bootstrapCluster | Assess cluster stability by bootstrapping |
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 Poisson noise |
modelGeneVarByPoisson-method | Model the per-gene variance with Poisson noise |
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 |
quickSubCluster | Quick and dirty subclustering |
quickSubCluster-method | Quick and dirty subclustering |
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 |