Abstract
Many metabolomics studies aim to find ‘biomarkers’: sets of molecules that are consistently elevated or decreased upon experimental manipulation. Biological effects, however, often manifest themselves along a continuum of individual differences between the biological replicates in the experiment. Such differences are overlooked or even diminished by methods in standard use for metabolomics, although they may contain a wealth of information on the experiment. Properly understanding individual differences is crucial for generating knowledge in fields like personalised medicine, evolution and ecology. We propose to use simultaneous component analysis with individual differences constraints (SCA-IND), a data analysis method from psychology that focuses on these differences. This method constructs axes along the natural biochemical differences between biological replicates, comparable to principal components. The model may shed light on changes in the individual differences between experimental groups, but also on whether these differences correspond to, e.g., responders and non-responders or to distinct chemotypes. Moreover, SCA-IND reveals the individuals that respond most to a manipulation and are best suited for further experimentation. The method is illustrated by the analysis of individual differences in the metabolic response of cabbage plants to herbivory. The model reveals individual differences in the response to shoot herbivory, where two ‘response chemotypes’ may be identified. In the response to root herbivory the model shows that individual plants differ strongly in response dynamics. Thereby SCA-IND provides a hitherto unavailable view on the chemical diversity of the induced plant response, that greatly increases understanding of the system.Electronic supplementary materialThe online version of this article (doi:10.1007/s11306-012-0414-8) contains supplementary material, which is available to authorized users.
Highlights
Ronald Fisher, in his landmark paper introducing Analysis of Variance (ANOVA), already stated that mendelian genetic variation is discrete, it may lead to continuous phenotypic differences between replicates (Fisher 1918)
We propose to use simultaneous component analysis with individual differences constraints (SCA-IND), a data analysis method from psychology that focuses on these differences
The levels of all SJA plants measured after 7 and 14 days are connected to dotted lines. These indicate the distance between the measured NEO and GBC levels in each sample and their prediction by the SCA-IND model that lies on the intersection with the continuous lines of each day
Summary
Ronald Fisher, in his landmark paper introducing Analysis of Variance (ANOVA), already stated that mendelian genetic variation is discrete, it may lead to continuous phenotypic differences between replicates (Fisher 1918). This stems from the main objectives in these fields, i.e., providing consistently high crop yields or curing as many people as possible with a given treatment This focus on reproducibility resonates into the statistical methods of choice: the heirs of Ronald Fisher at Rothamsted Research Centre 100 years later still quantify differences in plant phenotypes caused by bacterial infection (Ward et al 2010) with his ANOVA method (Sokal and Rohlf 1995), with stateof-the-art metabolomics technology. In this manuscript we propose a method to analyse and interpret individual differences on the individual and group-level simultaneously This method is called SCAIND and mixes the specific constraints from INDSCAL with the SCA model, such that entire experimental groups and individual biological replicates can be analysed simultaneously. The relations between metabolites, tied tightly together with individual differences metabolomics, have been proposed before as a very appropriate perspective to observe induced responses to biotic and abiotic plant stress (Broeckling et al 2005)
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