Run k-means clustering algorithm
cluster_kmeans(fsce, expt = "rnaseq", k, method = "PCA", n_dims = NULL, seed = NULL, ...)
fsce | An object of class |
---|---|
expt | Data to use for calculating variable features
(default is |
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 |
fsce with k_cluster
in expt
colData.
Other clustering functions: cluster_leiden
#>#>fsce <- cluster_kmeans(fsce, k = 6) SingleCellExperiment::colData(fsce[["rnaseq"]], "k_cluster")#> DataFrame with 250 rows and 4 columns #> cell_id k_cluster leiden_cluster cell_cycle #> <character> <character> <character> <character> #> TGCGGGTGTAGAGTGC TGCGGGTGTAGAGTGC 2 2 S #> TGGTTCCCATCTATGG TGGTTCCCATCTATGG 5 4 S #> CATATTCCACGTCAGC CATATTCCACGTCAGC 2 2 G2M #> TCACAAGTCCTGCTTG TCACAAGTCCTGCTTG 4 3 G1 #> GGAATAATCCAGGGCT GGAATAATCCAGGGCT 5 4 G1 #> ... ... ... ... ... #> CTACGTCCACCACGTG CTACGTCCACCACGTG 5 4 G1 #> TGTGGTAAGGTGCTTT TGTGGTAAGGTGCTTT 6 6 G1 #> AGCGTCGGTCGAGTTT AGCGTCGGTCGAGTTT 2 6 G1 #> CTCGAAAAGTTCGATC CTCGAAAAGTTCGATC 5 5 G2M #> TGACAACGTCGCATAT TGACAACGTCGCATAT 5 4 S