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  "Title": "An Umbrella Framework for Multi-Source and Multi-Omics\nClustering",
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  "Authors@R": "c(\nperson(\"Bernard Isekah\", \"Osang'ir\", email = \"bernard.osangir@sckcen.be\",\nrole = c(\"aut\", \"cre\"), comment = c(ORCID = \"0000-0002-5557-3602\")),\nperson(\"Marijke\", \"Van Moerbeke\", role = \"aut\"),\nperson(\"Ziv\", \"Shkedy\", role = \"ctb\"),\nperson(\"Surya\", \"Gupta\", role = \"ctb\"),\nperson(\"Jürgen\", \"Claesen\", role = \"ctb\"))",
  "Description": "An umbrella framework (\"MoSaIC\" = Multi-Omics\nSource-Agnostic Integration Clustering in R) that unifies a\nlarge collection of multi-source / multi-omics clustering\nmethodologies behind a single, consistent list-of-matrices\ninterface. It spans five integration paradigms - direct,\nsimilarity-based, graph-based, voting-based consensus, and\nhierarchy-based - and bundles a complete downstream workflow\nfor method comparison and evaluation. The package features the\nmulti-source the ability to compare many algorithms on the same\nfooting, a data-nugget based feature-weighting scheme as a\nrobust, big-data-friendly alternative to variance weighting,\nand a downstream suite for cluster characterisation,\nvisualisation and biological interpretation.",
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  "Repository": "https://lucp12891.r-universe.dev",
  "Date/Publication": "2026-09-15 11:18:07 UTC",
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    "Cyclogram",
    "DetermineWeight_SilClust",
    "DetermineWeight_SimClust",
    "DiffGenes",
    "DiffGenesSelection",
    "Distance",
    "Distance_v2",
    "EHC",
    "EnsembleClustering",
    "EvidenceAccumulation",
    "f.clustABC.MultiSource",
    "FeatSelection",
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    "M_ABCdist.WC",
    "M_ABCpp",
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    "Normalization",
    "nugget_cluster",
    "nugget_feature_weights",
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    "Pathways",
    "PathwaysIter",
    "PathwaysSelection",
    "PlotPathways",
    "PreparePathway",
    "ProfilePlot",
    "ReorderToReference",
    "SelectnrClusters",
    "SharedComps",
    "SharedGenesPathsFeat",
    "SharedSelection",
    "SharedSelectionLimma",
    "SharedSelectionMLP",
    "SimilarityHeatmap",
    "SimilarityMeasure",
    "SNF",
    "spectral_clustering",
    "TrackCluster",
    "WeightedClust",
    "Whclust",
    "Wkmeans",
    "WonM"
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      "title": "Single-source ABC with a deep-learning (autoencoder) Step 4 (ABCdeep)",
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        "ABCdeep.SingleInMultiple"
      ]
    },
    {
      "page": "ABCdist.SingleInMultiple",
      "title": "Single-source ABC with distance accumulation",
      "topics": [
        "ABCdist.SingleInMultiple"
      ]
    },
    {
      "page": "ABCpp.SingleInMultiple",
      "title": "Single-source Aggregating Bundles of Clusters (ABC), C++-accelerated engine",
      "topics": [
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      ]
    },
    {
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      "title": "Aggregated data clustering",
      "topics": [
        "ADC"
      ]
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      "title": "Aggregated data ensemble clustering",
      "topics": [
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    },
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      "topics": [
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    },
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    },
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      "title": "Determine the characteristic features of clusters",
      "topics": [
        "CharacteristicFeatures"
      ]
    },
    {
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      "title": "Interactive plot to determine DE Genes and DE features for a specific cluster",
      "topics": [
        "ChooseCluster"
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    },
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      "title": "Single-source base clustering",
      "topics": [
        "Cluster"
      ]
    },
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      "title": "Agreement between two partitions",
      "topics": [
        "cluster_agreement"
      ]
    },
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      "title": "Helper that colours dendrogram leaves by cluster membership",
      "topics": [
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      ]
    },
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      "title": "Clustering aggregation",
      "topics": [
        "ClusteringAggregation"
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    },
    {
      "page": "ClusterPlot",
      "title": "Colouring clusters in a dendrogram",
      "topics": [
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      ]
    },
    {
      "page": "ColorPalette",
      "title": "Create a color palette to be used in the plots",
      "topics": [
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      ]
    },
    {
      "page": "ColorsNames",
      "title": "Function that annotates colors to their names",
      "topics": [
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      ]
    },
    {
      "page": "compare_clusterings",
      "title": "Compare two clusterings derived from distance matrices",
      "topics": [
        "compare_clusterings"
      ]
    },
    {
      "page": "CompareInteractive",
      "title": "Interactive comparison of single and multiple source clustering results",
      "topics": [
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      ]
    },
    {
      "page": "ComparePlot",
      "title": "Comparison of clustering results over multiple methods",
      "topics": [
        "ComparePlot"
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    },
    {
      "page": "CompareSilCluster",
      "title": "Compares medoid clustering results based on silhouette widths",
      "topics": [
        "CompareSilCluster"
      ]
    },
    {
      "page": "CompareSvsM",
      "title": "Comparison of clustering results for the single and multiple source clustering.",
      "topics": [
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      ]
    },
    {
      "page": "ConsensusClustering",
      "title": "Voting-based consensus clustering",
      "topics": [
        "ConsensusClustering"
      ]
    },
    {
      "page": "ContFeaturesPlot",
      "title": "Plot of continuous features",
      "topics": [
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    },
    {
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      "topics": [
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    {
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      "title": "Cumulative Voting Aggregation (CVAA / W-CVAA)",
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      ]
    },
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      "title": "Comparison of clustering results over multiple results in circular format",
      "topics": [
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      ]
    },
    {
      "page": "DetermineWeight_SilClust",
      "title": "Determines an optimal weight for weighted clustering by silhouettes widths.",
      "topics": [
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      "page": "DiffGenes",
      "title": "Find differentially expressed genes",
      "topics": [
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      ]
    },
    {
      "page": "DiffGenesSelection",
      "title": "Differential expression for a selection of objects",
      "topics": [
        "DiffGenesSelection"
      ]
    },
    {
      "page": "Distance",
      "title": "Compute a distance matrix",
      "topics": [
        "Distance",
        "Distance_v2"
      ]
    },
    {
      "page": "distanceheatmaps",
      "title": "Determine the distance in a heatmap",
      "topics": [
        "distanceheatmaps"
      ]
    },
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      "title": "Ensemble hierarchical clustering via graph partitioning (METIS / MST)",
      "topics": [
        "EHC"
      ]
    },
    {
      "page": "EnsembleClustering",
      "title": "Ensemble clustering via graph partitioning (CSPA / HGPA / MCLA)",
      "topics": [
        "EnsembleClustering"
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    },
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      "title": "Evidence accumulation clustering (co-association)",
      "topics": [
        "EvidenceAccumulation"
      ]
    },
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      "title": "Consensus clustering from stacked ABC label matrices",
      "topics": [
        "f.clustABC.MultiSource"
      ]
    },
    {
      "page": "FeatSelection",
      "title": "Determine the characteristic features of a cluster or selection",
      "topics": [
        "FeatSelection"
      ]
    },
    {
      "page": "FeaturesOfCluster",
      "title": "List all features present in a selected cluster of objects",
      "topics": [
        "FeaturesOfCluster"
      ]
    },
    {
      "page": "FindCluster",
      "title": "Find a selection of objects in the output of 'ReorderToReference'",
      "topics": [
        "FindCluster"
      ]
    },
    {
      "page": "FindElement",
      "title": "Find an element in a data structure",
      "topics": [
        "FindElement"
      ]
    },
    {
      "page": "FindGenes",
      "title": "Find the genes shared across methods",
      "topics": [
        "FindGenes"
      ]
    },
    {
      "page": "Geneset.intersect",
      "title": "Intersection over resulting gene sets of 'PathwaysIter' function",
      "topics": [
        "Geneset.intersect"
      ]
    },
    {
      "page": "Geneset.intersectSelection",
      "title": "Intersection over resulting gene sets of 'PathwaysIter' function for a selection of objects",
      "topics": [
        "Geneset.intersectSelection"
      ]
    },
    {
      "page": "HBGF",
      "title": "Hybrid Bipartite Graph Formulation",
      "topics": [
        "HBGF"
      ]
    },
    {
      "page": "HeatmapPlot",
      "title": "A heatmap of the comparison of two clustering results.",
      "topics": [
        "HeatmapPlot"
      ]
    },
    {
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      "title": "A function to select a group of objects via the similarity heatmap.",
      "topics": [
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    },
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      "title": "Hierarchical ensemble clustering",
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