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
scrapper_1.7.3.zip(r-4.7)scrapper_1.7.3.zip(r-4.6)scrapper_1.7.3.zip(r-4.5)
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✨
DESCRIPTION |NEWS
card.svg |card.png
scrapper/json (API)

# 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-1.7.3(bioc 3.24)scrapper-1.6.3(bioc 3.23)

normalization rnaseq software geneexpression transcriptomics singlecell batcheffect qualitycontrol differentialexpression featureextraction principalcomponent clustering openblas cpp

8.73 score 8 stars 11 packages 131 scripts 115 exports 21 dependencies

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

Target	Result	Time
bioc-checks	WARNING	661
linux-devel-arm64	NOTE	834
linux-devel-x86_64	NOTE	963
source / vignettes	OK	1081
linux-release-arm64	NOTE	832
linux-release-x86_64	NOTE	901
macos-release-arm64	NOTE	484
macos-release-x86_64	FAIL	450
macos-oldrel-arm64	FAIL	218
macos-oldrel-x86_64	FAIL	291
windows-devel	NOTE	1936
windows-release	NOTE	1870
windows-oldrel	NOTE	1765
wasm-release	FAIL	1075

Exports:aggregateAcrossCells aggregateAcrossCells.se aggregateAcrossCellsDefaults aggregateAcrossGenes aggregateAcrossGenes.se aggregateAcrossGenesDefaults aggregateColData analyze analyze.se buildSnnGraph buildSnnGraphDefaults centerSizeFactors centerSizeFactorsDefaults centerSpikeInFactors centerSpikeInFactorsDefaults chooseHighlyVariableGenes chooseHighlyVariableGenesDefaults choosePseudoCount choosePseudoCountDefaults chooseRnaHvgs.se chooseRnaHvgsWithSpikeIns.se clusterGraph clusterGraph.se clusterGraphDefaults clusterKmeans clusterKmeans.se clusterKmeansDefaults combineFactors computeAdtQcMetrics computeAdtQcMetricsDefaults computeBlockWeights computeBlockWeightsDefaults computeClrm1Factors computeClrm1FactorsDefaults computeCrisprQcMetrics computeCrisprQcMetricsDefaults computeRnaQcMetrics computeRnaQcMetricsDefaults computeRnaQcMetricsWithAltExps convertAnalyzeResults correctMnn correctMnn.se correctMnnDefaults countGroupsByBlock DelayedArray extract_array extract_sparse_array filterAdtQcMetrics filterCrisprQcMetrics filterRnaQcMetrics fitVarianceTrend fitVarianceTrendDefaults formatComputeAdtQcMetricsResult formatComputeCrisprQcMetricsResult formatComputeRnaQcMetricsResult formatModelGeneVariancesResult formatScoreMarkersResult getTestAdtData.se getTestCrisprData.se getTestRnaData.se initializeCpp is_sparse LogNormalizedMatrix LogNormalizedMatrixSeed modelGeneVariances modelGeneVariancesDefaults normalizeAdtCounts.se normalizeCounts normalizeCountsDefaults normalizeCrisprCounts.se normalizeRnaCounts.se normalizeRnaCountsWithSpikeIns.se previewMarkers quickAdtQc.se quickCrisprQc.se quickRnaQc.se reportGroupMarkerStatistics runAllNeighborSteps runAllNeighborSteps.se runPca runPca.se runPcaDefaults runTsne runTsne.se runTsneDefaults runUmap runUmap.se runUmapDefaults sanitizeSizeFactors sanitizeSizeFactorsDefaults scaleByNeighbors scaleByNeighbors.se scaleByNeighborsDefaults scoreGeneSet scoreGeneSet.se scoreGeneSetDefaults scoreMarkers scoreMarkers.se scoreMarkersDefaults subsampleByNeighbors subsampleByNeighborsDefaults subsampleByPartition subsampleByPartitionDefaults suggestAdtQcThresholds suggestAdtQcThresholdsDefaults suggestCrisprQcThresholds suggestCrisprQcThresholdsDefaults suggestRnaQcThresholds suggestRnaQcThresholdsDefaults summarizeEffects summarizeEffectsDefaults testEnrichment testEnrichmentDefaults tsnePerplexityToNeighbors type

Dependencies:abind assorthead beachmat BiocGenerics biocmake BiocNeighbors DelayedArray dir.expiry filelock generics IRanges lattice Matrix MatrixGenerics matrixStats Rcpp Rigraphlib S4Arrays S4Vectors SparseArray XVector

Using scrapper to analyze single-cell data

Rendered fromuserguide.Rmdusingknitr::rmarkdown

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

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
scrapper: Bindings to C++ Libraries for Single-Cell Analysis	scrapper-package scrapper
Quality control for ADT count data	adt_quality_control computeAdtQcMetrics filterAdtQcMetrics suggestAdtQcThresholds
Default parameters for ADT quality control	adt_quality_control_defaults computeAdtQcMetricsDefaults suggestAdtQcThresholdsDefaults
Aggregate expression across cells	aggregateAcrossCells
Aggregate expression across cells in a SummarizedExperiment	aggregateAcrossCells.se aggregateColData
Default parameters for 'aggregateAcrossCells'	aggregateAcrossCellsDefaults
Aggregate expression across genes	aggregateAcrossGenes
Aggregate expression across gene sets in a SummarizedExperiment	aggregateAcrossGenes.se
Default parameters for 'aggregateAcrossGenes'	aggregateAcrossGenesDefaults
Defunct functions	analyze convertAnalyzeResults
Analyze single-cell data from a SummarizedExperiment	analyze.se
Build a shared nearest neighbor graph	buildSnnGraph
Default parameters for 'buildSnnGraph'	buildSnnGraphDefaults
Center size factors	centerSizeFactors
Default parameters for 'centerSizeFactors'	centerSizeFactorsDefaults
Center spike-in size factors	centerSpikeInFactors
Default parameters for 'centerSpikeInFactors'	centerSpikeInFactorsDefaults
Choose highly variable genes	chooseHighlyVariableGenes
Default parameters for 'chooseHighlyVariableGenes'	chooseHighlyVariableGenesDefaults
Choose a suitable pseudo-count	choosePseudoCount
Default parameters for 'choosePseudoCount'	choosePseudoCountDefaults
Choose highly variable genes from a SummarizedExperiment	chooseRnaHvgs.se formatModelGeneVariancesResult
Choose highly variable genes based on spike-ins	chooseRnaHvgsWithSpikeIns.se
Graph-based clustering of cells	clusterGraph
Graph-based clustering of cells in a SingleCellExperiment	clusterGraph.se
Default parameters for 'clusterGraph'	clusterGraphDefaults
K-means clustering	clusterKmeans
k-means clustering of cells in a SingleCellExperiment	clusterKmeans.se
Default parameters for 'clusterKmeans'	clusterKmeansDefaults
Combine multiple factors	combineFactors
Compute block weights	computeBlockWeights
Default parameters for 'computeBlockWeights'	computeBlockWeightsDefaults
Compute size factors for ADT counts	computeClrm1Factors
Default parameters for 'computeClrm1Factors'	computeClrm1FactorsDefaults
Batch correction with mutual nearest neighbors	correctMnn
MNN correction on a SingleCellExperiment	correctMnn.se
Default parameters for 'correctMnn'	correctMnnDefaults
Count cells in groups and blocks	countGroupsByBlock
Quality control for CRISPR count data	computeCrisprQcMetrics crispr_quality_control filterCrisprQcMetrics suggestCrisprQcThresholds
Default parameters for CRISPR quality control	computeCrisprQcMetricsDefaults crispr_quality_control_defaults suggestCrisprQcThresholdsDefaults
Fit a mean-variance trend	fitVarianceTrend
Default parameters for 'fitVarianceTrend'	fitVarianceTrendDefaults
Get datasets for testing	getTestAdtData.se getTestCrisprData.se getTestData.se getTestRnaData.se
Delayed log-normalization of a matrix	DelayedArray,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 expression	modelGeneVariances
Default parameters for 'modelGeneVariances'	modelGeneVariancesDefaults
Normalize ADT counts in a SummarizedExperiment	normalizeAdtCounts.se
Normalize the count matrix	normalizeCounts
Default parameters for 'normalizeCounts'	normalizeCountsDefaults
Normalize CRISPR counts in a SummarizedExperiment	normalizeCrisprCounts.se
Normalize RNA counts in a SummarizedExperiment	normalizeRnaCounts.se
Normalize RNA and spike-in counts	normalizeRnaCountsWithSpikeIns.se
Quick quality control for ADT data in a SummarizedExperiment	formatComputeAdtQcMetricsResult quickAdtQc.se
Quick quality control for CRISPR data in a SummarizedExperiment	formatComputeCrisprQcMetricsResult quickCrisprQc.se
Quick quality control for RNA data in a SummarizedExperiment	computeRnaQcMetricsWithAltExps formatComputeRnaQcMetricsResult quickRnaQc.se
Report marker statistics for a single group	reportGroupMarkerStatistics
Quality control for RNA count data	computeRnaQcMetrics filterRnaQcMetrics rna_quality_control suggestRnaQcThresholds
Default parameters for RNA quality control	computeRnaQcMetricsDefaults rna_quality_control_defaults suggestRnaQcThresholdsDefaults
Run all neighbor-related steps	runAllNeighborSteps
Run all nearest neighbor steps on a SummarizedExperiment	runAllNeighborSteps.se
Principal components analysis	runPca
Principal components analysis of a Summarizedexperiment	runPca.se
Default parameters for 'runPca'	runPcaDefaults
t-stochastic neighbor embedding	runTsne tsnePerplexityToNeighbors
t-SNE on a SummarizedExperiment	runTsne.se
Default parameters for 'runTsne'	runTsneDefaults
Uniform manifold approximation and projection	runUmap
UMAP on a SummarizedExperiment	runUmap.se
Default parameters for 'runUmap'	runUmapDefaults
Sanitize size factors	sanitizeSizeFactors
Default parameters for 'sanitizeSizeFactors'	sanitizeSizeFactorsDefaults
Scale and combine multiple embeddings	scaleByNeighbors
Scale and combine multiple embeddings in a SingleCellExperiment	scaleByNeighbors.se
Default parameters for 'scaleByNeighbors'	scaleByNeighborsDefaults
Score gene set activity for each cell	scoreGeneSet
Score a gene set in a SummarizedExperiment	scoreGeneSet.se
Default parameters for 'scoreGeneSet'	scoreGeneSetDefaults
Score marker genes	scoreMarkers
Score marker genes in a SummarizedExperiment	formatScoreMarkersResult previewMarkers scoreMarkers.se
Default parameters for 'scoreMarkers'	scoreMarkersDefaults
Subsample cells based on their neighbors	subsampleByNeighbors
Default parameters for 'subsampleByNeighbors'	subsampleByNeighborsDefaults
Subsample by partition	subsampleByPartition
Default parameters for 'subsampleByPartition'	subsampleByPartitionDefaults
Summarize pairwise effect sizes for each group	summarizeEffects
Default parameters for 'summarizeEffectsDefaults'	summarizeEffectsDefaults
Test for gene set enrichment	testEnrichment
Default parameters for 'testEnrichment'	testEnrichmentDefaults