Dunnart peak characterisation
lecook
2022-02-23
Last updated: 2022-03-07
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Knit directory: chipseq-cross-species/
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Introduction
This analysis looks through various features of the dunnart peaks including peak intensity, lengths, distance to TSS and nearest gene calls.
Load libraries
library(GenomicRanges)
library(GenomicFeatures)
library(rtracklayer)
library(AnnotationForge)
library(clusterProfiler)
library(ChIPseeker)
library(ggpubr)
library(AnnotationDbi)
library(AnnotationHub)
library(org.Mm.eg.db)
library(plyr)
library(tidyverse)
library(RColorBrewer)
library(ggplot2)
library(data.table)
library(ggridges)
library(reshape2)
library(RColorBrewer)
library(VennDiagram)
library(viridis)
library(hrbrthemes)
library(gghalves)
library(multcompView)
library(factoextra)
library(NbClust)
library(simplifyEnrichment)
library(formatR)Set up
Set fonts, plot and output directories etc.
plot_dir = "output/plots/"
fullPeak_dir = "output/peaks/"
annot_dir = "output/annotations/"
filterPeaks_dir = "output/filtered_peaks/"
## Set the fonts up so that each plot is the saved the same way.
font = theme(axis.text.x = element_text(size = 25),
axis.text.y = element_text(size = 25),
axis.title.x = element_text(size = 25),
axis.title.y = element_text(size = 25),
legend.title=element_text(size=25), legend.text=element_text(size=25))Peak features
Peak counts
fileList = fullPeak_dir
combined.tbl.stack = "dunnart_combined_stacked.pdf"
peak.length.plot = "dunnart_fullPeaks_length.pdf"
peak.intensity.plot = "dunnart_fullPeaks_intensity.pdf"
files <- list.files(fileList, pattern = "*r_peaks.narrowPeak|*_only.narrowPeak|*overlap_default.narrowPeak|*and_H3K27ac.narrowPeak", full.names = T) # create list of files in directory
files <- as.list(files)
data <- lapply(files, function(x) data.table::fread(x, header = FALSE, sep = "\t", quote = "", na.strings = c("", "NA")))
names(data) <- c("enhancers","promoters","H3K27ac_only","H3K27ac","H3K4me3_H3K27ac", "H3K4me3_only","H3K4me3")
df1 <- Map(dplyr::mutate, data, group = names(data))
df1$H3K27ac_only$cre <- "enhancers"
df1$H3K4me3_only$cre <- "promoters"
df1$H3K4me3_H3K27ac$cre <- "promoters"
fullPeaks <- rbind(df1$enhancers, df1$promoters, df1$H3K27ac, df1$H3K4me3)
combinedPeaks <- rbind(df1$H3K27ac_only, df1$H3K4me3_H3K27ac, df1$H3K4me3_only)
## Plot stacked bar graph with number of peaks for each histone mark
combined.tbl <- with(combinedPeaks, table(group, cre)) # nolint # nolint
combined.tbl <- as.data.frame(combined.tbl)
p <- ggplot(combined.tbl, aes(factor(cre), Freq, fill = group)) +
geom_bar(position = position_stack(reverse = TRUE), stat = "identity") +
scale_fill_manual(values = c("#FF9D40", "#37B6CE", "#04859D")) +
theme_minimal() + ylab("Number of peaks") + xlab("")
pdf(file = paste0(plot_dir, combined.tbl.stack), width = 10, height = 7)
print(p)
dev.off()svg
2 p
Average peak lengths
# Peak lengths for H3K4me3 and H3K27ac
fullPeaks$length <- (fullPeaks$V3 - fullPeaks$V2)
level_order <- c('promoter', 'enhancer', 'H3K4me3', 'H3K27ac')
## Plot peak lengths
p <- ggplot(fullPeaks, aes(factor(group), y = log10(length))) +
geom_violin(aes(fill = factor(group),
color = factor(group))) +
geom_boxplot(aes(color = factor(group)),
width = .15, outlier.shape = NA,
position = position_dodge(width = .1)) +
theme_bw() + xlab("") + ylab("Log10 Peak Length") +
scale_color_manual(values = c("#E9C46A","#E9C46A","#264653","#2A9D8F")) +
scale_fill_manual(values = c("#F1DAA2","#F1DAA2","#AEBABF","#7AC2B9"))
p
## Save as a pdf
pdf(file = paste0(plot_dir, peak.length.plot), width = 10, height = 7)
print(p + scale_color_manual(values = c("#E9C46A","#E9C46A","#264653","#2A9D8F")) +
scale_fill_manual(values = c("#F1DAA2","#F1DAA2","#AEBABF","#7AC2B9")) + coord_flip())
dev.off()svg
2 Average peak intensity
## Plot peak intensity
p <- ggplot(fullPeaks, aes(factor(group), y = log10(V7))) +
geom_half_violin(aes(fill = factor(group), color = factor(group)),
side = "r",
position = "dodge") +
geom_boxplot(aes(color = factor(group)),
width = .15,
outlier.shape = NA,
fill = c("#FCF8EC","#FCF8EC","#F5F6F7","#E4F3F1"),
position = position_dodge(width = .1)) +
coord_cartesian(xlim = c(1.2, NA), clip = "off") + theme_bw() +
xlab("") + ylab("Log10 Peak Intensity") +
scale_color_manual(values = c("#E9C46A","#E9C46A","#264653","#2A9D8F")) +
scale_fill_manual(values = c("#F1DAA2","#F1DAA2","#AEBABF","#7AC2B9"))
p
pdf(file = paste0(plot_dir, peak.intensity.plot), width = 10, height = 7)
print(p + scale_color_manual(values = c("#E9C46A","#E9C46A","#264653","#2A9D8F")) +
scale_fill_manual(values = c("#F1DAA2","#F1DAA2","#AEBABF","#7AC2B9")) + coord_flip())
dev.off() svg
2 print(p + scale_color_manual(values = c("#E9C46A","#E9C46A","#264653","#2A9D8F")) +
scale_fill_manual(values = c("#F1DAA2","#F1DAA2","#AEBABF","#7AC2B9")) + coord_flip())
Annotate dunnart peaks
The easiest way to call the nearest genes for the peaks in the dunnart is to use the ChIPseeker package (Guangchuang Yu 2021) as it allows easy integration of non-model organism genomes and has well documented instructions on incorporating BYO genomes with the package. To use the ChIPseeker to annotate peaks, firstly a txdb is needed for the dunnart annotation file. A TxDb class is a container for storing transcript annotations. The dunnart genome doesn’t have a de novo annotation so instead the Tasmanian devil annotation (RefSeq) has been lifted over to the dunnart genome using LiftOff (https://github.com/agshumate/Liftoff).
Build the annotation files for the dunnart
smiCra_txdb <- makeTxDbFromGFF("data/genomic_data/Scras_dunnart_assem1.0_pb-ont-illsr_flyeassem_red-rd-scfitr2_pil2xwgs2_60chr2.gff")Gene ID conversion tables
For downstream analyses, conversion tables between gene databases and between species is needed. This is because the ENSEMBL/ENTREZ IDs for the Tasmanian Devil have fewer links to databases such as GO terms etc. For this I have two conversion tables: 1. Converts Tasmanian devil RefSeq to Tasmanian Devil ENSEMBL IDs 2. Convert Tasmanian Devil ENSEMBL IDs to mouse ENSEMBL IDs
Additionally I have a list of background genes for calculating GO enrichment. This background list includes all devil genes that have an orthologous mouse gene ID in the ensembl database.
## geneID conversion tables
df2 <- read.table("output/annotations/devil_to_mouse_ensembl.txt", header=TRUE, sep="\t") ## conversion table for devil ENSEMBL to mouse ENSEMBL
df3 <- read.table("output/annotations/refseq_to_ensembl.txt", header=TRUE, sep="\t") ## convertsion table for devil refseq to devil ENSEMBL
bg = fread("output/annotations/go_background_orthologousENSEMBL_biomart.txt", header = FALSE)
bg = unlist(bg, use.names = FALSE)Annotate peak files with ChIPseeker
annotatePeaks <- function(peak, outFile, outFile1, outFile2, GOenrich, kegg, backg){
# Annotate peak file based on dunnart GFF
peakAnno <- annotatePeak(peak = peak, tssRegion = c(-3000, 3000), TxDb = smiCra_txdb)
# Write annotation to file
write.table(peakAnno, file=paste0(annot_dir, outFile), sep="\t", quote=F, row.names=F)
peakAnnoDF <- as.data.frame(peakAnno, row.names = NULL)
# # Convert refseq IDs and geneIDs to devil ensembl IDs
df2 <- read.table("output/annotations/devil_to_mouse_ensembl.txt", header=TRUE, sep="\t") ## conversion table for devil ENSEMBL to mouse ENSEMBL
df3 <- read.table("output/annotations/refseq_to_ensembl.txt", header=TRUE, sep="\t") ## convertsion table for devil refseq to devil ENSEMBL
peakAnnoDF$ensemblgeneID <- df2$Gene.stable.ID[match(unlist(peakAnnoDF$geneId), df2$Gene.name)]
peakAnnoDF$ensemblgeneID = replace(peakAnnoDF$ensemblgeneID,is.na(peakAnnoDF$ensemblgeneID),"-")
peakAnnoDF$transcriptIdAltered <- gsub("\\..*","", peakAnnoDF$transcriptId)
peakAnnoDF$refseqID <- df3$Ensembl.Gene.ID[match(unlist(peakAnnoDF$transcriptIdAltered), df3$RefSeq.mRNA.Accession)]
peakAnnoDF$refseqID = replace(peakAnnoDF$refseqID,is.na(peakAnnoDF$refseqID),"-")
peakAnnoDF$combined <- ifelse(peakAnnoDF$refseqID == "-", peakAnnoDF$ensemblgeneID, peakAnnoDF$refseqID)
peakAnnoDF$combined[peakAnnoDF$combined == as.character("-")] <- NA
peakAnnoDF <- peakAnnoDF[!is.na(peakAnnoDF$combined),]
write.table(peakAnnoDF, paste0(annot_dir, outFile2), sep="\t", quote=F, row.names=F)
# # Convert devil ensembl to mouse ensembl
peakAnnoDF$mouseensembl <- df2$Mouse.gene.stable.ID[match(unlist(peakAnnoDF$combined), df2$Gene.stable.ID)]
# # Write annotation with converted IDs
peakAnnoDF$mouseensembl[peakAnnoDF$mouseensembl == as.character("")] <- NA
peakAnnoDF <- peakAnnoDF[!is.na(peakAnnoDF$mouseensembl),]
write.table(peakAnnoDF, paste0(annot_dir, outFile1), sep="\t", quote=F, row.names=F)
# # GO enrichment analysis
GO <- enrichGO(gene = peakAnnoDF$mouseensembl,
keyType = "ENSEMBL",
OrgDb = org.Mm.eg.db,
ont = "BP",
universe = backg,
pAdjustMethod = "fdr",
qvalueCutoff = 0.01,
readable = TRUE)
write.table(GO, paste0(annot_dir, GOenrich),
sep="\t", quote=F, row.names=F)
# convert mouse ensembl to entrezID
suppressWarnings(gene.df <- bitr(geneID=peakAnnoDF$mouseensembl, fromType="ENSEMBL", toType="ENTREZID", OrgDb="org.Mm.eg.db" ))
bg.entrez <- bitr(geneID=backg, fromType="ENSEMBL", toType="ENTREZID", OrgDb="org.Mm.eg.db" )
bg.entrez = bg.entrez$ENTREZID
# ## kegg analysis
kk <- enrichKEGG(gene = gene.df$ENTREZID,
organism = "mmu", pAdjustMethod = "fdr",
qvalueCutoff = 0.01, universe = bg.entrez)
write.table(kk, paste0(annot_dir, kegg),
sep="\t",
quote=F,
row.names=F)
}
### Enhancer-associated peaks annotation
annotatePeaks(peak = paste0(fullPeak_dir, "dunnart_enhancer_peaks.narrowPeak"), outFile = "dunnart_enhancer_annotation.txt", backg = unlist(fread("output/annotations/go_background_orthologousENSEMBL_biomart.txt", header = FALSE), use.names = FALSE),
outFile1 = "dunnart_enhancer_annotationConvertedIDs.txt", GOenrich = "enhancer_mm10GOenrich.txt",
kegg="enhancer_KEGG.txt", outFile2 = "dunnart_enhancer_annotationConvertedIDs_t.devil.txt")>> loading peak file... 2022-03-07 15:52:54
>> preparing features information... 2022-03-07 15:52:55
>> identifying nearest features... 2022-03-07 15:52:55
>> calculating distance from peak to TSS... 2022-03-07 15:52:56
>> assigning genomic annotation... 2022-03-07 15:52:56
>> assigning chromosome lengths 2022-03-07 15:53:05
>> done... 2022-03-07 15:53:05 ### Promoter-associated peaks annotation
annotatePeaks(peak = paste0(fullPeak_dir, "dunnart_promoter_peaks.narrowPeak"), outFile = "dunnart_promoter_annotation.txt", backg = unlist(fread("output/annotations/go_background_orthologousENSEMBL_biomart.txt", header = FALSE), use.names = FALSE),
outFile1 = "dunnart_promoter_annotationConvertedIDs.txt" , GOenrich = "promoter_mm10GOenrich.txt",
kegg="promoter_KEGG.txt", outFile2 = "dunnart_promoter_annotationConvertedIDs_t.devil.txt")>> loading peak file... 2022-03-07 15:53:55
>> preparing features information... 2022-03-07 15:53:55
>> identifying nearest features... 2022-03-07 15:53:55
>> calculating distance from peak to TSS... 2022-03-07 15:53:56
>> assigning genomic annotation... 2022-03-07 15:53:56
>> assigning chromosome lengths 2022-03-07 15:53:58
>> done... 2022-03-07 15:53:58 ### H3K4me3-associated peaks annotation
annotatePeaks(peak = paste0(fullPeak_dir, "H3K4me3_overlap_default.narrowPeak"), outFile = "dunnart_H3K4me3_annotation.txt", backg = unlist(fread("output/annotations/go_background_orthologousENSEMBL_biomart.txt", header = FALSE), use.names = FALSE),
outFile1 = "dunnart_H3K4me3_annotationConvertedIDs.txt" , GOenrich = "H3K4me3_mm10GOenrich.txt",
kegg="H3K4me3_KEGG.txt", outFile2 = "dunnart_H3K4me3_annotationConvertedIDs_t.devil.txt")>> loading peak file... 2022-03-07 15:54:22
>> preparing features information... 2022-03-07 15:54:23
>> identifying nearest features... 2022-03-07 15:54:23
>> calculating distance from peak to TSS... 2022-03-07 15:54:23
>> assigning genomic annotation... 2022-03-07 15:54:23
>> assigning chromosome lengths 2022-03-07 15:54:25
>> done... 2022-03-07 15:54:25 ### H3K27ac-associated peaks annotation
annotatePeaks(peak = paste0(fullPeak_dir, "H3K27ac_overlap_default.narrowPeak"), outFile = "dunnart_H3K27ac_annotation.txt", backg = unlist(fread("output/annotations/go_background_orthologousENSEMBL_biomart.txt", header = FALSE), use.names = FALSE),
outFile1 = "dunnart_H3K27ac_annotationConvertedIDs.txt" , GOenrich = "H3K27ac_mm10GOenrich.txt",
kegg="H3K27ac_KEGG.txt", outFile2 = "dunnart_H3K27ac_annotationConvertedIDs_t.devil.txt")>> loading peak file... 2022-03-07 15:54:49
>> preparing features information... 2022-03-07 15:54:50
>> identifying nearest features... 2022-03-07 15:54:50
>> calculating distance from peak to TSS... 2022-03-07 15:54:51
>> assigning genomic annotation... 2022-03-07 15:54:51
>> assigning chromosome lengths 2022-03-07 15:54:53
>> done... 2022-03-07 15:54:53 Distance to the nearest TSS
Now see where the peaks are located in relation to the TSS. Promoters should be reasonably close to the TSS and enhancers more distal to the TSS. Plot distance to TSS for all unfiltered peaks
fileList = annot_dir
enhancerTSSplot="dunnart_enhancerTSS.pdf"
promoterTSSplot="dunnart_promoterTSS.pdf"
TSSplot = "dunnart_TSS.pdf"
files =list.files(fileList, pattern= "annotation.txt", full.names=T) # create list of files in directory
files = as.list(files)
data = lapply(files, function(x) fread(x, header=TRUE, sep="\t", quote = "", na.strings=c("", "NA"))) # read in all files
names(data) = c("enhancers","H3K27ac","H3K4me3","promoter")
df1 = Map(mutate, data, mark = names(data))
enhProm = list(as.data.table(df1$promoter), as.data.table(df1$enhancers))
enhProm = suppressWarnings(lapply(enhProm,
function(x)
x[,log10_abs_dist:=log10(abs(distanceToTSS)+1)][,log10_abs_dist:=ifelse(distanceToTSS<0,-log10_abs_dist,log10_abs_dist)]))
names(enhProm) = c("promoters","enhancers")
## Enhancers
p <- ggplot(enhProm$enhancers, aes(x=log10_abs_dist)) +
geom_density(alpha=0.5, color="#FF9D40", fill="#FF9D40") + theme_bw() +
ggtitle("Enhancer-associated peaks")
## Save as PDF
pdf(file = paste0(plot_dir, enhancerTSSplot), width=10, height = 7)
print(p)
dev.off()svg
2 ## Promoters
p <- ggplot(enhProm$promoters, aes(x=log10_abs_dist)) +
geom_density(alpha=0.5, color="#2A9D8F", fill="#2A9D8F") + theme_bw() +
ggtitle("Promoter-associated peaks")
p
| Version | Author | Date |
|---|---|---|
| 95a6294 | lecook | 2022-03-03 |
## Save as PDF
pdf(file = paste0(plot_dir, promoterTSSplot), width=10, height = 7)
print(p)
dev.off()svg
2 ## Overlay both on the same plots
enhProm = rbindlist(enhProm,)
p <- ggplot(enhProm, aes(x=log10_abs_dist, fill=factor(mark), color=factor(mark))) +
geom_density(alpha=0.5) +scale_color_manual(values=c("#E9C46A", "#2A9D8F")) +
scale_fill_manual(values=c("#E9C46A", "#2A9D8F")) +
scale_x_continuous(limits=c(-7, 7)) + theme_bw()
p
| Version | Author | Date |
|---|---|---|
| 95a6294 | lecook | 2022-03-03 |
## Save as PDF
pdf(file = paste0(plot_dir, TSSplot), width=10, height = 7)
print(p + font)
dev.off()svg
2 From this we can see that the promoter peaks have a large amount of peaks a long way from the TSS - suggests that these are either actually enhancers or they represent unannotated transcripts. Probably a mixture of both based on comparison with mouse peaks (where the annotation is better) there are not as many peaks in this distal regions. To look further into this I assessed the relationship between various peak features and the distances to the nearest genes as potential explanation for the distally-located promoter-associated peaks.
Assessing distal promoter-associated peaks
Gene length vs number of peaks per gene
peak.counts <- enhProm %>% as.data.frame() %>% group_by(geneId, mark, geneLength) %>%
summarise(count = n(), .groups = 'drop') %>% as.data.table()
p <- ggscatter(
peak.counts, x = "count", y = "geneLength",
color = "mark", palette = "jco", add="reg.line",
conf.int = TRUE, # Add confidence interval
cor.coef = TRUE, # Add correlation coefficient. see ?stat_cor
cor.coeff.args = list(method = "pearson", label.x = 3, label.sep = "\n"), xlab = "Gene Length",
ylab = "Number of peaks per gene"
) +
facet_wrap(~mark) + theme(panel.spacing = unit(3, "lines"), plot.margin = margin(2, 2, 2, 2, "cm"))
p
pdf(paste0(plot_dir, "geneLengthvsPeakCount.pdf"), width=10, height = 15)
print(p)
dev.off()svg
2 #FIX
#p <- ggplot(peak, aes(abs(distanceToTSS), y=log10_length, color=cre)) +
# geom_point(alpha=0.3) + theme_bw() + scale_color_manual(values=c("#F1DAA2", "#7AC2B9"))
#pdf(paste0(plot_dir, "peakLengthVsDist.pdf"), width=10, height = 7)
#dev.off()Peak intensity for distal enhancers marked by H3K4me3
# subset log10_abs_dist between 3.5 and 7 (3000bp and 10 million bps)
#H3K4me3 = fread("H3K4me3_annotation.txt", sep="\t", quote="", header=TRUE)
#H3K27ac = fread("H3K27ac_annotation.txt", sep="\t", quote="", header=TRUE)
#H3K4me3.subset.greater = subset(H3K4me3, abs(distanceToTSS) >=3000)
#H3K4me3.subset.greater$mark = "H3K4me3 > 3kb"
#H3K4me3.subset.less = subset(H3K4me3, abs(distanceToTSS) < 3000)
#H3K4me3.subset.less$mark = "H3K4me3 < 3kb"
#H3K27ac.subset = subset(H3K27ac, abs(distanceToTSS) >=3000)
#H3K27ac.subset$mark = "H3K27ac"
#data = rbind(H3K4me3.subset.less, H3K4me3.subset.greater, H3K27ac.subset)
#stat <- compare_means(V7 ~ mark, data = data)
#mean(H3K4me3.subset.greater$V7)
#mean(H3K4me3.subset.less$V7)
#enhancer <- fread("dunnart_enhancer_annotation.txt", sep="\t", quote="", header=TRUE)
#promoter <- fread("dunnart_promoter_annotation.txt", sep="\t", quote="", header=TRUE)
#enhancer$cre <- "enhancer"
#promoter$cre <- "promoter"
#enhancer = enhancer[,log10_abs_dist:=log10(abs(distanceToTSS)+1)]
#promoter = promoter[,log10_abs_dist:=log10(abs(distanceToTSS)+1)]
#promoter.subset=subset(promoter, log10_abs_dist >= 3.5)
#promoter = subset(promoter, log10_abs_dist <3.5)
#promoter.subset$cre = "subset"
#data = rbind(promoter, promoter.subset)
#data = data[,log10_intensity:=log10(V7)]
#p <- ggplot(data, aes(factor(cre), y = log10_intensity)) +
#geom_violin(aes(fill=factor(cre), color=factor(cre)),
# position = "dodge"
# )+
# geom_boxplot(aes(color=factor(cre)),
# outlier.shape = NA,
# width = .15,
# notch = TRUE,
# fill=c("#FCF8EC","#E4F3F1"),
# position = position_dodge(width=.1)
#) + theme_bw() + xlab("") + ylab("Peak intensity")
#pdf(paste0(plot_dir, "distalH3K4me3PeakIntensity.pdf"), width=9, height = 8)
#p + scale_color_manual(values = c("#E9C46A","#2A9D8F")) +
#scale_fill_manual(values = c("#F1DAA2","#7AC2B9")) + stat_compare_means(method = "wilcox.test" )
#dev.off()Number of peaks per gene
#enhancer <- fread("annotations/dunnart_enhancer_annotation.txt", sep="\t", quote="", header=TRUE)
#promoter <- fread("annotations/dunnart_promoter_annotation.txt", sep="\t", quote="", header=TRUE)
#enhancer$cre <- "enhancer"
#promoter$cre <- "promoter"
##peak <- rbind(enhancer, promoter)
#peak.counts <- enhancer %>% as.data.frame() %>% group_by(geneId, cre) %>%
# summarise(count = n(),.groups = 'drop') %>% as.data.table()
#peak.counts = peak.counts[,log10_abs_dist:=log10(count)][,log10_abs_dist:=ifelse(count<0,-log10_abs_dist,log10_abs_dist)]
#pick <- peak.counts %>% filter(count > 50)
#p <- ggplot(peak.counts, aes(factor(cre), y = log10_abs_dist)) +
# geom_violin(aes(fill=factor(cre), color=factor(cre))) +
#geom_boxplot(aes(color=factor(cre)),
# width = .15,
#outlier.shape = NA,
#fill=c("#FCF8EC","#E4F3F1"),
#position = position_dodge(width=.1)
#) +
#geom_text(data = pick, aes(factor(cre), log10_abs_dist), label = pick$geneId, check_overlap = TRUE, size=3, position = position_jitter(w = 0.3)) +
# theme_bw() + xlab("") + ylab("Log 10 number of peaks")
#pdf(paste0(plot_dir, "numberPeaksPerGene.pdf"), width=9, height = 8)
#p + scale_color_manual(values = c("#E9C46A","#2A9D8F")) +
#scale_fill_manual(values = c("#F1DAA2","#7AC2B9"))
#dev.off()Gene length and number of peaks per gene
#p <- ggplot(peak.counts, aes(x=log10_abs_length, y = log10(n))) +
# geom_point()+ theme_bw() + xlab("Gene Length") + ylab("Log 10 number of peaks")
#pdf(paste0(plot_dir, "numberPeaksvsGeneLength.pdf"), width=9, height = 8)
#p
#dev.off() k-means clustering
Use k-means clustering to group the peaks to decide on a cut off for “promoter” peaks. This will be more conservative for what we identify as promoters. We can use k-means clustering with the MacQueen algorithm (morissette_k-means_2013) to perform unsupervised clustering on promoter-associated peaks into 3 clusters based on distance to the closest TSS.
set.seed(3)
file = paste0(annot_dir, "dunnart_promoter_annotation.txt")
plot1 = paste0(plot_dir, "promoter_kmeans_bar.pdf")
plot2 = paste0(plot_dir, "promoter_kmeans_histogram.pdf")
output = "promoter_kmeans_peaks.txt"
data = fread(file, header=TRUE, sep="\t", quote = "") # read in all files
data = data[,log10_dist:=log10(abs(distanceToTSS)+1.1)][,log10_dist:=ifelse(distanceToTSS<0,-log10_dist,log10_dist)]
data = data[,abs_dist:=log10(abs(distanceToTSS)+1.1)]
data = data %>% dplyr::select("V4", "width", "V7", "distanceToTSS", "log10_dist", "abs_dist", "annotation")
## plot the number of peaks in each cluster
## Using the MacQueen algorithm instead of the default Lloyd
## The algorithm is more efficient as it updates centroids more often and usually needs to
## perform one complete pass through the cases to converge on a solution.
df = data[,5]
cre.kmeans = kmeans(df, 3, iter.max=100, nstart=25, algorithm="MacQueen")
cre.kmeans_table = data.frame(cre.kmeans$size, cre.kmeans$centers)
cre.kmeans_df = data.frame(Cluster = cre.kmeans$cluster, data)
p <- ggplot(data = cre.kmeans_df, aes(y = Cluster,
fill=as.factor(Cluster),
color=as.factor(Cluster))) +
geom_bar() + theme(plot.title = element_text(hjust = 0.5)) +
theme_bw() + scale_color_manual(values = c('#9EBCDA','#8C6BB1', "#4D004B")) +
scale_fill_manual(values = c('#9EBCDA','#8C6BB1', "#4D004B")) + theme_bw()
p + ggtitle("Number of peaks in clusters")
| Version | Author | Date |
|---|---|---|
| 95a6294 | lecook | 2022-03-03 |
pdf(plot1, width=10, height = 7)
print(p + font)
dev.off()svg
2 p <- ggplot(cre.kmeans_df, aes(x=log10_dist,
fill=as.factor(Cluster),
color=as.factor(Cluster))) +
geom_histogram(binwidth=0.1, position = 'identity') +
theme_bw() + scale_color_manual(values = c('#9EBCDA','#8C6BB1', "#4D004B")) +
scale_fill_manual(values = c('#9EBCDA','#8C6BB1', "#4D004B"))
p + ggtitle("Histogram of clustered peaks")
| Version | Author | Date |
|---|---|---|
| 95a6294 | lecook | 2022-03-03 |
pdf(plot2, width = 10, height = 7)
print(p + font)
dev.off() svg
2 write.table(cre.kmeans_df, paste0(filterPeaks_dir, output), sep="\t", quote=F, row.names=F)Filter for cluster 1 and make final promoter/enhancer plots
peakWidthPlot = paste0(plot_dir, "filteredPeaks_peakWidthPlot.pdf")
plotTSS = paste0(plot_dir, "filteredPeaks_peakDistanceToTSS.pdf")
peakIntensityPlot = paste0(plot_dir, "filteredPeaks_peakIntensityPlot.pdf")
annotation = paste0(plot_dir, "filteredPeaks_peakAnnotation.pdf")
x = paste0(filterPeaks_dir, "promoter_kmeans_peaks.txt")
enhancer = paste0(annot_dir, "dunnart_enhancer_annotation.txt")
promoter = paste0(annot_dir, "dunnart_promoter_annotation.txt")
enhancer.annot= fread(enhancer, header=TRUE, sep="\t", quote = "")
enhancer.annot = enhancer.annot[,log10_dist:=log10(abs(distanceToTSS)+1)][,log10_dist:=ifelse(distanceToTSS<0,-log10_dist,log10_dist)]
enhancer.annot = enhancer.annot[,abs_dist:=abs(distanceToTSS)]
promoter.annot = fread(promoter, header=TRUE, sep="\t", quote = "")
promoter.annot = promoter.annot[,log10_dist:=log10(abs(distanceToTSS)+1)][,log10_dist:=ifelse(distanceToTSS<0,-log10_dist,log10_dist)]
promoter.annot = promoter.annot[,abs_dist:=abs(distanceToTSS)]
promoter.annot$label = "promoter"
enhancer.annot$label = "enhancer"
enhancer.annot = enhancer.annot %>% dplyr::select("V4", "width", "V7", "distanceToTSS",
"log10_dist", "abs_dist", "annotation", "label")
promoter.annot = promoter.annot %>% dplyr::select("V4", "width", "V7", "distanceToTSS",
"log10_dist", "abs_dist", "annotation", "label")
promoter.kmeans = fread(x, header=TRUE, sep="\t", quote = "")
promoter.kmeans$cre = "promoter"
for (i in 1:nrow(promoter.kmeans)) {
if(promoter.kmeans$Cluster[i]=='1' & promoter.kmeans$cre[i]=='promoter') {
promoter.kmeans[i, 'label'] <- "promoter cluster 1"
}
else if(promoter.kmeans$Cluster[i]=='2' & promoter.kmeans$cre[i]=='promoter') {
promoter.kmeans[i, 'label'] <- "promoter cluster 2"
}
else if(promoter.kmeans$Cluster[i]=='3' & promoter.kmeans$cre[i]=='promoter') {
promoter.kmeans[i, 'label'] <- "promoter cluster 3"
}
}
data2 = promoter.kmeans %>% dplyr::select("V4", "width", "V7", "distanceToTSS", "log10_dist", "abs_dist", "annotation", "label")
all_peaks = rbind(data2, enhancer.annot, promoter.annot)
all_peaks = as.data.table(all_peaks)
all_peaks = all_peaks[,log10_intensity:=log10(V7)]
all_peaks = all_peaks[,log10_width:=log10(width)]Plot distance to TSS
colors = c("#E9C46A","#e34a33", "#fdbb84","#fee8c8", "#2A9D8F")
p <- ggplot(all_peaks, aes(x=log10_dist, colour = factor(label), fill=factor(label))) +
geom_density(aes(x = log10_dist, y = ..density..), position="stack", alpha=0.7) +
theme_bw() + scale_fill_manual(values = colors) + scale_color_manual(values = colors)
pdf(plotTSS, width=10, height = 7)
print(p + font)
dev.off()svg
2 my_comparisons = list( c("promoter cluster 1", "promoter cluster 2"), c("promoter cluster 1", "promoter cluster 3"), c("promoter cluster 1", "promoter"), c("promoter cluster 1", "enhancer") )
## Plot peak intensity
p <- ggplot(all_peaks, aes(factor(label), y = log10_intensity)) +
geom_violin(aes(fill = factor(label),
color = factor(label)),
position = "dodge") +
geom_boxplot(aes(color = factor(label)),
outlier.shape = NA,
width = .15,
notch = TRUE,
position = position_dodge(width=.1)) + theme_bw() +
xlab("") + ylab("Log 10 peak intensity") + scale_color_manual(values = colors) +
scale_fill_manual(values = colors) + stat_compare_means(comparisons = my_comparisons)
p + ggtitle("Peak intensity for clustered promoter-associated peaks compared to unfiltered peaks")
pdf(paste0(plot_dir, "test.pdf"),width=10, height = 8)
print(p + font)
dev.off()svg
2 ## Plot peak width
p <- ggplot(all_peaks, aes(factor(label), y = log10_width)) +
geom_violin(aes(fill = factor(label),
color = factor(label)),
position = "dodge") +
geom_boxplot(aes(color=factor(label)),
outlier.shape = NA,
width = .15,
notch = TRUE,
position = position_dodge(width=.1)) +
theme_bw() + xlab("") + ylab("Log 10 peak width") + scale_color_manual(values =colors) +
scale_fill_manual(values = colors) + stat_compare_means(comparisons = my_comparisons)
p + ggtitle("Peak widths for clustered promoter-associated peaks compared to unfiltered peaks")
pdf(peakWidthPlot, width=9, height = 8)
print(p + font)
dev.off()svg
2 ## Plot genomic regions
df2 = rbind(promoter.kmeans)
df2 = map_df(df2, ~ gsub(" ().*", "", .x))# remove everything after the first space
tbl = with(df2, table(annotation, Cluster)) %>% as.data.frame()
p <- ggplot(tbl, aes(factor(annotation), Freq, fill=factor(Cluster))) +
geom_bar(position=position_stack(reverse = TRUE), stat="identity") +
#geom_text(data=subset(data,Freq != 0),aes(label=Freq, y=pos), size=3)+
scale_fill_manual(values = c('#9EBCDA','#8C6BB1', "#4D004B")) +
theme_minimal() + ylab("Number of peaks") + xlab("")
p + ggtitle("Genomic features of clusters")
pdf(annotation)
print(p + font)
dev.off()svg
2 Extract cluster groups from narrowPeak files and save separately
promoter.kmeans = fread(paste0(filterPeaks_dir, "promoter_kmeans_peaks.txt"))
cluster1 <- promoter.kmeans$V4[promoter.kmeans$Cluster==1]
cluster2 <- promoter.kmeans$V4[promoter.kmeans$Cluster==2]
cluster3 <- promoter.kmeans$V4[promoter.kmeans$Cluster==3]
promoter = paste0(fullPeak_dir, "dunnart_promoter_peaks.narrowPeak")
out1 = paste0(filterPeaks_dir, "cluster1_promoters.narrowPeak")
out2 = paste0(filterPeaks_dir, "cluster2_promoters.narrowPeak")
out3 = paste0(filterPeaks_dir, "cluster3_promoters.narrowPeak")
cluster1 = cluster1
cluster2 = cluster2
cluster3 = cluster3
file= fread(promoter, header=FALSE, sep="\t", quote = "")
write.table(subset(file, V4 %in% cluster1), out1, quote=FALSE, col.names=FALSE, row.names=FALSE, sep="\t")
write.table(subset(file, V4 %in% cluster2), out2, quote=FALSE, col.names=FALSE, row.names=FALSE, sep="\t")
write.table(subset(file, V4 %in% cluster3), out3, quote=FALSE, col.names=FALSE, row.names=FALSE, sep="\t")Anotate promoter clusters
annotatePeaks(peak = paste0(filterPeaks_dir, "cluster1_promoters.narrowPeak"),
outFile = "cluster1_promoter_annotation.txt", outFile1 = "cluster1_promoter_annotationConvertedIDs.txt" , backg = unlist(fread("output/annotations/go_background_orthologousENSEMBL_biomart.txt", header = FALSE), use.names = FALSE),
GOenrich = "cluster1_mm10GOenrich.txt",
kegg="cluster1_KEGG.txt", outFile2 = "cluster1_annotationConvertedIDs_t.devil.txt")>> loading peak file... 2022-03-07 15:57:55
>> preparing features information... 2022-03-07 15:57:55
>> identifying nearest features... 2022-03-07 15:57:55
>> calculating distance from peak to TSS... 2022-03-07 15:57:55
>> assigning genomic annotation... 2022-03-07 15:57:55
>> assigning chromosome lengths 2022-03-07 15:57:56
>> done... 2022-03-07 15:57:56 annotatePeaks(peak = paste0(filterPeaks_dir, "cluster2_promoters.narrowPeak"),
outFile = "cluster2_promoter_annotation.txt", outFile1 = "cluster2_promoter_annotationConvertedIDs.txt" , backg = unlist(fread("output/annotations/go_background_orthologousENSEMBL_biomart.txt", header = FALSE), use.names = FALSE),
GOenrich = "cluster2_mm10GOenrich.txt",
kegg="cluster2_KEGG.txt", outFile2 = "cluster2_annotationConvertedIDs_t.devil.txt")>> loading peak file... 2022-03-07 15:58:17
>> preparing features information... 2022-03-07 15:58:17
>> identifying nearest features... 2022-03-07 15:58:17
>> calculating distance from peak to TSS... 2022-03-07 15:58:18
>> assigning genomic annotation... 2022-03-07 15:58:18
>> assigning chromosome lengths 2022-03-07 15:58:19
>> done... 2022-03-07 15:58:19 annotatePeaks(peak = paste0(filterPeaks_dir, "cluster3_promoters.narrowPeak"),
outFile = "cluster3_promoter_annotation.txt", outFile1 = "cluster3_promoter_annotationConvertedIDs.txt" , backg = unlist(fread("output/annotations/go_background_orthologousENSEMBL_biomart.txt", header = FALSE), use.names = FALSE),
GOenrich = "cluster3_mm10GOenrich.txt",
kegg="cluster3_KEGG.txt", outFile2 = "cluster3_annotationConvertedIDs_t.devil.txt")>> loading peak file... 2022-03-07 15:58:40
>> preparing features information... 2022-03-07 15:58:40
>> identifying nearest features... 2022-03-07 15:58:40
>> calculating distance from peak to TSS... 2022-03-07 15:58:40
>> assigning genomic annotation... 2022-03-07 15:58:40
>> assigning chromosome lengths 2022-03-07 15:58:41
>> done... 2022-03-07 15:58:41 Assess distance to TSS for clusters
fileList = annot_dir
enhancerTSSplot="dunnart_enhancerTSS.pdf"
promoterTSSplot="dunnart_filtered_promoterTSS.pdf"
TSSplot = "dunnart_all_TSS.pdf"
files =list.files(fileList, pattern= "_annotation.txt", full.names=T) # create list of files in directory
files = as.list(files)
data = lapply(files, function(x) fread(x, header=TRUE, sep="\t", quote = "", na.strings=c("", "NA"))) # read in all files
names(data) = c("cluster1", "cluster2", "cluster3", "enhancers","H3K27ac","H3K4me3","promoters")
enhProm = Map(mutate, data, mark = names(data))
#promoter_5kb = x
#promoter_5kb$mark = "promoter_5kb"
#enhProm = list(df1$enhancers, df1$promoters)
enhProm = suppressWarnings(lapply(enhProm, function(x) x[,log10_abs_dist:=log10(abs(distanceToTSS)+1)][,log10_abs_dist:=ifelse(distanceToTSS<0,-log10_abs_dist,log10_abs_dist)]))
enhProm = suppressWarnings(lapply(enhProm, function(x) x[,abs_dist:=abs(distanceToTSS)]))
#names(enhProm) = c("enhancers","promoters", "promoter_5kb")
level_order = c("cluster1", "cluster2", "cluster3", "promoters", "enhancers", "H3K4me3", "H3K27ac")
p <- ggplot(enhProm$enhancers, aes(x=log10_abs_dist,
color=factor(mark),
fill=factor(mark))) +
geom_density(alpha=0.5) + theme_bw() + scale_color_manual(values = c('#E9C46A')) +
scale_fill_manual(values = c('#E9C46A'))
p + ggtitle("Distance to the nearest TSS for enhancer-associated peaks")
pdf(file = paste0(plot_dir, enhancerTSSplot), width=10, height = 7)
print(p + font)
dev.off()svg
2 enhProm = rbind(enhProm$promoters, enhProm$cluster1, enhProm$cluster2, enhProm$cluster3 ,use.names=TRUE)
p <- ggplot(enhProm, aes(x=log10_abs_dist,
color = factor(mark, level=level_order),
fill = factor(mark, level=level_order))) +
geom_density(position = "stack", alpha=0.8) + theme_bw()
p + ggtitle("Distance to the nearest TSS for promoter-associated peaks")
pdf(file = paste0(plot_dir, TSSplot), width=15, height = 7)
print(p + font)
dev.off()svg
2 Now look at CpG% and GC% for these groups
Run Homer on the clusters
- Load modules and create genome db for use within homer
- Annotate peaks to extract GC and CpG content information using annotatePeaks.pl
module load perl
module load gcc
module load web_proxy
module load wget- Load genome for use with HOMER tools This will allow the genome to be called within the tools
loadGenome.pl -name smiCra1 -org null -fasta data/genomic_data/Scras_dunnart_assem1.0_pb-ont-illsr_flyeassem_red-rd-scfitr2_pil2xwgs2_60chr.fasta -gff data/genomic_data/Scras_dunnart_assem1.0_pb-ont-illsr_flyeassem_red-rd-scfitr2_pil2xwgs2_60chr2.gff- Annotate peaks to extract GC and CpG content information
annotatePeaks.pl output/filtered_peaks/cluster1_promoters.narrowPeak smiCra1 -gff data/genomic_data/Scras_dunnart_assem1.0_pb-ont-illsr_flyeassem_red-rd-scfitr2_pil2xwgs2_60chr2.gff -CpG -cons > output/annotations/cluster1_promoters_homerAnnot.txt
annotatePeaks.pl output/filtered_peaks/cluster2_promoters.narrowPeak smiCra1 -gff Scras_dunnart_assem1.0_pb-ont-illsr_flyeassem_red-rd-scfitr2_pil2xwgs2_60chr2.gff -CpG -cons > output/annotations/cluster2_promoters_homerAnnot.txt
annotatePeaks.pl output/filtered_peaks/cluster3_promoters.narrowPeak smiCra1 -gff Scras_dunnart_assem1.0_pb-ont-illsr_flyeassem_red-rd-scfitr2_pil2xwgs2_60chr2.gff -CpG -cons > output/annotations/cluster3_promoters_homerAnnot.txt
annotatePeaks.pl output/peaks/dunnart_promoter_peaks.narrowPeak smiCra1 -gff data/genomic_data/Scras_dunnart_assem1.0_pb-ont-illsr_flyeassem_red-rd-scfitr2_pil2xwgs2_60chr2.gff -CpG -cons > output/annotations/dunnart_promoters_homerAnnot.txt
annotatePeaks.pl output/peaks/dunnart_enhancer_peaks.narrowPeak smiCra1 -gff data/genomic_data/Scras_dunnart_assem1.0_pb-ont-illsr_flyeassem_red-rd-scfitr2_pil2xwgs2_60chr2.gff -CpG -cons > output/annotations/dunnart_enhancers_homerAnnot.txt fileList = annot_dir
gcPlot = paste0(plot_dir, "dunnart_GC.pdf")
cpgPlot = paste0(plot_dir, "dunnart_cpg.pdf")
files =list.files(fileList, pattern= "homerAnnot.txt", full.names=T) # create list of files in directory
files = as.list(files)
data = lapply(files, function(x) fread(x, header=TRUE, sep="\t", quote = "", na.strings=c("", "NA"))) # read in all files
names(data) = c("cluster1","cluster2","cluster3","enhancers", "promoters")
df1 = Map(mutate, data, group = names(data))
df1 = lapply(df1, function(x) x %>% dplyr::select("Chr", "Start", "End",
"Peak Score", "Distance to TSS",
"CpG%", "GC%", "group") %>% as.data.table())
df1 = rbind(df1$cluster1, df1$cluster2, df1$cluster3, df1$promoters, df1$enhancers)
colnames(df1)[5:7] <- c("distanceToTSS","CpG", "GC")
df1[is.na(df1)] <- 0
df1 = df1[,log10_abs_dist:=log10(abs(distanceToTSS)+1)][,log10_abs_dist:=ifelse(distanceToTSS<0,-log10_abs_dist,log10_abs_dist)]
my_comparisons = list( c("cluster1", "cluster2"), c("cluster1", "cluster3"),
c("cluster1", "promoters"), c("cluster1", "enhancers"),
c("promoters", "enhancers"), c("promoters", "cluster2"),
c("promoters", "cluster3"), c("enhancers", "cluster2"),
c("enhancers", "cluster3"), c("cluster2", "cluster3") )
p <- ggplot(df1, aes(factor(group), y = GC)) +
geom_violin(aes(fill=factor(group),
color=factor(group)),
position = "dodge") +
geom_boxplot(aes(color=factor(group)),
outlier.shape = NA,
width = .15,
notch = TRUE,
fill=c("#E7EEF6","#D4C7E2","#D3BFD2", "#FCF8EC","#E4F3F1"),
position = position_dodge(width=.1)) +
theme_bw() + xlab("") + ylab("GC content") +
scale_color_manual(values = c('#9EBCDA','#8C6BB1','#4D004B', '#E9C46A','#2A9D8F')) +
scale_fill_manual(values = c("#B6CDE3","#A990C4","#7A4078" ,"#F1DAA2","#7AC2B9"))
p + ggtitle("GC content")
pdf(gcPlot, width=9, height = 8)
print(p + font + stat_compare_means(p.adjust.method = "fdr", comparisons = my_comparisons))
dev.off()svg
2 ## Plot CpG
p <- ggplot(df1, aes(factor(group), y = CpG)) +
geom_violin(aes(fill=factor(group),
color=factor(group)),
position = "dodge") +
geom_boxplot(aes(color=factor(group)),
outlier.shape = NA,
width = .15,
notch = TRUE,
fill=c("#E7EEF6","#D4C7E2","#D3BFD2", "#FCF8EC","#E4F3F1"),
position = position_dodge(width=.1)) +
theme_bw() + xlab("") + ylab("CpG") +
scale_color_manual(values = c('#9EBCDA','#8C6BB1','#4D004B', '#E9C46A','#2A9D8F')) +
scale_fill_manual(values = c("#B6CDE3","#A990C4","#7A4078" ,"#F1DAA2","#7AC2B9"))
p + ggtitle("CpG content")
pdf(cpgPlot, width = 9, height = 8)
print(p + font + stat_compare_means(p.adjust.method = "fdr", comparisons = my_comparisons))
dev.off()svg
2 GO enrichment for high-confidence promoter-associated peaks and enhancer-associated peaks
promoter = fread(paste0(annot_dir, "cluster1_promoter_annotationConvertedIDs.txt"))
enhancer = fread(paste0(annot_dir, "dunnart_enhancer_annotationConvertedIDs.txt"))
data = list(promoter, enhancer)
genes = lapply(data, function(i) as.data.frame(i)$mouseensembl)
genes = lapply(genes, function(i) unique(i))
names(genes) = c("promoter-associated peaks", "enhancer-associated peaks")
go_cluster <- setReadable(
compareCluster(
geneCluster = genes,
fun = enrichGO,
ont="BP",
universe = bg,
keyType="ENSEMBL",
pvalueCutoff = 0.001,
OrgDb = org.Mm.eg.db),
OrgDb = org.Mm.eg.db,
keyType="ENSEMBL")Plot GO enrichment
p <- dotplot(go_cluster, showCategory = 10) + scale_color_viridis() +
theme(axis.text.x = element_text(angle = 45, hjust=1))
p
pdf(paste0(plot_dir, "dunnart_go_cluster.pdf"), width = 11, height = 9)
print(p)
dev.off()svg
2 write.table(go_cluster@compareClusterResult, file = paste0(annot_dir, "dunnart_peak_mm10_enrichGO_compareCluster_results.txt"), sep="\t", quote=F, row.names=F)
suppressWarnings(genes_ent <- lapply(genes, function(x) bitr(geneID=x, fromType="ENSEMBL", toType="ENTREZID", OrgDb="org.Mm.eg.db" )))
genes_ent = lapply(genes_ent, function(x) x$ENTREZID)
suppressWarnings(bg_ent <- bitr(geneID=bg, fromType="ENSEMBL", toType="ENTREZID", OrgDb="org.Mm.eg.db" ))
bg_entrez = bg_ent$ENTREZID
kegg_cluster <-
compareCluster(
geneCluster = genes_ent,
fun = 'enrichKEGG',
universe = bg_entrez,
pvalueCutoff = 0.001,
organism = 'mmu')
p <- dotplot(kegg_cluster, showCategory = 10) + scale_color_viridis() +
theme(axis.text.x = element_text(angle = 45, hjust=1))
p
pdf(paste0(plot_dir, "dunnart_kegg_cluster.pdf"), width = 8, height = 6)
print(p)
dev.off()svg
2 split <- split(as.data.frame(go_cluster@compareClusterResult),
f = factor(go_cluster@compareClusterResult$Cluster))
go_terms = lapply(split, function(x) x$ID)
pdf(paste0(plot_dir, "dunnart_simplifyGO.pdf"), width = 10, height = 14)
simplifyGOFromMultipleLists(go_terms,
db = org.Mm.eg.db,
measure = "Wang",
method = "binary_cut")334/334 GO IDs left for clustering.
Cluster 334 terms by 'binary_cut'... 27 clusters, used 0.1066508 secs.dev.off() svg
2
sessionInfo()R version 4.1.0 (2021-05-18)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Red Hat Enterprise Linux
Matrix products: default
BLAS/LAPACK: /usr/local/easybuild-2019/easybuild/software/compiler/gcc/10.2.0/openblas/0.3.12/lib/libopenblas_haswellp-r0.3.12.so
locale:
[1] LC_CTYPE=en_AU.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_AU.UTF-8 LC_COLLATE=en_AU.UTF-8
[5] LC_MONETARY=en_AU.UTF-8 LC_MESSAGES=en_AU.UTF-8
[7] LC_PAPER=en_AU.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_AU.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] grid stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] org.Hs.eg.db_3.14.0 formatR_1.11 simplifyEnrichment_1.4.0
[4] NbClust_3.0 factoextra_1.0.7 multcompView_0.1-8
[7] gghalves_0.1.1 hrbrthemes_0.8.0 viridis_0.6.2
[10] viridisLite_0.4.0 VennDiagram_1.7.1 futile.logger_1.4.3
[13] reshape2_1.4.4 ggridges_0.5.3 data.table_1.14.0
[16] RColorBrewer_1.1-2 forcats_0.5.1 stringr_1.4.0
[19] dplyr_1.0.8 purrr_0.3.4 readr_1.4.0
[22] tidyr_1.1.3 tibble_3.1.2 tidyverse_1.3.1
[25] plyr_1.8.6 org.Mm.eg.db_3.14.0 AnnotationHub_3.2.2
[28] BiocFileCache_2.2.1 dbplyr_2.1.1 ggpubr_0.4.0
[31] ggplot2_3.3.3 ChIPseeker_1.30.3 clusterProfiler_4.2.2
[34] AnnotationForge_1.36.0 rtracklayer_1.54.0 GenomicFeatures_1.46.5
[37] AnnotationDbi_1.56.2 Biobase_2.54.0 GenomicRanges_1.46.1
[40] GenomeInfoDb_1.30.1 IRanges_2.28.0 S4Vectors_0.32.3
[43] BiocGenerics_0.40.0 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] rappdirs_0.3.3
[2] bit64_4.0.5
[3] knitr_1.33
[4] DelayedArray_0.20.0
[5] doParallel_1.0.16
[6] KEGGREST_1.34.0
[7] RCurl_1.98-1.3
[8] generics_0.1.0
[9] callr_3.7.0
[10] lambda.r_1.2.4
[11] RSQLite_2.2.7
[12] shadowtext_0.1.1
[13] bit_4.0.4
[14] enrichplot_1.14.2
[15] xml2_1.3.2
[16] lubridate_1.7.10
[17] httpuv_1.6.1
[18] ggsci_2.9
[19] SummarizedExperiment_1.24.0
[20] assertthat_0.2.1
[21] xfun_0.23
[22] hms_1.1.0
[23] jquerylib_0.1.4
[24] evaluate_0.14
[25] promises_1.2.0.1
[26] fansi_0.5.0
[27] restfulr_0.0.13
[28] progress_1.2.2
[29] caTools_1.18.2
[30] readxl_1.3.1
[31] igraph_1.2.6
[32] DBI_1.1.1
[33] ellipsis_0.3.2
[34] backports_1.2.1
[35] RcppParallel_5.1.4
[36] biomaRt_2.50.3
[37] MatrixGenerics_1.6.0
[38] vctrs_0.3.8
[39] abind_1.4-5
[40] cachem_1.0.5
[41] withr_2.4.2
[42] ggforce_0.3.3
[43] GenomicAlignments_1.30.0
[44] treeio_1.18.1
[45] prettyunits_1.1.1
[46] cluster_2.1.2
[47] DOSE_3.20.1
[48] ape_5.5
[49] lazyeval_0.2.2
[50] crayon_1.4.1
[51] labeling_0.4.2
[52] slam_0.1-48
[53] pkgconfig_2.0.3
[54] tweenr_1.0.2
[55] nlme_3.1-152
[56] rlang_1.0.2
[57] lifecycle_1.0.1
[58] downloader_0.4
[59] filelock_1.0.2
[60] extrafontdb_1.0
[61] modelr_0.1.8
[62] cellranger_1.1.0
[63] rprojroot_2.0.2
[64] polyclip_1.10-0
[65] matrixStats_0.61.0
[66] Matrix_1.3-4
[67] aplot_0.1.2
[68] carData_3.0-4
[69] boot_1.3-28
[70] reprex_2.0.0
[71] GlobalOptions_0.1.2
[72] whisker_0.4
[73] processx_3.5.2
[74] png_0.1-7
[75] rjson_0.2.20
[76] bitops_1.0-7
[77] getPass_0.2-2
[78] KernSmooth_2.23-20
[79] Biostrings_2.62.0
[80] blob_1.2.1
[81] shape_1.4.6
[82] qvalue_2.26.0
[83] rstatix_0.7.0
[84] gridGraphics_0.5-1
[85] ggsignif_0.6.1
[86] scales_1.1.1
[87] memoise_2.0.0
[88] magrittr_2.0.1
[89] gplots_3.1.1
[90] zlibbioc_1.40.0
[91] compiler_4.1.0
[92] scatterpie_0.1.7
[93] BiocIO_1.4.0
[94] clue_0.3-59
[95] plotrix_3.8-1
[96] Rsamtools_2.10.0
[97] cli_2.5.0
[98] XVector_0.34.0
[99] patchwork_1.1.1
[100] ps_1.6.0
[101] mgcv_1.8-36
[102] MASS_7.3-54
[103] tidyselect_1.1.1
[104] stringi_1.6.2
[105] highr_0.9
[106] yaml_2.2.1
[107] GOSemSim_2.20.0
[108] ggrepel_0.9.1
[109] sass_0.4.0
[110] fastmatch_1.1-0
[111] tools_4.1.0
[112] parallel_4.1.0
[113] rio_0.5.26
[114] circlize_0.4.12
[115] rstudioapi_0.13
[116] foreach_1.5.1
[117] foreign_0.8-81
[118] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
[119] git2r_0.28.0
[120] gridExtra_2.3
[121] farver_2.1.0
[122] ggraph_2.0.5
[123] proxyC_0.2.4
[124] digest_0.6.27
[125] BiocManager_1.30.16
[126] shiny_1.6.0
[127] Rcpp_1.0.6
[128] car_3.0-10
[129] broom_0.7.6
[130] BiocVersion_3.14.0
[131] later_1.2.0
[132] httr_1.4.2
[133] gdtools_0.2.4
[134] ComplexHeatmap_2.10.0
[135] colorspace_2.0-1
[136] rvest_1.0.0
[137] XML_3.99-0.6
[138] fs_1.5.0
[139] splines_4.1.0
[140] yulab.utils_0.0.4
[141] tidytree_0.3.9
[142] graphlayouts_0.7.1
[143] ggplotify_0.1.0
[144] systemfonts_1.0.4
[145] xtable_1.8-4
[146] jsonlite_1.7.2
[147] ggtree_3.2.1
[148] futile.options_1.0.1
[149] tidygraph_1.2.0
[150] NLP_0.2-1
[151] ggfun_0.0.5
[152] R6_2.5.0
[153] tm_0.7-8
[154] pillar_1.6.1
[155] htmltools_0.5.1.1
[156] mime_0.10
[157] glue_1.4.2
[158] fastmap_1.1.0
[159] BiocParallel_1.28.3
[160] codetools_0.2-18
[161] interactiveDisplayBase_1.32.0
[162] fgsea_1.20.0
[163] utf8_1.2.1
[164] lattice_0.20-44
[165] bslib_0.2.5.1
[166] curl_4.3.1
[167] gtools_3.8.2
[168] magick_2.7.2
[169] zip_2.2.0
[170] GO.db_3.14.0
[171] openxlsx_4.2.3
[172] Rttf2pt1_1.3.8
[173] rmarkdown_2.8
[174] munsell_0.5.0
[175] GetoptLong_1.0.5
[176] DO.db_2.9
[177] GenomeInfoDbData_1.2.7
[178] iterators_1.0.13
[179] haven_2.4.1
[180] gtable_0.3.0
[181] extrafont_0.17