runKMeans: Get clustering with KMeans

In compbiomed/singleCellTK: Comprehensive and Interactive Analysis of Single Cell RNA-Seq Data

Get clustering with KMeans

Description

Perform KMeans clustering on a SingleCellExperiment object, with kmeans.

Usage

runKMeans(
  inSCE,
  nCenters,
  useReducedDim = "PCA",
  clusterName = "KMeans_cluster",
  nComp = 10,
  nIter = 10,
  nStart = 1,
  seed = 12345,
  algorithm = c("Hartigan-Wong", "Lloyd", "MacQueen")
)

Arguments

inSCE	A SingleCellExperiment object.
nCenters	An integer, the number of centroids (clusters).
useReducedDim	A single character, specifying which low-dimension representation to perform the clustering algorithm on. Default "PCA".
clusterName	A single character, specifying the name to store the cluster label in colData. Default "KMeans_cluster".
nComp	An integer. The number of components to use for K-Means. Default 10. See Detail.
nIter	An integer, the maximum number of iterations allowed. Default 10.
nStart	An integer, the number of random sets to choose. Default 1.
seed	An integer. The seed for the random number generator. Default 12345.
algorithm	A single character. Choose from "Hartigan-Wong", "Lloyd", "MacQueen". May be abbreviated. Default "Hartigan-Wong".

Value

The input SingleCellExperiment object with factor cluster labeling updated in colData(inSCE)[[clusterName]].

Examples

data("mouseBrainSubsetSCE")
mouseBrainSubsetSCE <- runKMeans(mouseBrainSubsetSCE,
                                 useReducedDim = "PCA_logcounts",
                                 nCenters = 2)

compbiomed/singleCellTK documentation built on Oct. 27, 2024, 3:26 a.m.