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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Vydáno v:Frontiers in microbiology Ročník 8; s. 2224
Hlavní autoři: Gloor, Gregory B., Macklaim, Jean M., Pawlowsky-Glahn, Vera, Egozcue, Juan J.
Médium: Journal Article Publikace
Jazyk:angličtina
Vydáno: Switzerland Frontiers Media S.A 15.11.2017
Témata:
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
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Shrnutí: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.
Bibliografie:ObjectType-Article-1
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ObjectType-Review-3
content type line 23
Edited by: Jessica Galloway-Pena, University of Texas MD Anderson Cancer Center, United States
Reviewed by: Ionas Erb, Centre for Genomic Regulation, Spain; Jennifer Stearns, McMaster University, Canada
This article was submitted to Systems Microbiology, a section of the journal Frontiers in Microbiology
ISSN:1664-302X
1664-302X
DOI:10.3389/fmicb.2017.02224