Pathway analysis in metabolomics: Recommendations for the use of over-representation analysis

Abstract

Over-representation analysis (ORA) is one of the commonest pathway analysis approaches used for the functional interpretation of metabolomics datasets. Despite the widespread use of ORA in metabolomics, the community lacks guidelines detailing its best-practice use. Many factors have a pronounced impact on the results, but to date their effects have received little systematic attention. Using five publicly available datasets, we demonstrated that changes in parameters such as the background set, differential metabolite selection methods, and pathway database used can result in profoundly different ORA results. The use of a non-assay-specific background set, for example, resulted in large numbers of false-positive pathways. Pathway database choice, evaluated using three of the most popular metabolic pathway databases (KEGG, Reactome, and BioCyc), led to vastly different results in both the number and function of significantly enriched pathways. Factors that are specific to metabolomics data, such as the reliability of compound identification and the chemical bias of different analytical platforms also impacted ORA results. Simulated metabolite misidentification rates as low as 4% resulted in both gain of false-positive pathways and loss of truly significant pathways across all datasets. Our results have several practical implications for ORA users, as well as those using alternative pathway analysis methods. We offer a set of recommendations for the use of ORA in metabolomics, alongside a set of minimal reporting guidelines, as a first step towards the standardisation of pathway analysis in metabolomics.

NMR in Metabolomics: From Conventional Statistics to Machine Learning and Neural Network Approaches

“…A powerful method to describe peculiar features of the cell metabolism is pathway analysis (PA), which provides a graphical representation of the relationships among the actors (mainly enzymes and metabolites) of precise catalyzed reactions. Therefore, PA is highly employed for the interpretation of high-dimensional molecular data [74]. In fact, taking advantage of the already acquired knowledge of biological pathways, proteins, metabolites and also genes can be mapped onto newly developed pathways with the objective to draw their collective functions and interactions in that specific biological environment [75].…”

“…Three are the necessary inputs in ORA analysis: (i) a set of pathways (or metabolite collections); (ii) a catalog of investigating metabolites and, (iii) a background collection of compounds. The list of investigating metabolites usually comes from experimental data after applying a statistical test to determine those metabolites whose signals can be associated with a precise result by choosing a threshold value usually associated to the p-values [74]. The background collection includes all metabolites that can be revealed in the considered measurement.…”

On the influence of several factors on pathway enrichment analysis

“…Furthermore, the authors argued that the choice of gene set collections should not be made arbitrarily as certain gene sets may be more or less suitable for a particular dataset than others. In a recent study on best practices for the popular ORA method on metabolomics data [ 65 ], the authors also found that the results of pathway analysis substantially differed based on the choice of pathway database (i.e. KEGG, Reactome and BioCyc [ 66 ]).…”

Studying Metabolism by NMR-Based Metabolomics

“…Details on various possibilities of handling NMR-based metabolomics data can be consulted elsewhere ( Blaise et al, 2021 ; Debik et al, 2022 ). Beyond statistical treatment, web-based tools like MetaboAnalyst ( Chong et al, 2018 ) allow to visualise metabolomics data in an user-friendly way, and are able to perform additional tasks, as for example pathway enrichment analysis ( Wieder et al, 2021 ).…”