title: Modeling Protein Expression and Protein Signaling Pathways.
abstract: High-throughput functional proteomic technologies provide a way to quantify the expression of proteins of interest. Statistical inference centers on identifying the activation state of proteins and their patterns of molecular interaction formalized as dependence structure. Inference on dependence structure is particularly important when proteins are selected because they are part of a common molecular pathway. In that case, inference on dependence structure reveals properties of the underlying pathway. We propose a probability model that represents molecular interactions at the level of hidden binary latent variables that can be interpreted as indicators for active versus inactive states of the proteins. The proposed approach exploits available expert knowledge about the target pathway to define an informative prior on the hidden conditional dependence structure. An important feature of this prior is that it provides an instrument to explicitly anchor the model space to a set of interactions of interest, favoring a local search approach to model determination. We apply our model to reverse-phase protein array data from a study on acute myeloid leukemia. Our inference identifies relevant subpathways in relation to the unfolding of the biological process under study.
authors.affiliation: Assistant Professor, Department of Biostatistics, UCLA School of Public Health, Los Angeles, CA.
authors.forename: Donatello
authors.initials: D
authors.lastname: Telesca
authors.affiliation: Professor, Department of Mathematics, University of Texas at Austin.
authors.forename: Peter
authors.initials: P
authors.lastname: Müller
authors.affiliation: Professor, Department of Stem Cell Transplantation, University of Texas M. D. Anderson Cancer Center, Houston, TX.
authors.forename: Steven M
authors.initials: SM
authors.lastname: Kornblau
authors.affiliation: Professor, Departments of Biomathematics and Human Genetics, David Geffen School of Medicine at UCLA, and Department of Biostatistics, UCLA School of Public Health, Los Angeles, CA.
authors.forename: Marc A
authors.initials: MA
authors.lastname: Suchard
authors.affiliation: Director of Cancer Research Informatics, North Shore University Health System.
authors.forename: Yuan
authors.initials: Y
authors.lastname: Ji
keywords: AML; Graphical models; Mixture models; POE; RJ-MCMC; RPPA