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 = "counts",
      useAltExp = NULL,
      sample = NULL,
      reducedDimName = "UMAP",
      logNorm = TRUE,
      nNeighbors = 30,
      nIterations = 200,
      alpha = 1,
      minDist = 0.01,
      spread = 1,
      pca = TRUE,
      initialDims = 50
    )

### Arguments

`inSCE` | Input SingleCellExperiment object.
`useAssay` | Assay to use for UMAP computation. If `useAltExp` is specified, `useAssay` has to exist in `assays(altExp(inSCE, useAltExp))`. Default `"counts"`.
`useAltExp` | The subset to use for UMAP computation, usually for the selected.variable features. Default `NULL`.
`sample` | Character vector. Indicates which sample each cell belongs to. If given a single character, will take the annotation from `colData`. Default `NULL`.
`reducedDimName` | A name to store the results of the dimension reduction coordinates obtained from this method. Default `"UMAP"`.
`logNorm` | Whether the counts will need to be log-normalized prior to generating the UMAP via `logNormCounts`. Default `TRUE`.
`nNeighbors` | The size of local neighborhood used for manifold approximation. Larger values result in more global views of the manifold, while smaller values result in more local data being preserved. Default `30`. See '?uwot::umap' for more information.
`nIterations` | The number of iterations performed during layout optimization. Default is `200`.
`alpha` | The initial value of "learning rate" of layout optimization. Default is `1`.
`minDist` | The effective minimum distance between embedded points. Smaller values will result in a more clustered/clumped embedding where nearby points on the manifold are drawn closer together, while larger values will result on a more even dispersal of points. Default `0.01`. See '?uwot::umap' for more information.
`spread` | The effective scale of embedded points. In combination with minDist, this determines how clustered/clumped the embedded points are. Default `1`. See '?uwot::umap' for more information.
`pca` | Logical. Whether to perform dimension reduction with PCA before UMAP. Default `TRUE`
`initialDims` | Number of dimensions from PCA to use as input in UMAP. Default `50`.

### Value

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

### Examples

    data(scExample, package = "singleCellTK")
    sce <- subsetSCECols(sce, colData = "type != 'EmptyDroplet'")
    umap_res <- getUMAP(inSCE = sce, useAssay = "counts",
                        reducedDimName = "UMAP", logNorm = TRUE,
                        nNeighbors = 30, alpha = 1,
                        nIterations = 200, spread = 1, pca = TRUE,
                        initialDims = 50)
    reducedDims(umap_res)

[Package singleCellTK version 2.0.0 Index]