Compare cluster outputs based on various clustering comparison measures.

compareClusters(
  se,
  func = c("ARI", "AMI", "MARI", "MARIraw", "RI", "NID", "NMI", "NVI"),
  digits = 3
)

Arguments

se: A SpatialExperiment, SingleCellExperiment or SummarizedExperiment object with cluster labels in colData(se).
func: A string scalar specifying what clustering comparison measure to compute. See ?aricode for more information.
digits: An integer scalar specifying the number of digits to round to.

Value

A matrix of cluster comparison measures.

Examples

data(rings)
spe <- runBanksyPCA(rings, assay_name = "counts", lambda = 0.2, npcs = 20)
#> Computing neighbors...
#> Spatial mode is kNN_median
#> Parameters: k_geom=15
#> Done
#> Building sparse weight matrix
#> Computing scaling parameters for own expression
#> Computing clipping excess for own expression
#> Computing scaling params and clipping for H0
#> H0 genes requiring clipping: 0 / 50
#> Clipping corrections: own=0 H0=0 entries
#> Computing BANKSY PCA (20 PCs) via C++ irlba (work=27)
#>   iter=1  mprod=54  sv[20]=8.5000e+00  t=0s
#>   iter=2  mprod=68  sv[20]=1.1893e+01  t=0s
#>   iter=5  mprod=110  sv[20]=1.4406e+01  t=0s
#>   iter=8  mprod=152  sv[20]=1.4489e+01  t=0s
#>   Converged: iter=8, mprod=152
#> Done.
spe <- clusterBanksy(spe, lambda = 0.2, resolution = c(0.1, 1))
spe <- connectClusters(spe)
#> clust_M0_lam0.2_k50_res0.1 --> cluster
#> clust_M0_lam0.2_k50_res1 --> clust_M0_lam0.2_k50_res0.1
compareClusters(spe)
#>                            cluster clust_M0_lam0.2_k50_res0.1
#> cluster                          1                          1
#> clust_M0_lam0.2_k50_res0.1       1                          1
#> clust_M0_lam0.2_k50_res1         1                          1
#>                            clust_M0_lam0.2_k50_res1
#> cluster                                           1
#> clust_M0_lam0.2_k50_res0.1                        1
#> clust_M0_lam0.2_k50_res1                          1