.logBH                  BH correction on log-p-values
DM                      Compute the distance-to-median statistic
bootstrapCluster        Assess cluster stability by bootstrapping
buildSNNGraph           Build a nearest-neighbor graph
cleanSizeFactors        Clean size factors
clusterModularity       Compute the cluster-wise modularity
clusterPurity           Evaluate cluster purity
clusterSNNGraph         Wrappers for graph-based clustering
coassignProb            Compute coassignment probabilities
combineBlocks           Combine blockwise statistics
combineMarkers          Combine pairwise DE results into a marker list
combinePValues          Combine p-values
combineVar              Combine variance decompositions
computeSpikeFactors     Normalization with spike-in counts
computeSumFactors       Normalization by deconvolution
convertTo               Convert to other classes
correlateGenes          Per-gene correlation statistics
correlateNull           Build null correlations
correlatePairs          Test for significant correlations
createClusterMST        Minimum spanning trees on cluster centroids
cyclone                 Cell cycle phase classification
decideTestsPerLabel     Decide tests for each label
defunct                 Defunct functions
denoisePCA              Denoise expression with PCA
doubletCells            Detect doublet cells
doubletCluster          Detect doublet clusters
doubletRecovery         Recover intra-sample doublets
findMarkers             Find marker genes
fitTrendCV2             Fit a trend to the CV2
fitTrendPoisson         Generate a trend for Poisson noise
fitTrendVar             Fit a trend to the variances of log-counts
getClusteredPCs         Use clusters to choose the number of PCs
getMarkerEffects        Get marker effect sizes
getTopHVGs              Identify HVGs
getTopMarkers           Get top markers
modelGeneCV2            Model the per-gene CV2
modelGeneCV2WithSpikes
                        Model the per-gene CV2 with spike-ins
modelGeneVar            Model the per-gene variance
modelGeneVarByPoisson   Model the per-gene variance with Poisson noise
modelGeneVarWithSpikes
                        Model the per-gene variance with spike-ins
multiMarkerStats        Combine multiple sets of marker statistics
pairwiseBinom           Perform pairwise binomial tests
pairwiseTTests          Perform pairwise t-tests
pairwiseWilcox          Perform pairwise Wilcoxon rank sum tests
pseudoBulkDGE           Quickly perform pseudo-bulk DE analyses
pseudoBulkSpecific      Label-specific pseudo-bulk DE
quickCluster            Quick clustering of cells
quickPseudotime         Quick MST-based pseudotime
quickSubCluster         Quick and dirty subclustering
sandbag                 Cell cycle phase training
scaledColRanks          Compute scaled column ranks
scran-gene-selection    Gene selection
summaryMarkerStats      Summary marker statistics
testLinearModel         Hypothesis tests with linear models
testPseudotime          Test for differences along pseudotime
