A Bayesian Approach for Inferring Local Causal Structure in Gene Regulatory Networks
This work addresses the challenge of extracting causal relationships in gene regulatory networks for biologists, but it is incremental as it builds on existing conditional independence test approaches with a Bayesian twist.
The authors tackled the problem of inferring local causal structures in gene regulatory networks from genetic data by proposing a novel Bayesian method that scores covariance patterns and incorporates background knowledge as priors. They demonstrated that their algorithm produces stable posterior probability estimates, achieving meaningful rankings of regulatory relationships on both simulated and real-world yeast data.
Gene regulatory networks play a crucial role in controlling an organism's biological processes, which is why there is significant interest in developing computational methods that are able to extract their structure from high-throughput genetic data. A typical approach consists of a series of conditional independence tests on the covariance structure meant to progressively reduce the space of possible causal models. We propose a novel efficient Bayesian method for discovering the local causal relationships among triplets of (normally distributed) variables. In our approach, we score the patterns in the covariance matrix in one go and we incorporate the available background knowledge in the form of priors over causal structures. Our method is flexible in the sense that it allows for different types of causal structures and assumptions. We apply the approach to the task of inferring gene regulatory networks by learning regulatory relationships between gene expression levels. We show that our algorithm produces stable and conservative posterior probability estimates over local causal structures that can be used to derive an honest ranking of the most meaningful regulatory relationships. We demonstrate the stability and efficacy of our method both on simulated data and on real-world data from an experiment on yeast.