Cluster Cells Using K-Means¶

doKmeans()

Cluster cells using kmeans algorithm.

doKmeans(
    gobject,
    expression_values = c("normalized", "scaled", "custom"),
    genes_to_use = NULL,
    dim_reduction_to_use = c("cells", "pca", "umap", "tsne"),
    dim_reduction_name = "pca",
    dimensions_to_use = 1:10,
    distance_method = c("original", "pearson", "spearman", "euclidean", "maximum",
        "manhattan", "canberra", "binary", "minkowski"),
    centers = 10,
    iter_max = 100,
    nstart = 1000,
    algorithm = "Hartigan-Wong",
    name = "kmeans",
    return_gobject = TRUE,
    set_seed = T,
    seed_number = 1234
)

Arguments¶

gobject	giotto object
expression_values	expression values to use
genes_to_use	subset of genes to use
dim_reduction_to_use	dimension reduction to use
dim_reduction_name	dimensions reduction name
dimensions_to_use	dimensions to use
distance_method	distance method
centers	number of final clusters
iter_max	kmeans maximum iterations
nstart	kmeans nstart
algorithm	kmeans algorithm
name	name for kmeans clustering
return_gobject	boolean: return giotto object (default = TRUE)
set_seed	set seed
seed_number	number for seed

Value¶

Giotto object with new clusters appended to cell metadata

Details¶

Description on how to use K-Means clustering method.

Examples¶

data(mini_giotto_single_cell)

mini_giotto_single_cell = doKmeans(mini_giotto_single_cell, centers = 4, name = 'kmeans_clus')
plotUMAP_2D(mini_giotto_single_cell, cell_color = 'kmeans_clus', point_size = 3)