Bayesian Statistics Improves Biological Interpretability of Metabolomics Data from Human Cohorts.

ABSTRACT: Univariate analyses of metabolomics data currently follow a frequentist approach, using p-values to reject a null hypothesis. We here propose the use of Bayesian statistics to quantify evidence supporting different hypotheses and discriminate between the null hypothesis versus the lack of statistical power. We used metabolomics data from three independent human cohorts that studied the plasma signatures of subjects with myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). The data are publicly available, covering 84-197 subjects in each study with 562-888 identified metabolites of which 777 were common between the two studies and 93 were compounds reported in all three studies. We show how Bayesian statistics incorporates results from one study as "prior information" into th

SUBMITTER: Brydges C 

PROVIDER: S-EPMC10535181 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature