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)
#> Running SCTransform on assay: RNA
#> Running SCTransform on layer: counts
#> vst.flavor='v2' set. Using model with fixed slope and excluding poisson genes.
#> `vst.flavor` is set to 'v2' but could not find glmGamPoi installed.
#> Please install the glmGamPoi package for much faster estimation.
#> --------------------------------------------
#> install.packages('BiocManager')
#> BiocManager::install('glmGamPoi')
#> --------------------------------------------
#> Falling back to native (slower) implementation.
#> 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
#> Found 95 outliers - those will be ignored in fitting/regularization step
#> Second step: Get residuals using fitted parameters for 2282 genes
#> Computing corrected count matrix for 2282 genes
#> Calculating gene attributes
#> Wall clock passed: Time difference of 22.42934 secs
#> Determine variable features
#> Centering data matrix
#> Set default assay to SCTransform