benchmark.clustering: Benchmarks the cluster assignments

In connorhknight/IBRAP: Integrated Benchmarking scRNA-seq Analytical Pipeline (IBRAP)

Benchmarks the cluster assignments

Description

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.

Usage

benchmark.clustering(
  object,
  assay,
  clustering,
  reduction,
  n.dims = 2,
  dist.method = "euclidean",
  ground.truth.column = NULL,
  threads = 1,
  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)

# 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)

connorhknight/IBRAP documentation built on March 9, 2023, 7:01 p.m.