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Weighted Kernel Method for Integrative Metabolomic and Metagenomic Pathway Analysis (309975)

Keywords: Kernel regression, Metagenomic, Metabolomic, Microbiome

Dysbiosis of the microbiome can lead to abnormal levels of microbe-produced metabolites, which has been linked to a variety of diseases and conditions. Innovations in high-throughput technology now allow rapid profiling of the metabolome and metagenome – the gene content of the bacteria – for characterizing microbial metabolism. Due to the small sample sizes and high-dimensionality of the data, pathway analysis (wherein the effect of multiple genes or metabolites on an outcome is cumulatively assessed) of metabolomic data is commonly conducted and also represents the standard for metagenomic analysis. However, how to integrate both data types remains unclear. Recognizing that a metabolic pathway can be complementarily characterized by both metagenomics and metabolomics, we propose a weighted kernel framework to test if the joint effect of genes and metabolites in a biological pathway is associated with outcomes. The approach allows analytic p-value calculation, correlation between data types, and optimal weighting. Simulations show that our approach often outperforms other strategies. The approach is illustrated on real data.