% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/WrapperFuncs.R
\name{runClustWrapper}
\alias{runClustWrapper}
\title{Wrapper for running cluster analysis}
\usage{
runClustWrapper(
  dat,
  NClust,
  proteins = NULL,
  VSClust = TRUE,
  cores,
  verbose = FALSE
)
}
\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}

\item{verbose}{Show more information during execution}
}
\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[,seq_len(4)],1)
clust_out <- runClustWrapper(data, NClust=3, cores=1)
clust_out$p
}