It is clear that the human’s microbiome is associated with various disease. Much work has been done to analyze microbiome data to identify microbiota related with specific disease. However, in the case of study using only microbiome data, even if we identify microbiota related to a particular disease, it is difficult to know the functional potential of the microbiome. The metabolomes produced from the microbial community are known to play a role in connecting host phenotype and microbiome function. Using both metabolomics and metagenomics has an advantage of understanding functional potentials of the microbiome and interactions with the host. However, integration of these two omics data remains a challenge, usually requiring a more advanced method. In this study, we proposed the hierarchical structural component model (HisCoM-mm) that integrates microbiome and metabolome data. In particular, we used pathway information for integrate these two omics datasets to provide insight into biological interactions between different biological layers in relation to host phenotype. We applied our model to analyze real datasets generated from specific diseases. These real datasets were used to demonstrate whether our model is able to identify the pathways known to be related with disease. This analysis shows our HisCoM-mm can identify disease related pathways from KEGG database and provide significant metabolomic and metagenomic components of pathway.<br/>

Pathway-based integration of metabolome and microbiome data

ACS Fall 2020 Virtual Meeting

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