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Sparse multi-block PLSR for biomarker discovery when integrating data from LC–MS and NMR metabolomics

Metabolomics volume 11, pages 367–379 (2015)Cite this article

Abstract

The objective of this study was to implement a multivariate method which analyzes multi-block metabolomics data and performs variable selection in order to discover potential biomarkers, simultaneously. We call this method sparse multi-block partial least squares regression (Sparse MBPLSR). To achieve this method, we first defined a nonlinear iterative partial least squares (NIPALS) algorithm for Sparse PLSR, thereafter we extended it to Sparse MBPLSR. Since over-fitting is an issue when variable selection is involved, we implemented a cross model validation (CMV) to assess the reliability and stability of the selected variables. The performance of the method was evaluated using a simulated data set and a multi-block data set from a dietary intervention study with pigs used as model for humans. The objective of the study was to investigate the biochemical effects in plasma after dietary intervention with breads varying in types of dietary fiber and to identify potential biomarkers. By introducing Sparse MBPLSR, we aimed at identifying the biomarkers where data from LC–MS and NMR instruments were analyzed simultaneously and therefore in addition we intended to explore the relationships among the measurement variables of this multi-block data set. The results showed that Sparse MBPLSR with CMV is a useful tool for analyzing multi-block metabolomics data with a good prediction and for identifying potential biomarkers.

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Acknowledgments

The authors are grateful for financial support by the Nordic Centre of Excellence on Food, Nutrition and Health “Systems biology in controlled dietary interventions and cohort studies” (SYSDIET; 070014) funded by NordForsk. In addition, this work was supported by the Grant 203699 (New statistical tools for integrating and exploiting complex genomic and phenotypic data sets) financed by the Research Council of Norway.

Conflict of interest

The authors declared no competing interests.

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Authors and Affiliations

  1. Department of Animal Science, Aarhus University, P.O. Box 50, 8830, Tjele, Denmark

    İbrahim Karaman, Natalja P. Nørskov, Mette Skou Hedemann & Knud Erik Bach Knudsen

  2. Department of Food Science, Aarhus University, 5792, Aarslev, Denmark

    Christian Clement Yde

  3. Department of Mathematical Sciences and Technology (IMT), Norwegian University of Life Sciences, 1432, Ås, Norway

    Achim Kohler

  4. Nofima AS, Osloveien 1, 1430, Ås, Norway

    Achim Kohler

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  1. İbrahim Karaman
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  2. Natalja P. Nørskov
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  3. Christian Clement Yde
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  4. Mette Skou Hedemann
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  5. Knud Erik Bach Knudsen
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  6. Achim Kohler
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Correspondence to İbrahim Karaman.

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Karaman, İ., Nørskov, N.P., Yde, C.C. et al. Sparse multi-block PLSR for biomarker discovery when integrating data from LC–MS and NMR metabolomics. Metabolomics 11, 367–379 (2015). https://doi.org/10.1007/s11306-014-0698-y

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  • DOI: https://doi.org/10.1007/s11306-014-0698-y

Keywords

  • Sparse PLSR
  • Multi-block
  • Cross model validation
  • Variable selection
  • Biomarker discovery