Package: scrapper 1.1.12

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. It is mostly intended for other Bioconductor package developers to build more user-friendly end-to-end workflows.

Authors:Aaron Lun [cre, aut]

scrapper_1.1.12.tar.gz
scrapper_1.1.12.zip(r-4.5)scrapper_1.1.12.zip(r-4.4)scrapper_1.1.12.zip(r-4.3)
scrapper_1.1.12.tgz(r-4.4-x86_64)scrapper_1.1.12.tgz(r-4.4-arm64)scrapper_1.1.12.tgz(r-4.3-x86_64)scrapper_1.1.12.tgz(r-4.3-arm64)
scrapper_1.1.12.tar.gz(r-4.5-noble)scrapper_1.1.12.tar.gz(r-4.4-noble)
scrapper_1.1.12.tgz(r-4.4-emscripten)scrapper_1.1.12.tgz(r-4.3-emscripten)
scrapper.pdf |scrapper.html✨
scrapper/json (API)
NEWS

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

Peer review:

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

On BioConductor:scrapper-1.1.11(bioc 3.21)scrapper-1.0.3(bioc 3.20)

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

normalizationrnaseqsoftwaregeneexpressiontranscriptomicssinglecellbatcheffectqualitycontroldifferentialexpressionfeatureextractionprincipalcomponentclusteringcpp

5.53 score 32 scripts 125 downloads 38 exports 27 dependencies

Last updated 16 days agofrom:0f251eefb4. Checks:6 OK, 3 ERROR. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKJan 07 2025
R-4.5-win-x86_64OKJan 07 2025
R-4.5-linux-x86_64OKJan 07 2025
R-4.4-win-x86_64OKJan 07 2025
R-4.4-mac-x86_64OKJan 07 2025
R-4.4-mac-aarch64OKJan 07 2025
R-4.3-win-x86_64ERRORJan 07 2025
R-4.3-mac-x86_64ERRORJan 07 2025
R-4.3-mac-aarch64ERRORJan 07 2025

Exports:aggregateAcrossCellsaggregateAcrossGenesanalyzebuildSnnGraphcenterSizeFactorschooseHighlyVariableGeneschoosePseudoCountclusterGraphclusterKmeanscombineFactorscomputeAdtQcMetricscomputeClrm1FactorscomputeCrisprQcMetricscomputeRnaQcMetricsconvertAnalyzeResultscorrectMnnfilterAdtQcMetricsfilterCrisprQcMetricsfilterRnaQcMetricsfitVarianceTrendmodelGeneVariancesnormalizeCountsreportGroupMarkerStatisticsrunAllNeighborStepsrunPcarunTsnerunUmapsanitizeSizeFactorsscaleByNeighborsscoreGeneSetscoreMarkerssubsampleByNeighborssuggestAdtQcThresholdssuggestCrisprQcThresholdssuggestRnaQcThresholdssummarizeEffectstestEnrichmenttsnePerplexityToNeighbors

Dependencies:abindassortheadbeachmatBiocGenericsBiocNeighborsclicpp11crayonDelayedArraygenericsglueigraphIRangeslatticelifecyclemagrittrMatrixMatrixGenericsmatrixStatspkgconfigRcpprlangS4ArraysS4VectorsSparseArrayvctrsXVector

Using scrapper to analyze single-cell data

Aaron Lun

Rendered fromuserguide.Rmdusingknitr::rmarkdownon Jan 07 2025.

Last update: 2025-01-06
Started: 2024-09-08

Readme and manuals

Help Manual

Help pageTopics
Quality control for ADT count dataadt_quality_control computeAdtQcMetrics filterAdtQcMetrics suggestAdtQcThresholds
Aggregate expression across cellsaggregateAcrossCells
Aggregate expression across genesaggregateAcrossGenes
Analyze single-cell dataanalyze
Build a shared nearest neighbor graphbuildSnnGraph
Center size factorscenterSizeFactors
Choose highly variable geneschooseHighlyVariableGenes
Choose a suitable pseudo-countchoosePseudoCount
Graph-based clustering of cellsclusterGraph
K-means clusteringclusterKmeans
Combine multiple factorscombineFactors
Compute size factors for ADT countscomputeClrm1Factors
Convert analysis results into a SingleCellExperimentconvertAnalyzeResults
Batch correction with mutual nearest neighborscorrectMnn
Quality control for CRISPR count datacomputeCrisprQcMetrics crispr_quality_control filterCrisprQcMetrics suggestCrisprQcThresholds
Fit a mean-variance trendfitVarianceTrend
Model per-gene variances in expressionmodelGeneVariances
Normalize the count matrixnormalizeCounts
Report marker statistics for a single groupreportGroupMarkerStatistics
Quality control for RNA count datacomputeRnaQcMetrics filterRnaQcMetrics rna_quality_control suggestRnaQcThresholds
Run all neighbor-related stepsrunAllNeighborSteps
Principal components analysisrunPca
t-stochastic neighbor embeddingrunTsne tsnePerplexityToNeighbors
Uniform manifold approxation and projectionrunUmap
Sanitize size factorssanitizeSizeFactors
Scale and combine multiple embeddingsscaleByNeighbors
Score gene set activity for each cellscoreGeneSet
Score marker genesscoreMarkers
Subsample cells based on their neighborssubsampleByNeighbors
Summarize pairwise effect sizes for each groupsummarizeEffects
Test for gene set enrichmenttestEnrichment