celdaUmap,celda_G-method {celda} | R Documentation |
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.
## S4 method for signature 'celda_G' celdaUmap(counts, celdaMod, maxCells = 25000, minClusterSize = 100, modules = NULL, seed = 12345, 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_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'. |
A two column matrix of umap coordinates
'celda_G()' for clustering features and cells and 'celdaHeatmap()' for displaying expression
data(celdaGSim, celdaGMod) umapRes <- celdaUmap(celdaGSim$counts, celdaGMod)