Calculate PCA.

Calculate PCA.

PCA(
  deobj,
  var.genes = NULL,
  remove.sample = NULL,
  transform.method = c("rlog", "vst", "ntd")
)

Arguments

deobj

Object created by DESeq2 or edgeR.
var.genes

Select genes with larger variance for PCA analysis. Default value is NULL, using all genes.
remove.sample

Sample(s) to remove. Default: NULL.
transform.method

Data transformation methods, chosen from rlog, vst and ntd. Default: rlog.

Value

List suitable for PCAtools.

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!