Increasing reproducibility, robustness, and generalizability of biomarker selection from meta-analysis using Bayesian methodology.
PLoS Comput Biol
; 18(6): e1010260, 2022 06.
Article
em En
| MEDLINE
| ID: mdl-35759523
ABSTRACT
A major limitation of gene expression biomarker studies is that they are not reproducible as they simply do not generalize to larger, real-world, heterogeneous populations. Frequentist multi-cohort gene expression meta-analysis has been frequently used as a solution to this problem to identify biomarkers that are truly differentially expressed. However, the frequentist meta-analysis framework has its limitations-it needs at least 4-5 datasets with hundreds of samples, is prone to confounding from outliers and relies on multiple-hypothesis corrected p-values. To address these shortcomings, we have created a Bayesian meta-analysis framework for the analysis of gene expression data. Using real-world data from three different diseases, we show that the Bayesian method is more robust to outliers, creates more informative estimates of between-study heterogeneity, reduces the number of false positive and false negative biomarkers and selects more generalizable biomarkers with less data. We have compared the Bayesian framework to a previously published frequentist framework and have developed a publicly available R package for use.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Teorema de Bayes
Tipo de estudo:
Prognostic_studies
/
Systematic_reviews
Limite:
Humans
Idioma:
En
Ano de publicação:
2022
Tipo de documento:
Article