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, 38 cells excluded from training.
#> iter=1, 37 cells excluded from training.
#> iter=2, 43 cells excluded from training.
#> Threshold found:0.65
#> 44 (3.8%) doublets called
clusterStickiness(sce)
#> Estimate Std. Error t value p.value FDR
#> 3 -0.748613444 0.4344829 -1.722998529 0.1454986 0.7274930
#> 2 0.412332379 0.2940423 1.402289438 0.2197603 0.8790414
#> 4 0.419660739 0.3022530 1.388442100 0.2236800 0.8790414
#> 1 0.154153478 0.3322079 0.464027066 0.6621300 1.0000000
#> 5 -0.002330167 0.3771950 -0.006177618 0.9953099 1.0000000