R/seuratFunctions.R
runSeuratSCTransform.Rd
runSeuratSCTransform Runs the SCTransform function to transform/normalize the input data
runSeuratSCTransform(
inSCE,
normAssayName = "SCTCounts",
useAssay = "counts",
verbose = TRUE
)
Input SingleCellExperiment object
Name for the output data assay. Default
"SCTCounts"
.
Name for the input data assay. Default "counts"
.
Logical value indicating if informative messages should
be displayed. Default is TRUE
.
Updated SingleCellExperiment object containing the transformed data
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 1.671999 secs
#> Determine variable features
#> Centering data matrix
#> Set default assay to SCTransform