getTSNE {singleCellTK} | R Documentation |
Run t-SNE dimensionality reduction method on a SingleCellExperiment Object
getTSNE( inSCE, useAssay = "logcounts", useAltExp = NULL, reducedDimName = "TSNE", n_iterations = 1000, perplexity = NULL, run_pca = TRUE, ntop = NULL )
inSCE |
Input SingleCellExperiment object. |
useAssay |
Assay to use for tSNE computation. If |
useAltExp |
The subset to use for tSNE computation, usually for the
selected.variable features. Default |
reducedDimName |
a name to store the results of the dimension
reductions. Default |
n_iterations |
maximum iterations. Default |
perplexity |
perplexity parameter. Default |
run_pca |
run tSNE on PCA components? Default |
ntop |
Number of top features to use as a further variable feature
selection. Default |
A SingleCellExperiment object with tSNE computation
updated in reducedDim(inSCE, reducedDimName)
.
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)