Cargando…
Comparison of Multivariate ANOVA-Based Approaches for the Determination of Relevant Variables in Experimentally Designed Metabolomic Studies
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...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9147242/ https://www.ncbi.nlm.nih.gov/pubmed/35630781 http://dx.doi.org/10.3390/molecules27103304 |
_version_ | 1784716759846092800 |
---|---|
author | Pérez-Cova, Miriam Platikanov, Stefan Stoll, Dwight R. Tauler, Romà Jaumot, Joaquim |
author_facet | Pérez-Cova, Miriam Platikanov, Stefan Stoll, Dwight R. Tauler, Romà Jaumot, Joaquim |
author_sort | Pérez-Cova, Miriam |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9147242 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-91472422022-05-29 Comparison of Multivariate ANOVA-Based Approaches for the Determination of Relevant Variables in Experimentally Designed Metabolomic Studies Pérez-Cova, Miriam Platikanov, Stefan Stoll, Dwight R. Tauler, Romà Jaumot, Joaquim Molecules Article 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. MDPI 2022-05-20 /pmc/articles/PMC9147242/ /pubmed/35630781 http://dx.doi.org/10.3390/molecules27103304 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Pérez-Cova, Miriam Platikanov, Stefan Stoll, Dwight R. Tauler, Romà Jaumot, Joaquim Comparison of Multivariate ANOVA-Based Approaches for the Determination of Relevant Variables in Experimentally Designed Metabolomic Studies |
title | Comparison of Multivariate ANOVA-Based Approaches for the Determination of Relevant Variables in Experimentally Designed Metabolomic Studies |
title_full | Comparison of Multivariate ANOVA-Based Approaches for the Determination of Relevant Variables in Experimentally Designed Metabolomic Studies |
title_fullStr | Comparison of Multivariate ANOVA-Based Approaches for the Determination of Relevant Variables in Experimentally Designed Metabolomic Studies |
title_full_unstemmed | Comparison of Multivariate ANOVA-Based Approaches for the Determination of Relevant Variables in Experimentally Designed Metabolomic Studies |
title_short | Comparison of Multivariate ANOVA-Based Approaches for the Determination of Relevant Variables in Experimentally Designed Metabolomic Studies |
title_sort | comparison of multivariate anova-based approaches for the determination of relevant variables in experimentally designed metabolomic studies |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9147242/ https://www.ncbi.nlm.nih.gov/pubmed/35630781 http://dx.doi.org/10.3390/molecules27103304 |
work_keys_str_mv | AT perezcovamiriam comparisonofmultivariateanovabasedapproachesforthedeterminationofrelevantvariablesinexperimentallydesignedmetabolomicstudies AT platikanovstefan comparisonofmultivariateanovabasedapproachesforthedeterminationofrelevantvariablesinexperimentallydesignedmetabolomicstudies AT stolldwightr comparisonofmultivariateanovabasedapproachesforthedeterminationofrelevantvariablesinexperimentallydesignedmetabolomicstudies AT taulerroma comparisonofmultivariateanovabasedapproachesforthedeterminationofrelevantvariablesinexperimentallydesignedmetabolomicstudies AT jaumotjoaquim comparisonofmultivariateanovabasedapproachesforthedeterminationofrelevantvariablesinexperimentallydesignedmetabolomicstudies |