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Simplivariate Models: Uncovering the Underlying Biology in Functional Genomics Data

Author

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  • Edoardo Saccenti
  • Johan A Westerhuis
  • Age K Smilde
  • Mariët J van der Werf
  • Jos A Hageman
  • Margriet M W B Hendriks

Abstract

One of the first steps in analyzing high-dimensional functional genomics data is an exploratory analysis of such data. Cluster Analysis and Principal Component Analysis are then usually the method of choice. Despite their versatility they also have a severe drawback: they do not always generate simple and interpretable solutions. On the basis of the observation that functional genomics data often contain both informative and non-informative variation, we propose a method that finds sets of variables containing informative variation. This informative variation is subsequently expressed in easily interpretable simplivariate components. We present a new implementation of the recently introduced simplivariate models. In this implementation, the informative variation is described by multiplicative models that can adequately represent the relations between functional genomics data. Both a simulated and two real-life metabolomics data sets show good performance of the method.

Suggested Citation

  • Edoardo Saccenti & Johan A Westerhuis & Age K Smilde & Mariët J van der Werf & Jos A Hageman & Margriet M W B Hendriks, 2011. "Simplivariate Models: Uncovering the Underlying Biology in Functional Genomics Data," PLOS ONE, Public Library of Science, vol. 6(6), pages 1-13, June.
  • Handle: RePEc:plo:pone00:0020747
    DOI: 10.1371/journal.pone.0020747
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    References listed on IDEAS

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    1. Hugh Chipman & Hong Gu, 2005. "Interpretable dimension reduction," Journal of Applied Statistics, Taylor & Francis Journals, vol. 32(9), pages 969-987.
    2. Robert Tibshirani & Guenther Walther & Trevor Hastie, 2001. "Estimating the number of clusters in a data set via the gap statistic," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(2), pages 411-423.
    3. Li, Baibing & Martin, Elaine B. & Morris, A. Julian, 2002. "On principal component analysis in L1," Computational Statistics & Data Analysis, Elsevier, vol. 40(3), pages 471-474, September.
    4. Bart Selman, 2008. "A hard statistical view," Nature, Nature, vol. 451(7179), pages 639-640, February.
    5. Turner, Heather & Bailey, Trevor & Krzanowski, Wojtek, 2005. "Improved biclustering of microarray data demonstrated through systematic performance tests," Computational Statistics & Data Analysis, Elsevier, vol. 48(2), pages 235-254, February.
    6. Jos A Hageman & Margriet M W B Hendriks & Johan A Westerhuis & Mariët J van der Werf & Ruud Berger & Age K Smilde, 2008. "Simplivariate Models: Ideas and First Examples," PLOS ONE, Public Library of Science, vol. 3(9), pages 1-12, September.
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    1. Johan Gottfries & Silvia Melgar & Erik Michaëlsson, 2012. "Modelling of Mouse Experimental Colitis by Global Property Screens: A Holistic Approach to Assess Drug Effects in Inflammatory Bowel Disease," PLOS ONE, Public Library of Science, vol. 7(1), pages 1-7, January.

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