Package: scrapper 1.6.3

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_1.6.3.tar.gz
scrapper_1.6.3.zip(r-4.7)scrapper_1.6.3.zip(r-4.6)scrapper_1.6.3.zip(r-4.5)
scrapper_1.6.3.tgz(r-4.6-arm64)scrapper_1.4.0.tgz(r-4.6-x86_64)scrapper_1.4.0.tgz(r-4.5-x86_64)scrapper_1.4.0.tgz(r-4.5-arm64)
scrapper_1.6.3.tar.gz(r-4.7-arm64)scrapper_1.6.3.tar.gz(r-4.7-x86_64)scrapper_1.6.3.tar.gz(r-4.6-arm64)scrapper_1.6.3.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-release.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-1.7.3(bioc 3.24)scrapper-1.6.3(bioc 3.23)

normalizationrnaseqsoftwaregeneexpressiontranscriptomicssinglecellbatcheffectqualitycontroldifferentialexpressionfeatureextractionprincipalcomponentclusteringopenblascpp

8.63 score 8 stars 9 packages 131 scripts 79 exports 21 dependencies

Last updated from:c1b691a3ca (on RELEASE_3_23). Checks:1 WARNING, 8 NOTE, 1 OK, 4 FAIL. Indexed: no.

TargetResultTimeFilesSyslog
bioc-checksWARNING410
linux-devel-arm64NOTE651
linux-devel-x86_64NOTE802
source / vignettesOK799
linux-release-arm64NOTE646
linux-release-x86_64NOTE710
macos-release-arm64NOTE520
macos-release-x86_64FAIL333
macos-oldrel-arm64FAIL139
macos-oldrel-x86_64FAIL364
windows-develNOTE1661
windows-releaseNOTE1811
windows-oldrelNOTE1717
wasm-releaseFAIL359

Exports:aggregateAcrossCellsaggregateAcrossCells.seaggregateAcrossGenesaggregateAcrossGenes.seaggregateColDataanalyzeanalyze.sebuildSnnGraphcenterSizeFactorschooseHighlyVariableGeneschoosePseudoCountchooseRnaHvgs.seclusterGraphclusterGraph.seclusterKmeansclusterKmeans.secombineFactorscomputeAdtQcMetricscomputeBlockWeightscomputeClrm1FactorscomputeCrisprQcMetricscomputeRnaQcMetricscomputeRnaQcMetricsWithAltExpsconvertAnalyzeResultscorrectMnncorrectMnn.secountGroupsByBlockDelayedArrayextract_arrayextract_sparse_arrayfilterAdtQcMetricsfilterCrisprQcMetricsfilterRnaQcMetricsfitVarianceTrendformatComputeAdtQcMetricsResultformatComputeCrisprQcMetricsResultformatComputeRnaQcMetricsResultformatModelGeneVariancesResultformatScoreMarkersResultgetTestAdtData.segetTestCrisprData.segetTestRnaData.seinitializeCppis_sparseLogNormalizedMatrixLogNormalizedMatrixSeedmodelGeneVariancesnormalizeAdtCounts.senormalizeCountsnormalizeCrisprCounts.senormalizeRnaCounts.sepreviewMarkersquickAdtQc.sequickCrisprQc.sequickRnaQc.sereportGroupMarkerStatisticsrunAllNeighborStepsrunAllNeighborSteps.serunPcarunPca.serunTsnerunTsne.serunUmaprunUmap.sesanitizeSizeFactorsscaleByNeighborsscaleByNeighbors.sescoreGeneSetscoreGeneSet.sescoreMarkersscoreMarkers.sesubsampleByNeighborssuggestAdtQcThresholdssuggestCrisprQcThresholdssuggestRnaQcThresholdssummarizeEffectstestEnrichmenttsnePerplexityToNeighborstype

Dependencies:abindassortheadbeachmatBiocGenericsbiocmakeBiocNeighborsDelayedArraydir.expiryfilelockgenericsIRangeslatticeMatrixMatrixGenericsmatrixStatsRcppRigraphlibS4ArraysS4VectorsSparseArrayXVector

Using scrapper to analyze single-cell data

Aaron Lun

Rendered fromuserguide.Rmdusingknitr::rmarkdownon Jun 18 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
Aggregate expression across cellsaggregateAcrossCells
Aggregate expression across cells in a SummarizedExperimentaggregateAcrossCells.se aggregateColData
Aggregate expression across genesaggregateAcrossGenes
Aggregate expression across gene sets in a SummarizedExperimentaggregateAcrossGenes.se
Analyze single-cell dataanalyze
Analyze single-cell data from a SummarizedExperimentanalyze.se
Build a shared nearest neighbor graphbuildSnnGraph
Center size factorscenterSizeFactors
Choose highly variable geneschooseHighlyVariableGenes
Choose a suitable pseudo-countchoosePseudoCount
Choose highly variable genes from a SummarizedExperimentchooseRnaHvgs.se formatModelGeneVariancesResult
Graph-based clustering of cellsclusterGraph
Graph-based clustering of cells in a SingleCellExperimentclusterGraph.se
K-means clusteringclusterKmeans
k-means clustering of cells in a SingleCellExperimentclusterKmeans.se
Combine multiple factorscombineFactors
Compute block weightscomputeBlockWeights
Compute size factors for ADT countscomputeClrm1Factors
Convert analysis results into a SingleCellExperimentconvertAnalyzeResults
Batch correction with mutual nearest neighborscorrectMnn
MNN correction on a SingleCellExperimentcorrectMnn.se
Count cells in groups and blockscountGroupsByBlock
Quality control for CRISPR count datacomputeCrisprQcMetrics crispr_quality_control filterCrisprQcMetrics suggestCrisprQcThresholds
Fit a mean-variance trendfitVarianceTrend
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
Normalize ADT counts in a SummarizedExperimentnormalizeAdtCounts.se
Normalize the count matrixnormalizeCounts
Normalize CRISPR counts in a SummarizedExperimentnormalizeCrisprCounts.se
Normalize RNA counts in a SummarizedExperimentnormalizeRnaCounts.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
Run all neighbor-related stepsrunAllNeighborSteps
Run all nearest neighbor steps on a SummarizedExperimentrunAllNeighborSteps.se
Principal components analysisrunPca
Principal components analysis of a SummarizedexperimentrunPca.se
t-stochastic neighbor embeddingrunTsne tsnePerplexityToNeighbors
t-SNE on a SummarizedExperimentrunTsne.se
Uniform manifold approximation and projectionrunUmap
UMAP on a SummarizedExperimentrunUmap.se
Sanitize size factorssanitizeSizeFactors
Scale and combine multiple embeddingsscaleByNeighbors
Scale and combine multiple embeddings in a SingleCellExperimentscaleByNeighbors.se
Score gene set activity for each cellscoreGeneSet
Score a gene set in a SummarizedExperimentscoreGeneSet.se
Score marker genesscoreMarkers
Score marker genes in a SummarizedExperimentformatScoreMarkersResult previewMarkers scoreMarkers.se
Subsample cells based on their neighborssubsampleByNeighbors
Summarize pairwise effect sizes for each groupsummarizeEffects
Test for gene set enrichmenttestEnrichment