Metabolomics

, Volume 3, Issue 1, pp 69–77 | Cite as

Applications of a new subspace clustering algorithm (COSA) in medical systems biology

  • Doris Damian
  • Matej Orešič
  • Elwin Verheij
  • Jacqueline Meulman
  • Jerome Friedman
  • Aram Adourian
  • Nicole Morel
  • Age Smilde
  • Jan van der Greef
Article

Abstract

A novel clustering approach named Clustering Objects on Subsets of Attributes (COSA) has been proposed (Friedman and Meulman, (2004). Clustering objects on subsets of attributes. J. R. Statist. Soc. B 66, 1–25.) for unsupervised analysis of complex data sets. We demonstrate its usefulness in medical systems biology studies. Examples of metabolomics analyses are described as well as the unsupervised clustering based on the study of disease pathology and intervention effects in rats and humans. In comparison to principal components analysis and hierarchical clustering based on Euclidean distance, COSA shows an enhanced capability to trace partial similarities in groups of objects enabling a new discovery approach in systems biology as well as offering a unique approach to reveal common denominators of complex multi-factorial diseases in animal and human studies.

Keywords

COSA subspace clustering metabolomics lipidomics biomarkers translational research metabolic syndrome Zucker rats ZDF rats 

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Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  • Doris Damian
    • 1
  • Matej Orešič
    • 2
  • Elwin Verheij
    • 3
    • 4
  • Jacqueline Meulman
    • 5
    • 6
  • Jerome Friedman
    • 7
  • Aram Adourian
    • 1
  • Nicole Morel
    • 1
  • Age Smilde
    • 3
    • 8
  • Jan van der Greef
    • 1
    • 3
    • 4
  1. 1.BG Medicine Inc.WalthamUSA
  2. 2.VTT Technical Research Centre of FinlandEspooFinland
  3. 3.TNO Quality of lifeZeistThe Netherlands
  4. 4.Center for Medical Systems Biology, LACDR, Leiden UniversityLeidenThe Netherlands
  5. 5.Data Theory GroupFaculty of Social and Behavioral Sciences, Leiden UniversityLeidenThe Netherlands
  6. 6.Nonlinear Dynamics of Natural SystemsMathematical Institute, Leiden UniversityLeidenThe Netherlands
  7. 7.Department of Statistics and Stanford Linear Accelerator CenterStanford UniversityStanfordUSA
  8. 8.Biosystems Data AnalysisFaculty of Sciences, University of AmsterdamAmsterdamThe Netherlands

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