Pathway analysis in metabolomics: Recommendations for the use of over-representation analysis.
Journal
PLoS computational biology
ISSN: 1553-7358
Titre abrégé: PLoS Comput Biol
Pays: United States
ID NLM: 101238922
Informations de publication
Date de publication:
09 2021
09 2021
Historique:
received:
14
05
2021
accepted:
23
08
2021
revised:
17
09
2021
pubmed:
8
9
2021
medline:
15
12
2021
entrez:
7
9
2021
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 34492007
doi: 10.1371/journal.pcbi.1009105
pii: PCOMPBIOL-D-21-00895
pmc: PMC8448349
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
e1009105Subventions
Organisme : NHLBI NIH HHS
ID : R01 HL133932
Pays : United States
Organisme : Medical Research Council
ID : MR/R008922/1
Pays : United Kingdom
Organisme : Wellcome Trust
Pays : United Kingdom
Organisme : Medical Research Council
ID : MR/S019669/1
Pays : United Kingdom
Organisme : Wellcome Trust
ID : 222837/Z/21/Z
Pays : United Kingdom
Organisme : Biotechnology and Biological Sciences Research Council
ID : BB/T007974/1
Pays : United Kingdom
Déclaration de conflit d'intérêts
The authors have declared that no competing interests exist.
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