celdaUmap {celda} | R Documentation

## Embeds cells in two dimensions using umap.

### Description

Embeds cells in two dimensions using umap.

### Usage

    celdaUmap(counts, celdaMod, maxCells = 25000, minClusterSize = 100,
      initialDims = 20, 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.
`initialDims` | Integer. PCA will be used to reduce the dimentionality of the dataset. The top 'initialDims' principal components will be used for umap. Default 20.
`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` | An object of class "umapConfig" specifying parameters to the UMAP algorithm.

### Value

Numeric Matrix of dimension 'ncol(counts)' x 2, colums representing the "X" and "Y" coordinates in the data's t-SNE represetation.

### Examples

    data(celdaCGSim, celdaCGMod)
    tsneRes <- celdaUmap(celdaCGSim$counts, celdaCGMod)

[Package celda version 1.0.4 Index]