getTSNE: Run t-SNE dimensionality reduction method on a...

In singleCellTK: Comprehensive and Interactive Analysis of Single Cell RNA-Seq Data

Description

Run t-SNE dimensionality reduction method on a SingleCellExperiment Object

Usage

getTSNE(
  inSCE,
  useAssay = "logcounts",
  useAltExp = NULL,
  reducedDimName = "TSNE",
  n_iterations = 1000,
  perplexity = NULL,
  run_pca = TRUE,
  ntop = 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.
reducedDimName	a name to store the results of the dimension reductions. Default "TSNE".
n_iterations	maximum iterations. Default 1000.
perplexity	perplexity parameter. Default NULL.
run_pca	run tSNE on PCA components? Default TRUE.
ntop	Number of top features to use as a further variable feature selection. Default NULL.

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")
reducedDims(mouseBrainSubsetSCE)

singleCellTK documentation built on Nov. 8, 2020, 5:21 p.m.