Package: scrapper 1.1.8

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_1.1.8.tar.gz
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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-1.1.7(bioc 3.21)scrapper-1.0.2(bioc 3.20)

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

normalizationrnaseqsoftwaregeneexpressiontranscriptomicssinglecellbatcheffectqualitycontroldifferentialexpressionfeatureextractionprincipalcomponentclusteringcpp

5.42 score 29 scripts 125 downloads 34 exports 27 dependencies

Last updated 14 hours agofrom:2fb5c04f93. Checks:OK: 9. Indexed: yes.

TargetResultDate
Doc / VignettesOKDec 22 2024
R-4.5-win-x86_64OKDec 20 2024
R-4.5-linux-x86_64OKDec 22 2024
R-4.4-win-x86_64OKDec 19 2024
R-4.4-mac-x86_64OKDec 22 2024
R-4.4-mac-aarch64OKDec 22 2024
R-4.3-win-x86_64OKDec 20 2024
R-4.3-mac-x86_64OKDec 22 2024
R-4.3-mac-aarch64OKDec 22 2024

Exports:aggregateAcrossCellsaggregateAcrossGenesbuildSnnGraphcenterSizeFactorschooseHighlyVariableGeneschoosePseudoCountclusterGraphclusterKmeanscombineFactorscomputeAdtQcMetricscomputeClrm1FactorscomputeCrisprQcMetricscomputeRnaQcMetricscorrectMnnfilterAdtQcMetricsfilterCrisprQcMetricsfilterRnaQcMetricsfitVarianceTrendmodelGeneVariancesnormalizeCountsrunAllNeighborStepsrunPcarunTsnerunUmapsanitizeSizeFactorsscaleByNeighborsscoreGeneSetscoreMarkerssubsampleByNeighborssuggestAdtQcThresholdssuggestCrisprQcThresholdssuggestRnaQcThresholdssummarizeEffectstsnePerplexityToNeighbors

Dependencies:abindassortheadbeachmatBiocGenericsBiocNeighborsclicpp11crayonDelayedArraygenericsglueigraphIRangeslatticelifecyclemagrittrMatrixMatrixGenericsmatrixStatspkgconfigRcpprlangS4ArraysS4VectorsSparseArrayvctrsXVector

Using scrapper to analyze single-cell data

Aaron Lun

Rendered fromuserguide.Rmdusingknitr::rmarkdownon Dec 22 2024.

Last update: 2024-09-19
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
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
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
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