title: Detecting differential patterns of interaction in molecular pathways.
abstract: We consider statistical inference for potentially heterogeneous patterns of association characterizing the expression of bio-molecular pathways across different biologic conditions. We discuss a modeling approach based on Gaussian-directed acyclic graphs and provide computational and methodological details needed for posterior inference. Our application finds motivation in reverse phase protein array data from a study on acute myeloid leukemia, where interest centers on contrasting refractory versus relapsed patients. We illustrate the proposed method through both synthetic and case study data.
authors.affiliation: Fred Hutchinson Cancer Research Center, WA 98109, USA myajima@fredhutch.org.
authors.forename: Masanao
authors.initials: M
authors.lastname: Yajima
authors.affiliation: UCLA, Department of Biostatistics, Los Angeles, CA 90095-1772, USA.
authors.forename: Donatello
authors.initials: D
authors.lastname: Telesca
authors.affiliation: Center for Clinical and Research Informatics, NorthShore University HealthSystem, Highland Park, IL 60035, USA.
authors.forename: Yuan
authors.initials: Y
authors.lastname: Ji
authors.affiliation: Department of Mathematics, The University of Texas at Austin, TX 78712, USA.
authors.forename: Peter
authors.initials: P
authors.lastname: Müller
mesh_terms: D006801:Humans; D007951:Leukemia, Myeloid / Q000378:metabolism; D053858:Metabolic Networks and Pathways / Q000502:physiology*; D008962:Models, Theoretical; D015398:Signal Transduction / Q000502:physiology*
keywords: Conditional independence; Directed acyclic graphs; Gaussian Markov models; Reversible jumps MCMC