# Integration of Multiple Data Sets with Clustering Techniques

## Documentation for package ‘IntClust’ version 0.1.0

- DESCRIPTION file.
- User guides, package vignettes and other documentation.

## Help Pages

IntClust-package | Integrated Clustering Methods.
ABC.SingleInMultiple | Single-source ABC clustering
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 | Determining the characteristic features of a cluster
ChooseCluster | Interactive plot to determine DE Genes and DE features for a specific cluster
Cluster | Single source clustering
ClusterCols | Matching clusters with colours
ClusteringAggregation | Clustering aggregation
ClusterPlot | Colouring clusters in a dendrogram
ColorPalette | Create a color palette to be used in the plots
Colors1 | Colour examples
ColorsNames | Function that annotates colors to their names
CompareInteractive | Interactive comparison of clustering results for a specific cluster or method.
ComparePlot | Comparison of clustering results over multiple results
CompareSilCluster | Compares medoid clustering results based on silhouette widths
CompareSvsM | Comparison of clustering results for the single and multiple source clustering.
ConsensusClustering | Consensus clustering
ContFeaturesPlot | Plot of continuous features
CVAA | Cumulative voting-based aggregation algorithm
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 | Differential gene expressions for multiple results
DiffGenesSelection | Differential expression for a selection of objects
Distance | Distance calculation
distanceheatmaps | Determine the distance in a heatmap
EHC | Ensemble for hierarchical clustering
EnsembleClustering | Ensemble clustering
EvidenceAccumulation | Evidence accumulation
f.clustABC.MultiSource | f.clustABC.MultiSoucre
f.gsample | f.gsample
f.rmv | f.rmv
f.t | ff
FeatSelection | feature selection for a selection of objects
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 | Investigates whether genes are differential expressed in multiple clusters
fingerprintMat | Fingerprint data
GeneInfo | Information of the genes Gene info in a data frame
geneMat | Gene expression data
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
GS | List of GO Annotations
HBGF | Hybrid bipartite graph formulation
HeatmapPlot | Comparing two clustering results with a heatmap
HeatmapSelection | A function to select a group of objects via the similarity heatmap.
HierarchicalEnsembleClustering | Hierarchical ensemble clustering
IntClust | Integrated Clustering Methods.
LabelCols | Colouring labels
LabelPlot | Coloring specific leaves of a dendrogram
LinkBasedClustering | Link based clustering
M_ABC | Multi-source ABC clustering
Normalization | Normalization of features
PathwayAnalysis | Pathway Analysis
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.
PreparePathway | Preparing a data set for pathway analysis
ProfilePlot | Plotting gene profiles
ReorderToReference | Order the outputs of the clustering methods against a reference
SelectnrClusters | Determines an optimal number of clusters based on silhouette widths
SharedComps | Intersection of clusters across multiple methods
SharedGenesPathsFeat | Intersection of genes and pathways over multiple 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 | A measure of similarity for the outputs of the different methods
SNF | Similarity network fusion
targetMat | Target prediction data
TrackCluster | Follow a cluster over multiple methods
WeightedClust | Weighted clustering
WonM | Weighting on membership