ANOVA simultaneous component analysis: A tutorial review

ABSTRACT: When analyzing experimental chemical data, it is often necessary to incorporate the structure of the study design into the chemometric/statistical models to effectively address the research questions of interest. ANOVA-Simultaneous Component Analysis (ASCA) is one of the most prominent methods to include such information in the quantitative analysis of multivariate data, especially when the number of variables is large. This tutorial review intends to explain in a simple way how ASCA works, how it is operated and how to correctly interpret ASCA results, with approachable mathematical and visual descriptions. Two examples are given: the first, a simulated chemical reaction, serves to illustrate the ASCA steps and the second, from a real chemical ecology data set, the interpretation of results. An overview of methods closely related to ASCA is also provided, pointing out their differences and scope, to give a wide-ranging picture of the available options to build multivariate models that take experimental design into account. Graphical abstract Image 1 Highlights • ASCA is a multivariate method for analysis of multi-factor data.• An overview of the (mathematical) principles of ASCA is presented.• Key aspects for practical application of ASCA are discussed.• Detailed explanation of ASCA output in terms of score and loading plots is given.• Literature review of other multivariate techniques for analysis of multi-factor data.

SUBMITTER: Bertinetto C 

PROVIDER: S-EPMC7772684 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature