Automated Prior Elicitation for Bayesian Metabolomics Analysis

Music City Center 

Modern metabolomics experiments generate rich, high-dimensional data that capture complex biochemical relationships. While public databases like KEGG, HMDB, and Reactome contain extensive prior knowledge about metabolic networks, incorporating this information systematically into statistical analyses remains challenging. We present a novel framework for automatically constructing informative prior distributions from metabolic databases for Bayesian analysis of metabolomics data. Our method extracts network topology, reaction directionality, and uses modern NLP techniques to utilize qualitative information to build hierarchical prior distributions. We demonstrate our approach on both targeted and untargeted LC-MS data, showing improved power for differential abundance testing and more biologically plausible pathway-level effect estimates compared to standard methods. This work provides a principled bridge between accumulated biochemical knowledge and modern Bayesian methods for metabolomics.

Bayesian Statistics

Metabolomics

Prior Solicitation

Differential Abundance

Pathway Analysis

Biomarker Discovery 

Section on Bayesian Statistical Science