celdaUmap: Uniform Manifold Approximation and Projection (UMAP)...
In celda: CEllular Latent Dirichlet Allocation

Description Usage Arguments Value Examples

Embeds cells in two dimensions using umap based on a celda model. For celda_C sce objects, PCA on the normalized counts is used to reduce the number of features before applying UMAP. For celda_CG sce object, UMAP is run on module probabilities to reduce the number of features instead of using PCA. Module probabilities are square-root transformed before applying UMAP.

celdaUmap(sce, ...)

## S4 method for signature 'SingleCellExperiment'
celdaUmap(
  sce,
  useAssay = "counts",
  altExpName = "featureSubset",
  maxCells = NULL,
  minClusterSize = 100,
  modules = NULL,
  seed = 12345,
  nNeighbors = 30,
  minDist = 0.75,
  spread = 1,
  pca = TRUE,
  initialDims = 50,
  normalize = "proportion",
  scaleFactor = NULL,
  transformationFun = sqrt,
  cores = 1,
  ...
)

`sce`	A SingleCellExperiment object returned by celda_C, celda_G, or celda_CG.
`...`	Additional parameters to pass to umap.
`useAssay`	A string specifying which assay slot to use. Default "counts".
`altExpName`	The name for the altExp slot to use. Default "featureSubset".
`maxCells`	Integer. Maximum number of cells to plot. Cells will be randomly subsampled if `ncol(sce) > maxCells`. Larger numbers of cells requires more memory. If NULL, no subsampling will be performed. Default NULL.
`minClusterSize`	Integer. Do not subsample cell clusters below this threshold. Default 100.
`modules`	Integer vector. Determines which features modules to use for UMAP. If NULL, all modules will be used. Default NULL.
`seed`	Integer. Passed to with_seed. For reproducibility, a default value of 12345 is used. If NULL, no calls to with_seed are made.
`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 umap for more information.
`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 0.75. See umap for more information.
`spread`	The effective scale of embedded points. In combination with `min_dist`, this determines how clustered/clumped the embedded points are. Default 1. See umap for more information.
`pca`	Logical. Whether to perform dimensionality reduction with PCA before UMAP. Only works for celda_C `sce` objects.
`initialDims`	Integer. Number of dimensions from PCA to use as input in UMAP. Default 50. Only works for celda_C `sce` objects.
`normalize`	Character. Passed to normalizeCounts in normalization step. Divides counts by the library sizes for each cell. One of 'proportion', 'cpm', 'median', or 'mean'. 'proportion' uses the total counts for each cell as the library size. 'cpm' divides the library size of each cell by one million to produce counts per million. 'median' divides the library size of each cell by the median library size across all cells. 'mean' divides the library size of each cell by the mean library size across all cells.
`scaleFactor`	Numeric. Sets the scale factor for cell-level normalization. This scale factor is multiplied to each cell after the library size of each cell had been adjusted in `normalize`. Default `NULL` which means no scale factor is applied.
`transformationFun`	Function. Applys a transformation such as 'sqrt', 'log', 'log2', 'log10', or 'log1p'. If `NULL`, no transformation will be applied. Occurs after applying normalization and scale factor. Default `NULL`.
`cores`	Number of threads to use. Default 1.