# Feature-based variance-sensitive quantitative clustering

## Documentation for package ‘vsclust’ version 1.3.0

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

## Help Pages

vsclust-package | VSClust provides a powerful method to run variance-sensitive clustering
artificial_clusters | Synthetic/artificial data comprising 5 clusters
averageCond | Calculate mean over replicates
calcBHI | Calculate "biological homogeneity index"
ClustComp | Function to run clustering with automatic fuzzifier settings (might become obsolete)
cvalidate.xiebeni | Xie Beni Index of clustering object
determine_fuzz | Determine individual fuzzifier values
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
PrepareForVSClust | Wrapper for statistical analysis
PrepareSEForVSClust | Wrapper for statistical analysis for SummarizedExperiment object
protein_expressions | Data from a typical proteomics experiment
runClustWrapper | Wrapper for running cluster analysis
runVSClustApp | Run VSClust as Shiny app
SignAnalysis | Unpaired statistical testing
SignAnalysisPaired | Paired statistical testing
SwitchOrder | arrange cluster member numbers from largest to smallest
vsclust | VSClust provides a powerful method to run variance-sensitive clustering
vsclust_algorithm | Run the vsclust clustering algorithm