title: Comparing the performance of biomedical clustering methods.
abstract: Identifying groups of similar objects is a popular first step in biomedical data analysis, but it is error-prone and impossible to perform manually. Many computational methods have been developed to tackle this problem. Here we assessed 13 well-known methods using 24 data sets ranging from gene expression to protein domains. Performance was judged on the basis of 13 common cluster validity indices. We developed a clustering analysis platform, ClustEval (http://clusteval.mpi-inf.mpg.de), to promote streamlined evaluation, comparison and reproducibility of clustering results in the future. This allowed us to objectively evaluate the performance of all tools on all data sets with up to 1,000 different parameter sets each, resulting in a total of more than 4 million calculated cluster validity indices. We observed that there was no universal best performer, but on the basis of this wide-ranging comparison we were able to develop a short guideline for biomedical clustering tasks. ClustEval allows biomedical researchers to pick the appropriate tool for their data type and allows method developers to compare their tool to the state of the art.
authors.affiliation: Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark.
authors.forename: Christian
authors.initials: C
authors.lastname: Wiwie
authors.affiliation: Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark.|Computational Systems Biology, Max Planck Institute for Informatics, Saarbrücken, Germany.|Cluster of Excellence for Multimodal Computing and Interaction, Saarland University, Saarbrücken, Germany.
authors.forename: Jan
authors.initials: J
authors.lastname: Baumbach
authors.affiliation: Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark.
authors.forename: Richard
authors.initials: R
authors.lastname: Röttger
mesh_terms: D000465:Algorithms; D000818:Animals; D001331:Automation; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods; D010363:Pattern Recognition, Automated / Q000379:methods*; D017434:Protein Structure, Tertiary; D011786:Quality Control; D015203:Reproducibility of Results; D012984:Software