Bayesian Statistics Improves Biological Interpretability of Metabolomics Data from Human Cohorts

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 statistica...

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Detalles Bibliográficos
Autores principales: Brydges, Christopher, Che, Xiaoyu, Lipkin, Walter Ian, Fiehn, Oliver
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10535181/
https://www.ncbi.nlm.nih.gov/pubmed/37755264
http://dx.doi.org/10.3390/metabo13090984

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10535181/
https://www.ncbi.nlm.nih.gov/pubmed/37755264
http://dx.doi.org/10.3390/metabo13090984

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