Simplivariate Models: Uncovering the Underlying Biology in Functional Genomics Data

E. Saccenti, J.A. Westerhuis, A.K. Smilde, M.J. van der Werf, J.A. Hageman, M.M.W.B. Hendriks

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11 Citations (Scopus)

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.
Original languageEnglish
Article numbere20747
JournalPLoS ONE
Volume6
Issue number6
DOIs
Publication statusPublished - 2011

Keywords

  • metabolomics data
  • multiple-regression
  • genetic algorithms
  • escherichia-coli
  • microarray data
  • decomposition
  • number
  • indole

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    Saccenti, E., Westerhuis, J. A., Smilde, A. K., van der Werf, M. J., Hageman, J. A., & Hendriks, M. M. W. B. (2011). Simplivariate Models: Uncovering the Underlying Biology in Functional Genomics Data. PLoS ONE, 6(6), [e20747]. https://doi.org/10.1371/journal.pone.0020747