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Abstract:
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Understanding the relationship between microbiome and other omics data types is important both for obtaining a more comprehensive view of biological systems as well as for elucidating mechanisms underlying outcomes and response to exposures. However, the key features of microbiome data, including high-dimensionality, compositionality, sparsity, phylogenetic constraints, and complexity of relationships among taxa, pose a grand challenge for statistical analysis. This is compounded by the inherent complexity of the other omics data types as well. Recognizing these difficulties, we illustrate frameworks for integrative analysis of multiple omics data types in conjunction with microbiome data. Specifically, we consider community level associations between microbiome and individual or groups (e.g. pathways) of other omics features. We further discuss how to incorporate other omics data to facilitate analyses of microbiome data. Simulations and real data are used to illustrate our strategies.
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