Benchmarks the cluster assignments — benchmark.clustering • IBRAP

Supervised (ARI and NMI) and unsupervised (ASW, Dunn Index, and Connectivity) benchmarking metrics are calculated for cluster assignments. assays, clustering and reduction for distance calculations are iterated through.

benchmark.clustering(
  object,
  assay,
  clustering,
  reduction,
  n.dims = 2,
  dist.method = "euclidean",
  ground.truth.column = NULL,
  verbose = FALSE,
  seed = 1234
)

Arguments

object: IBRAP S4 class object
assay: Character. String containing indicating which assay to use
clustering: Character. The names of the cluster assignment dataframes to use
reduction: Character. Which reduction(s) within the assay should be supplied for distance calcultions
n.dims: Numerical. How many dimensions of the reduction should be supplied. Default = 1:3
dist.method: Character. Which distance method should be used, options: 'euclidean', 'maximum', 'manhattan', 'canberra', 'binary', 'minkowski'. Default = 'euclidean'
ground.truth.column: Character. If available, supply the column in the object metadata that contains ground truth labels, i.e. true cell type labels. If this is not supplied, only unsupervised methods will be supplied. Default = NULL
verbose: Logical. Should system information be printed. Default = FALSE
seed: Numeric. What should the seed be set as. Default = 1234

Value

Benchmarking scores for the supplied cluster assignments

Examples


# without ground truth labels 

object <- benchmark.clustering(object = object, assay = c('SCT', 'SCRAN', 'SCANPY'), 
                                         clustering = c("pca_harmony_nn:louvain"), 
                                         reduction = c('pca_harmony_umap'), 
                                         n.dims = 1:2)
#> Error in is(object = object, class2 = "IBRAP"): object 'object' not found

# With ground truth labels 

object <- benchmark.clustering(object = object, assay = c('SCT', 'SCRAN', 'SCANPY'), 
                                         clustering = c("pca_harmony_nn.v1:louvain"), 
                                         reduction = c('pca_harmony_umap'), 
                                         n.dims = 1:2, ground.truth = metadata$celltypes)
#> Error in is(object = object, class2 = "IBRAP"): object 'object' not found