Package: scran 1.33.2

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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scran.pdf |scran.html
scran/json (API)
NEWS

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

Peer review:

Bug tracker:https://github.com/marionilab/scran/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3

On BioConductor:scran-1.33.2(bioc 3.20)scran-1.32.0(bioc 3.19)

immunooncologynormalizationsequencingrnaseqsoftwaregeneexpressiontranscriptomicssinglecellclusteringbioconductor-packagehuman-cell-atlassingle-cell-rna-seq

75 exports 40 stars 13.15 score 64 dependencies 34 dependents 113 mentions 7.1k scripts 8.2k downloads

Last updated 1 months agofrom:60f8278584. Checks:OK: 1 ERROR: 2 WARNING: 6. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 06 2024
R-4.5-win-x86_64WARNINGOct 06 2024
R-4.5-linux-x86_64ERROROct 06 2024
R-4.4-win-x86_64WARNINGOct 06 2024
R-4.4-mac-x86_64WARNINGOct 06 2024
R-4.4-mac-aarch64WARNINGSep 06 2024
R-4.3-win-x86_64WARNINGOct 06 2024
R-4.3-mac-x86_64WARNINGOct 06 2024
R-4.3-mac-aarch64ERROROct 06 2024

Exports:.logBHbootstrapClusterbuildKNNGraphbuildSNNGraphcalculateSumFactorsclusterCellsclusterKNNGraphclusterModularityclusterPurityclusterSNNGraphcoassignProbcombineBlockscombineCV2combineMarkerscombinePValuescombineVarcomputeMinRankcomputeSumFactorsconnectClusterMSTconvertTocorrelateGenescorrelateNullcorrelatePairscreateClusterMSTcyclonedecideTestsPerLabeldecomposeVardenoisePCAdenoisePCANumberDMdoubletCellsdoubletClusterdoubletRecoveryfindMarkersfitTrendCV2fitTrendPoissonfitTrendVarfixedPCAgetClusteredPCsgetDenoisedPCsgetMarkerEffectsgetTopHVGsgetTopMarkersimprovedCV2makeTechTrendmodelGeneCV2modelGeneCV2WithSpikesmodelGeneVarmodelGeneVarByPoissonmodelGeneVarWithSpikesmultiBlockNormmultiBlockVarmultiMarkerStatsorderClusterMSToverlapExprspairwiseBinompairwiseTTestspairwiseWilcoxparallelPCApseudoBulkDGEpseudoBulkSpecificquickClusterquickPseudotimequickSubClusterrhoToPValuesandbagscaledColRanksscoreMarkerssummarizeTestsPerLabelsummaryMarkerStatstechnicalCV2testLinearModeltestPseudotimetestVartrendVar

Dependencies:abindaskpassassortheadbeachmatBHBiobaseBiocGenericsBiocNeighborsBiocParallelBiocSingularblustercliclustercodetoolscpp11crayoncurlDelayedArraydqrngedgeRformatRfutile.loggerfutile.optionsGenomeInfoDbGenomeInfoDbDataGenomicRangesgluehttrigraphIRangesirlbajsonlitelambda.rlatticelifecyclelimmalocfitmagrittrMatrixMatrixGenericsmatrixStatsmetapodmimeopensslpkgconfigR6RcpprlangrsvdS4ArraysS4VectorsScaledMatrixscuttleSingleCellExperimentsitmosnowSparseArraystatmodSummarizedExperimentsysUCSC.utilsvctrsXVectorzlibbioc

Using scran to analyze single-cell RNA-seq data

Rendered fromscran.Rmdusingknitr::rmarkdownon Oct 06 2024.

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

Readme and manuals

Help Manual

Help pageTopics
BH correction on log-p-values.logBH
Build a nearest-neighbor graphbuildKNNGraph buildKNNGraph,ANY-method buildKNNGraph,SingleCellExperiment-method buildSNNGraph buildSNNGraph,ANY-method buildSNNGraph,SingleCellExperiment-method buildSNNGraph,SummarizedExperiment-method
Cluster cells in a SingleCellExperimentclusterCells
Combine blockwise statisticscombineBlocks
Combine pairwise DE results into a marker listcombineMarkers
Combine p-valuescombinePValues
Combine variance decompositionscombineCV2 combineVar
Compute the minimum rankcomputeMinRank
Normalization by deconvolutioncalculateSumFactors computeSumFactors
Convert to other classesconvertTo
Per-gene correlation statisticscorrelateGenes
Build null correlationscorrelateNull
Test for significant correlationscorrelatePairs correlatePairs,ANY-method correlatePairs,SummarizedExperiment-method
Cell cycle phase classificationcyclone cyclone,ANY-method cyclone,SummarizedExperiment-method
Decide tests for each labeldecideTestsPerLabel summarizeTestsPerLabel
Defunct functionsbootstrapCluster 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 PCAdenoisePCA denoisePCANumber getDenoisedPCs getDenoisedPCs,ANY-method getDenoisedPCs,SummarizedExperiment-method
Compute the distance-to-median statisticDM
Find marker genesfindMarkers findMarkers,ANY-method findMarkers,SingleCellExperiment-method findMarkers,SummarizedExperiment-method
Fit a trend to the CV2fitTrendCV2
Generate a trend for Poisson noisefitTrendPoisson
Fit a trend to the variances of log-countsfitTrendVar
PCA with a fixed number of componentsfixedPCA
Gene selectionscran-gene-selection
Use clusters to choose the number of PCsgetClusteredPCs
Get marker effect sizesgetMarkerEffects
Identify HVGsgetTopHVGs
Get top markersgetTopMarkers
Model the per-gene CV2modelGeneCV2 modelGeneCV2,ANY-method modelGeneCV2,SingleCellExperiment-method modelGeneCV2,SummarizedExperiment-method
Model the per-gene CV2 with spike-insmodelGeneCV2WithSpikes modelGeneCV2WithSpikes,ANY-method modelGeneCV2WithSpikes,SingleCellExperiment-method modelGeneCV2WithSpikes,SummarizedExperiment-method
Model the per-gene variancemodelGeneVar modelGeneVar,ANY-method modelGeneVar,SingleCellExperiment-method modelGeneVar,SummarizedExperiment-method
Model the per-gene variance with Poisson noisemodelGeneVarByPoisson modelGeneVarByPoisson,ANY-method modelGeneVarByPoisson,SingleCellExperiment-method modelGeneVarByPoisson,SummarizedExperiment-method
Model the per-gene variance with spike-insmodelGeneVarWithSpikes modelGeneVarWithSpikes,ANY-method modelGeneVarWithSpikes,SingleCellExperiment-method modelGeneVarWithSpikes,SummarizedExperiment-method
Combine multiple sets of marker statisticsmultiMarkerStats
Perform pairwise binomial testspairwiseBinom pairwiseBinom,ANY-method pairwiseBinom,SingleCellExperiment-method pairwiseBinom,SummarizedExperiment-method
Perform pairwise t-testspairwiseTTests pairwiseTTests,ANY-method pairwiseTTests,SingleCellExperiment-method pairwiseTTests,SummarizedExperiment-method
Perform pairwise Wilcoxon rank sum testspairwiseWilcox pairwiseWilcox,ANY-method pairwiseWilcox,SingleCellExperiment-method pairwiseWilcox,SummarizedExperiment-method
Quickly perform pseudo-bulk DE analysespseudoBulkDGE pseudoBulkDGE,ANY-method pseudoBulkDGE,SummarizedExperiment-method
Label-specific pseudo-bulk DEpseudoBulkSpecific pseudoBulkSpecific,ANY-method pseudoBulkSpecific,SummarizedExperiment-method
Quick clustering of cellsquickCluster quickCluster,ANY-method quickCluster,SummarizedExperiment-method
Quick and dirty subclusteringquickSubCluster quickSubCluster,ANY-method quickSubCluster,SingleCellExperiment-method quickSubCluster,SummarizedExperiment-method
Spearman's rho to a p-valuerhoToPValue
Cell cycle phase trainingsandbag sandbag,ANY-method sandbag,SummarizedExperiment-method
Compute scaled column ranksscaledColRanks
Score marker genesscoreMarkers scoreMarkers,ANY-method scoreMarkers,SingleCellExperiment-method scoreMarkers,SummarizedExperiment-method
Summary marker statisticssummaryMarkerStats summaryMarkerStats,ANY-method summaryMarkerStats,SummarizedExperiment-method
Hypothesis tests with linear modelstestLinearModel testLinearModel,ANY-method testLinearModel,SummarizedExperiment-method