Statistical and Computational Methods for Microbiome Multi-Omics Data

Culture-independent studies of the human microbiome and microbial communities using multiple high-throughput functional profiling technologies, including metagenomics, metatranscriptomics, metaproteomics, and metabolomics have become a powerful tool for surveying the whole community. This has been highlighted by an increase in longitudinal and population-level microbiome-wide studies that rely on multi-omics profiling to simultaneously characterize community function, dynamics, and biochemical signatures across diverse disease states and environments. The field of microbiome multi-omics, however, has not yet reached the maturity attained in other established molecular epidemiology fields such as cancer biomarker discovery and genome-wide association studies for making the leap from ‘omics survey to rational microbiome-based therapeutics.

One of the primary limitations to leveraging this large body of ‘big data’ is computational and statistical. Among these are the technical nature of the data associated: high-dimensionality, count and compositional data structure, sparsity (zero-inflation), over-dispersion, and hierarchical, spatial, and temporal dependence, among others. To combat these challenges, specialized methods and software are needed to accurately characterize microbial communities within and across large studies, while maintaining both statistical rigor and biological relevance.

This Research Topic thus focuses on studies (e.g. original research, perspectives, reviews, commentaries, and opinion papers) that investigate and discuss novel experimental design and downstream biostatistical considerations for integrated analysis of microbial community multi-omics profiles (16S amplicon, metagenomics, metatranscriptomics, metaproteomics, metabolomics, and other culture-independent molecular data). We believe this topic is both timely and fundamental for improving our current understanding of the microbiome. The diverse collection of articles on this topic will (i) provide a useful reference for both current and future investigators in translational and clinical microbiome research, and (ii) establish best practice guidelines for analyzing and integrating microbial multi-omics data, including but not limited to:

• biologically informed strain- or species-level ecological interaction discovery
• meta-analysis for batch effect correction and population structure discovery
• integrative analysis for precision medicine
• longitudinal and time-series analyses
• machine learning methods for predictive analyses