Bayesian Copula Directional Dependence for causal inference on gene expression data

TL;DR

This paper introduces a Bayesian extension of Copula Directional Dependence (CDD) for inferring causal gene networks, demonstrating improved robustness and accuracy over the frequentist approach in real biological datasets.

Contribution

A novel Bayesian CDD method is proposed, extending the frequentist version to better model directional gene interactions without strict distributional assumptions.

Findings

Bayesian CDD identified 4 out of 6 true gene interactions.

The method showed increased robustness compared to the frequentist approach.

Validated on both single-cell and bulk gene expression datasets.

Abstract

Modelling and understanding directional gene networks is a major challenge in biology as they play an important role in the architecture and function of genetic systems. Copula Directional Dependence (CDD) can measure the directed connectivity among variables without any strict requirements of distributional and linearity assumptions. Furthermore, copulas can achieve that by isolating the dependence structure of a joint distribution. In this work, a novel extension of the frequentist CDD in the Bayesian setting is introduced. The new method is compared against the frequentist CDD and validated on six gene interactions, three coming from a mouse scRNA-seq dataset and three coming from a bulk epigenome dataset. The results illustrate that the novel proposed Bayesian CDD was able to identify four out of six true interactions with increased robustness compared to the frequentist method. Therefore, the Bayesian CDD can be considered as an alternative way for modeling the information flow in gene networks.