runSeuratICA Computes ICA on the input sce object and stores the calculated independent components within the sce object

runSeuratICA(
  inSCE,
  useAssay = "seuratScaledData",
  useFeatureSubset = NULL,
  scale = TRUE,
  reducedDimName = "seuratICA",
  nics = 20,
  seed = NULL,
  verbose = FALSE
)

Arguments

inSCE: (sce) object on which to compute ICA
useAssay: Assay containing scaled counts to use in ICA.
useFeatureSubset: Subset of feature to use for dimension reduction. A character string indicating a rowData variable that stores the logical vector of HVG selection, or a vector that can subset the rows of inSCE. Default NULL.
scale: Logical scalar, whether to standardize the expression values using ScaleData. Default TRUE.
reducedDimName: Name of new reducedDims object containing Seurat ICA Default seuratICA.
nics: Number of independent components to compute. Default 20.
seed: Random seed for reproducibility of results. Default NULL will use global seed in use by the R environment.
verbose: Logical value indicating if informative messages should be displayed. Default is TRUE.

Value

Updated SingleCellExperiment object which now contains the computed independent components

Details

For features used for computation, it can be controlled by features or useFeatureSubset. When features is specified, the scaling and dimensionality reduction will only be processed with these features. When features is NULL but useFeatureSubset is specified, will use the features that the HVG list points to. If both parameters are NULL, the function will see if any Seurat's variable feature detection has been ever performed, and use them if found. Otherwise, all features are used.

Examples

data(scExample, package = "singleCellTK")
if (FALSE) {
sce <- runSeuratNormalizeData(sce, useAssay = "counts")
sce <- runSeuratFindHVG(sce, useAssay = "counts")
sce <- runSeuratScaleData(sce, useAssay = "counts")
sce <- runSeuratICA(sce, useAssay = "counts")
}