Run t-SNE dimensionality reduction method on a SingleCellExperiment Object

getTSNE(
  inSCE,
  useAssay = "logcounts",
  useAltExp = NULL,
  useReducedDim = NULL,
  reducedDimName = "TSNE",
  nIterations = 1000,
  perplexity = 30,
  run_pca = TRUE,
  ntop = NULL,
  seed = NULL
)

Arguments

inSCE: Input SingleCellExperiment object.
useAssay: Assay to use for tSNE computation. If useAltExp is specified, useAssay has to exist in assays(altExp(inSCE, useAltExp)). Default "logcounts"
useAltExp: The subset to use for tSNE computation, usually for the selected.variable features. Default NULL.
useReducedDim: The low dimension representation to use for UMAP computation. Default NULL.
reducedDimName: a name to store the results of the dimension reductions. Default "TSNE".
nIterations: maximum iterations. Default 1000.
perplexity: perplexity parameter. Default 30.
run_pca: run tSNE on PCA components? Default TRUE.
ntop: Number of top features to use as a further variable feature selection. Default NULL.
seed: Random seed for reproducibility of tSNE results. Default NULL will use global seed in use by the R environment.

Value

A SingleCellExperiment object with tSNE computation updated in reducedDim(inSCE, reducedDimName).

Examples

data("mouseBrainSubsetSCE")
#add a CPM assay
assay(mouseBrainSubsetSCE, "cpm") <- apply(
  assay(mouseBrainSubsetSCE, "counts"), 2, function(x) {
    x / (sum(x) / 1000000)
  })
mouseBrainSubsetSCE <- getTSNE(mouseBrainSubsetSCE, useAssay = "cpm",
                               reducedDimName = "TSNE_cpm",
                               perplexity = NULL)