ArticleMolecules (Basel, Switzerland)2022
Comparison of Multivariate ANOVA-Based Approaches for the Determination of Relevant Variables in Experimentally Designed Metabolomic Studies.
Article in Molecules (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Exploration and morphological variation of liberoid coffee (Coffea liberica) germplasm as a basis for genetic resource conservation.BMC plant biology · 2026Article
- A systems microbiology framework for reproducible multi-dataset omics integration with application to long COVID.Frontiers in systems biology · 2026Article
- Multi-Statistical Pragmatic Framework to Study UV Exposure Effects via VIS Reflectance in Automotive Polymer Components.Polymers · 2025Article
- Design and Analysis of Untargeted Metabolomics Experiments.Current protocols · 2025Article
- Wolbachia modify host cell metabolite profiles in response to short-term temperature stress.Environmental microbiology reports · 2024Article
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5 authors.
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Abstract
The use of chemometric methods based on the analysis of variances (ANOVA) allows evaluation of the statistical significance of the experimental factors used in a study. However, classical multivariate ANOVA (MANOVA) has a number of requirements that make it impractical for dealing with metabolomics data. For this reason, in recent years, different options have appeared that overcome these limitations. In this work, we evaluate the performance of three of these multivariate ANOVA-based methods (ANOVA simultaneous component analysis-ASCA, regularized MANOVA-rMANOVA, and Group-wise ANOVA-simultaneous component analysis-GASCA) in the framework of metabolomics studies. Our main goals are to compare these various ANOVA-based approaches and evaluate their performance on experimentally designed metabolomic studies to find the significant factors and identify the most relevant variables (potential markers) from the obtained results. Two experimental data sets were generated employing liquid chromatography coupled to mass spectrometry (LC-MS) with different complexity in the design to evaluate the performance of the statistical approaches. Results show that the three considered ANOVA-based methods have a similar performance in detecting statistically significant factors. However, relevant variables pointed by GASCA seem to be more reliable as there is a strong similarity with those variables detected by the widely used partial least squares discriminant analysis (PLS-DA) method.
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