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.

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.

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. However, the analytical methodology needed to model microbiome data and integrate them with other data constructs remains nascent. The advent of large-scale microbiome studies affords newfound analytical opportunities to understand how these communities of microbes operate and relate to their environment. 2Department of Microbiology, Oregon State University, Corvallis, OR, United States.1Department of Statistics, Oregon State University, Corvallis, OR, United States.Armour 2, Chenxiao Hu 1, Meng Mei 1, Chuan Tian 1, Thomas J.

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