| buildKNNGraph | Build a nearest-neighbor graph |
| buildKNNGraph-method | Build a nearest-neighbor graph |
| buildSNNGraph | Build a nearest-neighbor graph |
| buildSNNGraph-method | Build a nearest-neighbor graph |
| calculateSumFactors | Normalization by deconvolution |
| calculateSumFactors-method | Normalization by deconvolution |
| cleanSizeFactors | Clean size factors |
| clusterModularity | Compute the cluster-wise modularity |
| combineCV2 | Combine variance decompositions |
| 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 |
| correlatePairs-method | Test for significant correlations |
| cyclone | Cell cycle phase classification |
| cyclone-method | Cell cycle phase classification |
| decomposeVar | Decompose the gene-level variance |
| decomposeVar-method | Decompose the gene-level variance |
| denoisePCA | Denoise expression with PCA |
| denoisePCANumber | Denoise expression with PCA |
| DM | Compute the distance-to-median statistic |
| doubletCells | Detect doublet cells |
| doubletCells-method | Detect doublet cells |
| doubletCluster | Detect doublet clusters |
| doubletCluster-method | Detect doublet clusters |
| findMarkers | Find marker genes |
| findMarkers-method | 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 |
| getDenoisedPCs | Denoise expression with PCA |
| getDenoisedPCs-method | Denoise expression with PCA |
| getTopHVGs | Identify HVGs |
| getTopMarkers | Get top markers |
| improvedCV2 | Stably model the technical coefficient of variation |
| improvedCV2-method | Stably model the technical coefficient of variation |
| makeTechTrend | Make a technical trend |
| modelGeneCV2 | Model the per-gene CV2 |
| modelGeneCV2-method | Model the per-gene CV2 |
| modelGeneCV2WithSpikes | Model the per-gene CV2 with spike-ins |
| modelGeneCV2WithSpikes-method | Model the per-gene CV2 with spike-ins |
| modelGeneVar | Model the per-gene variance |
| modelGeneVar-method | Model the per-gene variance |
| modelGeneVarByPoisson | Model the per-gene variance with spike-ins |
| modelGeneVarByPoisson-method | Model the per-gene variance with spike-ins |
| modelGeneVarWithSpikes | Model the per-gene variance with spike-ins |
| modelGeneVarWithSpikes-method | Model the per-gene variance with spike-ins |
| multiBlockNorm | Per-block scaling normalization |
| multiBlockVar | Per-block variance statistics |
| neighborsToKNNGraph | Build a nearest-neighbor graph |
| neighborsToSNNGraph | Build a nearest-neighbor graph |
| overlapExprs | Overlap expression profiles |
| overlapExprs-method | Overlap expression profiles |
| pairwiseBinom | Perform pairwise binomial tests |
| pairwiseTTests | Perform pairwise t-tests |
| pairwiseWilcox | Perform pairwise Wilcoxon rank sum tests |
| parallelPCA | Parallel analysis for PCA |
| parallelPCA-method | Parallel analysis for PCA |
| quickCluster | Quick clustering of cells |
| quickCluster-method | Quick clustering of cells |
| sandbag | Cell cycle phase training |
| sandbag-method | Cell cycle phase training |
| scaledColRanks | Compute scaled column ranks |
| scran-gene-selection | Gene selection |
| technicalCV2 | Model the technical coefficient of variation |
| technicalCV2-method | Model the technical coefficient of variation |
| testVar | Test for significantly large variances |
| trendVar | Fit a variance trend |
| trendVar-method | Fit a variance trend |