Tests for enrichment of doublets created from each cluster (i.e. cluster's stickiness). Only applicable with >=4 clusters. Note that when applied to an multisample object, this functions assumes that the cluster labels match across samples.
Usage
clusterStickiness(
x,
type = c("quasibinomial", "nbinom", "binomial", "poisson"),
inclDiff = NULL,
verbose = TRUE
)Arguments
- x
A table of double statistics, or a SingleCellExperiment on which scDblFinder was run using the cluster-based approach.
- type
The type of test to use (quasibinomial recommended).
- inclDiff
Logical; whether to include the difficulty in the model. If NULL, will be used only if there is a significant trend with the enrichment.
- verbose
Logical; whether to print additional running information.
Examples
sce <- mockDoubletSCE(rep(200,5), dbl.rate=0.2)
sce <- scDblFinder(sce, clusters=TRUE, artificialDoublets=500)
#> Warning: Some cells in `sce` have an extremely low read counts; note that these could trigger errors and might best be filtered out
#> Clustering cells...
#> 5 clusters
#> Creating ~500 artificial doublets...
#> Dimensional reduction
#> Evaluating kNN...
#> Training model...
#> iter=0, 43 cells excluded from training.
#> iter=1, 46 cells excluded from training.
#> iter=2, 43 cells excluded from training.
#> Threshold found:0.658
#> 48 (4.1%) doublets called
clusterStickiness(sce)
#> Estimate Std. Error t value p.value FDR
#> 5 0.46478892 0.1847161 2.5162333 0.05341973 0.2670987
#> 2 -0.13701315 0.2532653 -0.5409867 0.61173819 1.0000000
#> 3 -0.10894496 0.2168956 -0.5022922 0.63679654 1.0000000
#> 1 -0.08931500 0.2379699 -0.3753205 0.72281691 1.0000000
#> 4 0.03613934 0.2128837 0.1697609 0.87185298 1.0000000