doKmeans#

Last Updated: Oct 02, 2023

Description#

cluster cells using kmeans algorithm

Usage#

doKmeans(
  gobject,
  feat_type = NULL,
  spat_unit = NULL,
  expression_values = c("normalized", "scaled", "custom"),
  feats_to_use = NULL,
  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 = TRUE,
  seed_number = 1234
)

Arguments#

`gobject`	giotto object
`feat_type`	feature type (e.g. “cell”)
`spat_unit`	spatial unit (e.g. “rna”, “dna”, “protein”)
`expression_values`	expression values to use (e.g. “normalized”, “scaled”, “custom”)
`feats_to_use`	subset of features to use
`genes_to_use`	deprecated use feats_to_use
`dim_reduction_to_use`	dimension reduction to use (e.g. “cells”, “pca”, “umap”, “tsne”)
`dim_reduction_name`	dimensions reduction name, default to “pca”
`dimensions_to_use`	dimensions to use, default = 1:10
`distance_method`	distance method (e.g. “original”, “pearson”, “spearman”, “euclidean”, “maximum”, “manhattan”, “canberra”, “binary”, “minkowski”)
`centers`	number of final clusters, default = 10
`iter_max`	kmeans maximum iterations, default = 100
`nstart`	kmeans nstart, default = 1000
`algorithm`	kmeans algorithm, default to “Hartigan-Wong”
`name`	name for kmeans clustering, default to “kmeans”
`return_gobject`	boolean: return giotto object (default = TRUE)
`set_seed`	set seed (default = TRUE)
`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