Package: scran 1.33.0

Aaron Lun

scran: Methods for Single-Cell RNA-Seq Data Analysis

Implements miscellaneous functions for interpretation of single-cell RNA-seq data. Methods are provided for assignment of cell cycle phase, detection of highly variable and significantly correlated genes, identification of marker genes, and other common tasks in routine single-cell analysis workflows.

Authors:Aaron Lun [aut, cre], Karsten Bach [aut], Jong Kyoung Kim [ctb], Antonio Scialdone [ctb]

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NEWS

# Install 'scran' in R:

install.packages('scran', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Uses libs:

openblas– Optimized BLAS
c++– GNU Standard C++ Library v3

On BioConductor:scran-1.33.0(bioc 3.20)scran-1.32.0(bioc 3.19)

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

bioconductor-package

75 exports 4.28 score 66 dependencies 34 dependents 113 mentions

Last updated 2 months agofrom:edd02ba626

Exports:.logBH bootstrapCluster buildKNNGraph buildSNNGraph calculateSumFactors clusterCells clusterKNNGraph clusterModularity clusterPurity clusterSNNGraph coassignProb combineBlocks combineCV2 combineMarkers combinePValues combineVar computeMinRank computeSumFactors connectClusterMST convertTo correlateGenes correlateNull correlatePairs createClusterMST cyclone decideTestsPerLabel decomposeVar denoisePCA denoisePCANumber DM doubletCells doubletCluster doubletRecovery findMarkers fitTrendCV2 fitTrendPoisson fitTrendVar fixedPCA getClusteredPCs getDenoisedPCs getMarkerEffects getTopHVGs getTopMarkers improvedCV2 makeTechTrend modelGeneCV2 modelGeneCV2WithSpikes modelGeneVar modelGeneVarByPoisson modelGeneVarWithSpikes multiBlockNorm multiBlockVar multiMarkerStats orderClusterMST overlapExprs pairwiseBinom pairwiseTTests pairwiseWilcox parallelPCA pseudoBulkDGE pseudoBulkSpecific quickCluster quickPseudotime quickSubCluster rhoToPValue sandbag scaledColRanks scoreMarkers summarizeTestsPerLabel summaryMarkerStats technicalCV2 testLinearModel testPseudotime testVar trendVar

Dependencies:abind askpass beachmat BH Biobase BiocGenerics BiocNeighbors BiocParallel BiocSingular bluster cli cluster codetools cpp11 crayon curl DelayedArray DelayedMatrixStats dqrng edgeR formatR futile.logger futile.options GenomeInfoDb GenomeInfoDbData GenomicRanges glue httr igraph IRanges irlba jsonlite lambda.r lattice lifecycle limma locfit magrittr Matrix MatrixGenerics matrixStats metapod mime openssl pkgconfig R6 Rcpp RcppHNSW rlang rsvd S4Arrays S4Vectors ScaledMatrix scuttle SingleCellExperiment sitmo snow SparseArray sparseMatrixStats statmod SummarizedExperiment sys UCSC.utils vctrs XVector zlibbioc

Using scran to analyze single-cell RNA-seq data

Aaron Lun

Rendered fromscran.Rmdusingknitr::rmarkdownon Jun 18 2024.

Last update: 2021-05-11
Started: 2016-03-31

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
BH correction on log-p-values	.logBH
Build a nearest-neighbor graph	buildKNNGraph buildKNNGraph,ANY-method buildKNNGraph,SingleCellExperiment-method buildSNNGraph buildSNNGraph,ANY-method buildSNNGraph,SingleCellExperiment-method buildSNNGraph,SummarizedExperiment-method
Cluster cells in a SingleCellExperiment	clusterCells
Combine blockwise statistics	combineBlocks
Combine pairwise DE results into a marker list	combineMarkers
Combine p-values	combinePValues
Combine variance decompositions	combineCV2 combineVar
Compute the minimum rank	computeMinRank
Normalization by deconvolution	calculateSumFactors computeSumFactors
Convert to other classes	convertTo
Per-gene correlation statistics	correlateGenes
Build null correlations	correlateNull
Test for significant correlations	correlatePairs correlatePairs,ANY-method correlatePairs,SummarizedExperiment-method
Cell cycle phase classification	cyclone cyclone,ANY-method cyclone,SummarizedExperiment-method
Decide tests for each label	decideTestsPerLabel summarizeTestsPerLabel
Defunct functions	bootstrapCluster clusterKNNGraph clusterModularity clusterPurity clusterSNNGraph coassignProb connectClusterMST createClusterMST decomposeVar defunct doubletCells doubletCluster doubletRecovery improvedCV2 makeTechTrend multiBlockNorm multiBlockVar orderClusterMST overlapExprs parallelPCA quickPseudotime technicalCV2 testPseudotime testVar trendVar
Denoise expression with PCA	denoisePCA denoisePCANumber getDenoisedPCs getDenoisedPCs,ANY-method getDenoisedPCs,SummarizedExperiment-method
Compute the distance-to-median statistic	DM
Find marker genes	findMarkers findMarkers,ANY-method findMarkers,SingleCellExperiment-method findMarkers,SummarizedExperiment-method
Fit a trend to the CV2	fitTrendCV2
Generate a trend for Poisson noise	fitTrendPoisson
Fit a trend to the variances of log-counts	fitTrendVar
PCA with a fixed number of components	fixedPCA
Gene selection	scran-gene-selection
Use clusters to choose the number of PCs	getClusteredPCs
Get marker effect sizes	getMarkerEffects
Identify HVGs	getTopHVGs
Get top markers	getTopMarkers
Model the per-gene CV2	modelGeneCV2 modelGeneCV2,ANY-method modelGeneCV2,SingleCellExperiment-method modelGeneCV2,SummarizedExperiment-method
Model the per-gene CV2 with spike-ins	modelGeneCV2WithSpikes modelGeneCV2WithSpikes,ANY-method modelGeneCV2WithSpikes,SingleCellExperiment-method modelGeneCV2WithSpikes,SummarizedExperiment-method
Model the per-gene variance	modelGeneVar modelGeneVar,ANY-method modelGeneVar,SingleCellExperiment-method modelGeneVar,SummarizedExperiment-method
Model the per-gene variance with Poisson noise	modelGeneVarByPoisson modelGeneVarByPoisson,ANY-method modelGeneVarByPoisson,SingleCellExperiment-method modelGeneVarByPoisson,SummarizedExperiment-method
Model the per-gene variance with spike-ins	modelGeneVarWithSpikes modelGeneVarWithSpikes,ANY-method modelGeneVarWithSpikes,SingleCellExperiment-method modelGeneVarWithSpikes,SummarizedExperiment-method
Combine multiple sets of marker statistics	multiMarkerStats
Perform pairwise binomial tests	pairwiseBinom pairwiseBinom,ANY-method pairwiseBinom,SingleCellExperiment-method pairwiseBinom,SummarizedExperiment-method
Perform pairwise t-tests	pairwiseTTests pairwiseTTests,ANY-method pairwiseTTests,SingleCellExperiment-method pairwiseTTests,SummarizedExperiment-method
Perform pairwise Wilcoxon rank sum tests	pairwiseWilcox pairwiseWilcox,ANY-method pairwiseWilcox,SingleCellExperiment-method pairwiseWilcox,SummarizedExperiment-method
Quickly perform pseudo-bulk DE analyses	pseudoBulkDGE pseudoBulkDGE,ANY-method pseudoBulkDGE,SummarizedExperiment-method
Label-specific pseudo-bulk DE	pseudoBulkSpecific pseudoBulkSpecific,ANY-method pseudoBulkSpecific,SummarizedExperiment-method
Quick clustering of cells	quickCluster quickCluster,ANY-method quickCluster,SummarizedExperiment-method
Quick and dirty subclustering	quickSubCluster quickSubCluster,ANY-method quickSubCluster,SingleCellExperiment-method quickSubCluster,SummarizedExperiment-method
Spearman's rho to a p-value	rhoToPValue
Cell cycle phase training	sandbag sandbag,ANY-method sandbag,SummarizedExperiment-method
Compute scaled column ranks	scaledColRanks
Score marker genes	scoreMarkers scoreMarkers,ANY-method scoreMarkers,SingleCellExperiment-method scoreMarkers,SummarizedExperiment-method
Summary marker statistics	summaryMarkerStats summaryMarkerStats,ANY-method summaryMarkerStats,SummarizedExperiment-method
Hypothesis tests with linear models	testLinearModel testLinearModel,ANY-method testLinearModel,SummarizedExperiment-method