Bayesian hierarchical modeling for signaling pathway inference from single cell interventional data

Annals of Applied Statistics

Volumn 5, Issue 2 A, 2011, Pages 725-745

  Luo, Ruiyan ^a   Zhao, Hongyu ^a  

^a YALE UNIVERSITY   (United States)

Author keywords

Bayesian network; Dependency network; Gaussian graphical model; Hierarchical model; Interventional data; Markov chain monte carlo; Mixture distribution; Signaling pathway; Single cell measurements

Indexed keywords

EID: 84866688958     PISSN: 19326157     EISSN: 19417330     Source Type: Journal    
DOI: 10.1214/10-AOAS425     Document Type: Article

References (17)

1. Dobra, A., Hans, C., Jones, B., Nevins, J., Yao, G. and West, M. (2004). Sparse graphical models for exploring gene expression data. J. Multivariate Anal. 90 196-212. Mathematical Reviews (MathSciNet): MR2064941 Zentralblatt MATH: 1047.62104 Digital Object Identifier: doi:10.1016/j.jmva.2004.02.009
2. Ellis, B. and Wong, W. H. (2008). Learning causal Bayesian network structures from experimental data. J. Amer. Statist. Assoc. 103 778-789. Mathematical Reviews (MathSciNet): MR2524009 Zentralblatt MATH: 05564531 Digital Object Identifier: doi:10.1198/016214508000000193
3. Friedman, N. and Killer, D. (2003). Being Bayesian about network structure. Machine Learning 50 95-126.
4. Heckerman, D., Chickering, D. M., Meek, C., Rounthwaite, R. and Kadie, C. (2000). Dependency networks for inference, collaborative filtering, and data visulization. J. Mach. Learn. Res. 1 49-75.
5. Herzenberg, L. A., Parks, D., Sahaf, B., Perez, O., Roederer, M. and Herzenberg, L. A. (2002). The history and future of the fluorescence activated cell sorter and flow cytometry: A view from Stanford. Clinical Chemistry 48 1819-1827.
6. Lauritzen, S. L. (1996). Graphical Models. Clarendon Press, Oxford. Mathematical Reviews (MathSciNet): MR1419991
7. Liu, Y. and Ringnér, M. (2007). Revealing signaling pathway deregulation by using gene expression signatures and regulatory motif analysis. Genome Biology 8 R77.1-R77.10.
8. Luo, R. and Zhao, H. (2010). Supplementary material for "Bayesian hierarchical modeling for signaling pathway inference from single cell interventional data." DOI: 10.1214/10-AOAS425SUPP.
9. Mathematical Reviews (MathSciNet): MR2840173 Zentralblatt MATH: 1223.62014 Digital Object Identifier: doi:10.1214/10-AOAS425 Project Euclid: euclid.aoas/1310562203
10. Pe'er, D. (2005). Bayesian network analysis of signaling networks: A primer. Science's STKE 281 1-12.
11. Pe'er, D., Regev, A., Elidan, G. and Friedman, N. (2001). Inferring subnetworks from perturbed expression profiles. Bioinformatics 17 Suppl. S215-S224.
12. Perez, O. D. and Nolan, G. (2002). Simultaneous measurement of multiple active kinase states using polychromatic flow cytometry. Nature Biotechnology 20 155-162.
13. Sachs, K., Perez, O., Pe'er, D., Lauffenburger, D. A. and Nolan, G. P. (2005). Causal protein-signaling networks derived from multiparameter single-cell data. Science 308 523-529.
14. Schäfer, J. and Strimmer, K. (2005). An empirical Bayes approach to inferring large-scale gene association networks. Bioinformatics 21 754-764.
15. Wei, W. and Li, H. (2007). A Markov random field model for network-based analysis of genomic data. Bioinformatics 23 1537-1544.
16. Wei, W. and Li, H. (2008). A hidden spatial-temporal Markov random field model for network-based analysis of time course gene expression data. Ann. Appl. Statist. 2 408-429. Mathematical Reviews (MathSciNet): MR2415609 Zentralblatt MATH: 1137.62081 Digital Object Identifier: doi:10.1214/07--AOAS145 Project Euclid: euclid.aoas/1206367827
17. Werhli, A. V., Grzegorczyk, M. and Husmeier, D. (2006). Comparative evaluation of reverse engineering gene regulatory networks with relevance networks, graphical Gaussian models and Bayesian networks. Systems Biology 22 2523-2531.