Increasing reproducibility, robustness, and generalizability of biomarker selection from meta-analysis using Bayesian methodology.

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 outlier

SUBMITTER: Kalesinskas L 

PROVIDER: S-EPMC9269905 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature