Run GSVA analysis on a SingleCellExperiment object

runGSVA(
  inSCE,
  useAssay = "logcounts",
  resultNamePrefix = NULL,
  geneSetCollectionName,
  ...
)

Arguments

inSCE: Input SingleCellExperiment object.
useAssay: Indicate which assay to use. The default is "logcounts"
resultNamePrefix: Character. Prefix to the name the GSVA results which will be stored in the reducedDim slot of inSCE. The names of the output matrix will be resultNamePrefix_Scores. If this parameter is set to NULL, then "GSVA_geneSetCollectionName_" will be used. Default NULL.
geneSetCollectionName: Character. The name of the gene set collection to use.
...: Parameters to pass to gsva()

Value

A SingleCellExperiment object with pathway activity scores from GSVA stored in reducedDim as GSVA_geneSetCollectionName_Scores.

Examples

data(scExample, package = "singleCellTK")
sce <- subsetSCECols(sce, colData = "type != 'EmptyDroplet'")
sce <- scaterlogNormCounts(sce, assayName = "logcounts")
gs1 <- rownames(sce)[seq(10)]
gs2 <- rownames(sce)[seq(11,20)]
gs <- list("geneset1" = gs1, "geneset2" = gs2)

sce <- importGeneSetsFromList(inSCE = sce,geneSetList = gs,
                                           by = "rownames")
sce <- runGSVA(inSCE = sce, 
               geneSetCollectionName = "GeneSetCollection", 
               useAssay = "logcounts")
#> Wed Apr 23 11:41:20 2025 ... Running GSVA
#> ℹ GSVA version 2.0.5
#> ℹ Calculating GSVA ranks
#> ℹ kcdf='auto' (default)
#> ℹ GSVA dense (classical) algorithm
#> ℹ Row-wise ECDF estimation with Gaussian kernels
#> ℹ Calculating GSVA column ranks
#> ! No annotation metadata available in the input expression data object
#> ! Attempting to directly match identifiers in expression data to gene sets
#> ℹ Calculating GSVA scores
#> ✔ Calculations finished