Bayesian Copula Directional Dependence is Cross-Network Robust for Gene-Regulatory Pair Direction: A Benchmark Study on DREAM5

Abstract

Inferring the direction of a gene-regulatory relationship is harder than inferring whether a relationship exists, and most direction-inference methods are validated mainly on a single in silico benchmark. We ask which method remains reliable as the data move from a synthetic network to real organisms and as sample size decreases. We embed a copula-based measure of directional dependence (CDD) in a Bayesian framework that returns, for each candidate pair, a posterior distribution over a directional contrast, a 95% credible interval, a posterior sign-support score, and a principled no-call. We benchmark this estimator against eight direction-inference methods, including two Bayesian DAG-posterior baselines, on the three core DREAM5 networks (in silico, S. aureus, and E. coli), with S. cerevisiae used as an out-of-domain eukaryotic stress test. Across the three core networks, Bayesian CDD is the only method whose called accuracy is always above 60%, whose coverage is always above 88%, and whose direction AUROC is always above 0.6; every competing method falls to chance or below on at least one network. CDD ranks first on both real-organism networks, remains stable on the smallest-sample network where bootstrap-interval methods collapse, and is the only Bayesian method that is simultaneously above chance and high-coverage under a 95% posterior gate. We position CDD as a post-screening, uncertainty-aware direction-refinement tool for candidate regulatory pairs.