Create Integrated Summary Plot.

Create Integrated Summary Plot.

PlotDEbPeak(
  de.peak,
  peak.type = c("ChIP", "ATAC", "Peak"),
  peak.mode = c("consensus", "diff"),
  gene.col = c("geneId", "ENSEMBL", "SYMBOL"),
  ...
)

Arguments

de.peak

Dataframe contains integrated results.
peak.type

The source of peaks, chosen from ATAC, ChIP and Peak (ChIP and ATAC). Default: ChIP.
peak.mode

The source of peak results, choose from consensus (peak annotation) and diff (differential expression analysis). Default: consensus.
gene.col

Column of inte.res contains genes. Same as merge.key in DEbPeak.
...

Parameters for ggvenn.

Value

A ggplot2 object.

Examples

library(DEbPeak)
library(DESeq2)
# ChIP-Seq data
peak.file <- system.file("extdata", "debchip_peaks.bed", package = "DEbPeak")
peak.df <- GetConsensusPeak(peak.file = peak.file)
peak.profile <- PeakProfile(peak.df, species = "Mouse", by = "gene", region.type = "body", nbin = 800)

#> >> preparing promoter regions...	 2023-07-02 17时23分15秒 
#> >> preparing tag matrix...		 2023-07-02 17时23分15秒 
#> >> preparing start_site regions by ... 2023-07-02 17时23分15秒
#> >> preparing tag matrix...  2023-07-02 17时23分15秒 
#> >> generating figure...		 2023-07-02 17时23分22秒 
#> >> done...			 2023-07-02 17时23分22秒 
#> >> binning method is used...2023-07-02 17时23分22秒
#> >> preparing start_site regions by gene... 2023-07-02 17时23分22秒
#> >> preparing tag matrix by binning...  2023-07-02 17时23分22秒 
#> >> Running bootstrapping for tag matrix...		 2023-07-02 17时23分30秒 
#> >> binning method is used...2023-07-02 17时23分31秒
#> >> preparing body regions by gene... 2023-07-02 17时23分31秒
#> >> preparing tag matrix by binning...  2023-07-02 17时23分31秒 
#> >> preparing matrix with extension from (TSS-20%)~(TTS+20%)... 2023-07-02 17时23分31秒
#> >> 1 peaks(0.1536098%), having lengths smaller than 800bp, are filtered... 2023-07-02 17时23分34秒
#> >> Running bootstrapping for tag matrix...		 2023-07-02 17时24分14秒 
peak.anno <- AnnoPeak(
  peak.df = peak.df, species = "Mouse",
  seq.style = "UCSC", up.dist = 20000, down.dist = 20000
)

#> >> preparing features information...		 2023-07-02 17时24分15秒 
#> >> identifying nearest features...		 2023-07-02 17时24分15秒 
#> >> calculating distance from peak to TSS...	 2023-07-02 17时24分15秒 
#> >> assigning genomic annotation...		 2023-07-02 17时24分15秒 
#> >> adding gene annotation...			 2023-07-02 17时24分18秒 
#> 'select()' returned 1:many mapping between keys and columns
#> >> assigning chromosome lengths			 2023-07-02 17时24分18秒 
#> >> done...					 2023-07-02 17时24分18秒 
#> Warning: Removed 6 rows containing non-finite values (`stat_count()`).
# RNA-Seq data
count.file <- system.file("extdata", "debchip_count.txt", package = "DEbPeak")
meta.file <- system.file("extdata", "debchip_meta.txt", package = "DEbPeak")
count.matrix <- read.table(file = count.file, header = TRUE, sep = "\t")
meta.info <- read.table(file = meta.file, header = TRUE)
# create DESeqDataSet object
dds <- DESeq2::DESeqDataSetFromMatrix(
  countData = count.matrix, colData = meta.info,
  design = ~condition
)

#> Warning: some variables in design formula are characters, converting to factors
# set control level
dds$condition <- relevel(dds$condition, ref = "NF")
# conduct differential expressed genes analysis
dds <- DESeq(dds)

#> estimating size factors
#> estimating dispersions
#> gene-wise dispersion estimates
#> mean-dispersion relationship
#> final dispersion estimates
#> fitting model and testing
# extract results
dds.results <- results(dds, contrast = c("condition", "RX", "NF"))
dds.results.ordered <- dds.results[order(dds.results$log2FoldChange, decreasing = TRUE), ]
# Integrated with RNA-Seq
debchip.res <- DEbPeak(
  de.res = dds.results.ordered, peak.res = peak.anno[["df"]],
  peak.anno.key = "Promoter", merge.key = "SYMBOL"
)

#> Differential expression analysis with DESeq2!
# DE and ChIP venn plot
debchip.plot <- PlotDEbPeak(
  de.peak = debchip.res, peak.type = "ChIP", gene.col = "SYMBOL",
  show_percentage = FALSE
)

#> Warning: 条件的长度大于一，因此只能用其第一元素
#> Warning: 条件的长度大于一，因此只能用其第一元素