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.