celdaUmap,celda_C-method {celda} | R Documentation |
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.
## S4 method for signature 'celda_C' celdaUmap(counts, celdaMod, maxCells = 25000, minClusterSize = 100, modules = NULL, umapConfig = umap::umap.defaults)
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. |
umapConfig |
An object of class "umap.config" specifying parameters to the UMAP algorithm. |
... |
Additional parameters. |
A two column matrix of umap coordinates
'celda_C()' for clustering cells and 'celdaHeatmap()' for displaying expression.
data(celdaCSim, celdaCMod) umapRes <- celdaUmap(celdaCSim$counts, celdaCMod)