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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...
| Autores principales: | , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
MDPI
2023
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| Materias: | |
| 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 |