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Fusing metabolomics data sets with heterogeneous measurement errors

Author

Listed:
  • Sandra Waaijenborg
  • Oksana Korobko
  • Ko Willems van Dijk
  • Mirjam Lips
  • Thomas Hankemeier
  • Tom F Wilderjans
  • Age K Smilde
  • Johan A Westerhuis

Abstract

Combining different metabolomics platforms can contribute significantly to the discovery of complementary processes expressed under different conditions. However, analysing the fused data might be hampered by the difference in their quality. In metabolomics data, one often observes that measurement errors increase with increasing measurement level and that different platforms have different measurement error variance. In this paper we compare three different approaches to correct for the measurement error heterogeneity, by transformation of the raw data, by weighted filtering before modelling and by a modelling approach using a weighted sum of residuals. For an illustration of these different approaches we analyse data from healthy obese and diabetic obese individuals, obtained from two metabolomics platforms. Concluding, the filtering and modelling approaches that both estimate a model of the measurement error did not outperform the data transformation approaches for this application. This is probably due to the limited difference in measurement error and the fact that estimation of measurement error models is unstable due to the small number of repeats available. A transformation of the data improves the classification of the two groups.

Suggested Citation

  • Sandra Waaijenborg & Oksana Korobko & Ko Willems van Dijk & Mirjam Lips & Thomas Hankemeier & Tom F Wilderjans & Age K Smilde & Johan A Westerhuis, 2018. "Fusing metabolomics data sets with heterogeneous measurement errors," PLOS ONE, Public Library of Science, vol. 13(4), pages 1-19, April.
    • Handle: RePEc:plo:pone00:0195939
    DOI: 10.1371/journal.pone.0195939
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    1. Marieke Timmerman & Henk Kiers, 2003. "Four simultaneous component models for the analysis of multivariate time series from more than one subject to model intraindividual and interindividual differences," Psychometrika, Springer;The Psychometric Society, vol. 68(1), pages 105-121, March.
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