% 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 }