Genetic algorithm based two-mode clustering of metabolomics data

J.A. Hageman, R.A. van den Berg, J.A. Westerhuis, M.J. van der Werf, A.K. Smilde

Research output: Contribution to journalArticleAcademicpeer-review

25 Citations (Scopus)


Metabolomics and other omics tools are generally characterized by large data sets with many variables obtained under different environmental conditions. Clustering methods and more specifically two-mode clustering methods are excellent tools for analyzing this type of data. Two-mode clustering methods allow for analysis of the behavior of subsets of metabolites under different experimental conditions. In addition, the results are easily visualized. In this paper we introduce a two-mode clustering method based on a genetic algorithm that uses a criterion that searches for homogeneous clusters. Furthermore we introduce a cluster stability criterion to validate the clusters and we provide an extended knee plot to select the optimal number of clusters in both experimental and metabolite modes. The genetic algorithm-based two-mode clustering gave biological relevant results when it was applied to two real life metabolomics data sets. It was, for instance, able to identify a catabolic pathway for growth on several of the carbon sources.
Original languageEnglish
Pages (from-to)141-149
Number of pages8
Issue number2
Publication statusPublished - 2008


  • discriminant-analysis
  • mass-spectrometry
  • degradation
  • number


Dive into the research topics of 'Genetic algorithm based two-mode clustering of metabolomics data'. Together they form a unique fingerprint.

Cite this