getUMAP {singleCellTK}R Documentation

Uniform Manifold Approximation and Projection(UMAP) algorithm for dimension reduction.

Description

Uniform Manifold Approximation and Projection(UMAP) algorithm for dimension reduction.

Usage

getUMAP(inSCE, useAssay = "logcounts", reducedDimName = "UMAP",
  n_neighbors = 5, n_iterations = 200, alpha = 1)

Arguments

inSCE

Input SCtkExperiment object. Required

useAssay

Indicate which assay to use. The default is "logcounts".

reducedDimName

a name to store the results of the dimension reduction coordinates obtained from this method. This is stored in the SingleCellExperiment object in the reducedDims slot. Required.

n_neighbors

specify the number of nearest neighbors. Default is 5.

n_iterations

number of iterations performed during layout optimization. Default is 200.

alpha

initial value of "learning rate" of layout optimization. Default is 1.

Value

a SCtkExperiment object with the reduced dimensions updated under reducedDimName specified.

Examples

umap_res <- getUMAP(inSCE = mouseBrainSubsetSCE, useAssay = "counts",
                    reducedDimName = "UMAP", n_neighbors = 3, n_iterations = 200,
                    alpha = 1)
reducedDims(umap_res)


[Package singleCellTK version 1.4.2 Index]