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

getUMAP(
  inSCE,
  useAssay = "counts",
  useAltExp = NULL,
  useReducedDim = NULL,
  sample = NULL,
  reducedDimName = "UMAP",
  logNorm = TRUE,
  nNeighbors = 30,
  nIterations = 200,
  alpha = 1,
  minDist = 0.01,
  spread = 1,
  pca = TRUE,
  initialDims = 25,
  nTop = 2000,
  seed = NULL
)

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`.
useReducedDim	The low dimension representation to use for UMAP computation. 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`. Will not normalize when using `useReducedDim`. 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 `?scater::calculateUMAP` 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 `?scater::calculateUMAP` 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 `?scater::calculateUMAP` for more information.
pca	Logical. Whether to perform dimension reduction with PCA before UMAP. Will not perform PCA if using `useReducedDim`. Default `TRUE`
initialDims	Number of dimensions from PCA to use as input in UMAP. Default `25`.
nTop	Number of features with the highest variances to use for dimensionality reduction. Default `2000`.
seed	Random seed for reproducibility of UMAP results. Default `NULL` will use global seed in use by the R environment.

Value

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

Examples

data(scExample, package = "singleCellTK")
sce <- subsetSCECols(sce, colData = "type != 'EmptyDroplet'")
sce <- getUMAP(inSCE = sce, useAssay = "counts", reducedDimName = "UMAP")