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

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 im...

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Veröffentlicht in:PLoS computational biology 2021-09, Vol.17 (9), p.e1009105
Hauptverfasser: Wieder, Cecilia, Frainay, Clément, Poupin, Nathalie, Rodríguez-Mier, Pablo, Vinson, Florence, Cooke, Juliette, Lai, Rachel Pj, Bundy, Jacob G, Jourdan, Fabien, Ebbels, Timothy
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container_title PLoS computational biology
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creator Wieder, Cecilia
Frainay, Clément
Poupin, Nathalie
Rodríguez-Mier, Pablo
Vinson, Florence
Cooke, Juliette
Lai, Rachel Pj
Bundy, Jacob G
Jourdan, Fabien
Ebbels, Timothy
description 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.
doi_str_mv 10.1371/journal.pcbi.1009105
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subjects Biology and Life Sciences
Computational Biology - methods
Computer and Information Sciences
Datasets
Datasets as Topic
Electronic data processing
Guidelines
Life Sciences
Mathematical analysis
Metabolic Networks and Pathways
Metabolic pathways
Metabolites
Metabolomics
Methods
Physical Sciences
Reliability analysis
Reliability aspects
Representations
Reproducibility of Results
Statistical methods
title Pathway analysis in metabolomics: Recommendations for the use of over-representation analysis
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