% Generated by roxygen2: do not edit by hand % Please edit documentation in R/scater_PCA.R \name{scaterPCA} \alias{scaterPCA} \title{Perform scater PCA on a SingleCellExperiment Object} \usage{ scaterPCA( inSCE, useAssay = "logcounts", useFeatureSubset = "hvg2000", scale = TRUE, reducedDimName = "PCA", nComponents = 50, ntop = 2000, useAltExp = NULL, seed = 12345, BPPARAM = BiocParallel::SerialParam() ) } \arguments{ \item{inSCE}{Input \linkS4class{SingleCellExperiment} object.} \item{useAssay}{Assay to use for PCA computation. If \code{useAltExp} is specified, \code{useAssay} has to exist in \code{assays(altExp(inSCE, useAltExp))}. Default \code{"logcounts"}} \item{useFeatureSubset}{Subset of feature to use for dimension reduction. A character string indicating a \code{rowData} variable that stores the logical vector of HVG selection, or a vector that can subset the rows of \code{inSCE}. Default \code{"hvg2000"}.} \item{scale}{Logical scalar, whether to standardize the expression values. Default \code{TRUE}.} \item{reducedDimName}{Name to use for the reduced output assay. Default \code{"PCA"}.} \item{nComponents}{Number of principal components to obtain from the PCA computation. Default \code{50}.} \item{ntop}{Automatically detect this number of variable features to use for dimension reduction. Ignored when using \code{useReducedDim} or using \code{useFeatureSubset}. Default \code{2000}.} \item{useAltExp}{The subset to use for PCA computation, usually for the selected.variable features. Default \code{NULL}.} \item{seed}{Integer, random seed for reproducibility of PCA results. Default \code{NULL}.} \item{BPPARAM}{A \linkS4class{BiocParallelParam} object specifying whether the PCA should be parallelized.} } \value{ A \linkS4class{SingleCellExperiment} object with PCA computation updated in \code{reducedDim(inSCE, reducedDimName)}. } \description{ A wrapper to \link[scater]{runPCA} function to compute principal component analysis (PCA) from a given \linkS4class{SingleCellExperiment} object. } \examples{ data(scExample, package = "singleCellTK") sce <- subsetSCECols(sce, colData = "type != 'EmptyDroplet'") sce <- scaterlogNormCounts(sce, "logcounts") # Example of ranking variable genes, selecting the top variable features, # and running PCA. Make sure to increase the number of highly variable # features (hvgNumber) and the number of principal components (nComponents) # for real datasets sce <- runModelGeneVar(sce, useAssay = "logcounts") sce <- setTopHVG(sce, method = "modelGeneVar", hvgNumber = 100, featureSubsetName = "hvf") sce <- scaterPCA(sce, useAssay = "logcounts", scale = TRUE, useFeatureSubset = "hvf", nComponents = 5) # Alternatively, let the scater PCA function select the top variable genes sce <- scaterPCA(sce, useAssay = "logcounts", scale = TRUE, useFeatureSubset = NULL, ntop = 100, nComponents = 5) }