% 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
}