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Review ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Genet. | doi: 10.3389/fgene.2019.00995

Microbiome Multi-Omics Network Analysis: Statistical Considerations, Limitations, and Opportunities

 Duo Jiang1,  Courtney R. Armour1, Chenxiao Hu1, Meng Mei1, Chuan Tian1,  Thomas J. Sharpton1 and  Yuan Jiang1*
  • 1Oregon State University, United States

The advent of large-scale microbiome studies affords newfound analytical opportunities to understand how these communities of microbes operate and relate to their environment. However, the analytical methodology needed to model microbiome data and integrate them with other data constructs remains nascent. This emergent analytical toolset frequently ports over techniques developed in other multi-omics investigations, especially the growing array of statistical and computational techniques for integrating and representing data through networks. While network analysis has emerged as a powerful approach to modeling microbiome data, oftentimes by integrating these data with other types of omics data to discern their functional linkages, it is not always evident if the statistical details of the approach being applied are consistent with the assumptions of microbiome data or how they impact data interpretation. In this review, we overview some of the most important network methods for integrative analysis, with an emphasis on methods that have been applied or have great potential to be applied to the analysis of multi-omics integration of microbiome data. We compare advantages and disadvantages of various statistical tools, assess their applicability to microbiome data, and discuss their biological interpretability. We also highlight on-going statistical challenges and opportunities for integrative network analysis of microbiome data.

Keywords: compositionality, heterogeneity, microbiome networks, Multi-omics data Integration, Network analysis, normalization, sparsity

Received: 13 Feb 2019; Accepted: 18 Sep 2019.

Copyright: © 2019 Jiang, Armour, Hu, Mei, Tian, Sharpton and Jiang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Dr. Yuan Jiang, Oregon State University, Corvallis, 97331, Oregon, United States, yuan.jiang@stat.oregonstate.edu