A Bayesian Informative Shrinkage Approach for Large-scale Multiple Hypothesis Testing (BISHOT): with Applications in Differential Analysis of Omics Data

A major goal of many omics studies is to identify differential features, e.g. differentially expressed genes, between experimental groups. However, existing methods typically analyze only the data from the current study, without leveraging relevant information from prior studies. We address this limitation using a Bayesian framework that enables the incorporation of prior knowledge obtained from different platforms or organisms. We propose a new test statistic, Bayesian Credible Ratio (BCR), based on a heteroscedastic global local shrinkage prior, and a new multiple testing criterion, sign-adjusted FDR (SFDR), that emphasize information regarding the direction of the differentially features. We prove that BCR achieves the largest count of sign-based true positives among all legitimate SFDR-controlling methods. Simulation results offer numerical evidence of its advantage compared to an empirical Bayesian method. The approach is demonstrated through the analysis of RNAseq and single-cell RNAseq datasets.