`doHclust`¶

doHclust

Description¶

cluster cells using hierarchical clustering algorithm

Usage¶

doHclust(
  gobject,
  spat_unit = NULL,
  feat_type = NULL,
  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¶

Argument	Description
`gobject`	giotto object
`spat_unit`	spatial unit
`feat_type`	feature type
`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

Details¶

Description on how to use Kmeans clustering method.

Value¶

giotto object with new clusters appended to cell metadata

Seealso¶

``hclust` <#hclust>`_