Improving insights from metabolomic functional analysis combining multivariate tools.
Cluster cross validation
Functional analysis
Metabolomics
Multivariate analysis
OPLS-DA
Journal
Analytica chimica acta
ISSN: 1873-4324
Titre abrégé: Anal Chim Acta
Pays: Netherlands
ID NLM: 0370534
Informations de publication
Date de publication:
22 Sep 2024
22 Sep 2024
Historique:
received:
23
05
2024
revised:
31
07
2024
accepted:
05
08
2024
medline:
26
8
2024
pubmed:
26
8
2024
entrez:
25
8
2024
Statut:
ppublish
Résumé
Metabolomics is a scientific field that relies on the comprehensive analysis of metabolites to provide direct insights into functional processes in biological systems. Metabolomic data provides valuable insights into the functional processes of biological systems, often analyzed through univariate and multivariate approaches, and well as with functional or pathway analysis using different methods such as mummichog. Yet, the integration of results from these sources to aid the interpretation of their biological significance remains challenging. This represents a significant bottleneck limiting the applicability of multivariate analysis of metabolomic data, despite its potential for providing deep biological insights. In this work we propose two straightforward methods to facilitate the interpretation of results from multivariate analysis and functional metabolic analysis using: i) p-values from multivariate tests as input in functional analysis, and ii) cluster-CV to assess the impact on the predictive performance of a multivariate model at the pathway level. Four simulated data sets were analyzed including a data set with no class separation, and three data sets with a statistically significant discrimination between classes by including either univariate, multivariate, or both types of discriminant effects. The data sets were analyzed using univariate tests and OPLS-DA. Furthermore, p-values for each feature estimated by univariate analysis and OPLS-DA were used as input for functional analysis in mummichog. Cluster-CV was then used to assess the effect of detected metabolic pathways on the class separation observed by OPLS-DA. Through simulated data, we show how these approaches enhance the interpretation of biological effects driving multivariate models and support the identification of altered pathways not detected by univariate analysis. By providing a deeper understanding of metabolic phenotypes, these methods might improve the biological insights derived from statistical and functional analysis of future or previous studies.
Sections du résumé
BACKGROUND
BACKGROUND
Metabolomics is a scientific field that relies on the comprehensive analysis of metabolites to provide direct insights into functional processes in biological systems. Metabolomic data provides valuable insights into the functional processes of biological systems, often analyzed through univariate and multivariate approaches, and well as with functional or pathway analysis using different methods such as mummichog. Yet, the integration of results from these sources to aid the interpretation of their biological significance remains challenging. This represents a significant bottleneck limiting the applicability of multivariate analysis of metabolomic data, despite its potential for providing deep biological insights.
RESULTS
RESULTS
In this work we propose two straightforward methods to facilitate the interpretation of results from multivariate analysis and functional metabolic analysis using: i) p-values from multivariate tests as input in functional analysis, and ii) cluster-CV to assess the impact on the predictive performance of a multivariate model at the pathway level. Four simulated data sets were analyzed including a data set with no class separation, and three data sets with a statistically significant discrimination between classes by including either univariate, multivariate, or both types of discriminant effects. The data sets were analyzed using univariate tests and OPLS-DA. Furthermore, p-values for each feature estimated by univariate analysis and OPLS-DA were used as input for functional analysis in mummichog. Cluster-CV was then used to assess the effect of detected metabolic pathways on the class separation observed by OPLS-DA.
SIGNIFICANCE
CONCLUSIONS
Through simulated data, we show how these approaches enhance the interpretation of biological effects driving multivariate models and support the identification of altered pathways not detected by univariate analysis. By providing a deeper understanding of metabolic phenotypes, these methods might improve the biological insights derived from statistical and functional analysis of future or previous studies.
Identifiants
pubmed: 39182979
pii: S0003-2670(24)00863-8
doi: 10.1016/j.aca.2024.343062
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
343062Informations de copyright
Copyright © 2024 Elsevier B.V. All rights reserved.
Déclaration de conflit d'intérêts
Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.