Changes in version 2.7.6                        

    o   Changed factor for normalization of UCell scores, to account
	for minimal rank. For typical use cases the behaviour is
	similar to the previous implementation, but has an impact with
	very large signatures (UCell score distributions are now more
	homogeneous). See function UCell:::u_stat()) for details.

                        Changes in version 2.7.4                        

    o   Add support for Seurat v5 assay and datasets in multiple
	layers. Added dependency on Seurat >= 5.0.

    o   Change default for chunk.size to 100. On parallelized jobs,
	this can improve up to 2-fold execution time.

    o   Smaller test dataset (30 cells), to speed up package function
	checks.

                        Changes in version 2.1.2                        

    o   New function SmoothKNN() for k-nearest neighbor smoothing of
	UCell scores. It can be applied both on SingleCellExperiment
	and Seurat objects (S3 method).

    o   Add two new vignettes: along with basic usage (vignette 1),
	there are now dedicated vignettes for running UCell with
	SingleCellExperiment objects (vignette 2) and Seurat objects
	(vignette 3). kNN smoothing is illustrated for both object
	types.

    o   Fixing a bug that prevented storing of feature ranks.

                        Changes in version 2.0.0                        

    o   Update code to pass all BioC checks.

    o   The function ScoreSignatures_UCell() and StoreRankings_UCell()
	accept directly sce objects.

    o   Takes custom BiocParallel::bpparam() object as input to specify
	parallelisation.

                        Changes in version 1.3.1                        

    o   Restructure code to conform to BioC standards.

    o   Switch from future to BiocParallel to parallelize jobs.

    o   Add support for SingleCellExperiment - new function
	ScoreSignatures_UCell_sce() interacts directly with sce
	objects.

    o   Signatures cannot be larger than maxRank parameter.

    o   Do not rank more genes (maxRank) than there are in the input
	matrix.