getPCA: Perform PCA on a SingleCellExperiment Object A wrapper to...

In singleCellTK: Comprehensive and Interactive Analysis of Single Cell RNA-Seq Data

Description

Perform PCA on a SingleCellExperiment Object A wrapper to runPCA function to compute principal component analysis (PCA) from a given SingleCellExperiment object.

Usage

getPCA(
  inSCE,
  useAssay = "logcounts",
  useAltExp = NULL,
  reducedDimName = "PCA",
  ndim = 50,
  scale = TRUE,
  ntop = NULL
)

Arguments

inSCE	Input SingleCellExperiment object.
useAssay	Assay to use for PCA computation. If useAltExp is specified, useAssay has to exist in assays(altExp(inSCE, useAltExp)). Default "logcounts"
useAltExp	The subset to use for PCA computation, usually for the selected.variable features. Default NULL.
reducedDimName	Name to use for the reduced output assay. Default "PCA".
ndim	Number of principal components to obtain from the PCA computation. Default 50.
scale	Logical scalar, whether to standardize the expression values. Default TRUE.
ntop	Number of top features to use as a further variable feature selection. Default NULL.

Value

A SingleCellExperiment object with PCA computation updated in reducedDim(inSCE, reducedDimName).

Examples

data(scExample, package = "singleCellTK")
sce <- subsetSCECols(sce, colData = "type != 'EmptyDroplet'")
logcounts(sce) <- log(counts(sce) + 1)
sce <- getPCA(sce, ntop = 500)

singleCellTK documentation built on Nov. 8, 2020, 5:21 p.m.