% Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/HelperFuncs.R
 \name{runClustWrapper}
 \alias{runClustWrapper}

 \title{Wrapper for running cluster analysis}

 \usage{

 runClustWrapper(dat, NClust, proteins = NULL, VSClust = TRUE, cores)

 }

 \arguments{
 \item{dat}{matrix or data frame with feature values for different conditions}
 
 \item{NClust}{Number of cluster for running the clustering}
 
 \item{proteins}{vector with additional feature information (default is NULL) to be added to the results}
 
 \item{VSClust}{boolean. TRUE for running the variance-sensitive clustering. Otherwise, the function will call standard fuzzy c-means clustering}
 
 \item{cores}{Number of threads for the parallelization}
 }
 \value{
 list with the items `dat`(the original data), `Bestcl` clustering results (same as from vsclust_algorithm), `p` (plot object with mfuzz plots), `outFileClust`(suitable matrix with complete information) , `ClustInd` (information about being member of any cluster, feature needs on membership values > 0.5)
 }

 \description{

 This function runs the clustering and visualizes the results.
 }
 \examples{
 data(iris)
 data <- cbind(iris[,1:4],1)
 clust_out <- runClustWrapper(data, NClust=3, cores=1)
 clust_out$p

 }