cluster_kmeans: Run k-means clustering algorithm

Description Usage Arguments Value See Also Examples

View source: R/clustering.R

Description

Run k-means clustering algorithm

Usage

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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

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# 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.