Differential Patterns of Interaction and Gaussian Graphical Models

• Activity Number: 181
• Type: Contributed
• Date/Time: Monday, July 30, 2012 : 10:30 AM to 12:20 PM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): Masanao Yajima*+ and Donatello Telesca and Yuan Ji and Peter Mueller
• Companies: University of California at Los Angeles and University of California at Los Angeles Fielding School of Public Health and Northshore University Healthcare System and The University of Texas at Austin
• Keywords: DAG ; RJMCMC ; covariance selection models ; differential association

Abstract

We propose a methodological framework to assess heterogeneous patterns of association amongst components of a random vector expressed as a Gaussian directed acyclic graph. The proposed framework is likely to be useful when primary interest focuses on potential contrasts characterizing the association structure between known subgroups of a given sample. We provide inferential frameworks as well as an efficient computational algorithm to fit such a model and illustrate its validity through a simulation. We apply the model to a Reverse Phase Protein Array data on Acute Myeloid Leukemia patients to show the contrast of association structure between the refractory patients and the relapsed patients.