PCA loading plot.

LoadingPlot(
  pca,
  deobj = NULL,
  type = c("bar", "heat"),
  data.type = c("RNA", "ChIP", "ATAC"),
  peak.anno.key = c("Promoter", "5' UTR", "3' UTR", "Exon", "Intron", "Downstream",
    "Distal Intergenic", "All"),
  pc = 1:5,
  gene.num = 10,
  ncol = 2
)

Arguments

pca	PCA results of `PCA`.
deobj	Object created by DESeq2 or edgeR.
type	loading plot type, chosen from bar, heat. Default: bar.
data.type	Input data type, choose from RNA, ChIP, ATAC. Default: RNA.
peak.anno.key	Peak location, chosen from "Promoter", "5' UTR", "3' UTR", "Exon", "Intron", "Downstream", "Distal Intergenic","All". Default: "Promoter".
pc	Specify PC to export genes. Default: 1:5.
gene.num	Gene number to export for every PC. Default: 10.
ncol	Column of final plots. Default: 2.

Value

Loading plot.

Examples

library(DESeq2)
library(DEbPeak)
count.file <- system.file("extdata", "snon_count.txt", package = "DEbPeak")
meta.file <- system.file("extdata", "snon_meta.txt", package = "DEbPeak")
count.matrix <- read.table(file = count.file, header = TRUE, sep = "\t")
meta.info <- read.table(file = meta.file, header = TRUE)
dds <- DESeq2::DESeqDataSetFromMatrix(countData = count.matrix, colData = meta.info, design = ~condition)
#> Warning: some variables in design formula are characters, converting to factors
keep.genes <- rowSums(DESeq2::counts(dds, normalized = FALSE)) >= 10
dds <- dds[keep.genes, ]
pca_res <- PCA(deobj = dds, transform.method = "rlog")
#> Differential expression analysis with DESeq2!
#> Use all genes for PCA!
LoadingPlot(pca = pca_res, type = "bar")
#> Selecting by Loadding
#> Selecting by Loadding
LoadingPlot(pca = pca_res, deobj = dds, type = "heat")
#> Differential expression analysis with DESeq2!
#> Selecting by Loadding
#> Selecting by Loadding