Journal of Chemical Ecology

, Volume 44, Issue 3, pp 215–234 | Cite as

Multivariate Analysis of Multiple Datasets: a Practical Guide for Chemical Ecology

  • Maxime R. Hervé
  • Florence Nicolè
  • Kim-Anh Lê Cao
Article

Abstract

Chemical ecology has strong links with metabolomics, the large-scale study of all metabolites detectable in a biological sample. Consequently, chemical ecologists are often challenged by the statistical analyses of such large datasets. This holds especially true when the purpose is to integrate multiple datasets to obtain a holistic view and a better understanding of a biological system under study. The present article provides a comprehensive resource to analyze such complex datasets using multivariate methods. It starts from the necessary pre-treatment of data including data transformations and distance calculations, to the application of both gold standard and novel multivariate methods for the integration of different omics data. We illustrate the process of analysis along with detailed results interpretations for six issues representative of the different types of biological questions encountered by chemical ecologists. We provide the necessary knowledge and tools with reproducible R codes and chemical-ecological datasets to practice and teach multivariate methods.

Keywords

Discriminant analyses Distance-based analyses Integrative analyses Metabolomics Multi-block methods Ordination methods 

Notes

Acknowledgments

We are very grateful to Bernard Banaigs, Lucie Conchou, Laurent Dormont, Stéphane Greff, Maria Cristina Lorenzi, Thierry Pérez, Bertrand Schatz, Oriol Sacristán-Soriano and Olivier Thomas who kindly provided their data to illustrate the examples, Stéphane Dray and Denis Poinsot for their insightful comments on the manuscript and Zoe Welham for proof reading of the manuscript.

Compliance with Ethical Standards

Conflict of Interest

The authors declare that they have no conflict of interest.

Supplementary material

10886_2018_932_MOESM1_ESM.zip (1.9 mb)
ESM 1 (zip 1.86 MB)
10886_2018_932_MOESM2_ESM.pdf (298 kb)
ESM 2 (PDF 297 KB)

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Authors and Affiliations

  • Maxime R. Hervé
    • 1
  • Florence Nicolè
    • 2
  • Kim-Anh Lê Cao
    • 3
  1. 1.University of Rennes, Inra, Agrocampus Ouest, IGEPP - UMR-A 1349RennesFrance
  2. 2.University of Lyon, UJM-Saint-Etienne, CNRS, LBVpam FRE 3727, EA 3061Saint-EtienneFrance
  3. 3.Melbourne Integrative Genomics and School of Mathematics and StatisticsUniversity of MelbourneParkvilleAustralia

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