Joint conditional Gaussian graphical models with multiple sources of genomic data

Frontiers in Genetics

Volumn 4, Issue DEC, 2013, Pages

  Chun, Hyonho ^a   Chen, Min ^b   Li, Bing ^c   Zhao, Hongyu ^d  

^a PURDUE UNIVERSITY   (United States)

^b UNIVERSITY OF TEXAS AT DALLAS   (United States)

^c PENNSYLVANIA STATE UNIVERSITY   (United States)

^d YALE UNIVERSITY   (United States)

Author keywords

Conditional GGMs; Gaussian graphical models; Gene networks; GGMs; Joint sparsity

Indexed keywords

EID: 84892410725     PISSN: None     EISSN: 16648021     Source Type: Journal    
DOI: 10.3389/fgene.2013.00294     Document Type: Article

References (18)

1. Barabasi, A. L., and Albert, R. (1999). Emergence of scaling in random networks. Science 286, 509-512. doi: 10.1126/science.286.5439.509.
2. Cai, T., Li, H., Liu, W., and Xie, J. (2013). Covariate-adjusted precision matrix estination with an application in genetical genomics. Biometrika 100, 139-156. doi: 10.1093/biomet/ass058.
3. Campbell, N. A., Williamson, B., and Heyden, R. J. (2006). Biology: Exploring Life. Boston, MA: Pearson Prentice Hall.
4. Danaher, P., Wang, P., and Witten, D. M. (2013). The joint graphical lasso for inverse covariance estimation across multiple classes. J. R. Stat. Soc. Ser. B. Stat. Methodol. doi: 10.1111/rssb.12033.
5. Friedman, J., Hastie, T., and Tibshirani, R. (2008). Sparse inverse covariance estimation with the graphical lasso. Biostatistics 9, 432-441. doi: 10.1093/biostatistics/kxm045.
6. Gerrits, A., Li, Y., Tesson, B. M., Bystrykh, L. V., Weersing, E., Ausema, A., et al. (2009). Expression quantitative trait loci are highly sensitive to cellular differentiation state. PLoS Genet. 5:e1000692. doi: 10.1371/journal.pgen.1000692.
7. Guo, J., Levina, E., Michailidis, G., and Zhu, J. (2011). Joint estimation of multiple graphical models. Biometrika 98, 1-15. doi: 10.1093/biomet/asq060.
8. Hubner, N., Wallace, C. A., Zimdahl, H., Petretto, E., Schulz, H., Maciver, F., et al. (2005). Integrated transcriptional profiling and linkage analysis for identification of genes underlying disease. Nat. Genet. 37, 243-253. doi: 10.1038/ng1522.
9. Jansen, R., and Nap, J. (2001). Genetical genomics: the added value from segregation. Trends Genet. 17, 388-391. doi: 10.1016/S0168-9525(01)02310-1.
10. Lauritzen, S. L. (1996). Graphical Models. Oxford: Clarendon Press.
11. Li, B., Chun, H., and Zhao, H. (2012). Sparse estimation of conditonal graphical models with application to gene networks. J. Am. Stat. Assoc. 107, 152-167. doi: 10.1080/01621459.2011.644498.
12. Li, H., and Gui, J. (2006). Gradient directed regularization for sparse gaussian concentration graphs, with applications to inference of genetic networks. Biostatistics 7, 302-317. doi: 10.1093/biostatistics/kxj008.
13. Petretto, E., Mangion, J., Dickens, N. J., Cook, S. A., Kumaran, M. K., Lu, H., et al. (2006). Heritability and tissue specificity of expression quantitative trait loci. PLoS Genet. 2:e172. doi: 10.1371/journal.pgen.0020172.
14. Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. J. R. Stat. Soc. B 58, 267-288.
15. Watson, R., Kanzaki, M., and Pessin, J. (2004). Regulated membrane trafficking of the insulin-responsive glucose transporter 4 in adipocytes. Endocr. Rev. 25, 177-204. doi: 10.1210/er.2003-0011.
16. Yin, J., and Li, H. (2011). A sparse conditional gaussian graphical model for analysis of genetical genomics data. Ann. Appl. Stat. 5, 2630-2650. doi: 10.1214/11-AOAS494.
17. Yuan, M., and Lin, Y. (2007). Model selection and estimation in the gaussian graphical model. Biometrika 94, 19-35. doi: 10.1093/biomet/asm018.
18. Zou, H. (2006). The adaptive lasso and its oracle properties. J. Am. Stat. Assoc. 101, 1418-1429. doi: 10.1198/016214506000000735.