doKmeans: doKmeans

In RubD/Giotto: Spatial Single-Cell Transcriptomics Toolbox

doKmeans

Description

cluster cells using kmeans algorithm

Usage

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

Details

Description on how to use Kmeans clustering method.

Value

giotto object with new clusters appended to cell metadata

See Also

kmeans

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)

RubD/Giotto documentation built on April 29, 2023, 5:52 p.m.