  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.
