Clustering with respect to undirected networks

Thomas M. Menino Convention & Exhibition Center 

In this talk, a Bayesian framework is proposed to cluster observations sharing a common undirected network. The clustering, denoted as Multi-center Graph Clustering (McGC), is driven by network structures and strength of edges and allows the variables in each cluster to have different profiles. Pseudo nodes designed with no expected connections with the original nodes are introduced to control false connections aiming to facilitate graph constructions. Extensive simulations demonstrate the feasibility of the proposed approach and applications of McGC to epigenetic data support its value in practice with a potential to benefit future studies in predicting disease risk at a much earlier stage of life.

Gaussian graphs

Bayesian inference

Pseudo nodes

Cluster analysis

Tuning parameter

Variable selection 

Section on Statistics in Genomics and Genetics