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Microbiome datasets are compositional: and this is not optional
dc.contributor.author | Gloor, Gregory B. |
dc.contributor.author | Macklaim, Jean M. |
dc.contributor.author | Pawlowsky Glahn, Vera |
dc.contributor.author | Egozcue Rubí, Juan José |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament d'Enginyeria Civil i Ambiental |
dc.date.accessioned | 2017-11-28T14:39:31Z |
dc.date.available | 2017-11-28T14:39:31Z |
dc.date.issued | 2017-11 |
dc.identifier.citation | Gloor, G., Macklaim, J., Pawlowsky, V., Egozcue, J. J. Microbiome datasets are compositional: and this is not optional. "Frontiers in Microbiology", Novembre 2017, vol. 8, p. 1-6. |
dc.identifier.issn | 1664-302X |
dc.identifier.uri | http://hdl.handle.net/2117/111278 |
dc.description.abstract | Datasets collected by high-throughput sequencing (HTS) of 16S rRNA gene amplimers, metagenomes or metatranscriptomes are commonplace and being used to study human disease states, ecological differences between sites, and the built environment. There is increasing awareness that microbiome datasets generated by HTS are compositional because they have an arbitrary total imposed by the instrument. However, many investigators are either unaware of this or assume specific properties of the compositional data. The purpose of this review is to alert investigators to the dangers inherent in ignoring the compositional nature of the data, and point out that HTS datasets derived from microbiome studies can and should be treated as compositions at all stages of analysis. We briefly introduce compositional data, illustrate the pathologies that occur when compositional data are analyzed inappropriately, and finally give guidance and point to resources and examples for the analysis of microbiome datasets using compositional data analysis. |
dc.format.extent | 6 p. |
dc.language.iso | eng |
dc.rights | Attribution 3.0 Spain |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ |
dc.subject | Àrees temàtiques de la UPC::Ciències de la salut::Medicina::Dietètica i nutrició |
dc.subject | Àrees temàtiques de la UPC::Matemàtiques i estadística::Estadística aplicada |
dc.subject.lcsh | Microbiota |
dc.subject.lcsh | Bayesian statistical decision theory |
dc.subject.other | microbiota |
dc.subject.other | compositional data |
dc.subject.other | high-throughput sequencing |
dc.subject.other | correlation |
dc.subject.other | Bayesian estimation |
dc.subject.other | count normalization |
dc.subject.other | relative abundance |
dc.title | Microbiome datasets are compositional: and this is not optional |
dc.type | Article |
dc.subject.lemac | Aparell digestiu |
dc.subject.lemac | Estadística aplicada -- Biologia |
dc.contributor.group | Universitat Politècnica de Catalunya. COSDA-UPC - COmpositional and Spatial Data Analysis |
dc.identifier.doi | 10.3389/fmicb.2017.02224 |
dc.description.peerreviewed | Peer Reviewed |
dc.relation.publisherversion | https://www.frontiersin.org/articles/10.3389/fmicb.2017.02224/full |
dc.rights.access | Open Access |
local.identifier.drac | 21637898 |
dc.description.version | Postprint (published version) |
local.citation.author | Gloor, G.; Macklaim, J.; Pawlowsky, V.; Egozcue, J. J. |
local.citation.publicationName | Frontiers in Microbiology |
local.citation.volume | 8 |
local.citation.startingPage | 1 |
local.citation.endingPage | 6 |
dc.identifier.pmid | 29187837 |
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