Seurat (version 1.4.0.16)

DoKMeans: K-Means Clustering

Description

Perform k=means clustering on both genes and single cells

Usage

DoKMeans(object, genes.use = NULL, k.genes = NULL, k.cells = NULL,
  k.seed = 1, do.plot = TRUE, data.cut = 2.5, k.cols = pyCols,
  pc.row.order = NULL, pc.col.order = NULL, rev.pc.order = FALSE,
  use.imputed = FALSE, set.ident = TRUE, do.constrained = F, ...)

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 TRUE)

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

pc.row.order

Order gene clusters based on the average PC score within a cluster. Can be useful if you want to visualize clusters, for example, based on their average score for PC1.

pc.col.order

Order cell clusters based on the average PC score within a cluster

rev.pc.order

Use the reverse PC ordering for gene and cell clusters (since the sign of a PC is arbitrary)

use.imputed

Cluster imputed values (default is FALSE)

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.

…

Additional parameters passed to kmeans (or tkmeans)

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)

Details

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