Open Access
June 2011 Bayesian hierarchical modeling for signaling pathway inference from single cell interventional data
Ruiyan Luo, Hongyu Zhao
Ann. Appl. Stat. 5(2A): 725-745 (June 2011). DOI: 10.1214/10-AOAS425


Recent technological advances have made it possible to simultaneously measure multiple protein activities at the single cell level. With such data collected under different stimulatory or inhibitory conditions, it is possible to infer the causal relationships among proteins from single cell interventional data. In this article we propose a Bayesian hierarchical modeling framework to infer the signaling pathway based on the posterior distributions of parameters in the model. Under this framework, we consider network sparsity and model the existence of an association between two proteins both at the overall level across all experiments and at each individual experimental level. This allows us to infer the pairs of proteins that are associated with each other and their causal relationships. We also explicitly consider both intrinsic noise and measurement error. Markov chain Monte Carlo is implemented for statistical inference. We demonstrate that this hierarchical modeling can effectively pool information from different interventional experiments through simulation studies and real data analysis.


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Ruiyan Luo. Hongyu Zhao. "Bayesian hierarchical modeling for signaling pathway inference from single cell interventional data." Ann. Appl. Stat. 5 (2A) 725 - 745, June 2011.


Published: June 2011
First available in Project Euclid: 13 July 2011

zbMATH: 1223.62014
MathSciNet: MR2840173
Digital Object Identifier: 10.1214/10-AOAS425

Keywords: Bayesian network , dependency network , Gaussian graphical model , hierarchical model , interventional data , Markov chain Monte Carlo , mixture distribution , signaling pathway , single cell measurements

Rights: Copyright © 2011 Institute of Mathematical Statistics

Vol.5 • No. 2A • June 2011
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