Package: MOFSR 2.3.0

MOFSR: Multi-Omics Fusion for Subtype Recognition

A comprehensive toolkit for integrating multi-modal biological data to discover disease subtypes and biological mechanisms. MOFSR provides 15 state-of-the-art multi-omics clustering algorithms (SNF, wSNF, CPCA, iClusterBayes, IntNMF, LRAcluster, MCIA, MOFA, NEMO, PINSPlus, RGCCA, SGCCA, CIMLR, BCC, LateFusion), 17 classification methods, parallel computing support, comprehensive visualization (UMAP, heatmaps, survival curves), data preprocessing (normalization, filtering, batch correction, QC), feature selection with bootstrap validation, and cluster quality assessment. All core algorithms are implemented internally for maximum compatibility, cross-platform support, and optimized performance. Works on Windows, macOS, and Linux without external dependencies for core functionality.

Authors:Zaoqu Liu [aut, cre]

MOFSR_2.3.0.tar.gz
MOFSR_2.3.0.zip(r-4.7)MOFSR_2.3.0.zip(r-4.6)MOFSR_2.3.0.zip(r-4.5)
MOFSR_2.3.0.tgz(r-4.6-any)MOFSR_2.3.0.tgz(r-4.5-any)
MOFSR_2.3.0.tar.gz(r-4.7-any)MOFSR_2.3.0.tar.gz(r-4.6-any)
MOFSR_2.3.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
MOFSR/json (API)

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

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

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

Datasets:

On CRAN:

Conda:

3.54 score 1 stars 14 scripts 118 exports 0 dependencies

Last updated from:ebcefdc51e (on master). Checks:7 ERROR, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64ERROR237
source / vignettesOK285
linux-release-x86_64ERROR227
macos-release-arm64ERROR161
macos-oldrel-arm64ERROR145
windows-develERROR181
windows-releaseERROR146
windows-oldrelERROR149
wasm-releaseOK219

Exports:align_samplesbcc_clusterbcc_cluster_fastcalc_chicalc_pacCalCHICalPACcheck_sample_alignmentcimlr_clustercimlr_feature_rankingClassifier.AdaboostClassifier.DTClassifier.EnetClassifier.EnrichmentClassifier.GBDTClassifier.kNNClassifier.LASSOClassifier.LDAClassifier.NBayesClassifier.NNetClassifier.PCAClassifier.RFClassifier.RidgeClassifier.ssGSEAClassifier.StepLRClassifier.SVDClassifier.SVMClassifier.XGBoostcompare_clusteringscompute_umapconsensus_clustercorrect_batchcpcaFeatureSelectionWithBootstrapfilter_by_madfilter_low_varianceFind.OptClusterFeaturesget_consensus_classget.binary.clustersget.classget.Jaccard.Distancehandle_missingicluster_bayesicluster_bayes_fastinitintnmf_clusterintnmf_opt_klist_clustering_algorithmslraclustermciamofa_analysismulti_view_factor_analysisnemo_affinity_graphnemo_clusteringnemo_num_clustersnormalize_omicsparallel_bootstrap_featuresparallel_consensus_clusterPathDEAperturbation_clusteringplot_algorithm_comparisonplot_cluster_qualityplot_consensus_heatmapplot_silhouetteplot_survivalplot_umapqc_summaryrgccarun_bccrun_cimlrrun_cpcarun_iclusterbayesrun_integrationrun_intnmfrun_late_fusionrun_lraclusterrun_mciarun_mofarun_multiple_algorithmsrun_nemorun_parallel_algorithmsrun_pinsplusrun_rgccarun_sgccarun_snfrun_wsnfRunBCCRunCCRunCIMLRRunClassifierRunCOCARunCPCARunEnsembleRunGSVARuniClusterBayesRunIFRunIntegrationRunIntNMFRunLRAclusterRunMCIARunMOFSRunNEMORunPCARunPINSPlusRunRGCCARunSGCCARunSNFSelect.Featuressetup_parallelsgccasnf_affinity_matrixsnf_fusespectral_clusteringssMwwGSTstop_parallelsubtyping_omics_dataWangGBMWuGBM

Dependencies:

Advanced Usage
Introduction | Parallel Computing | Setup Parallel Backend | Parallel Consensus Clustering | Parallel Feature Selection | Cleanup | Data Preprocessing Pipeline | Complete Preprocessing Workflow | Handling Missing Values | Batch Effect Correction | Custom Analysis Pipelines | Multi-Algorithm Comparison | Optimal K Selection | Ensemble Clustering | Feature Selection | Bootstrap-Based Selection | MAD-Based Filtering | Classification | Train Subtype Classifier | Ensemble Classifier | Low-Level API Access | Direct Algorithm Functions | Factor Analysis Components | Integration with Bioconductor | Gene Set Analysis | Survival Analysis | Performance Tips | Memory Management | Algorithm Selection by Data Size | Parallelization Strategy | Troubleshooting | Common Issues | Debug Mode | Session Info
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Last update: 2026-01-29
Started: 2026-01-29

Algorithm Principles in MOFSR
Overview | Algorithm Classification | Network-Based Methods | Similarity Network Fusion (SNF) | CIMLR (Cancer Integration via Multi-kernel Learning) | Matrix Factorization Methods | Integrative Non-negative Matrix Factorization (IntNMF) | LRAcluster (Low-Rank Approximation) | Factor Analysis Methods | Multi-Omics Factor Analysis (MOFA) | Multiple Co-Inertia Analysis (MCIA) | Canonical Correlation Methods | RGCCA (Regularized Generalized CCA) | SGCCA (Sparse GCCA) | Bayesian Methods | iClusterBayes | Bayesian Consensus Clustering (BCC) | Ensemble Methods | PINSPlus (Perturbation Clustering) | Algorithm Selection Guide | References | Session Info
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Last update: 2026-01-29
Started: 2026-01-29

Benchmark Analysis
Introduction | Simulation Framework | Algorithm Benchmark | Define Algorithms | Run Benchmark | Summary Statistics | Visualization | ARI Comparison | Runtime Comparison | Trade-off Analysis | Noise Sensitivity Analysis | Sample Size Scaling | Recommendations | Algorithm Selection Flowchart | Session Info
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Last update: 2026-01-29
Started: 2026-01-29

Getting Started with MOFSR
Introduction | Author | Installation | Quick Start | Generate Simulated Multi-Omics Data | Run SNF Clustering | UMAP Visualization | Compare Multiple Algorithms | Consensus Clustering | Data Preprocessing | Session Info
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Visualization Guide
Introduction | Setup | UMAP Visualization | Basic UMAP Plot | Customized UMAP | UMAP Parameters | Consensus Matrix Heatmap | Generate Consensus Matrix | Basic Heatmap | Ordered by Clusters | Custom Colors | Cluster Quality Metrics | PAC (Proportion of Ambiguous Clustering) | Combined Metrics | Silhouette Analysis | Interpretation | Algorithm Comparison | ARI Matrix | Survival Analysis (Optional) | Base R vs ggplot2 | Check ggplot2 Availability | Consistent API | Saving Plots | With ggplot2 | With Base R | Color Palettes | Default Palette | Nature-Style Palettes | Summary | Session Info
Rendered fromvisualization-guide.Rmdusingknitr::rmarkdown

Last update: 2026-01-29
Started: 2026-01-29

Readme and manuals

Help Manual

Help pageTopics
Multi-View Factor Analysisalgo-factor multi_view_factor_analysis
Bayesian Integrative Clustering (iClusterBayes)algo-iclusterbayes icluster_bayes
Neighborhood-based Multi-Omics clustering (NEMO).NEMO_NEIGHBORS_RATIO algo-nemo
Similarity Network Fusion (SNF) Algorithmalgo-snf snf_affinity_matrix
Align Samples Across Datasetsalign_samples
BCC Core Algorithmbcc_cluster
Fast BCCbcc_cluster_fast
Calculate Calinski-Harabasz Indexcalc_chi
Calculate Proportion of Ambiguous Clustering (PAC)calc_pac
Calinski-Harabasz Index CalculationCalCHI
Calculate Proportion of Ambiguous Clustering (PAC)CalPAC
Check Sample Alignmentcheck_sample_alignment
CIMLR Core Algorithmcimlr_cluster
CIMLR Feature Rankingcimlr_feature_ranking
AdaBoost Classifier for Cluster PredictionClassifier.Adaboost
Decision Tree Classifier for Cluster PredictionClassifier.DT
Elastic Net Classifier for Cluster PredictionClassifier.Enet
Enrichment-Based Neural Network Classifier for Cluster PredictionClassifier.Enrichment
Gradient Boosted Decision Trees (GBDT) Classifier for Cluster PredictionClassifier.GBDT
k-Nearest Neighbors (kNN) Classifier for Cluster PredictionClassifier.kNN
LASSO Classifier for Cluster PredictionClassifier.LASSO
Linear Discriminant Analysis (LDA) Classifier for Cluster PredictionClassifier.LDA
Naive Bayes Classifier for Cluster PredictionClassifier.NBayes
Neural Network Classifier for Cluster PredictionClassifier.NNet
PCA-Based Neural Network Classifier for Cluster PredictionClassifier.PCA
Random Forest Classifier for Cluster PredictionClassifier.RF
Ridge Classifier for Cluster PredictionClassifier.Ridge
Perform ssGSEA-based Subtyping Using Marker Gene SetsClassifier.ssGSEA
Stepwise Logistic Regression Classifier for Cluster PredictionClassifier.StepLR
SVD-Based Neural Network Classifier for Cluster PredictionClassifier.SVD
Support Vector Machine (SVM) Classifier for Cluster PredictionClassifier.SVM
XGBoost Classifier for Cluster PredictionClassifier.XGBoost
Create Cluster Comparison Matrixcompare_clusterings
UMAP Dimensionality Reductioncompute_umap
Consensus Clusteringconsensus_cluster
Simple Batch Correctioncorrect_batch
Consensus PCAcpca
Calculate Coefficient of Variation for a Numeric VectorCV
Calculate Coefficient of Variation for a Data FrameCV.df
Feature Selection with Bootstrap for Each ClusterFeatureSelectionWithBootstrap
Filter by MAD (Median Absolute Deviation)filter_by_mad
Filter Low-Variance Featuresfilter_low_variance
Optimal Feature Combination for Multi-Modality ClusteringFind.OptClusterFeatures
Functional Gene Setsgene_sets
Get Cluster Assignments from Consensus Resultsget_consensus_class
Get Binary Clusters from Clustering Resultsget.binary.clusters
Get Cluster Assignmentsget.class
Jaccard Distance Calculation for Binary Matrixget.Jaccard.Distance
Handle Missing Valueshandle_missing
Simplified iClusterBayesicluster_bayes_fast
Initialize MOFSR Optional Dependenciesinit
IntNMF Core Algorithmintnmf_cluster
Optimal K Selection for IntNMFintnmf_opt_k
LRAcluster Core Algorithmlracluster
Calculate Median Absolute Deviation for a Data FrameMAD.df
Multiple Co-Inertia Analysismcia
Calculate Mean Value for a Data FrameMean.df
Calculate Median Value for a Data FrameMedian.df
Minmax Normalization for a Numeric Vectorminmax
Minmax Normalization for a Data Frameminmax.df
NEMO Affinity Graph Constructionnemo_affinity_graph
NEMO Clusteringnemo_clustering
Estimate Number of Clusters from Affinity Graphnemo_num_clusters
Parallel Computing Support for MOFSRparallel setup_parallel
Parallel Bootstrap Feature Selectionparallel_bootstrap_features
Parallel Consensus Clusteringparallel_consensus_cluster
Pathway Differential Expression Analysis (PathDEA)PathDEA
Perturbation Clusteringperturbation_clustering
Plot Algorithm Comparisonplot_algorithm_comparison
Plot Cluster Quality Metricsplot_cluster_quality
Plot Consensus Matrix Heatmapplot_consensus_heatmap
Plot Silhouette Analysisplot_silhouette
Plot Kaplan-Meier Survival Curvesplot_survival
Plot UMAP with Clustersplot_umap
Data Preprocessing Functions for MOFSRnormalize_omics preprocessing
Quality Control Summaryqc_summary
Regularized Generalized Canonical Correlation Analysisrgcca
Run BCC Clusteringrun_bcc
Run CIMLR Clusteringrun_cimlr
Run CPCA Clusteringrun_cpca
Run iClusterBayes Clusteringrun_iclusterbayes
Run Multi-Omics Integration and Clusteringrun_integration
Run IntNMF Clusteringrun_intnmf
Late Fusion Clusteringrun_late_fusion
Run LRAclusterrun_lracluster
Run MCIA Clusteringrun_mcia
Run Factor-Based Clusteringrun_mofa
Run Multiple Clustering Algorithmsrun_multiple_algorithms
Run NEMO Clusteringrun_nemo
Run Multiple Algorithms in Parallelrun_parallel_algorithms
Run PINSPlus Clusteringrun_pinsplus
Run RGCCA Clusteringrun_rgcca
Run SGCCA Clusteringrun_sgcca
Run SNF Clusteringrun_snf
Weighted Similarity Network Fusionrun_wsnf
Unified Multi-Omics Clustering Interfacelist_clustering_algorithms run-clustering
Run Bayesian Consensus Clustering (BCC)RunBCC
Run Consensus ClusteringRunCC
Run Cancer Integrative Multi-kernel Learning (CIMLR)RunCIMLR
Run Classifiers for Cluster PredictionRunClassifier
Run Consensus Clustering Analysis (COCA)RunCOCA
Run Consensus Principal Component Analysis (CPCA)RunCPCA
Run Ensemble of Multiple Classifiers for Cluster PredictionRunEnsemble
Generate Single-Sample Gene-Set Enrichment ScoreRunGSVA
Run Bayesian Integrative Clustering (iClusterBayes)RuniClusterBayes
Run Multi-Omics Integration ClusteringRunIF RunIntegration
Run Integrative Non-negative Matrix Factorization (IntNMF)RunIntNMF
Run Low-Rank Approximation Clustering (LRAcluster)RunLRAcluster
Run Multiple Co-Inertia Analysis (MCIA)RunMCIA
Run MultiModality Fusion Subtyping (MOFS) for Multi-Modality Data IntegrationRunMOFS
Run Neighborhood-based Multi-Omics clustering (NEMO)RunNEMO
Run Principal Component AnalysisRunPCA
Run Perturbation Clustering (PINSPlus)RunPINSPlus
Run Regularized Generalized Canonical Correlation Analysis (RGCCA)RunRGCCA
Run Sparse Generalized Canonical Correlation Analysis (SGCCA)RunSGCCA
Run Similarity Network Fusion (SNF) for Multi-Modality Data IntegrationRunSNF
Calculate Standard Deviation for a Data FrameSD.df
Select Hypervariable FeaturesSelect.Features
Sparse Generalized Canonical Correlation Analysissgcca
Similarity Network Fusionsnf_fuse
Spectral Clusteringspectral_clustering
Single-Sample Pathway Activity Analysis Using MWW-GSTssMwwGST
Stop Parallel Backendstop_parallel
Subtyping Multi-Omics Data with PINSPlussubtyping_omics_data
Perform ssGSEA-based Subtyping for GBM Samples (Wang et al. 2017)WangGBM
Perform ssGSEA-based Subtyping Using Wu et al. 2024 MarkersWuGBM