celdaTsne,celda_C-method {celda} | R Documentation

## tSNE for celda_C

### Description

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

### Usage

    ## S4 method for signature 'celda_C'
    celdaTsne(
      counts,
      celdaMod,
      maxCells = NULL,
      minClusterSize = 100,
      initialDims = 20,
      perplexity = 20,
      maxIter = 2500,
      seed = 12345
    )

### 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.
`initialDims` | Integer. PCA will be used to reduce the dimentionality of the dataset. The top 'initialDims' principal components will be used for tSNE. Default 20.
`perplexity` | Numeric. Perplexity parameter for tSNE. Default 20.
`maxIter` | Integer. Maximum number of iterations in tSNE generation. Default 2500.
`seed` | Integer. Passed to with_seed. For reproducibility, a default value of 12345 is used. If NULL, no calls to with_seed are made.

### Value

A two column matrix of t-SNE coordinates

### See Also

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

### Examples

    data(celdaCSim, celdaCMod)
    tsneRes <- celdaTsne(celdaCSim$counts, celdaCMod)

[Package celda version 1.3.4 Index]