ClustComp               Function to run clustering with automatic
                        fuzzifier settings (might become obsolete)
PrepareForVSClust       Functions for running VSClust analysis
PrepareSEForVSClust     Wrapper for statistical analysis for
                        SummarizedExperiment object
SignAnalysis            Unpaired statistical testing
SignAnalysisPaired      Paired statistical testing
SwitchOrder             arrange cluster member numbers from largest to
                        smallest
artificial_clusters     Synthetic/artificial data comprising 5 clusters
averageCond             Calculate mean over replicates
calcBHI                 Calculate "biological homogeneity index"
cvalidate.xiebeni       Xie Beni Index of clustering object
determine_fuzz          Determine individual fuzzifier values
enrichSTRING_API        Enrichment Analysis via STRING REST API
estimClust.plot         Plotting results from estimating the cluster
                        number
estimClustNum           Wrapper for estimation of cluster number
mfuzz.plot              Plotting vsclust results
optimalClustNum         Determine optimal cluster number from validity
                        index
pcaWithVar              Visualize using principal component analysis
                        (both loadings and scoring) including the
                        variance from the replicates
protein_expressions     Data from a typical proteomics experiment
runClustWrapper         Wrapper for running cluster analysis
runFuncEnrich           Functional Enrichment with STRING
runVSClustApp           Run VSClust as Shiny app
vsclust-package         VSClust provides a powerful method to run
                        variance-sensitive clustering
vsclust_algorithm       Run the vsclust clustering algorithm
