Perform KMeans clustering on a
SingleCellExperiment object, with kmeans
.
runKMeans(
inSCE,
nCenters,
useReducedDim = "PCA",
clusterName = "KMeans_cluster",
nComp = 10,
nIter = 10,
nStart = 1,
seed = 12345,
algorithm = c("Hartigan-Wong", "Lloyd", "MacQueen")
)
A SingleCellExperiment object.
An integer
, the number of centroids (clusters).
A single character
, specifying which
low-dimension representation to perform the clustering algorithm on. Default
"PCA"
.
A single character
, specifying the name to store
the cluster label in colData
. Default "KMeans_cluster"
.
An integer
. The number of components to use for K-Means.
Default 10
. See Detail.
An integer
, the maximum number of iterations allowed.
Default 10
.
An integer
, the number of random sets to choose. Default
1
.
An integer
. The seed for the random number generator.
Default 12345
.
A single character
. Choose from
"Hartigan-Wong"
, "Lloyd"
, "MacQueen"
. May be
abbreviated. Default "Hartigan-Wong"
.
The input SingleCellExperiment object with
factor
cluster labeling updated in
colData(inSCE)[[clusterName]]
.
data("mouseBrainSubsetSCE")
mouseBrainSubsetSCE <- runKMeans(mouseBrainSubsetSCE,
useReducedDim = "PCA_logcounts",
nCenters = 2)
#> Sat Mar 18 10:31:04 2023 ... Running 'KMeans clustering' with Hartigan-Wong algorithm.
#> Sat Mar 18 10:31:04 2023 ... Identified 2 clusters