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

• Published in: Metabolomics 2019-10, Vol.15 (10), p.134-9, Article 134
• Main Authors: Brandolini-Bunlon, Marion, Pétéra, Mélanie, Gaudreau, Pierrette, Comte, Blandine, Bougeard, Stéphanie, Pujos-Guillot, Estelle
• Format: Article
• Language: English
• Subjects: Biochemistry; Biomedical and Life Sciences; Biomedicine; Cell Biology; Computer Science; Developmental Biology; Discriminant analysis; Environmental factors; Epidemiology; Food and Nutrition; Life Sciences; Metabolomics; Molecular Medicine; Operating Systems; Phenotypes; Phenotyping; Software/Database; Statistical analysis
• ISSN: 1573-3882; 1573-3890
• DOI: 10.1007/s11306-019-1598-y

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.