celdaUmap,celda_C-method {celda} | R Documentation

## umap for celda_C

### Description

Embeds cells in two dimensions using umap based on a 'celda_C' model. PCA on the normalized counts is used to reduce the number of features before applying umap.

### Usage

    ## S4 method for signature 'celda_C'
    celdaUmap(
      counts,
      celdaMod,
      maxCells = NULL,
      minClusterSize = 100,
      seed = 12345,
      nNeighbors = 30,
      minDist = 0.75,
      spread = 1,
      pca = TRUE,
      initialDims = 50,
      cores = 1,
      ...
    )

### Arguments

`counts` | Integer matrix. Rows represent features and columns represent cells. This matrix should be the same as the one used to generate 'celdaMod'.
`celdaMod` | Celda object of class 'celda_C'.
`maxCells` | Integer. Maximum number of cells to plot. Cells will be randomly subsampled if ncol(counts) > 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.
`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 '?uwot::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 0.2. See '?uwot::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 '?uwot::umap' for more information.
`pca` | Logical. Whether to perform dimensionality reduction with PCA before UMAP.
`initialDims` | Integer. Number of dimensions from PCA to use as input in UMAP. Default 50.
`cores` | Number of threads to use. Default 1.
`...` | Other parameters to pass to 'uwot::umap'.

### Value

A two column matrix of UMAP coordinates

### See Also

'celda_C()' for clustering cells and 'celdaHeatmap()' for displaying expression.

### Examples

    data(celdaCSim, celdaCMod)
    umapRes <- celdaUmap(celdaCSim$counts, celdaCMod)

[Package celda version 1.3.4 Index]