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 Kmeans clustering method.

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)