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MosaiClusteR 0.1.0

First public release. MosaiClusteR is an umbrella framework for multi-source and multi-omics clustering: it gathers a broad catalogue of integration methods behind one consistent interface, together with the tooling needed to preprocess, select, evaluate, compare, characterise and biologically interpret the results.

Framework

• Unified interface. Every method takes the same input — a List of numeric matrices over the same objects (objects in rows) — and returns the same {DistM, Clust} contract, so methods are interchangeable and directly comparable. Precomputed dissimilarities are accepted via type = "dist".
• Four-layer workflow. Preprocess → baseline → integrate → evaluate.

Integration methods (five paradigms)

• Direct: ADC(), ADEC().
• Similarity-based: WeightedClust(), WonM(), SNF(), NEMO().
• Graph-based: EnsembleClustering() (CSPA/HGPA/MCLA), HBGF(), ClusteringAggregation().
• Voting / consensus: CEC(), CVAA(), ConsensusClustering(), EvidenceAccumulation(), LinkBasedClustering() (CTS/SRS/ASRS), and the Aggregating Bundles of Clusters family M_ABCpp() / M_ABCdist() / M_ABCdist.WC() / M_ABCdeep() (a deep-learning, autoencoder-based variant).
• Hierarchy-based: HierarchicalEnsembleClustering(), EHC().
• Factor / low-rank / spectral: intNMF(), spectral_clustering(), LUCID() (supervised, outcome-guided).
• Cluster() provides the single-source baseline primitive.

Preprocessing, selection and evaluation

• Preprocessing: Normalization() and Distance() (Euclidean, Manhattan, Tanimoto/Jaccard for binary data, correlation-based, and more).
• Cluster-number selection: SelectnrClusters(), ChooseCluster().
• Evaluation: mosaic_labels(), cluster_agreement() (ARI / NMI / Jaccard / purity), compare_clusterings(), CompareSilCluster(), CompareSvsM(), and data-driven source weighting (DetermineWeight_SilClust(), DetermineWeight_SimClust()).

Feature weighting and data nuggets

• A data-nugget compression provides a robust, big-data-friendly feature weighting as an alternative to variance: create_data_nuggets(), nugget_feature_weights(), nugget_cluster(), Wkmeans(), Whclust().

Characterisation, visualisation and interpretation

• Characterisation / navigation: CharacteristicFeatures(), FeatSelection(), FeaturesOfCluster(), FindCluster(), FindElement(), TrackCluster(), ReorderToReference(), SharedComps(), SimilarityMeasure().
• Visualisation: ComparePlot(), ClusterPlot(), Cyclogram(), SimilarityHeatmap(), HeatmapPlot(), ProfilePlot(), ContFeaturesPlot(), BinFeaturesPlot_SingleData()/_MultipleData(), BoxPlotDistance(), and colour helpers (ColorPalette(), ColorsNames(), ClusterCols(), LabelCols()).
• Biological interpretation (omics): DiffGenes(), PathwayAnalysis(), Pathways(), Geneset.intersect(), SharedGenesPathsFeat() and related functions connect clusters to genes and pathways.

Data and tests

• Bundled mosaic_toy dataset and the mosaic_sim() structured-data generator.
• A testthat suite covering distances, weighting schemes, the integration methods, evaluation metrics and data nuggets.

Notes

• A compiled kernel (src/) accelerates the consensus step; building from source needs a C/C++ toolchain (Rtools on Windows). Heavier and specialised dependencies — SNFtool, circlize, plotrix, ggplot2, igraph, limma, MLP, biomaRt, org.Hs.eg.db and others — are declared under Suggests and loaded only when the corresponding method is called.
• A few methods rely on an external graph-partitioning executable (e.g. METIS for EnsembleClustering(), the METIS path of EHC(), and LinkBasedClustering(linkBasedMethod = "asrs")) and raise an informative error when it is absent; pure-R alternatives (HBGF(), EvidenceAccumulation(), CTS/SRS) cover the same need.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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