Modeling Protein Expression and Protein Signaling Pathways

• 成果类型: Article
• 署名作者: Telesca, Donatello; Mueller, Peter; Kornblau, Steven M.; Suchard, Marc A.; Ji, Yuan
• 署名单位: University of California System; University of California Los Angeles; University of California System; University of California Los Angeles; University of California Los Angeles Medical Center; David Geffen School of Medicine at UCLA; University of California System; University of California Los Angeles; University of California Los Angeles Medical Center; David Geffen School of Medicine at UCLA; University of Texas System; University of Texas Austin; University of Texas System; UTMD Anderson Cancer Center
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2012.706121
• 发表日期: 2012
• 页码: 1372-1384
• 关键词: bayesian-inference; leukemia; selection; network; stat3; computation; activation; survival; modulate; kinase

摘要

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.