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@article{Wieder:2021:10.1101/2021.05.24.445406,
author = {Wieder, C and Frainay, C and Poupin, N and Rodríguez-Mier, P and Vinson, F and Cooke, J and Lai, RPJ and Bundy, JG and Jourdan, F and Ebbels, T},
doi = {10.1101/2021.05.24.445406},
title = {Pathway analysis in metabolomics: pitfalls and best practice for the use of over-representation analysis},
url = {http://dx.doi.org/10.1101/2021.05.24.445406},
year = {2021}
}

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TY  - JOUR
AB  - <jats:title>Abstract</jats:title><jats:p>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 in the field. We developed <jats:italic>in-silico</jats:italic> simulations using five publicly available datasets and illustrated that changes in parameters, such as the background set, differential metabolite selection methods, and pathway database choice, could all lead to 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. Metabolomics data specific factors, such as reliability of compound identification and assay chemical bias 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.</jats:p><jats:sec><jats:title>Author summary</jats:title><jats:p>Metabolomics is a rapidly growing field of study involving the profiling of small molecules within an organism. It allows researchers to understand the effects of biologi
AU  - Wieder,C
AU  - Frainay,C
AU  - Poupin,N
AU  - Rodríguez-Mier,P
AU  - Vinson,F
AU  - Cooke,J
AU  - Lai,RPJ
AU  - Bundy,JG
AU  - Jourdan,F
AU  - Ebbels,T
DO  - 10.1101/2021.05.24.445406
PY  - 2021///
TI  - Pathway analysis in metabolomics: pitfalls and best practice for the use of over-representation analysis
UR  - http://dx.doi.org/10.1101/2021.05.24.445406
ER  -

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