celdaUmap {celda} | R Documentation |
Embeds cells in two dimensions using umap.
celdaUmap(counts, celdaMod, maxCells = 25000, minClusterSize = 100, initialDims = 20, 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_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. |
umapConfig |
An object of class "umapConfig" specifying parameters to the UMAP algorithm. |
... |
Additional parameters. |
Numeric Matrix of dimension 'ncol(counts)' x 2, colums representing the "X" and "Y" coordinates in the data's t-SNE represetation.
data(celdaCGSim, celdaCGMod) tsneRes <- celdaUmap(celdaCGSim$counts, celdaCGMod)