It's all relative: analyzing microbiome data as compositions
dc.contributor.author
dc.date.accessioned
2016-09-07T11:23:54Z
dc.date.available
2017-03-23T04:00:06Z
dc.date.issued
2016-03-23
dc.identifier.issn
1047-2797
dc.identifier.uri
dc.description.abstract
Purpose: The ability to properly analyze and interpret large microbiome datasets has lagged behind our ability to acquire such datasets from environmental or clinical samples. Sequencing instruments impose a structure on these data: the natural sample space of a 16S rRNA gene sequencing dataset is a simplex, which is a part of real space that is restricted to non-negative values with a constant sum. Such data are compositional, and should be analyzed using compositionally appropriate tools and approaches. However, the majority of the tools for 16S rRNA gene sequencing analysis assume these data are unrestricted. Methods: We show that existing tools for compositional data (CoDa) analysis can be readily adapted to analyze high throughput sequencing datasets. Results: The Human Microbiome Project tongue vs. buccal mucosa dataset shows how the CoDa approach can address the major elements of microbiome analysis. Reanalysis of a publicly available autism microbiome dataset shows that the CoDa approach in concert with multiple hypothesis test corrections prevent false positive identifications. Conclusions: The CoDa approach is readily scalable to microbiome-sized analyses. We provide example code, and make recommendations to improve the analysis and reporting of microbiome datasets
dc.description.sponsorship
Work in G.B. Gloor’s lab has been supported by a Discovery Grant from the National Science and
Engineering Research Council of Canada. J.R. Wu was supported by a CIHR grant to Dr. J. Allard
and GBG. Drs J.J. Egozcue and V. Pawlowsky-Glahn have been supported by the Spanish Ministry of
Economy and Competitiveness under the project METRICS (Ref. MTM2012-33236); and by the
Agència de Gestió d’Ajuts Universitaris i de Recerca of the Generalitat de Catalunya under the
project COSDA (Ref: 2014SGR551).
dc.format.extent
8 p.
dc.format.mimetype
application/pdf
dc.language.iso
eng
dc.publisher
Elsevier
dc.relation
info:eu-repo/grantAgreement/MINECO//MTM2012-33236/ES/METODOS ESTADISTICOS EN ESPACIOS RESTRINGIDOS/
AGAUR/2014-2016/2014 SGR-551
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Versió postprint del document publicat a: http://dx.doi.org/10.1016/j.annepidem.2016.03.003
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© Annals of Epidemiology, 2016, vol. 26, núm. 5, p. 322-329
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Articles publicats (D-IMAE)
dc.rights
Reconeixement-NoComercial-SenseObraDerivada 4.0 Internacional
dc.rights.uri
dc.source
Gloor, G.B. Wu, J.R. Pawlowsky-Glahn, Vera Egozcue, Juan José 2016 It's all relative: analyzing microbiome data as compositions. Annals of Epidemiology 26 5 322 329
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dc.title
It's all relative: analyzing microbiome data as compositions
dc.type
info:eu-repo/semantics/article
dc.rights.accessRights
info:eu-repo/semantics/openAccess
dc.type.version
info:eu-repo/semantics/acceptedVersion
dc.identifier.doi
dc.identifier.idgrec
025458
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dc.relation.ProjectAcronym