INTRODUCTION:
Plant metabolomics experiments yield large amounts of data, too much to be interpretable by eye. Multivariate data analyses are therefore essential to extract and visualise the information of interest.
OBJECTIVE:
Because multivariate statistical methods may be remote from the expertise of many scientists working in the metabolomics field, this overview provides a step-by-step description of a multivariate data analysis, starting from the experiment and ending with the figures appearing in scientific journals.
METHODOLOGY:
We developed a thought experiment that explores the relationship between the differences in nutrient levels and three plant developmental descriptors through photography of the greenhouse they grow in. Through this, multivariate data analysis, data preprocessing and model validation are illustrated. Finally some of the presented methods are illustrated by the analysis of a plant metabolomics dataset.
CONCLUSION:
This paper will familiarize non-specialised researchers with the main concepts in multivariate data analysis and allow them to develop and evaluate metabolomic data analyses more critically.