list %>%
dplyr::filter(genes %in% H3K9me3_set2$.) %>%
dplyr::select(group,genes,logFC) %>%
mutate(group=factor(group, levels = c("H3K9me3_24T","H3K9me3_24R","H3K9me3_144R")),
logFC=abs(logFC)) %>%
group_by(group) %>%
summarize(med_Set2=median(logFC),.groups="drop")set3
# H3K9me3_toptable_list %>%
# dplyr::filter(genes %in% H3K9me3_set3$.) %>%
# dplyr::select(group,genes,logFC) %>%
# mutate(group=factor(group, levels = c("H3K9me3_24T","H3K9me3_24R","H3K9me3_144R"))) %>%
# ggplot(., aes(x=group, y=logFC))+
# geom_boxplot(aes(fill=group))+
# geom_signif(comparisons = list(c("H3K9me3_24T", "H3K9me3_144R"),
# c("H3K9me3_24T","H3K9me3_24R"),
# c("H3K9me3_24R", "H3K9me3_144R")),
# step_increase = 0.1,
# map_signif_level = FALSE,
# test = "wilcox.test")+
# theme_bw()+
# ggtitle("LFC across time for H3K9me3 set3")
H3K9me3_toptable_list %>%
dplyr::filter(genes %in% H3K9me3_set3$.) %>%
dplyr::select(group,genes,logFC) %>%
mutate(group=factor(group, levels = c("H3K9me3_24T","H3K9me3_24R","H3K9me3_144R")),
logFC=abs(logFC)) %>%
ggplot(., aes(x=group, y=logFC))+
geom_boxplot(aes(fill=group))+
geom_signif(comparisons = list(c("H3K9me3_24T", "H3K9me3_144R"),
c("H3K9me3_24T","H3K9me3_24R"),
c("H3K9me3_24R", "H3K9me3_144R")),
step_increase = 0.1,
map_signif_level = FALSE,
test = "wilcox.test")+
theme_bw()+
ggtitle("Absolute LFC across time for H3K9me3 set3")
H3K9me3_toptable_list %>%
dplyr::filter(genes %in% H3K9me3_set3$.) %>%
dplyr::select(group,genes,logFC) %>%
mutate(group=factor(group, levels = c("H3K9me3_24T","H3K9me3_24R","H3K9me3_144R")),
logFC=abs(logFC)) %>%
group_by(group) %>%
summarize(med_Set3=median(logFC),.groups="drop") %>%
left_join(set1_9me3, by="group") %>%
left_join(set2_9me3, by="group") %>%
pivot_longer(cols=!group, values_to = "median_LFC", names_to = "set") %>%
ggplot(., aes(x=group, y=median_LFC, group=set, color=set))+
geom_point()+
geom_line(aes(alpha = 0.3))+
theme_bw()+
ggtitle("LFC across time for H3K9me3 sets")
sessionInfo()R version 4.4.2 (2024-10-31 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 26100)
Matrix products: default
locale:
[1] LC_COLLATE=English_United States.utf8
[2] LC_CTYPE=English_United States.utf8
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C
[5] LC_TIME=English_United States.utf8
time zone: America/Chicago
tzcode source: internal
attached base packages:
[1] stats4 grid stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] ChIPseeker_1.42.1
[2] TxDb.Hsapiens.UCSC.hg38.knownGene_3.20.0
[3] GenomicFeatures_1.58.0
[4] AnnotationDbi_1.68.0
[5] Biobase_2.66.0
[6] BiocParallel_1.40.2
[7] ggsignif_0.6.4
[8] ggVennDiagram_1.5.4
[9] smplot2_0.2.5
[10] cowplot_1.2.0
[11] ggrastr_1.0.2
[12] Rsubread_2.20.0
[13] gcplyr_1.12.0
[14] ggpmisc_0.6.2
[15] ggpp_0.5.9
[16] corrplot_0.95
[17] ggpubr_0.6.1
[18] GenomicRanges_1.58.0
[19] GenomeInfoDb_1.42.3
[20] IRanges_2.40.1
[21] S4Vectors_0.44.0
[22] BiocGenerics_0.52.0
[23] genomation_1.38.0
[24] kableExtra_1.4.0
[25] DT_0.33
[26] viridis_0.6.5
[27] viridisLite_0.4.2
[28] data.table_1.17.8
[29] ComplexHeatmap_2.22.0
[30] edgeR_4.4.2
[31] limma_3.62.2
[32] lubridate_1.9.4
[33] forcats_1.0.0
[34] stringr_1.5.1
[35] dplyr_1.1.4
[36] purrr_1.1.0
[37] readr_2.1.5
[38] tidyr_1.3.1
[39] tibble_3.3.0
[40] ggplot2_3.5.2
[41] tidyverse_2.0.0
[42] workflowr_1.7.1
loaded via a namespace (and not attached):
[1] fs_1.6.6
[2] matrixStats_1.5.0
[3] bitops_1.0-9
[4] enrichplot_1.26.6
[5] httr_1.4.7
[6] RColorBrewer_1.1-3
[7] doParallel_1.0.17
[8] tools_4.4.2
[9] backports_1.5.0
[10] R6_2.6.1
[11] lazyeval_0.2.2
[12] GetoptLong_1.0.5
[13] withr_3.0.2
[14] gridExtra_2.3
[15] quantreg_6.1
[16] cli_3.6.5
[17] textshaping_1.0.1
[18] labeling_0.4.3
[19] sass_0.4.10
[20] Rsamtools_2.22.0
[21] systemfonts_1.2.3
[22] yulab.utils_0.2.1
[23] foreign_0.8-90
[24] DOSE_4.0.1
[25] svglite_2.2.1
[26] R.utils_2.13.0
[27] dichromat_2.0-0.1
[28] plotrix_3.8-4
[29] BSgenome_1.74.0
[30] pwr_1.3-0
[31] rstudioapi_0.17.1
[32] impute_1.80.0
[33] RSQLite_2.4.3
[34] generics_0.1.4
[35] gridGraphics_0.5-1
[36] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
[37] shape_1.4.6.1
[38] BiocIO_1.16.0
[39] vroom_1.6.5
[40] gtools_3.9.5
[41] car_3.1-3
[42] GO.db_3.20.0
[43] Matrix_1.7-3
[44] ggbeeswarm_0.7.2
[45] abind_1.4-8
[46] R.methodsS3_1.8.2
[47] lifecycle_1.0.4
[48] whisker_0.4.1
[49] yaml_2.3.10
[50] carData_3.0-5
[51] SummarizedExperiment_1.36.0
[52] gplots_3.2.0
[53] qvalue_2.38.0
[54] SparseArray_1.6.2
[55] blob_1.2.4
[56] promises_1.3.3
[57] crayon_1.5.3
[58] ggtangle_0.0.7
[59] lattice_0.22-7
[60] KEGGREST_1.46.0
[61] pillar_1.11.0
[62] knitr_1.50
[63] fgsea_1.32.4
[64] rjson_0.2.23
[65] boot_1.3-31
[66] codetools_0.2-20
[67] fastmatch_1.1-6
[68] glue_1.8.0
[69] getPass_0.2-4
[70] ggfun_0.2.0
[71] treeio_1.30.0
[72] vctrs_0.6.5
[73] png_0.1-8
[74] gtable_0.3.6
[75] cachem_1.1.0
[76] xfun_0.52
[77] S4Arrays_1.6.0
[78] survival_3.8-3
[79] iterators_1.0.14
[80] statmod_1.5.0
[81] nlme_3.1-168
[82] ggtree_3.14.0
[83] bit64_4.6.0-1
[84] rprojroot_2.1.0
[85] bslib_0.9.0
[86] vipor_0.4.7
[87] KernSmooth_2.23-26
[88] rpart_4.1.24
[89] colorspace_2.1-1
[90] DBI_1.2.3
[91] Hmisc_5.2-3
[92] seqPattern_1.38.0
[93] nnet_7.3-20
[94] tidyselect_1.2.1
[95] processx_3.8.6
[96] bit_4.6.0
[97] compiler_4.4.2
[98] curl_7.0.0
[99] git2r_0.36.2
[100] htmlTable_2.4.3
[101] SparseM_1.84-2
[102] xml2_1.4.0
[103] DelayedArray_0.32.0
[104] rtracklayer_1.66.0
[105] caTools_1.18.3
[106] checkmate_2.3.3
[107] scales_1.4.0
[108] callr_3.7.6
[109] rappdirs_0.3.3
[110] digest_0.6.37
[111] rmarkdown_2.29
[112] XVector_0.46.0
[113] htmltools_0.5.8.1
[114] pkgconfig_2.0.3
[115] base64enc_0.1-3
[116] MatrixGenerics_1.18.1
[117] fastmap_1.2.0
[118] rlang_1.1.6
[119] GlobalOptions_0.1.2
[120] htmlwidgets_1.6.4
[121] UCSC.utils_1.2.0
[122] farver_2.1.2
[123] jquerylib_0.1.4
[124] zoo_1.8-14
[125] jsonlite_2.0.0
[126] GOSemSim_2.32.0
[127] R.oo_1.27.1
[128] RCurl_1.98-1.17
[129] magrittr_2.0.3
[130] polynom_1.4-1
[131] Formula_1.2-5
[132] GenomeInfoDbData_1.2.13
[133] ggplotify_0.1.2
[134] patchwork_1.3.1
[135] Rcpp_1.1.0
[136] ape_5.8-1
[137] stringi_1.8.7
[138] zlibbioc_1.52.0
[139] MASS_7.3-65
[140] plyr_1.8.9
[141] ggrepel_0.9.6
[142] parallel_4.4.2
[143] Biostrings_2.74.1
[144] splines_4.4.2
[145] hms_1.1.3
[146] circlize_0.4.16
[147] locfit_1.5-9.12
[148] ps_1.9.1
[149] igraph_2.1.4
[150] reshape2_1.4.4
[151] XML_3.99-0.18
[152] evaluate_1.0.4
[153] tzdb_0.5.0
[154] foreach_1.5.2
[155] httpuv_1.6.16
[156] MatrixModels_0.5-4
[157] clue_0.3-66
[158] gridBase_0.4-7
[159] broom_1.0.9
[160] restfulr_0.0.16
[161] tidytree_0.4.6
[162] rstatix_0.7.2
[163] later_1.4.2
[164] aplot_0.2.8
[165] memoise_2.0.1
[166] beeswarm_0.4.0
[167] GenomicAlignments_1.42.0
[168] cluster_2.1.8.1
[169] timechange_0.3.0