Package: recall 0.1.0

recall: Calibrated Clustering with Artificial Variables to Avoid Over-Clustering in Single-Cell RNA-Sequencing

recall (Calibrated Clustering with Artificial Variables) is a method for protecting against over-clustering by controlling for the impact of double-dipping. The approach can be applied to any clustering algorithm (implemented are the Louvain and Leiden algorithms with plans for K-means, and hierarchical clustering algorithms). The method provides state-of-the-art clustering performance and can rapidly analyze large-scale scRNA-seq studies and is compatible with the Seurat library (V4 and V5).

Authors:Zaoqu Liu [aut, cre]

recall_0.1.0.tar.gz
recall_0.1.0.zip(r-4.7)recall_0.1.0.zip(r-4.6)recall_0.1.0.zip(r-4.5)
recall_0.1.0.tgz(r-4.6-any)recall_0.1.0.tgz(r-4.5-any)
recall_0.1.0.tar.gz(r-4.7-any)recall_0.1.0.tar.gz(r-4.6-any)
recall_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html
DESCRIPTION
card.svg |card.png
recall/json (API)

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

Bug tracker:https://github.com/zaoqu-liu/recall/issues

Pkgdown/docs site:https://zaoqu-liu.github.io

On CRAN:

Conda:

3.81 score 32 scripts 3 exports 183 dependencies

Last updated from:ebad0cdf97 (on main). Checks:1 FAIL, 2 OK, 6 ERROR. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64FAIL167
source / vignettesOK242
linux-release-x86_64ERROR202
macos-release-arm64ERROR106
macos-oldrel-arm64ERROR91
windows-develERROR141
windows-releaseERROR110
windows-oldrelERROR113
wasm-releaseOK220

Exports:FindClustersCountsplitFindClustersRecallseurat_workflow

Dependencies:abindaskpassbase64encBHBiobaseBiocGenericsBiocParallelbitopsbslibcachemcaToolscliclustercodetoolscommonmarkcoopcorpcorcountsplitcowplotcpp11crosstalkcurldata.tableDelayedArraydeldirdigestdotCall64dplyrdqrngevaluatefarverfastDummiesfastmapfitdistrplusFNNfontawesomeforeachformatRfsfutile.loggerfutile.optionsfuturefuture.applygamlssgamlss.datagamlss.distgenericsGenomicRangesggplot2ggrepelggridgesglmnetglobalsgluegoftestgplotsgridExtragtablegtoolsherehighrhtmltoolshtmlwidgetshttpuvhttricaigraphIRangesirlbaisobanditeratorsjquerylibjsonliteKernSmoothknitrknockofflabelinglambda.rlamWlaterlatticelazyevallifecyclelistenvlmtestmagrittrMASSMatrixMatrixGenericsmatrixStatsmclustmemoisemgcvmimeminiUImvtnormnlmeopensslotelparallellypatchworkpbapplypbmcapplypillarpkgconfigplotlyplyrpngpolyclipprogressrpromisespurrrR6RANNrappdirsRColorBrewerRcppRcppAnnoyRcppArmadilloRcppEigenRcppHNSWRcppParallelRcppProgressRcppTOMLRdsdpreshape2reticulaterlangrmarkdownROCRrprojrootRSpectraRtsneS4ArraysS4VectorsS7sassscalesscattermorescDesign3sctransformSeqinfoSeuratSeuratObjectshapeshinySingleCellExperimentsitmosnowsourcetoolsspspamSparseArraysparseMVNspatstat.dataspatstat.explorespatstat.geomspatstat.randomspatstat.sparsespatstat.univarspatstat.utilsstringistringrSummarizedExperimentsurvivalsystensortibbletidyrtidyselecttinytexumaputf8uwotvctrsviridisviridisLitewithrxfunxtableXVectoryamlzoo

Advanced Usage
Introduction | Null Distribution Selection | Available Methods | Choosing the Right Method | Assessing Data Characteristics | Parallel Computing | Cross-Platform Configuration | Memory Management | Count Splitting Alternative | Method Comparison | Resolution Tuning | Iterative Resolution Reduction | Finding Optimal Starting Resolution | Clustering Algorithm Selection | Louvain vs Leiden | Integration with Seurat Workflow | Complete Analysis Pipeline | Using seurat_workflow Helper | Batch Processing Multiple Samples | Troubleshooting | Common Issues | Session Information

Last update: 2026-01-23
Started: 2026-01-23

Algorithm and Methodology
Introduction | The Double-Dipping Problem | Mathematical Framework | The Knockoff Filter | The W Statistic | FDR-Controlled Selection | The recall Algorithm | Stage 1: Synthetic Null Variable Generation | Supported Distributions | Stage 2: Joint Analysis | Stage 3: Iterative Calibration | Zero-Inflated Poisson Estimation | Maximum Likelihood Estimation | Implementation | Negative Binomial Estimation | Copula Models | Gaussian Copula | Theoretical Guarantees | FDR Control | Comparison with Count Splitting | References

Last update: 2026-01-23
Started: 2026-01-23

Basic Usage
Introduction | Setup | Loading Data | Standard Preprocessing | Running recall | Accessing Results | Visualization | Comparison with Standard Clustering | Next Steps | Session Info

Last update: 2026-01-23
Started: 2026-01-23

Visualization Guide
Introduction | Setup | Data Preparation | Comparing Clustering Methods | Standard vs Calibrated Clustering | Side-by-Side UMAP Comparison | Cluster Quality Assessment | Cluster Size Distribution | Marker Gene Heatmap | Feature Visualization | Expression Patterns | Violin Plots | Resolution Analysis | Multi-Resolution Comparison | Publication-Ready Figures | Custom Theme | Exporting Figures | Session Info

Last update: 2026-01-23
Started: 2026-01-23