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Microbiome Datasets Are Compositional: And This Is Not Optional
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 g...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2017
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5695134/ https://www.ncbi.nlm.nih.gov/pubmed/29187837 http://dx.doi.org/10.3389/fmicb.2017.02224 |
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author | Gloor, Gregory B. Macklaim, Jean M. Pawlowsky-Glahn, Vera Egozcue, Juan J. |
author_facet | Gloor, Gregory B. Macklaim, Jean M. Pawlowsky-Glahn, Vera Egozcue, Juan J. |
author_sort | Gloor, Gregory B. |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-5695134 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-56951342017-11-29 Microbiome Datasets Are Compositional: And This Is Not Optional Gloor, Gregory B. Macklaim, Jean M. Pawlowsky-Glahn, Vera Egozcue, Juan J. Front Microbiol Microbiology 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. Frontiers Media S.A. 2017-11-15 /pmc/articles/PMC5695134/ /pubmed/29187837 http://dx.doi.org/10.3389/fmicb.2017.02224 Text en Copyright © 2017 Gloor, Macklaim, Pawlowsky-Glahn and Egozcue. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Microbiology Gloor, Gregory B. Macklaim, Jean M. Pawlowsky-Glahn, Vera Egozcue, Juan J. Microbiome Datasets Are Compositional: And This Is Not Optional |
title | Microbiome Datasets Are Compositional: And This Is Not Optional |
title_full | Microbiome Datasets Are Compositional: And This Is Not Optional |
title_fullStr | Microbiome Datasets Are Compositional: And This Is Not Optional |
title_full_unstemmed | Microbiome Datasets Are Compositional: And This Is Not Optional |
title_short | Microbiome Datasets Are Compositional: And This Is Not Optional |
title_sort | microbiome datasets are compositional: and this is not optional |
topic | Microbiology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5695134/ https://www.ncbi.nlm.nih.gov/pubmed/29187837 http://dx.doi.org/10.3389/fmicb.2017.02224 |
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