Seurat (version 2.1.0)

KClustDimension: Perform spectral k-means clustering on single cells

Description

Find point clounds single cells in a low-dimensional space using k-means clustering. Can be useful for smaller datasets, where graph-based clustering can perform poorly

Usage

KClustDimension(object, dims.use = c(1, 2), reduction.use = "tsne",
  k.use = 5, set.ident = TRUE, seed.use = 1)

Arguments

object

A Seurat object

dims.use

Dimensions to use for clustering

reduction.use

Dimmensional Reduction to use for k-means clustering

k.use

Number of clusters

set.ident

Set identity of Seurat object

seed.use

Random seed to use

Value

Object with clustering information

Examples

# NOT RUN {
pbmc_small
# K-means clustering on the first two tSNE dimensions
pbmc_small <- KClustDimension(pbmc_small)

# }