runSeuratSCTransform Runs the SCTransform function to transform/normalize the input data

runSeuratSCTransform(
  inSCE,
  normAssayName = "SCTCounts",
  useAssay = "counts",
  verbose = TRUE
)

Arguments

inSCE: Input SingleCellExperiment object
normAssayName: Name for the output data assay. Default "SCTCounts".
useAssay: Name for the input data assay. Default "counts".
verbose: Logical value indicating if informative messages should be displayed. Default is TRUE.

Value

Updated SingleCellExperiment object containing the transformed data

Examples

data("mouseBrainSubsetSCE", package = "singleCellTK")
mouseBrainSubsetSCE <- runSeuratSCTransform(mouseBrainSubsetSCE)
#> Calculating cell attributes from input UMI matrix: log_umi
#> Variance stabilizing transformation of count matrix of size 2282 by 30
#> Model formula is y ~ log_umi
#> Get Negative Binomial regression parameters per gene
#> Using 2000 genes, 30 cells
#> 
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#> Second step: Get residuals using fitted parameters for 2282 genes
#> 
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#> Calculating gene attributes
#> Wall clock passed: Time difference of 1.383423 secs
#> Determine variable features
#> Centering data matrix
#> Set default assay to SCTransform