Statistical Methods for Detecting Differentially Abundant Features in Clinical Metagenomic Samples

Abstract

Numerous studies are currently underway to characterize the microbial communities inhabiting our world. These studies aim to dramatically expand our understanding of the microbial biosphere and, more importantly, hope to reveal the secrets of the complex symbiotic relationship between us and our commensal bacterial microflora. An important prerequisite for such discoveries are computational tools that are able to rapidly and accurately compare large datasets generated from complex bacterial communities to identify features that distinguish them.

We present a statistical method for comparing clinical metagenomic samples from two treatment populations on the basis of count data (e.g. as obtained through sequencing) to detect differentially abundant features. Our method, Metastats, employs the false discovery rate to improve specificity in high-complexity environments, and separately handles sparsely-sampled features using Fisher's exact test. Under a variety of simulations, we show that Metastats performs well compared to previously used methods, and significantly outperforms other methods for features with sparse counts. We demonstrate the utility of our method on several datasets including a 16S rRNA survey of obese and lean human gut microbiomes, COG functional profiles of infant and mature gut microbiomes, and bacterial and viral metabolic subsystem data inferred from random sequencing of 85 metagenomes. The application of our method to the obesity dataset reveals differences between obese and lean subjects not reported in the original study. For the COG and subsystem datasets, we provide the first statistically rigorous assessment of the differences between these populations. The methods described in this paper are the first to address clinical metagenomic datasets comprising samples from multiple subjects. Our methods are robust across datasets of varied complexity and sampling level. While designed for metagenomic applications, our software can also be applied to digital gene expression studies (e.g. SAGE). A web server implementation of our methods and freely available source code can be found at http://metastats.cbcb.umd.edu/.

Author Summary

The emerging field of metagenomics aims to understand the structure and function of microbial communities solely through DNA analysis. Current metagenomics studies comparing communities resemble large-scale clinical trials with multiple subjects from two general populations (e.g. sick and healthy). To improve analyses of this type of experimental data, we developed a statistical methodology for detecting differentially abundant features between microbial communities, that is, features that are enriched or depleted in one population versus another. We show our methods are applicable to various metagenomic data ranging from taxonomic information to functional annotations. We also provide an assessment of taxonomic differences in gut microbiota between lean and obese humans, as well as differences between the functional capacities of mature and infant gut microbiomes, and those of microbial and viral metagenomes. Our methods are the first to statistically address differential abundance in comparative metagenomics studies with multiple subjects, and we hope will give researchers a more complete picture of how exactly two environments differ.