Bayesian functional analysis for untargeted metabolomics data with matching uncertainty and small sample sizes.

ABSTRACT: 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 are 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 integra

SUBMITTER: Ma G 

PROVIDER: S-EPMC10998539 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature