Bayesian Structure Learning for Graphical Models With Symmetry Constraints

PAM50 gene expression profiling, a popular and widely used tool, is employed to identify and assess the functional relationships and pathways among genes in patients with breast cancer. Motivated by a study aimed at concurrently recovering dependency and symmetric networks for the PAM50 gene data set, we consider the graphical Gaussian model with symmetry constraints on edges and vertices. The symmetry constraints in the model are represented by imposing equality constraints on the concentration matrix. This model allows us to simultaneously explore the dependency relationships and symmetrical structure among the variables. The symmetrical structure of PAM50 gene expression can deepen our understanding of their functional similarities and the inherent symmetrical properties of gene regulatory behavior. Prioritizing candidate genes with high functional similarity will help elucidate the underlying biological mechanisms for the disease progression. To effectively capture the network's structure, we utilize a birth-death Markov Chain Monte Carlo method. This method is a continuous-time and transdimensional search algorithm that is particularly effective in this context. To further improve the efficiency of the algorithm, we propose a stepwise model learning strategy combined with an approximation method for the posterior distribution. To validate the effectiveness of our approach, we finally apply it in various simulation studies as well as in a practical application involving the PAM50 gene expression data set.