cluster_kmeans: Run k-means clustering algorithm

In hesselberthlab/scrunchy: Single Cell Reconstruction of Functional Heterogeneity

Description

Run k-means clustering algorithm

Usage

cluster_kmeans(fsce, expt = "rnaseq", k, method = "PCA",
  n_dims = NULL, seed = NULL, ...)

Arguments

fsce	An object of class FunctionalSingleCellExperiment
expt	Data to use for calculating variable features (default is rnaseq). Must be present in names(fsce).
k	number of classes
method	dimensionality reduction method for clustering (defaults to PCA)
n_dims	specify the number of dimensions from "dr" to use for clustering, defaults to all dimensions
seed	seed for reproducible result
...	additional arguments to pass to stats::kmeans()

Value

fsce with k_cluster in expt colData.

See Also

Other clustering functions: cluster_leiden

Examples

# calculate PCA for k-means default method
fsce <- calc_pca(fsce_small)

fsce <- cluster_kmeans(fsce, k = 6)

SingleCellExperiment::colData(fsce[["rnaseq"]], "k_cluster")

hesselberthlab/scrunchy documentation built on Nov. 11, 2019, 2:29 p.m.