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))
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, 83 cells excluded from training.
#> iter=1, 83 cells excluded from training.
#> iter=2, 83 cells excluded from training.
#> Threshold found:0.992
#> 83 (7.7%) doublets called
clusterStickiness(sce)
#> Estimate Std. Error t value p.value FDR
#> 4 0.17105050 0.3551707 0.4816008 0.6504287 1
#> 3 0.13707656 0.3542400 0.3869596 0.7147080 1
#> 5 -0.12097021 0.4492524 -0.2692700 0.7984804 1
#> 1 0.08987382 0.3622676 0.2480868 0.8139339 1
#> 2 0.07532340 0.3744404 0.2011626 0.8484988 1