# Methods for Single-Cell RNA-Seq Data Analysis

## Documentation for package ‘scran’ version 1.14.1

- DESCRIPTION file.
- User guides, package vignettes and other documentation.
- Package NEWS.

## Help Pages

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