Imperial College London
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DrRachelLai

Faculty of Medicine, Department of Infectious Disease

Non-Clinical Lecturer in Antimicrobial Resistance and Infect
 
 
 
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rachel.lai

 
 
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Location

 

8N12Commonwealth BuildingHammersmith Campus

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Summary

 

Publications

Citation

BibTex format

@article{Wieder:2021:10.1371/journal.pcbi.1009105,
author = {Wieder, C and Frainay, C and Poupin, N and Rodriguez-Mier, P and Vinson, F and Cooke, J and Lai, RPJ and Bundy, JG and Jourdan, F and Ebbels, T},
doi = {10.1371/journal.pcbi.1009105},
journal = {PLoS Computational Biology},
title = {Pathway analysis in metabolomics: recommendations for the use of over-representation analysis},
url = {http://dx.doi.org/10.1371/journal.pcbi.1009105},
volume = {17},
year = {2021}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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.
AU  - Wieder,C
AU  - Frainay,C
AU  - Poupin,N
AU  - Rodriguez-Mier,P
AU  - Vinson,F
AU  - Cooke,J
AU  - Lai,RPJ
AU  - Bundy,JG
AU  - Jourdan,F
AU  - Ebbels,T
DO  - 10.1371/journal.pcbi.1009105
PY  - 2021///
SN  - 1553-734X
TI  - Pathway analysis in metabolomics: recommendations for the use of over-representation analysis
T2  - PLoS Computational Biology
UR  - http://dx.doi.org/10.1371/journal.pcbi.1009105
UR  - https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000697187000013&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=a2bf6146997ec60c407a63945d4e92bb
UR  - https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1009105
UR  - http://hdl.handle.net/10044/1/110383
VL  - 17
ER  -
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