runKMeans {singleCellTK} | R Documentation

## Get clustering with KMeans

### Description

Perform KMeans clustering on a `SingleCellExperiment` object, with `kmeans`.

### Usage

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

### Arguments

`inSCE` | A `SingleCellExperiment` object.
`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 `"scranSNN_cluster"`.
`nCenters` | An `integer`, the number of centroids (clusters).
`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)

[Package singleCellTK version 2.0.0 Index]