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

作者：Herve Maxime R^*; Nicole Florence; Le Cao Kim Anh

来源：Journal of Chemical Ecology, 2018, 44(3): 215-234.

DOI：10.1007/s10886-018-0932-6

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

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.

• 出版日期2018-3