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Gloor GB, Macklaim JM, Pawlowsky-Glahn V, et al. Microbiome Datasets Are Compositional: And This Is Not Optional. Front Microbiol. 2017;8:2224doi: 10.3389/fmicb.2017.02224.
Gloor, G. B., Macklaim, J. M., Pawlowsky-Glahn, V., & Egozcue, J. J. (2017). Microbiome Datasets Are Compositional: And This Is Not Optional. Frontiers in microbiology, 82224. https://doi.org/10.3389/fmicb.2017.02224
Gloor, Gregory B, et al. "Microbiome Datasets Are Compositional: And This Is Not Optional." Frontiers in microbiology vol. 8 (2017): 2224. doi: https://doi.org/10.3389/fmicb.2017.02224
Gloor GB, Macklaim JM, Pawlowsky-Glahn V, Egozcue JJ. Microbiome Datasets Are Compositional: And This Is Not Optional. Front Microbiol. 2017 Nov 15;8:2224. doi: 10.3389/fmicb.2017.02224. eCollection 2017. PMID: 29187837; PMCID: PMC5695134.
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Front Microbiol. 2017 Nov 15;8:2224. doi: 10.3389/fmicb.2017.02224. eCollection 2017.

Microbiome Datasets Are Compositional: And This Is Not Optional.

Frontiers in microbiology

Gregory B Gloor, Jean M Macklaim, Vera Pawlowsky-Glahn, Juan J Egozcue

Affiliations

  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.

PMID: 29187837 PMCID: PMC5695134 DOI: 10.3389/fmicb.2017.02224

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.

Keywords: Bayesian estimation; compositional data; correlation; count normalization; high-throughput sequencing; microbiota; relative abundance

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