Bayesian Sparse Graphical Models for Classification with Application to Protein Expression Data

• Activity Number: 384
• Type: Topic Contributed
• Date/Time: Tuesday, August 5, 2014 : 2:00 PM to 3:50 PM
• Sponsor: Section on Physical and Engineering Sciences
• Author(s): Rajesh Talluri*+
• Companies: MD Anderson Cancer Center
• Keywords: Bayesian ; RPPA ; Classification ; Networks

Abstract

Reverse-phase protein arrays(RPPA) are increasingly being used for high-throughput, quantitative analysis of protein networks. Motivated by RPPA data, we propose a Bayesian sparse graphical modeling approach that uses selection priors on the conditional relationships in the presence of class information. The novelty of our Bayesian model lies in the ability to draw information from the network data as well as from the associated categorical outcome in a unified hierarchical model for classification. Applying our methodology to an RPPA data set generated from panels of human breast cancer and ovarian cancer cell lines, we demonstrate that the model is able to distinguish the different cancer cell types more accurately than several existing models and to identify differential regulation of components of a critical signaling network (the PI3K-AKT pathway) between these two types of cancer. This approach represents a powerful new tool that can be used to improve our understanding of protein networks in cancer.