% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/HelperFuncs.R
\name{estimClust.plot}
\alias{estimClust.plot}
\title{Plotting results from estimating the cluster number}
\usage{
estimClust.plot(ClustInd)
}
\arguments{
\item{ClustInd}{Matrix with values from validity indices}
}
\value{
Multiple panels showing expression profiles of clustered features passing the min.mem threshold
}
\description{
This function visualizes the output from estimClustNumber, and there particularly the
two validity indices Minimum Centroid Distance and Xie Beni Index.
}
\examples{
data("artificial_clusters")
dat <- averageCond(artificial_clusters, 5, 10)
dat <- scale(dat)
dat <- cbind(dat, 1)
ClustInd <- estimClustNum(dat, 10)
estimClust.plot(ClustInd)
}
\references{
Schwaemmle V, Jensen ON. VSClust: feature-based variance-sensitive clustering of omics data. Bioinformatics. 2018 Sep 1;34(17):2965-2972. doi: 10.1093/bioinformatics/bty224. PMID: 29635359.

Schwaemmle V, Hagensen CE. A Tutorial for Variance-Sensitive Clustering and the Quantitative Analysis of Protein Complexes. Methods Mol Biol. 2021;2228:433-451. doi: 10.1007/978-1-0716-1024-4_30. PMID: 33950508.

Schwaemmle V, Jensen ON. A simple and fast method to determine the parameters for fuzzy c-means cluster analysis. Bioinformatics. 2010 Nov 15;26(22):2841-8. doi: 10.1093/bioinformatics/btq534. Epub 2010 Sep 29. PMID: 20880957.
}