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Testing Associations Between Microbiome and Other Omics Data Types

Banff International Research Station for Mathematical Innovation and Discovery

Joint analysis of microbiome and other genomic data types offers to simultaneously improve power to identify novel associations and elucidate the mechanisms underlying established relationships with outcomes. However, microbiome data are subject to high dimensionality, compositionality, sparsity, phylogenetic constraints, and complexity of relationships among taxa. Combined with the myriad challenges specific to other omics data types, how to conduct integrative analysis continues to pose a grand challenge. To move towards joint analysis, we propose development of methods for identifying individual and groups of genomic features related to microbiome community structure. Specifically, using kernels to capture microbiome community structure, we develop approaches for rapidly screening genomic features that collectively, marginally or conditionally affecting beta diversity.

Mathematics; Statistics; Biology and other natural sciences; Applied statistics

video/mp4

Author affiliation: Fred Hutchinson Cancer Research Center

2020-03-15

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/73742

Unreviewed

http://creativecommons.org/licenses/by-nc-nd/4.0/