DoKMeans: K-Means Clustering

In mayer-lab/SeuratForMayer2018: Seurat : R Toolkit for Single Cell Genomics

Description

Perform k=means clustering on both genes and single cells

Usage

DoKMeans(object, genes.use = NULL, k.genes = NULL, k.cells = 0,
  k.seed = 1, do.plot = FALSE, data.cut = 2.5,
  k.cols = PurpleAndYellow(), set.ident = TRUE, do.constrained = FALSE,
  assay.type = "RNA", ...)

Arguments

object	Seurat object
genes.use	Genes to use for clustering
k.genes	K value to use for clustering genes
k.cells	K value to use for clustering cells (default is NULL, cells are not clustered)
k.seed	Random seed
do.plot	Draw heatmap of clustered genes/cells (default is FALSE).
data.cut	Clip all z-scores to have an absolute value below this. Reduces the effect of huge outliers in the data.
k.cols	Color palette for heatmap
set.ident	If clustering cells (so k.cells>0), set the cell identity class to its K-means cluster (default is TRUE)
do.constrained	FALSE by default. If TRUE, use the constrained K-means function implemented in the tclust package.
assay.type	Type of data to normalize for (default is RNA), but can be changed for multimodal analyses.
...	Additional parameters passed to kmeans (or tkmeans)

Details

K-means and heatmap are calculated on [e-mail]

Value

Seurat object where the k-means results for genes is stored in [e-mail][[1]], and the k-means results for cells is stored in [e-mail][[1]]. The cluster for each cell is stored in [e-mail][,"kmeans.ident"] and also object@ident (if set.ident=TRUE)

mayer-lab/SeuratForMayer2018 documentation built on May 25, 2019, 9:34 p.m.