# An Umbrella Framework for Multi-Source and Multi-Omics Clustering

## Documentation for package ‘MosaiClusteR’ version 0.1.0

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package NEWS
- Package README

## Help Pages

ABCdeep.SingleInMultiple | Single-source ABC with a deep-learning (autoencoder) Step 4 (ABCdeep)
ABCdist.SingleInMultiple | Single-source ABC with distance accumulation
ABCpp.SingleInMultiple | Single-source Aggregating Bundles of Clusters (ABC), C++-accelerated engine
ADC | Aggregated data clustering
ADEC | Aggregated data ensemble clustering
BinFeaturesPlot_MultipleData | Visualization of characteristic binary features of multiple data sets
BinFeaturesPlot_SingleData | Visualization of characteristic binary features of a single data set
BoxPlotDistance | Box plots of one distance matrix categorized against another distance matrix.
CEC | Complementary ensemble clustering
CharacteristicFeatures | Determine the characteristic features of clusters
characterize_clusters | Characterise clusters with external (biological / clinical) variables
ChooseCluster | Interactive plot to determine DE Genes and DE features for a specific cluster
Cluster | Single-source base clustering
ClusterCols | Helper that colours dendrogram leaves by cluster membership
ClusteringAggregation | Clustering aggregation
clustering_concordance | Compare clustering solutions to each other (no ground truth needed)
ClusterPlot | Colouring clusters in a dendrogram
cluster_agreement | Agreement between two partitions
cluster_biology_panel | Panel of cluster characterisation plots
cluster_k_sweep | Show the clustering at every k (see how it deteriorates)
cluster_selection_plot | Justify the number of clusters (silhouette + elbow / scree)
ColorPalette | Create a color palette to be used in the plots
ColorsNames | Function that annotates colors to their names
CompareInteractive | Interactive comparison of single and multiple source clustering results
ComparePlot | Comparison of clustering results over multiple methods
CompareSilCluster | Compares medoid clustering results based on silhouette widths
CompareSvsM | Comparison of clustering results for the single and multiple source clustering.
compare_clusterings | Compare two clusterings derived from distance matrices
compare_methods_plot | Visually compare clustering solutions with ComparePlot (no ground truth)
ConsensusClustering | Voting-based consensus clustering
ContFeaturesPlot | Plot of continuous features
create_data_nuggets | Create data nuggets from a data matrix
CVAA | Cumulative Voting Aggregation (CVAA / W-CVAA)
Cyclogram | Comparison of clustering results over multiple results in circular format
DetermineWeight_SilClust | Determines an optimal weight for weighted clustering by silhouettes widths.
DetermineWeight_SimClust | Determines an optimal weight for weighted clustering by similarity weighted clustering.
DiffGenes | Find differentially expressed genes
DiffGenesSelection | Differential expression for a selection of objects
Distance | Compute a distance matrix
distanceheatmaps | Determine the distance in a heatmap
Distance_v2 | Compute a distance matrix
EHC | Ensemble hierarchical clustering via graph partitioning (METIS / MST)
embedding_plot | Two-dimensional cluster visualisation (PCA / UMAP / t-SNE)
EnsembleClustering | Ensemble clustering via graph partitioning (CSPA / HGPA / MCLA)
EvidenceAccumulation | Evidence accumulation clustering (co-association)
f.clustABC.MultiSource | Consensus clustering from stacked ABC label matrices
FeatSelection | Determine the characteristic features of a cluster or selection
FeaturesOfCluster | List all features present in a selected cluster of objects
FindCluster | Find a selection of objects in the output of 'ReorderToReference'
FindElement | Find an element in a data structure
FindGenes | Find the genes shared across methods
Geneset.intersect | Intersection over resulting gene sets of 'PathwaysIter' function
Geneset.intersectSelection | Intersection over resulting gene sets of 'PathwaysIter' function for a selection of objects
HBGF | Hybrid Bipartite Graph Formulation
HeatmapPlot | A heatmap of the comparison of two clustering results.
HeatmapSelection | A function to select a group of objects via the similarity heatmap.
HierarchicalEnsembleClustering | Hierarchical ensemble clustering
intNMF | Integrative NMF clustering (intNMF)
LabelCols | Helper that colours specific dendrogram leaves
LabelPlot | Coloring specific leaves of a dendrogram
LinkBasedClustering | Link-based cluster ensembles
LUCID | LUCID: latent unknown clustering integrating omics with an outcome
mosaic_labels | Extract a flat partition from a MosaiClusteR result
mosaic_sim | Simulate a multi-source data set with planted clusters
mosaic_toy | Toy multi-omics example data
M_ABCdeep | Multi-source M-ABC with a deep-learning (autoencoder) Step 4 (M-ABCdeep)
M_ABCdist | Multi-source M-ABC with distance accumulation
M_ABCdist.WC | Multi-source M-ABC fused with WeightedClust
M_ABCpp | Multi-source Aggregating Bundles of Clusters (M-ABC), C++-accelerated
NEMO | NEMO: neighborhood-based multi-omics clustering
Normalization | Normalisation of features
nugget_cluster | Data-nugget clustering
nugget_feature_weights | Feature weights derived from data nuggets
PathwayAnalysis | Pathway analysis with intersection over iterations
Pathways | Pathway analysis for multiple clustering results
PathwaysIter | Iterations of the pathway analysis
PathwaysSelection | Pathway analysis for a selection of objects
PlotPathways | A GO plot of a pathway analysis output.
plot_cluster_profile | Plot one external variable by cluster
PreparePathway | Prepare data for pathway analysis
ProfilePlot | Plotting gene profiles
ReorderToReference | Relabel and reorder clusterings to a reference
SelectnrClusters | Select the number of clusters via silhouette widths
SharedComps | Objects shared across clusterings
SharedGenesPathsFeat | Shared genes, pathways and features across methods
SharedSelection | Intersection of genes and pathways over multiple methods for a selection of objects.
SharedSelectionLimma | Intersection of genes over multiple methods for a selection of objects.
SharedSelectionMLP | Intersection of pathways over multiple methods for a selection of objects.
SimilarityHeatmap | A heatmap of similarity values between objects
SimilarityMeasure | Similarity of a set of clusterings to a reference
simulate_weighting_regime | Simulate data for evaluating feature-weighting schemes (variance vs CV)
SNF | Similarity network fusion
spectral_clustering | Spectral clustering of an affinity matrix
TrackCluster | Track a cluster or a selection of objects across multiple methods
WeightedClust | Weighted clustering
Whclust | Weighted hierarchical clustering (weighted Ward)
Wkmeans | Weighted k-means (minimises WWCSS)
WonM | Weighting on Membership clustering