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Independent Component Analysis for Compositional Data

In: Advances in Contemporary Statistics and Econometrics

Author

Listed:
  • Christoph Muehlmann

    (Institute of Statistics & Mathematical Methods in Economics, Vienna University of Technology)

  • Kamila Fačevicová

    (Palacký University Olomouc, Department of Mathematical Analysis and Applications of Mathematics)

  • Alžběta Gardlo

    (University Hospital Olomouc and Palacký University Olomouc, Department of Clinical Biochemistry)

  • Hana Janečková

    (University Hospital Olomouc and Palacký University Olomouc, Laboratory for Inherited Metabolic Disorders, Department of Clinical Biochemistry)

  • Klaus Nordhausen

    (Institute of Statistics & Mathematical Methods in Economics, Vienna University of Technology)

Abstract

Compositional data represent a specific family of multivariate data, where the information of interest is contained in the ratios between parts rather than in absolute values of single parts. The analysis of such specific data is challenging as the application of standard multivariate analysis tools on the raw observations can lead to spurious results. Hence, it is appropriate to apply certain transformations prior to further analysis. One popular multivariate data analysis tool is independent component analysis. Independent component analysis aims to find statistically independent components in the data and as such might be seen as an extension to principal component analysis. In this paper, we examine an approach of how to apply independent component analysis on compositional data by respecting the nature of the latter and demonstrate the usefulness of this procedure on a metabolomics dataset.

Suggested Citation

  • Christoph Muehlmann & Kamila Fačevicová & Alžběta Gardlo & Hana Janečková & Klaus Nordhausen, 2021. "Independent Component Analysis for Compositional Data," Springer Books, in: Abdelaati Daouia & Anne Ruiz-Gazen (ed.), Advances in Contemporary Statistics and Econometrics, pages 525-545, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-73249-3_27
    DOI: 10.1007/978-3-030-73249-3_27
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