Package: MultiK 1.0.0

MultiK: Multi-Resolution Consensus Clustering for Single-Cell RNA-seq

Identifies optimal cluster numbers in single-cell RNA-seq data through subsampling-based consensus clustering. MultiK performs repeated subsampling and clustering across multiple resolution parameters, then evaluates clustering stability using the Proportion of Ambiguous Clustering (PAC) metric. Statistical significance of cluster separability is assessed using SigClust.

Authors:Zaoqu Liu [aut, cre], Siyao Liu [aut]

MultiK_1.0.0.tar.gz
MultiK_1.0.0.zip(r-4.7)MultiK_1.0.0.zip(r-4.6)MultiK_1.0.0.zip(r-4.5)
MultiK_1.0.0.tgz(r-4.6-any)MultiK_1.0.0.tgz(r-4.5-any)
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MultiK_1.0.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
MultiK/json (API)

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

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

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

Datasets:
  • p3cl - Three Cell Line Mixture Dataset

On CRAN:

Conda:

3.60 score 20 scripts 5 exports 143 dependencies

Last updated from:780a0075c8 (on main). Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK321
source / vignettesOK792
linux-release-x86_64OK300
macos-release-arm64OK170
macos-oldrel-arm64OK204
windows-develOK220
windows-releaseOK221
windows-oldrelOK235
wasm-releaseOK249

Exports:CalcSigClustDiagMultiKPlotgetClustersMultiKPlotSigClust

Dependencies:abindaskpassbase64encBHbitopsbslibcachemcaToolscliclustercodetoolscommonmarkcowplotcpp11crosstalkcurldata.tabledeldirdigestdotCall64dplyrdqrngevaluatefarverfastDummiesfastmapfitdistrplusFNNfontawesomefsfuturefuture.applygenericsggplot2ggrepelggridgesglobalsgluegoftestgplotsgridExtragtablegtoolsherehighrhtmltoolshtmlwidgetshttpuvhttricaigraphirlbaisobandjquerylibjsonliteKernSmoothknitrlabelinglaterlatticelazyevallifecyclelistenvlmtestmagrittrMASSMatrixmatrixStatsmemoisemimeminiUInlmeopensslotelparallellypatchworkpbapplypillarpkgconfigplotlyplyrpngpolyclipprogressrpromisespurrrR6RANNrappdirsRColorBrewerRcppRcppAnnoyRcppArmadilloRcppEigenRcppHNSWRcppProgressRcppTOMLreshape2reticulaterlangrmarkdownROCRrprojrootRSpectraRtsneS7sassscalesscattermoresctransformSeuratSeuratObjectshinysigclustsitmosourcetoolsspspamspatstat.dataspatstat.explorespatstat.geomspatstat.randomspatstat.sparsespatstat.univarspatstat.utilsstringistringrsurvivalsystensortibbletidyrtidyselecttinytexutf8uwotvctrsviridisLitewithrxfunxtableyamlzoo

Algorithm Details and Mathematical Framework
Mathematical Framework | 1. Consensus Clustering | 1.1 Subsampling Strategy | 1.2 Consensus Matrix Construction | 1.3 Visualizing the Consensus Matrix | 2. Proportion of Ambiguous Clustering (PAC) | 2.1 Definition | 2.2 Interpretation | 2.3 PAC Visualization | 2.4 Relative PAC (rPAC) | 3. Optimal K Selection | 3.1 Multi-Objective Optimization | 3.2 Pareto Frontier | 4. SigClust Statistical Testing | 4.1 Hypothesis Framework | 4.2 Cluster Index | 4.3 P-value Heatmap Interpretation | 4.4 Interpretation Guide | 5. Complete Workflow Visualization | 6. Computational Complexity | 7. Parameter Recommendations | References | Author
Rendered fromalgorithm-details.Rmdusingknitr::rmarkdown

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

Introduction to MultiK
Overview | The Challenge | The MultiK Solution | Installation | Quick Start | Load Package and Data | Step 1: Run MultiK Algorithm | Step 2: Diagnostic Visualization | Step 3: Extract Clusters | Step 4: Visualize Clusters | Step 5: Statistical Validation | Key Functions | Summary | Author | Session Info
Rendered fromMultiK-introduction.Rmdusingknitr::rmarkdown

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

Visualization Guide
Overview | 1. Diagnostic Plots (DiagMultiKPlot) | 1.1 K Frequency Distribution | 1.2 Relative PAC (rPAC) | 1.3 Frequency vs. Stability Trade-off | 2. SigClust Visualization (PlotSigClust) | 2.1 Hierarchical Dendrogram | 2.2 P-value Heatmap | 3. Consensus Matrix Visualization | 3.1 Understanding the Consensus Matrix | 4. Color Schemes | 5. Publication-Ready Figures | 5.1 Saving Figures | 5.2 Recommended Dimensions | Author
Rendered fromvisualization-guide.Rmdusingknitr::rmarkdown

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

Best Practices and Troubleshooting
Best Practices | 1. Data Preprocessing | 1.1 Quality Control | 1.2 Recommended Preprocessing | 2. Parameter Selection | 2.1 Number of Repetitions (reps) | 2.2 Subsampling Proportion (pSample) | 2.3 Resolution Range | 2.4 PCA Dimensions (nPC) | 3. Computational Considerations | 3.1 Parallel Processing | 3.2 Memory Management | 3.3 Runtime Estimates | 4. Interpreting Results | 4.1 Clear Optimal K | 4.2 Multiple Candidate K Values | 4.3 Hierarchical Relationships | 5. Troubleshooting | 5.1 "No valid consensus matrices found" | 5.2 High PAC for All K | 5.3 SigClust Returns NA | 5.4 Long Runtime | 6. Validation Strategies | 6.1 Biological Validation | 6.2 Technical Validation | 6.3 Cross-Validation | 7. Reporting Guidelines | Example Methods Text | Author
Rendered frombest-practices.Rmdusingknitr::rmarkdown

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