Extracting consistent biological information from functional results of metabolomic pathway analysis using the Mantel's test

Extraction of meaningful biological information from the vast array of data that metabolomics analyses generate is a major challenge in the field. A variety of computational and visual tools that help to identify changes in metabolic pathways have been proposed including functional analysis and pathway analysis. Meta-analysis of metabolomic data has emerged as a powerful source of information. In this work, the applicability of the Mantel's test for the correlation of functional results from metabolic pathway analysis is shown using experimental and simulated data sets as evaluation examples. The statistical significance of the correlation coefficient can be assessed by permutation testing requiring practically no computation time. The use of the Mantel's test can assist the critical comparison of different phenotypes, studies, methods, platforms, or data preprocessing strategies, as well as help to identify inconsistencies between metabolomic study outcomes, making this algorithm attractive for data interpretation and meta-analysis on a routine basis. (c) 2021 Elsevier B.V. All rights reserved.

Automated summary

Extracting meaningful biological information from metabolomics data is challenging, but the Mantel test can help assess the significance of correlation in metabolic pathway analysis. It allows critical comparisons between different phenotypes, studies, and methods, aiding data interpretation and meta-analysis.