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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Autores principales:	Cecilia Wieder, Clément Frainay, Nathalie Poupin, Pablo Rodríguez-Mier, Florence Vinson, Juliette Cooke, Rachel Pj Lai, Jacob G Bundy, Fabien Jourdan, Timothy Ebbels
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/38f5454156524f19b62777d46227717a
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spelling	oai:doaj.org-article:38f5454156524f19b62777d46227717a2021-12-02T19:57:50ZPathway analysis in metabolomics: Recommendations for the use of over-representation analysis.1553-734X1553-735810.1371/journal.pcbi.1009105https://doaj.org/article/38f5454156524f19b62777d46227717a2021-09-01T00:00:00Zhttps://doi.org/10.1371/journal.pcbi.1009105https://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Over-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.Cecilia WiederClément FrainayNathalie PoupinPablo Rodríguez-MierFlorence VinsonJuliette CookeRachel Pj LaiJacob G BundyFabien JourdanTimothy EbbelsPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 17, Iss 9, p e1009105 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Cecilia Wieder Clément Frainay Nathalie Poupin Pablo Rodríguez-Mier Florence Vinson Juliette Cooke Rachel Pj Lai Jacob G Bundy Fabien Jourdan Timothy Ebbels Pathway analysis in metabolomics: Recommendations for the use of over-representation analysis.
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.
format	article
author	Cecilia Wieder Clément Frainay Nathalie Poupin Pablo Rodríguez-Mier Florence Vinson Juliette Cooke Rachel Pj Lai Jacob G Bundy Fabien Jourdan Timothy Ebbels
author_facet	Cecilia Wieder Clément Frainay Nathalie Poupin Pablo Rodríguez-Mier Florence Vinson Juliette Cooke Rachel Pj Lai Jacob G Bundy Fabien Jourdan Timothy Ebbels
author_sort	Cecilia Wieder
title	Pathway analysis in metabolomics: Recommendations for the use of over-representation analysis.
title_short	Pathway analysis in metabolomics: Recommendations for the use of over-representation analysis.
title_full	Pathway analysis in metabolomics: Recommendations for the use of over-representation analysis.
title_fullStr	Pathway analysis in metabolomics: Recommendations for the use of over-representation analysis.
title_full_unstemmed	Pathway analysis in metabolomics: Recommendations for the use of over-representation analysis.
title_sort	pathway analysis in metabolomics: recommendations for the use of over-representation analysis.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/38f5454156524f19b62777d46227717a
work_keys_str_mv	AT ceciliawieder pathwayanalysisinmetabolomicsrecommendationsfortheuseofoverrepresentationanalysis AT clementfrainay pathwayanalysisinmetabolomicsrecommendationsfortheuseofoverrepresentationanalysis AT nathaliepoupin pathwayanalysisinmetabolomicsrecommendationsfortheuseofoverrepresentationanalysis AT pablorodriguezmier pathwayanalysisinmetabolomicsrecommendationsfortheuseofoverrepresentationanalysis AT florencevinson pathwayanalysisinmetabolomicsrecommendationsfortheuseofoverrepresentationanalysis AT juliettecooke pathwayanalysisinmetabolomicsrecommendationsfortheuseofoverrepresentationanalysis AT rachelpjlai pathwayanalysisinmetabolomicsrecommendationsfortheuseofoverrepresentationanalysis AT jacobgbundy pathwayanalysisinmetabolomicsrecommendationsfortheuseofoverrepresentationanalysis AT fabienjourdan pathwayanalysisinmetabolomicsrecommendationsfortheuseofoverrepresentationanalysis AT timothyebbels pathwayanalysisinmetabolomicsrecommendationsfortheuseofoverrepresentationanalysis
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Pathway analysis in metabolomics: Recommendations for the use of over-representation analysis.

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