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dc.contributor.authorPeluso, Alina
dc.contributor.authorGlen, Robert
dc.contributor.authorEbbels, Timothy MD
dc.date.accessioned2021-02-12T17:30:38Z
dc.date.available2021-02-12T17:30:38Z
dc.date.issued2021-02-12
dc.date.submitted2020-10-12
dc.identifier.issn1471-2105
dc.identifier.others12859-021-03975-2
dc.identifier.other3975
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/317564
dc.description.abstractBACKGROUND: The search for statistically significant relationships between molecular markers and outcomes is challenging when dealing with high-dimensional, noisy and collinear multivariate omics data, such as metabolomic profiles. Permutation procedures allow for the estimation of adjusted significance levels without assuming independence among metabolomic variables. Nevertheless, the complex non-normal structure of metabolic profiles and outcomes may bias the permutation results leading to overly conservative threshold estimates i.e. lower than those from a Bonferroni or Sidak correction. METHODS: Within a univariate permutation procedure we employ parametric simulation methods based on the multivariate (log-)Normal distribution to obtain adjusted significance levels which are consistent across different outcomes while effectively controlling the type I error rate. Next, we derive an alternative closed-form expression for the estimation of the number of non-redundant metabolic variates based on the spectral decomposition of their correlation matrix. The performance of the method is tested for different model parametrizations and across a wide range of correlation levels of the variates using synthetic and real data sets. RESULTS: Both the permutation-based formulation and the more practical closed form expression are found to give an effective indication of the number of independent metabolic effects exhibited by the system, while guaranteeing that the derived adjusted threshold is stable across outcome measures with diverse properties.
dc.languageen
dc.publisherSpringer Science and Business Media LLC
dc.rightsAttribution 4.0 International (CC BY 4.0)
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectResearch Article
dc.subjectProteomics
dc.subjectFWER
dc.subjectMWAS
dc.subjectMWSL
dc.subjectMultiple testing
dc.subjectPermutation
dc.subjectCorrelated tests
dc.titleMultiple-testing correction in metabolome-wide association studies.
dc.typeArticle
dc.date.updated2021-02-12T17:30:38Z
prism.issueIdentifier1
prism.publicationNameBMC Bioinformatics
prism.volume22
dc.identifier.doi10.17863/CAM.64677
dcterms.dateAccepted2021-01-19
rioxxterms.versionofrecord10.1186/s12859-021-03975-2
rioxxterms.versionVoR
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by/4.0/
dc.contributor.orcidPeluso, Alina [0000-0003-2895-0406]
dc.identifier.eissn1471-2105
cam.issuedOnline2021-02-12


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Attribution 4.0 International (CC BY 4.0)
Except where otherwise noted, this item's licence is described as Attribution 4.0 International (CC BY 4.0)