  Untargeted metabolomics based on liquid chromatography-mass spectrometry
technology is quickly gaining widespread application given its ability to
depict the global metabolic pattern in biological samples. However, the data is
noisy and plagued by the lack of clear identity of data features measured from
samples. Multiple potential matchings exist between data features and known
metabolites, while the truth can only be one-to-one matches. Some existing
methods attempt to reduce the matching uncertainty, but are far from being able
to remove the uncertainty for most features. The existence of the uncertainty
causes major difficulty in downstream functional analysis. To address these
issues, we develop a novel approach for Bayesian Analysis of Untargeted
Metabolomics data (BAUM) to integrate previously separate tasks into a single
framework, including matching uncertainty inference, metabolite selection, and
functional analysis. By incorporating the knowledge graph between variables and
using relatively simple assumptions, BAUM can analyze datasets with small
sample sizes. By allowing different confidence levels of feature-metabolite
matching, the method is applicable to datasets in which feature identities are
partially known. Simulation studies demonstrate that, compared with other
existing methods, BAUM achieves better accuracy in selecting important
metabolites that tend to be functionally consistent and assigning confidence
scores to feature-metabolite matches. We analyze a COVID-19 metabolomics
dataset and a mouse brain metabolomics dataset using BAUM. Even with a very
small sample size of 16 mice per group, BAUM is robust and stable. It finds
pathways that conform to existing knowledge, as well as novel pathways that are
biologically plausible.
