getUMAP: Uniform Manifold Approximation and Projection(UMAP) algorithm...
In singleCellTK: Comprehensive and Interactive Analysis of Single Cell RNA-Seq Data

Description Usage Arguments Value Examples

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

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
)

`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`.

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

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)