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Visualization and Analysis of Molecular Data

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Metabolomics

Part of the book series: Methods in Molecular Biology™ ((MIMB,volume 358))

Abstract

This chapter provides an overview of visualization and analysis techniques applied to large-scale datasets from genomics, metabolomics, and proteomics. The aim is to reduce the number of variables (genes, metabolites, or proteins) by extracting a small set of new relevant variables, usually termed components. The advantages and disadvantages of the classical principal component analysis (PCA) are discussed and a link is given to the closely related singular value decomposition and multidimensional scaling. Special emphasis is given to the recent trend toward the use of independent component analysis, which aims to extract statistically independent components and, therefore, provides usually more meaningful components than PCA. We also discuss normalization techniques and their influence on the result of different analytical techniques.

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Authors and Affiliations

  1. Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany

    Matthias Scholz & Joachim Selbig

Authors
  1. Matthias Scholz
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  2. Joachim Selbig
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Editor information

Editors and Affiliations

  1. Department of Metabolic Networks, Max Planck Institute of Molecular Plant Physiology, Potsdam-Golm, Germany

    Wolfram Weckwerth

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© 2007 Humana Press Inc.

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Scholz, M., Selbig, J. (2007). Visualization and Analysis of Molecular Data. In: Weckwerth, W. (eds) Metabolomics. Methods in Molecular Biology™, vol 358. Humana Press. https://doi.org/10.1007/978-1-59745-244-1_6

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  • DOI: https://doi.org/10.1007/978-1-59745-244-1_6

  • Publisher Name: Humana Press

  • Print ISBN: 978-1-58829-561-3

  • Online ISBN: 978-1-59745-244-1

  • eBook Packages: Springer Protocols

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