celdaUmap,celda_G-method {celda} | R Documentation

## umap for celda_G

### Description

Embeds cells in two dimensions using umap based on a 'celda_G' model. umap is run on module probabilities to reduce the number of features instead of using PCA. Module probabilities square-root trasformed before applying tSNE.

### Usage

    ## S4 method for signature 'celda_G'
    celdaUmap(counts, celdaMod, maxCells = 25000,
      minClusterSize = 100, modules = NULL, seed = 12345,
      umapConfig = umap::umap.defaults)

### 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_CG'.
`maxCells` | Integer. Maximum number of cells to plot. Cells will be randomly subsampled if ncol(counts) > maxCells. Larger numbers of cells requires more memory. Default 25000.
`minClusterSize` | Integer. Do not subsample cell clusters below this threshold. Default 100.
`modules` | Integer vector. Determines which features modules to use for tSNE. 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.
`umapConfig` | Object of class 'umap.config'. Configures parameters for umap. Default 'umap::umap.defaults'.

### Value

A two column matrix of umap coordinates

### See Also

'celda_G()' for clustering features and cells and 'celdaHeatmap()' for displaying expression

### Examples

    data(celdaGSim, celdaGMod)
    umapRes <- celdaUmap(celdaGSim$counts, celdaGMod)

[Package celda version 1.0.4 Index]