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...

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Bibliographic Details
Published in:	Frontiers in microbiology Vol. 8; p. 2224
Main Authors:	Gloor, Gregory B., Macklaim, Jean M., Pawlowsky-Glahn, Vera, Egozcue, Juan J.
Format:	Journal Article Publication
Language:	English
Published:	Switzerland Frontiers Media S.A 15.11.2017
Subjects:	Aparell digestiu Bayesian estimation Bayesian statistical decision theory Biologia Ciències de la salut compositional data correlation count normalization Dietètica i nutrició Estadística aplicada high-throughput sequencing Matemàtiques i estadística Medicina Microbiology Microbiota relative abundance Àrees temàtiques de la UPC microbiota Bayesian estimation compositional data correlation relative abundance count normalization high-throughput sequencing
ISSN:	1664-302X, 1664-302X
Online Access:	Get full text
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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.
AbstractList	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. 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.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. 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. Peer Reviewed
Author	Macklaim, Jean M. Pawlowsky-Glahn, Vera Egozcue, Juan J. Gloor, Gregory B.
AuthorAffiliation	1 Department of Biochemistry, University of Western Ontario , London, ON , Canada 2 Departments of Computer Science, Applied Mathematics, and Statistics, Universitat de Girona , Girona , Spain 3 Department of Applied Mathematics, Universitat Politècnica de Catalunya , Barcelona , Spain
AuthorAffiliation_xml	– name: 1 Department of Biochemistry, University of Western Ontario , London, ON , Canada – name: 3 Department of Applied Mathematics, Universitat Politècnica de Catalunya , Barcelona , Spain – name: 2 Departments of Computer Science, Applied Mathematics, and Statistics, Universitat de Girona , Girona , Spain
Author_xml	– sequence: 1 givenname: Gregory B. surname: Gloor fullname: Gloor, Gregory B. – sequence: 2 givenname: Jean M. surname: Macklaim fullname: Macklaim, Jean M. – sequence: 3 givenname: Vera surname: Pawlowsky-Glahn fullname: Pawlowsky-Glahn, Vera – sequence: 4 givenname: Juan J. surname: Egozcue fullname: Egozcue, Juan J.
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/29187837$$D View this record in MEDLINE/PubMed
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Copyright	Attribution 3.0 Spain info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/3.0/es Copyright © 2017 Gloor, Macklaim, Pawlowsky-Glahn and Egozcue. 2017 Gloor, Macklaim, Pawlowsky-Glahn and Egozcue
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Keywords	microbiota Bayesian estimation compositional data correlation relative abundance count normalization high-throughput sequencing
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References	Gorvitovskaia (B14) 2016; 4 Lovell (B18) 2011 Kurtz (B17) 2015; 11 Mandal (B22) 2015; 26 McMurdie (B26) 2014; 10 Wong (B43) 2016; 11 Shaffer (B36) 1981; 31 Thorsen (B38) 2016; 4 Aitchison (B1) 1983; 70 Hawinkel (B15) 2017 Bian (B6) 2017; 2 Gloor (B11); 45 Jaynes (B16) 2003 Morton (B28) 2017; 2 Ortego (B29) 2013 Weiss (B42) 2017; 5 Friedman (B10) 2012; 8 McMillan (B24) 2015; 5 McMurrough (B27) 2014; 111 Quinn (B33) 2017 Gloor (B12) 2016; 62 Anders (B5) 2010; 11 Silverman (B37) 2017; 6 Weiss (B41) 2016; 10 Palarea-Albaladejo (B30) 2015; 143 Van den Boogaart (B40) 2013 Pearson (B32) 1897; 60 Robinson (B35) 2010; 11 Aitchison (B4) 2002; 51 Martín-Fernández (B23) 1998; 98 Fernandes (B8) 2013; 8 Lozupone (B20) 2011; 5 Robinson (B34) 2016; 26 Tsilimigras (B39) 2016; 26 Aitchison (B3) 2000; 32 Lovell (B19) 2015; 11 McMurdie (B25) 2013; 8 Fernandes (B9) 2014; 2 Macklaim (B21) 2013; 1 Pawlowsky-Glahn (B31) 2015 Erb (B7) 2016; 135 Aitchison (B2) 1986 Gloor (B13); 26
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SubjectTerms	Aparell digestiu Bayesian estimation Bayesian statistical decision theory Biologia Ciències de la salut compositional data correlation count normalization Dietètica i nutrició Estadística aplicada high-throughput sequencing Matemàtiques i estadística Medicina Microbiology Microbiota relative abundance Àrees temàtiques de la UPC
Title	Microbiome Datasets Are Compositional: And This Is Not Optional
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