A Bayesian Approach for Inferring Local Causal Structure in Gene Regulatory Networks

TL;DR

This paper introduces a Bayesian method for inferring local causal relationships in gene regulatory networks, leveraging covariance patterns and prior knowledge to improve stability and interpretability of causal inference.

Contribution

It presents a novel efficient Bayesian approach for discovering local causal structures among gene expression variables, incorporating background knowledge and handling various causal assumptions.

Findings

Produces stable and conservative posterior probabilities for causal relationships.

Effective on both simulated and real yeast gene expression data.

Provides a reliable ranking of regulatory relationships.

Abstract

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.