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Metabolomics pp 105-126 | Cite as

A Gentle Guide to the Analysis of Metabolomic Data

  • Ralf Steuer
  • Katja Morgenthal
  • Wolfram Weckwerth
  • Joachim Selbig
Part of the Methods in Molecular Biology™ book series (MIMB, volume 358)

Abstract

Modern molecular biology crucially relies on computational tools to handle and interpret the large amounts of data that are generated by high-throughput measurements. To this end, much effort is dedicated to devise novel sophisticated methods that allow one to integrate, evaluate, and analyze biological data. However, prior to an application of specifically designed methods, simple and well-known statistical approaches often provide a more appropriate starting point for further analysis.

This chapter seeks to describe several well-established approaches to data analysis, including various clustering techniques, discriminant function analysis, principal component analysis, multidimensional scaling, and classification trees.

The chapter is accompanied by a webpage, describing the application of all algorithms in a ready-to-use format.

Keywords

Singular Value Decomposition Independent Component Analysis Independent Component Analysis Discriminant Function Analysis Metabolomic Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

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    Steuer, R., Kurths, J., Fiehn, O., and Weckwerth, W. (2003) Observing and interpreting correlations in metabolomic networks. Bioinformatics 19, 1019–1026.PubMedCrossRefGoogle Scholar

Copyright information

© Humana Press Inc. 2007

Authors and Affiliations

  • Ralf Steuer
    • 1
  • Katja Morgenthal
    • 2
  • Wolfram Weckwerth
    • 2
  • Joachim Selbig
    • 1
  1. 1.Institute of Biochemistry and BiologyUniversity of PotsdamPotsdamGermany
  2. 2.Department of Metabolic NetworksMax Planck Institute of Molecular Plant PhysiologyPotsdam-GolmGermany

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