Feature-based variance-sensitive quantitative clustering

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Documentation for package ‘vsclust’ version 1.11.1

• 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
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
PrepareForVSClust	Functions for running VSClust analysis
PrepareSEForVSClust	Wrapper for statistical analysis for SummarizedExperiment object
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
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