Bayesian Statistics Improves Biological Interpretability of Metabolomics Data from Human Cohorts

Bayesian Statistics Improves Biological Interpretability of Metabolomics Data from Human Cohorts

Univariate analyses of metabolomics data currently follow a frequentist approach, using <i>p</i>-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...

Full description

Bibliographic Details
Main Authors: Christopher Brydges, Xiaoyu Che, Walter Ian Lipkin, Oliver Fiehn
Format: Article
Language:English
Published: MDPI AG 2023-08-01
Series:Metabolites
Subjects:
Bayesian
<i>p</i>-value
metabolomics
ME/CFS
statistics
Online Access:https://www.mdpi.com/2218-1989/13/9/984

Internet

https://www.mdpi.com/2218-1989/13/9/984

Similar Items