It’s all relative: analyzing microbiome data as compositions

• Published in: Annals of epidemiology Vol. 26; no. 5; pp. 322 - 329
• Main Authors: Gloor, Gregory B, Wu, Jia Rong, Pawlowsky-Glahn, Vera, Egozcue, Juan José
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01-05-2016; Elsevier
• Subjects: 16S rRNA gene sequencing; Anàlisi multivariable; Compositional data; Datasets as Topic; Female; High-Throughput Nucleotide Sequencing - methods; Humans; Internal Medicine; Male; Microbiologia; Microbiology; Microbiome; Microbiota - genetics; Multivariate analysis; Mètodes estadístics; RNA, Ribosomal, 16S - genetics; Sensitivity and Specificity; Statistical methods; microbiome; 16S rRNA gene sequencing; compositional data; Multivariate analysis; Compositional data; Microbiome
• ISSN: 1047-2797; 1873-2585
• DOI: 10.1016/j.annepidem.2016.03.003

Abstract Purpose The ability to properly analyze and interpret large microbiome datasets has lagged behind our ability to acquire such datasets from environmental or clinical samples. Sequencing instruments impose a structure on these data: the natural sample space of a 16S rRNA gene sequencing dataset is a simplex, which is a part of real space that is restricted to non-negative values with a constant sum. Such data are compositional, and should be analyzed using compositionally appropriate tools and approaches. However, the majority of the tools for 16S rRNA gene sequencing analysis assume these data are unrestricted. Methods We show that existing tools for compositional data (CoDa) analysis can be readily adapted to analyze high throughput sequencing datasets. Results The Human Microbiome Project tongue vs. buccal mucosa dataset shows how the CoDa approach can address the major elements of microbiome analysis. Reanalysis of a publicly available autism microbiome dataset shows that the CoDa approach in concert with multiple hypothesis test corrections prevent false positive identifications. Conclusions The CoDa approach is readily scalable to microbiome-sized analyses. We provide example code, and make recommendations to improve the analysis and reporting of microbiome datasets.