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, 40 cells excluded from training.
#> iter=1, 37 cells excluded from training.
#> iter=2, 37 cells excluded from training.
#> Threshold found:0.699
#> 41 (3.5%) doublets called
clusterStickiness(sce)
#> Estimate Std. Error t value p.value FDR
#> 1 0.570949414 0.2367866 2.41124002 0.06077094 0.3038547
#> 4 0.537158468 0.2379533 2.25741087 0.07359007 0.3038547
#> 5 -1.061269307 0.5524059 -1.92117660 0.11275844 0.3382753
#> 3 -0.463794435 0.3297274 -1.40659974 0.21855358 0.4371072
#> 2 0.004126825 0.2783859 0.01482412 0.98874583 0.9887458