Changes in version 1.4.0                        

    o   Multiple changes to scoreMarkers():
	
	  • Setting all.pairwise= to an integer will now return the top
	    markers by effect size for each pairwise comparison. This
	    is more efficient than creating the 3D array of effects and
	    then ranking the genes in each comparison.
	
	  • The min-rank summary is now limited to the top
	    min.rank.limit= genes, for efficiency. All other genes will
	    have their reported min-rank set to a maximum placeholder.
	
	  • Added compute.group.mean= and compute.group.detected=
	    options to disable reporting of the group-wise means.

    o   Setting universe=NULL in testEnrichment() will automatically
	define the universe as the union of all genes.

    o   Updated correctMnn() to the new algorithm used by the
	mnncorrect C++ library. This aims to improve correction
	accuracy and reduce sensitivity to the choice of reference.

    o   Bugfix to runAllNeighborSteps() to return an empty list when no
	step is requested.

    o   Set bound=0 by default in chooseHighlyVariableGenes(), for more
	convenient use with residuals.

    o   Modified testEnrichment() to return a data frame with the
	number of overlapping genes and the size of each gene set.

                        Changes in version 1.2.0                        

    o   Added the aggregateAcrossGenes() function, to compute an
	aggregate expression value for gene sets.

    o   Added compute.cohens.d=, compute.delta.mean= and
	compute.delta.detected= options to scoreMarkers().

    o   Support top=Inf in chooseHighlyVariableGenes(). Also added the
	bound= argument to set a hard upper/lower bound.

    o   Bugfix for correct filtering with block= in the various
	filter*QcMetrics() functions when not all blocking levels are
	present.

    o   Bugfix to clusterKmeans() to respect the user-supplied seed= in
	relevant initialization methods.

    o   Added a return.graph= option to return the SNN graph from
	runAllNeighborSteps().

    o   Added a testEnrichment() function for quick and dirty gene set
	enrichment testing.

    o   Modified runPca() so that it caps number= to the maximum number
	of available PCs.

    o   Added an analyze() function that provides a one-click approach
	for analyzing single-cell data.

    o   Added a reportGroupMarkerStatistics() function to combine all
	marker statistics for a single group into one data frame.

    o   Switch clusterGraph() to use C++ wrappers around the igraph
	community detection algorithms via Rigraphlib. This replaces
	the dependency on the igraph R package.

    o   Added a delayed= option to avoid wrapping the matrix in a
	DelayedArray in normalizeCounts().