celdaTsne.celda_C: tSNE for celda_C

In compbiomed/celda: CEllular Latent Dirichlet Allocation

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

## S3 method for class 'celda_C'
celdaTsne(counts, celda.mod, max.cells = 25000,
  min.cluster.size = 100, initial.dims = 20, perplexity = 20,
  max.iter = 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 'celda.mod'.
celda.mod	Celda object of class 'celda_C'.
max.cells	Integer. Maximum number of cells to plot. Cells will be randomly subsampled if ncol(counts) > max.cells. Larger numbers of cells requires more memory. Default 25000.
min.cluster.size	Integer. Do not subsample cell clusters below this threshold. Default 100.
initial.dims	Integer. PCA will be used to reduce the dimentionality of the dataset. The top 'initial.dims' principal components will be used for tSNE. Default 20.
perplexity	Numeric. Perplexity parameter for tSNE. Default 20.
max.iter	Integer. Maximum number of iterations in tSNE generation. Default 2500.
seed	Integer. Passed to 'set.seed()'. Default 12345.
...	Additional parameters.

Value

A two column matrix of t-SNE coordinates

See Also

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

Examples

tsne.res = celdaTsne(celda.C.sim$counts, celda.C.mod)

compbiomed/celda documentation built on May 25, 2019, 3:58 a.m.