It's all relative: analyzing microbiome data as compositions

Abstract

Purpose: The ability to properly analyze and interpret large microbiome data sets has lagged behind our ability to acquire such data sets from environmental or clinical samples. Sequencing instruments impose a structure on these data: the natural sample space of a 16S rRNA gene sequencing data set is a simplex, which is a part of real space that is restricted to nonnegative values with a constant sum. Such data are compositional and should be analyzed using compositionally appropriate tools and approaches. However, most 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 data sets.

Results: The Human Microbiome Project tongue versus buccal mucosa data set shows how the CoDa approach can address the major elements of microbiome analysis. Reanalysis of a publicly available autism microbiome data set 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 data sets.

Keywords: 16S rRNA gene sequencing; Compositional data; Microbiome; Multivariate analysis.