PCA related functions used in quality control.

QCPCA(
  deobj,
  var.genes = NULL,
  remove.sample = NULL,
  transform.method = c("rlog", "vst", "ntd"),
  batch = NULL,
  raw.deobj = NULL,
  min.count = 10,
  colby = NULL,
  legend.pos = c("bottom", "top", "left", "right", "none"),
  outlier.detection = TRUE,
  rpca.method = c("PcaGrid", "PcaHubert"),
  k = 2,
  ...
)

Arguments

deobj	Object created by DESeq2 or edgeR.
var.genes	Select genes with larger variance for PCA analysis. Default: all genes.
remove.sample	Sample(s) to remove. Default: NULL.
transform.method	Data transformation methods, chosen from rlog, vst and ntd. Default: rlog.
batch	Batch column to conduct batch correction. Default value is NULL, do not conduct batch correction.
raw.deobj	Object created by DESeq2 or edgeR before filtering with counts. Default: NULL.
min.count	A feature is considered to be detected if the corresponding number of read counts is > `min.count`. By default, `min.count` = 10.
colby	Group information to color samples. Default: NULL.
legend.pos	Position of legend ('top', 'bottom', 'left', 'right', 'none'). Default: bottom.
outlier.detection	Logical value. If TRUE, conduct outlier detection with robust PCA. Default: TRUE.
rpca.method	Robust PCA method, chosen from `PcaGrid`, `PcaHubert`. Default: PcaGrid.
k	Number of principal components to compute, for `PcaGrid`, `PcaHubert`. Default: 2.
...	Parameter for `PcaGrid`, `PcaHubert`.

Value

List contains all step plots, final PCA results, final deobj.

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, ]
pcs_res <- QCPCA(
  deobj = dds, var.genes = NULL, remove.sample = NULL, transform.method = "rlog",
  outlier.detection = TRUE, rpca.method = "PcaGrid"
)
#> Conduct PCA analysis!
#> Differential expression analysis with DESeq2!
#> Use all genes for PCA!
#> Conduct outlier detection!
#> Differential expression analysis with DESeq2!
#> Use all genes for PCA!
#> Detecting 2 outlier(s): KO3,WT1
#> Differential expression analysis with DESeq2!
#> Use all genes for PCA!