doHclust

cluster cells using hierarchical clustering algorithm

doHclust(
  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("pearson", "spearman", "original", "euclidean", "maximum",
    "manhattan", "canberra", "binary", "minkowski"),
  agglomeration_method = c("ward.D2", "ward.D", "single", "complete", "average",
    "mcquitty", "median", "centroid"),
  k = 10,
  h = NULL,
  name = "hclust",
  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

agglomeration_method

agglomeration method for hclust

k

number of final clusters

h

cut hierarchical tree at height = h

name

name for hierarchical 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

hclust

Examples

data(mini_giotto_single_cell)

mini_giotto_single_cell = doHclust(mini_giotto_single_cell, k = 4, name = 'hier_clus')
plotUMAP_2D(mini_giotto_single_cell, cell_color = 'hier_clus', point_size = 3)