runScanpyPCA Computes PCA on the input sce object and stores the calculated principal components within the sce object

runScanpyPCA(
  inSCE,
  useAssay = "scanpyScaledData",
  reducedDimName = "scanpyPCA",
  nPCs = 50,
  method = c("arpack", "randomized", "auto", "lobpcg"),
  use_highly_variable = TRUE,
  seed = 12345
)

Arguments

inSCE: (sce) object on which to compute PCA
useAssay: Assay containing scaled counts to use in PCA. Default "scanpyScaledData".
reducedDimName: Name of new reducedDims object containing Scanpy PCA. Default scanpyPCA.
nPCs: numeric value of how many components to compute. Default 50.
method: selected method to use for computation of pca. One of 'arpack', 'randomized', 'auto' or 'lobpcg'. Default "arpack".
use_highly_variable: boolean value of whether to use highly variable genes only. By default uses them if they have been determined beforehand.
seed: Specify numeric value to set as a seed. Default 12345.

Value

Updated SingleCellExperiment object which now contains the computed principal components

Examples

data(scExample, package = "singleCellTK")
if (FALSE) {
sce <- runScanpyNormalizeData(sce, useAssay = "counts")
sce <- runScanpyFindHVG(sce, useAssay = "scanpyNormData", method = "seurat")
sce <- runScanpyScaleData(sce, useAssay = "scanpyNormData")
sce <- runScanpyPCA(sce, useAssay = "scanpyScaledData")
}