Package: MosaiClusteR 0.1.0

Bernard Isekah Osangir

MosaiClusteR: An Umbrella Framework for Multi-Source and Multi-Omics Clustering

An umbrella framework ("MoSaIC" = Multi-Omics Source-Agnostic Integration Clustering in R) that unifies a large collection of multi-source / multi-omics clustering methodologies behind a single, consistent list-of-matrices interface. It spans five integration paradigms - direct, similarity-based, graph-based, voting-based consensus, and hierarchy-based - and bundles a complete downstream workflow for method comparison and evaluation. The package features the multi-source the ability to compare many algorithms on the same footing, a data-nugget based feature-weighting scheme as a robust, big-data-friendly alternative to variance weighting, and a downstream suite for cluster characterisation, visualisation and biological interpretation.

Authors:Bernard Isekah Osang'ir [aut, cre], Marijke Van Moerbeke [aut], Ziv Shkedy [ctb], Surya Gupta [ctb], Jürgen Claesen [ctb]

MosaiClusteR_0.1.0.tar.gz
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manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
MosaiClusteR/json (API)

# Install 'MosaiClusteR' in R:
install.packages('MosaiClusteR', repos = c('https://lucp12891.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/lucp12891/mosaicluster/issues

Uses libs:
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

cpp

3.48 score 2 stars 537 downloads 87 exports 31 dependencies

Last updated from:b58a4a257a. Checks:14 ERROR, 1 OK. Indexed: yes.

TargetResultTimeFilesSyslog
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linux-devel-x86_64ERROR227
source / vignettesERROR325
linux-release-arm64ERROR198
linux-release-x86_64ERROR246
macos-release-arm64ERROR211
macos-release-x86_64ERROR549
macos-oldrel-arm64ERROR192
macos-oldrel-x86_64ERROR418
windows-devel-arm64ERROR368
windows-devel-x86_64ERROR224
windows-release-arm64ERROR375
windows-release-x86_64ERROR203
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wasm-releaseOK192

Exports:ABCdeep.SingleInMultipleABCdist.SingleInMultipleABCpp.SingleInMultipleADCADECBinFeaturesPlot_MultipleDataBinFeaturesPlot_SingleDataBoxPlotDistanceCECCharacteristicFeaturesChooseClusterClustercluster_agreementClusterColsClusteringAggregationClusterPlotColorPaletteColorsNamescompare_clusteringsCompareInteractiveComparePlotCompareSilClusterCompareSvsMConsensusClusteringContFeaturesPlotcreate_data_nuggetsCVAACyclogramDetermineWeight_SilClustDetermineWeight_SimClustDiffGenesDiffGenesSelectionDistanceDistance_v2EHCEnsembleClusteringEvidenceAccumulationf.clustABC.MultiSourceFeatSelectionFeaturesOfClusterFindClusterFindElementFindGenesGeneset.intersectGeneset.intersectSelectionHBGFHeatmapPlotHeatmapSelectionHierarchicalEnsembleClusteringintNMFLabelColsLabelPlotLinkBasedClusteringLUCIDM_ABCdeepM_ABCdistM_ABCdist.WCM_ABCppmosaic_labelsmosaic_simNEMONormalizationnugget_clusternugget_feature_weightsPathwayAnalysisPathwaysPathwaysIterPathwaysSelectionPlotPathwaysPreparePathwayProfilePlotReorderToReferenceSelectnrClustersSharedCompsSharedGenesPathsFeatSharedSelectionSharedSelectionLimmaSharedSelectionMLPSimilarityHeatmapSimilarityMeasureSNFspectral_clusteringTrackClusterWeightedClustWhclustWkmeansWonM

Dependencies:abindade4apeclassclusterdata.tabledigeste1071fastclusterFDgeometrygtoolslatticelinproglpSolvemagicMASSMatrixmatrixStatsmgcvnlmepermutepixmapproxyrbibutilsRcppRcppArmadilloRcppProgressRdpackspvegan

Readme and manuals

Help Manual

Help pageTopics
Single-source ABC with a deep-learning (autoencoder) Step 4 (ABCdeep)ABCdeep.SingleInMultiple
Single-source ABC with distance accumulationABCdist.SingleInMultiple
Single-source Aggregating Bundles of Clusters (ABC), C++-accelerated engineABCpp.SingleInMultiple
Aggregated data clusteringADC
Aggregated data ensemble clusteringADEC
Visualization of characteristic binary features of multiple data setsBinFeaturesPlot_MultipleData
Visualization of characteristic binary features of a single data setBinFeaturesPlot_SingleData
Box plots of one distance matrix categorized against another distance matrix.BoxPlotDistance
Complementary ensemble clusteringCEC
Determine the characteristic features of clustersCharacteristicFeatures
Interactive plot to determine DE Genes and DE features for a specific clusterChooseCluster
Single-source base clusteringCluster
Agreement between two partitionscluster_agreement
Helper that colours dendrogram leaves by cluster membershipClusterCols
Clustering aggregationClusteringAggregation
Colouring clusters in a dendrogramClusterPlot
Create a color palette to be used in the plotsColorPalette
Function that annotates colors to their namesColorsNames
Compare two clusterings derived from distance matricescompare_clusterings
Interactive comparison of single and multiple source clustering resultsCompareInteractive
Comparison of clustering results over multiple methodsComparePlot
Compares medoid clustering results based on silhouette widthsCompareSilCluster
Comparison of clustering results for the single and multiple source clustering.CompareSvsM
Voting-based consensus clusteringConsensusClustering
Plot of continuous featuresContFeaturesPlot
Create data nuggets from a data matrixcreate_data_nuggets
Cumulative Voting Aggregation (CVAA / W-CVAA)CVAA
Comparison of clustering results over multiple results in circular formatCyclogram
Determines an optimal weight for weighted clustering by silhouettes widths.DetermineWeight_SilClust
Determines an optimal weight for weighted clustering by similarity weighted clustering.DetermineWeight_SimClust
Find differentially expressed genesDiffGenes
Differential expression for a selection of objectsDiffGenesSelection
Compute a distance matrixDistance Distance_v2
Determine the distance in a heatmapdistanceheatmaps
Ensemble hierarchical clustering via graph partitioning (METIS / MST)EHC
Ensemble clustering via graph partitioning (CSPA / HGPA / MCLA)EnsembleClustering
Evidence accumulation clustering (co-association)EvidenceAccumulation
Consensus clustering from stacked ABC label matricesf.clustABC.MultiSource
Determine the characteristic features of a cluster or selectionFeatSelection
List all features present in a selected cluster of objectsFeaturesOfCluster
Find a selection of objects in the output of 'ReorderToReference'FindCluster
Find an element in a data structureFindElement
Find the genes shared across methodsFindGenes
Intersection over resulting gene sets of 'PathwaysIter' functionGeneset.intersect
Intersection over resulting gene sets of 'PathwaysIter' function for a selection of objectsGeneset.intersectSelection
Hybrid Bipartite Graph FormulationHBGF
A heatmap of the comparison of two clustering results.HeatmapPlot
A function to select a group of objects via the similarity heatmap.HeatmapSelection
Hierarchical ensemble clusteringHierarchicalEnsembleClustering
Integrative NMF clustering (intNMF)intNMF
Helper that colours specific dendrogram leavesLabelCols
Coloring specific leaves of a dendrogramLabelPlot
Link-based cluster ensemblesLinkBasedClustering
LUCID: latent unknown clustering integrating omics with an outcomeLUCID
Multi-source M-ABC with a deep-learning (autoencoder) Step 4 (M-ABCdeep)M_ABCdeep
Multi-source M-ABC with distance accumulationM_ABCdist
Multi-source M-ABC fused with WeightedClustM_ABCdist.WC
Multi-source Aggregating Bundles of Clusters (M-ABC), C++-acceleratedM_ABCpp
Extract a flat partition from a MosaiClusteR resultmosaic_labels
Simulate a multi-source data set with planted clustersmosaic_sim
Toy multi-omics example datamosaic_toy
NEMO: neighborhood-based multi-omics clusteringNEMO
Normalisation of featuresNormalization
Data-nugget clusteringnugget_cluster
Feature weights derived from data nuggetsnugget_feature_weights
Pathway analysis with intersection over iterationsPathwayAnalysis
Pathway analysis for multiple clustering resultsPathways
Iterations of the pathway analysisPathwaysIter
Pathway analysis for a selection of objectsPathwaysSelection
A GO plot of a pathway analysis output.PlotPathways
Prepare data for pathway analysisPreparePathway
Plotting gene profilesProfilePlot
Relabel and reorder clusterings to a referenceReorderToReference
Select the number of clusters via silhouette widthsSelectnrClusters
Objects shared across clusteringsSharedComps
Shared genes, pathways and features across methodsSharedGenesPathsFeat
Intersection of genes and pathways over multiple methods for a selection of objects.SharedSelection
Intersection of genes over multiple methods for a selection of objects.SharedSelectionLimma
Intersection of pathways over multiple methods for a selection of objects.SharedSelectionMLP
A heatmap of similarity values between objectsSimilarityHeatmap
Similarity of a set of clusterings to a referenceSimilarityMeasure
Similarity network fusionSNF
Spectral clustering of an affinity matrixspectral_clustering
Track a cluster or a selection of objects across multiple methodsTrackCluster
Weighted clusteringWeightedClust
Weighted hierarchical clustering (weighted Ward)Whclust
Weighted k-means (minimises WWCSS)Wkmeans
Weighting on Membership clusteringWonM