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 = 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_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. Default 25000.

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.

umapConfig

An object of class "umap.config" specifying parameters to the UMAP algorithm.

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 1.0.4 Index]