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Multi-block PLS discriminant analysis for the joint analysis of metabolomic and epidemiological data

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Abstract

Introduction

Metabolomics is a powerful phenotyping tool in nutrition and health research, generating complex data that need dedicated treatments to enrich knowledge of biological systems. In particular, to investigate relations between environmental factors, phenotypes and metabolism, discriminant statistical analyses are generally performed separately on metabolomic datasets, complemented by associations with metadata. Another relevant strategy is to simultaneously analyse thematic data blocks by a multi-block partial least squares discriminant analysis (MBPLSDA) allowing determining the importance of variables and blocks in discriminating groups of subjects, taking into account data structure.

Objective

The present objective was to develop a full open-source standalone tool, allowing all steps of MBPLSDA for the joint analysis of metabolomic and epidemiological data.

Methods

This tool was based on the mbpls function of the ade4 R package, enriched with functionalities, including some dedicated to discriminant analysis. Provided indicators help to determine the optimal number of components, to check the MBPLSDA model validity, and to evaluate the variability of its parameters and predictions.

Results

To illustrate the potential of this tool, MBPLSDA was applied to a real case study involving metabolomics, nutritional and clinical data from a human cohort. The availability of different functionalities in a single R package allowed optimizing parameters for an efficient joint analysis of metabolomics and epidemiological data to obtain new insights into multidimensional phenotypes.

Conclusion

In particular, we highlighted the impact of filtering the metabolomic variables beforehand, and the relevance of a MBPLSDA approach in comparison to a standard PLS discriminant analysis method.

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Data availability

The tool is developed under the R software and is available via the packMBPLSDA CRAN R package.

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Acknowledgements

The authors would like to thank Charlotte Joly for the analyses by LC–MS and Stéphanie Monnerie for the provision of the data. All metabolomics analyses were performed within the metaboHUB French infrastructure (ANR-INBS-0010). The NuAge Study was supported by a research grant from the Canadian Institutes of Health Research (CIRH; MOP-62842). The NuAge Database and Biobank are supported by the Fonds de Recherche du Québec (FRQ; 2020-VICO-279753), the Quebec Network for Research on Aging funded by the Fonds de Recherche du Québec - Santé (FRQS) and by the Merck-Frosst Chair funded by La Fondation de l'Université de Sherbrooke.

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Authors and Affiliations

  1. Université Clermont Auvergne, INRA, UNH, Plateforme d’Exploration du Métabolisme, MetaboHUB Clermont, 63000, Clermont-Ferrand, France

    Marion Brandolini-Bunlon, Mélanie Pétéra & Estelle Pujos-Guillot

  2. Centre de Recherche du Centre hospitalier de l’Université de Montréal, Montréal, Canada

    Pierrette Gaudreau

  3. Département de médecine, Université de Montréal, Montréal, Canada

    Pierrette Gaudreau

  4. Université Clermont Auvergne, INRA, UNH, 63000, Clermont-Ferrand, France

    Blandine Comte & Estelle Pujos-Guillot

  5. Anses, BP53, Technopole Saint Brieuc Armor, 22440, Ploufragan, France

    Stéphanie Bougeard

Authors
  1. Marion Brandolini-Bunlon
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  2. Mélanie Pétéra
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  3. Pierrette Gaudreau
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  4. Blandine Comte
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  5. Stéphanie Bougeard
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  6. Estelle Pujos-Guillot
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Contributions

MP, MBB, BC and EPG conceived the study. MBB and SB designed the tool. EP, BC and PG provided the data. MBB analyzed the data. MBB, MP, BC and EPG wrote the manuscript. All authors read and approved the manuscript.

Corresponding author

Correspondence to Marion Brandolini-Bunlon.

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Conflict of interest

All authors declare they have no conflict of interest.

Human and animal rights

All procedures performed in the study involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study. The NuAge Study has been approved by the Research Ethics Board (REB) of both the Geriatric University Institutes of Montreal and Sherbrooke. The management framework of the NuAge Database and Biobank has been approved by the REB of the CIUSSS-de-l'Estrie-CHUS.

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Brandolini-Bunlon, M., Pétéra, M., Gaudreau, P. et al. Multi-block PLS discriminant analysis for the joint analysis of metabolomic and epidemiological data. Metabolomics 15, 134 (2019). https://doi.org/10.1007/s11306-019-1598-y

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