Package: scran 1.33.2
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:
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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')) |
Bug tracker:https://github.com/marionilab/scran/issues
On BioConductor:scran-1.33.2(bioc 3.20)scran-1.32.0(bioc 3.19)
immunooncologynormalizationsequencingrnaseqsoftwaregeneexpressiontranscriptomicssinglecellclusteringbioconductor-packagehuman-cell-atlassingle-cell-rna-seq
Last updated 1 months agofrom:60f8278584. Checks:OK: 1 ERROR: 2 WARNING: 6. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Oct 06 2024 |
R-4.5-win-x86_64 | WARNING | Oct 06 2024 |
R-4.5-linux-x86_64 | ERROR | Oct 06 2024 |
R-4.4-win-x86_64 | WARNING | Oct 06 2024 |
R-4.4-mac-x86_64 | WARNING | Oct 06 2024 |
R-4.4-mac-aarch64 | WARNING | Sep 06 2024 |
R-4.3-win-x86_64 | WARNING | Oct 06 2024 |
R-4.3-mac-x86_64 | WARNING | Oct 06 2024 |
R-4.3-mac-aarch64 | ERROR | Oct 06 2024 |
Exports:.logBHbootstrapClusterbuildKNNGraphbuildSNNGraphcalculateSumFactorsclusterCellsclusterKNNGraphclusterModularityclusterPurityclusterSNNGraphcoassignProbcombineBlockscombineCV2combineMarkerscombinePValuescombineVarcomputeMinRankcomputeSumFactorsconnectClusterMSTconvertTocorrelateGenescorrelateNullcorrelatePairscreateClusterMSTcyclonedecideTestsPerLabeldecomposeVardenoisePCAdenoisePCANumberDMdoubletCellsdoubletClusterdoubletRecoveryfindMarkersfitTrendCV2fitTrendPoissonfitTrendVarfixedPCAgetClusteredPCsgetDenoisedPCsgetMarkerEffectsgetTopHVGsgetTopMarkersimprovedCV2makeTechTrendmodelGeneCV2modelGeneCV2WithSpikesmodelGeneVarmodelGeneVarByPoissonmodelGeneVarWithSpikesmultiBlockNormmultiBlockVarmultiMarkerStatsorderClusterMSToverlapExprspairwiseBinompairwiseTTestspairwiseWilcoxparallelPCApseudoBulkDGEpseudoBulkSpecificquickClusterquickPseudotimequickSubClusterrhoToPValuesandbagscaledColRanksscoreMarkerssummarizeTestsPerLabelsummaryMarkerStatstechnicalCV2testLinearModeltestPseudotimetestVartrendVar
Dependencies:abindaskpassassortheadbeachmatBHBiobaseBiocGenericsBiocNeighborsBiocParallelBiocSingularblustercliclustercodetoolscpp11crayoncurlDelayedArraydqrngedgeRformatRfutile.loggerfutile.optionsGenomeInfoDbGenomeInfoDbDataGenomicRangesgluehttrigraphIRangesirlbajsonlitelambda.rlatticelifecyclelimmalocfitmagrittrMatrixMatrixGenericsmatrixStatsmetapodmimeopensslpkgconfigR6RcpprlangrsvdS4ArraysS4VectorsScaledMatrixscuttleSingleCellExperimentsitmosnowSparseArraystatmodSummarizedExperimentsysUCSC.utilsvctrsXVectorzlibbioc
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 |