Package: scrapper 1.7.3

Aaron Lun

scrapper: Bindings to C++ Libraries for Single-Cell Analysis

Implements R bindings to C++ code for analyzing single-cell (expression) data, mostly from various libscran libraries. Each function performs an individual step in the single-cell analysis workflow, ranging from quality control to clustering and marker detection. Additional wrappers are provided for easy construction of end-to-end workflows involving Bioconductor objects like SingleCellExperiments.

Authors:Aaron Lun [cre, aut]

scrapper_1.7.3.tar.gz
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scrapper_1.7.3.tgz(r-4.6-arm64)scrapper_1.5.17.tgz(r-4.6-x86_64)scrapper_1.5.17.tgz(r-4.5-x86_64)scrapper_1.5.17.tgz(r-4.5-arm64)
scrapper_1.7.3.tar.gz(r-4.7-arm64)scrapper_1.7.3.tar.gz(r-4.7-x86_64)scrapper_1.7.3.tar.gz(r-4.6-arm64)scrapper_1.7.3.tar.gz(r-4.6-x86_64)
manual.pdf |manual.html✨
card.svg |card.png
scrapper/json (API)
NEWS

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

Bug tracker:https://github.com/libscran/scrapper/issues

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

On BioConductor:scrapper-1.7.2(bioc 3.24)scrapper-1.6.2(bioc 3.23)

normalizationrnaseqsoftwaregeneexpressiontranscriptomicssinglecellbatcheffectqualitycontroldifferentialexpressionfeatureextractionprincipalcomponentclusteringopenblascpp

8.50 score 8 stars 6 packages 109 scripts 1.2k downloads 115 exports 21 dependencies

Last updated from:defe76d393. Checks:1 WARNING, 8 NOTE, 1 OK, 4 FAIL. Indexed: yes.

TargetResultTimeFilesSyslog
bioc-checksWARNING428
linux-devel-arm64NOTE658
linux-devel-x86_64NOTE832
source / vignettesOK891
linux-release-arm64NOTE660
linux-release-x86_64NOTE757
macos-release-arm64NOTE590
macos-release-x86_64FAIL416
macos-oldrel-arm64FAIL168
macos-oldrel-x86_64FAIL421
windows-develNOTE1674
windows-releaseNOTE2056
windows-oldrelNOTE1992
wasm-releaseFAIL378

Exports:aggregateAcrossCellsaggregateAcrossCells.seaggregateAcrossCellsDefaultsaggregateAcrossGenesaggregateAcrossGenes.seaggregateAcrossGenesDefaultsaggregateColDataanalyzeanalyze.sebuildSnnGraphbuildSnnGraphDefaultscenterSizeFactorscenterSizeFactorsDefaultscenterSpikeInFactorscenterSpikeInFactorsDefaultschooseHighlyVariableGeneschooseHighlyVariableGenesDefaultschoosePseudoCountchoosePseudoCountDefaultschooseRnaHvgs.sechooseRnaHvgsWithSpikeIns.seclusterGraphclusterGraph.seclusterGraphDefaultsclusterKmeansclusterKmeans.seclusterKmeansDefaultscombineFactorscomputeAdtQcMetricscomputeAdtQcMetricsDefaultscomputeBlockWeightscomputeBlockWeightsDefaultscomputeClrm1FactorscomputeClrm1FactorsDefaultscomputeCrisprQcMetricscomputeCrisprQcMetricsDefaultscomputeRnaQcMetricscomputeRnaQcMetricsDefaultscomputeRnaQcMetricsWithAltExpsconvertAnalyzeResultscorrectMnncorrectMnn.secorrectMnnDefaultscountGroupsByBlockDelayedArrayextract_arrayextract_sparse_arrayfilterAdtQcMetricsfilterCrisprQcMetricsfilterRnaQcMetricsfitVarianceTrendfitVarianceTrendDefaultsformatComputeAdtQcMetricsResultformatComputeCrisprQcMetricsResultformatComputeRnaQcMetricsResultformatModelGeneVariancesResultformatScoreMarkersResultgetTestAdtData.segetTestCrisprData.segetTestRnaData.seinitializeCppis_sparseLogNormalizedMatrixLogNormalizedMatrixSeedmodelGeneVariancesmodelGeneVariancesDefaultsnormalizeAdtCounts.senormalizeCountsnormalizeCountsDefaultsnormalizeCrisprCounts.senormalizeRnaCounts.senormalizeRnaCountsWithSpikeIns.sepreviewMarkersquickAdtQc.sequickCrisprQc.sequickRnaQc.sereportGroupMarkerStatisticsrunAllNeighborStepsrunAllNeighborSteps.serunPcarunPca.serunPcaDefaultsrunTsnerunTsne.serunTsneDefaultsrunUmaprunUmap.serunUmapDefaultssanitizeSizeFactorssanitizeSizeFactorsDefaultsscaleByNeighborsscaleByNeighbors.sescaleByNeighborsDefaultsscoreGeneSetscoreGeneSet.sescoreGeneSetDefaultsscoreMarkersscoreMarkers.sescoreMarkersDefaultssubsampleByNeighborssubsampleByNeighborsDefaultssubsampleByPartitionsubsampleByPartitionDefaultssuggestAdtQcThresholdssuggestAdtQcThresholdsDefaultssuggestCrisprQcThresholdssuggestCrisprQcThresholdsDefaultssuggestRnaQcThresholdssuggestRnaQcThresholdsDefaultssummarizeEffectssummarizeEffectsDefaultstestEnrichmenttestEnrichmentDefaultstsnePerplexityToNeighborstype

Dependencies:abindassortheadbeachmatBiocGenericsbiocmakeBiocNeighborsDelayedArraydir.expiryfilelockgenericsIRangeslatticeMatrixMatrixGenericsmatrixStatsRcppRigraphlibS4ArraysS4VectorsSparseArrayXVector

Using scrapper to analyze single-cell data

Aaron Lun

Rendered fromuserguide.Rmdusingknitr::rmarkdownon May 19 2026.

Last update: 2025-12-26
Started: 2024-09-08

Readme and manuals

Help Manual

Help pageTopics
scrapper: Bindings to C++ Libraries for Single-Cell Analysisscrapper-package scrapper
Quality control for ADT count dataadt_quality_control computeAdtQcMetrics filterAdtQcMetrics suggestAdtQcThresholds
Default parameters for ADT quality controladt_quality_control_defaults computeAdtQcMetricsDefaults suggestAdtQcThresholdsDefaults
Aggregate expression across cellsaggregateAcrossCells
Aggregate expression across cells in a SummarizedExperimentaggregateAcrossCells.se aggregateColData
Default parameters for 'aggregateAcrossCells'aggregateAcrossCellsDefaults
Aggregate expression across genesaggregateAcrossGenes
Aggregate expression across gene sets in a SummarizedExperimentaggregateAcrossGenes.se
Default parameters for 'aggregateAcrossGenes'aggregateAcrossGenesDefaults
Defunct functionsanalyze convertAnalyzeResults
Analyze single-cell data from a SummarizedExperimentanalyze.se
Build a shared nearest neighbor graphbuildSnnGraph
Default parameters for 'buildSnnGraph'buildSnnGraphDefaults
Center size factorscenterSizeFactors
Default parameters for 'centerSizeFactors'centerSizeFactorsDefaults
Center spike-in size factorscenterSpikeInFactors
Default parameters for 'centerSpikeInFactors'centerSpikeInFactorsDefaults
Choose highly variable geneschooseHighlyVariableGenes
Default parameters for 'chooseHighlyVariableGenes'chooseHighlyVariableGenesDefaults
Choose a suitable pseudo-countchoosePseudoCount
Default parameters for 'choosePseudoCount'choosePseudoCountDefaults
Choose highly variable genes from a SummarizedExperimentchooseRnaHvgs.se formatModelGeneVariancesResult
Choose highly variable genes based on spike-inschooseRnaHvgsWithSpikeIns.se
Graph-based clustering of cellsclusterGraph
Graph-based clustering of cells in a SingleCellExperimentclusterGraph.se
Default parameters for 'clusterGraph'clusterGraphDefaults
K-means clusteringclusterKmeans
k-means clustering of cells in a SingleCellExperimentclusterKmeans.se
Default parameters for 'clusterKmeans'clusterKmeansDefaults
Combine multiple factorscombineFactors
Compute block weightscomputeBlockWeights
Default parameters for 'computeBlockWeights'computeBlockWeightsDefaults
Compute size factors for ADT countscomputeClrm1Factors
Default parameters for 'computeClrm1Factors'computeClrm1FactorsDefaults
Batch correction with mutual nearest neighborscorrectMnn
MNN correction on a SingleCellExperimentcorrectMnn.se
Default parameters for 'correctMnn'correctMnnDefaults
Count cells in groups and blockscountGroupsByBlock
Quality control for CRISPR count datacomputeCrisprQcMetrics crispr_quality_control filterCrisprQcMetrics suggestCrisprQcThresholds
Default parameters for CRISPR quality controlcomputeCrisprQcMetricsDefaults crispr_quality_control_defaults suggestCrisprQcThresholdsDefaults
Fit a mean-variance trendfitVarianceTrend
Default parameters for 'fitVarianceTrend'fitVarianceTrendDefaults
Get datasets for testinggetTestAdtData.se getTestCrisprData.se getTestData.se getTestRnaData.se
Delayed log-normalization of a matrixDelayedArray,LogNormalizedMatrixSeed-method dim,LogNormalizedMatrixSeed-method dimnames,LogNormalizedMatrixSeed-method extract_array,LogNormalizedMatrixSeed-method extract_sparse_array,LogNormalizedMatrixSeed-method initializeCpp,LogNormalizedMatrixSeed-method is_sparse,LogNormalizedMatrixSeed-method LogNormalizedMatrix LogNormalizedMatrix-class LogNormalizedMatrixSeed LogNormalizedMatrixSeed-class matrixClass,LogNormalizedMatrixSeed-method type,LogNormalizedMatrixSeed-method
Model per-gene variances in expressionmodelGeneVariances
Default parameters for 'modelGeneVariances'modelGeneVariancesDefaults
Normalize ADT counts in a SummarizedExperimentnormalizeAdtCounts.se
Normalize the count matrixnormalizeCounts
Default parameters for 'normalizeCounts'normalizeCountsDefaults
Normalize CRISPR counts in a SummarizedExperimentnormalizeCrisprCounts.se
Normalize RNA counts in a SummarizedExperimentnormalizeRnaCounts.se
Normalize RNA and spike-in countsnormalizeRnaCountsWithSpikeIns.se
Quick quality control for ADT data in a SummarizedExperimentformatComputeAdtQcMetricsResult quickAdtQc.se
Quick quality control for CRISPR data in a SummarizedExperimentformatComputeCrisprQcMetricsResult quickCrisprQc.se
Quick quality control for RNA data in a SummarizedExperimentcomputeRnaQcMetricsWithAltExps formatComputeRnaQcMetricsResult quickRnaQc.se
Report marker statistics for a single groupreportGroupMarkerStatistics
Quality control for RNA count datacomputeRnaQcMetrics filterRnaQcMetrics rna_quality_control suggestRnaQcThresholds
Default parameters for RNA quality controlcomputeRnaQcMetricsDefaults rna_quality_control_defaults suggestRnaQcThresholdsDefaults
Run all neighbor-related stepsrunAllNeighborSteps
Run all nearest neighbor steps on a SummarizedExperimentrunAllNeighborSteps.se
Principal components analysisrunPca
Principal components analysis of a SummarizedexperimentrunPca.se
Default parameters for 'runPca'runPcaDefaults
t-stochastic neighbor embeddingrunTsne tsnePerplexityToNeighbors
t-SNE on a SummarizedExperimentrunTsne.se
Default parameters for 'runTsne'runTsneDefaults
Uniform manifold approximation and projectionrunUmap
UMAP on a SummarizedExperimentrunUmap.se
Default parameters for 'runUmap'runUmapDefaults
Sanitize size factorssanitizeSizeFactors
Default parameters for 'sanitizeSizeFactors'sanitizeSizeFactorsDefaults
Scale and combine multiple embeddingsscaleByNeighbors
Scale and combine multiple embeddings in a SingleCellExperimentscaleByNeighbors.se
Default parameters for 'scaleByNeighbors'scaleByNeighborsDefaults
Score gene set activity for each cellscoreGeneSet
Score a gene set in a SummarizedExperimentscoreGeneSet.se
Default parameters for 'scoreGeneSet'scoreGeneSetDefaults
Score marker genesscoreMarkers
Score marker genes in a SummarizedExperimentformatScoreMarkersResult previewMarkers scoreMarkers.se
Default parameters for 'scoreMarkers'scoreMarkersDefaults
Subsample cells based on their neighborssubsampleByNeighbors
Default parameters for 'subsampleByNeighbors'subsampleByNeighborsDefaults
Subsample by partitionsubsampleByPartition
Default parameters for 'subsampleByPartition'subsampleByPartitionDefaults
Summarize pairwise effect sizes for each groupsummarizeEffects
Default parameters for 'summarizeEffectsDefaults'summarizeEffectsDefaults
Test for gene set enrichmenttestEnrichment
Default parameters for 'testEnrichment'testEnrichmentDefaults