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 2017-11, Vol.8, p.2224-2224
Main Authors: Gloor, Gregory B, Macklaim, Jean M, Pawlowsky-Glahn, Vera, Egozcue, Juan J
Format: Article
Language:English
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
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Summary: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.
ISSN:1664-302X
1664-302X
DOI:10.3389/fmicb.2017.02224