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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, such analyses are challenging. Issues inherent to microbiome data include dimensionality, compositionality, sparsity, phylogenetic constraints, and complexity of relationships among taxa. It remains unclear how to address these issues, much less to address these issues in combination with problems specific to other omics data types and problems in modeling relationships between microbial taxa and other omics features. To move towards joint analysis, we propose development of methods for studying both community level correlations between microbiome and other data types as well as for correlating individual taxa with other omics data. Real data analyses demonstrate that our approach for correlating microbial taxa with other omics features can reveal new biological findings.
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