runScanpyTSNE Computes tSNE from the given sce object and stores the tSNE computations back into the sce object

runScanpyTSNE(
  inSCE,
  useAssay = NULL,
  useReducedDim = "scanpyPCA",
  reducedDimName = "scanpyTSNE",
  dims = 40,
  perplexity = 30,
  externalReduction = NULL,
  seed = 12345
)

Arguments

inSCE: (sce) object on which to compute the tSNE
useAssay: Specify name of assay to use. Default is NULL, so useReducedDim param will be used instead.
useReducedDim: selected reduction method to use for computing tSNE. Default "scanpyPCA".
reducedDimName: Name of new reducedDims object containing Scanpy tSNE Default scanpyTSNE.
dims: Number of reduction components to use for tSNE computation. Default 40.
perplexity: Adjust the perplexity tuneable parameter for the underlying tSNE call. Default 30.
externalReduction: Pass DimReduc object if PCA computed through other libraries. Default NULL.
seed: Specify numeric value to set as a seed. Default 12345.

Value

Updated sce object with tSNE computations stored

Examples

data(scExample, package = "singleCellTK")
if (FALSE) { # \dontrun{
sce <- runScanpyNormalizeData(sce, useAssay = "counts")
sce <- runScanpyFindHVG(sce, useAssay = "scanpyNormData", method = "seurat")
sce <- runScanpyScaleData(sce, useAssay = "scanpyNormData")
sce <- runScanpyPCA(sce, useAssay = "scanpyScaledData")
sce <- runScanpyFindClusters(sce, useReducedDim = "scanpyPCA")
sce <- runScanpyTSNE(sce, useReducedDim = "scanpyPCA")
} # }