Direct estimation of differential networks

Biometrika

Volumn 101, Issue 2, 2014, Pages 253-268

  Zhao, Sihai Dave ^a   Cai, T Tony ^b   Li, Hongzhe ^a  

^a UNIVERSITY OF PENNSYLVANIA   (United States)

^b UNIVERSITY OF PENNSYLVANIA   (United States)

Author keywords

Differential network; Graphical model; High dimensionality; Precision matrix

Indexed keywords

EID: 84901451119     PISSN: 00063444     EISSN: 14643510     Source Type: Journal    
DOI: 10.1093/biomet/asu009     Document Type: Article

References (48)

1. Bandyopadhyay, S., Mehta, M., Kuo, D., Sung, M-K., Chuang, R., Jaehnig, E. J., Bodenmiller, B., Licon, K., Copeland, W., Shales, M., Fiedler, D., Dutkowski, J., Gúenoĺe, A., Van ATTIKUM, H., Shokat, K. M., Kolodner, R. D., Huh, W-K., Aebersold, R., Keogh, M-C., Krogan, N. J. & Ideker, T. (2010). Rewiring of genetic networks in response to DNA damage. Sci. Signal. 330, 1385-9.
2. Barabási, A-L., Gulbahce, N. & Loscalzo, J. (2011). Network medicine: A network-based approach to human disease. Nature Rev. Genet. 12, 56-68.
3. Barabási, A-L. & Oltvai, Z. N. (2004). Network biology: Understanding the cell's functional organization. Nature Rev. Genet. 5, 101-13. (Pubitemid 38160277)
4. Bellail, A. C., Qi, L., Mulligan, P., Chhabra, V. & Hao, C. (2009). TRAIL agonists on clinical trials for cancer therapy: The promises and the challenges. Rev. Recent Clin. Trials 4, 34-41.
5. Boyd, S., Parikh, N., Chu, E., Peleato, B. & Eckstein, J. (2011). Distributed optimization and statistical learning via the alternating direction method of multipliers. Foundat. Trends Mach. Learn. 3, 1-122.
6. Cai, T. & Liu, W. (2011). A direct estimation approach to sparse linear discriminant analysis. J. Am. Statist. Assoc. 106, 1566-77.
7. Cai, T., Liu, W. & Luo, X. (2011). A constrained-1 minimization approach to sparse precision matrix estimation. J. Am. Statist. Assoc. 106, 594-607.
8. Cai, T., Wang, L. & Xu, G. (2010). Shifting inequality and recovery of sparse signals. IEEE Trans. Sig. Proces. 58, 1300-8.
9. Cai, T., Wang, L. & Xu, G. (2010). Stable recovery of sparse signals and an oracle inequality. IEEE Trans. Info. Theory 56, 3516-22.
10. Cai, T., Xu, G. & Zhang, J. (2009). On recovery of sparse signals via-1 minimization. IEEE Trans. Info. Theory 55, 3388-97.
11. Chiquet, J., Grandvalet, Y. & Ambroise, C. (2011). Inferring multiple graphical structures. Statist. Comp. 21, 537-53.
12. Danaher, P., Wang, P. & Witten, D. M. (2014). The joint graphical lasso for inverse covariance estimation across multiple classes. J. R. Statist. Soc. B 76, 373-97.
13. De LA Fuente, A. (2010). From 'differential expression' to 'differential networking'-identification of dysfunctional regulatory networks in diseases. Trends Genet. 26, 326-33.
14. Donoho, D. & Huo, X. (2001). Uncertainty principles and ideal atomic decomposition. IEEE Trans. Info. Theory 47, 2845-62. (Pubitemid 33053488)
15. Emmert-Streib, F. & Dehmer, M. (2011). Networks for systems biology: Conceptual connection of data and function. IET Syst. Biol. 5, 185-207.
16. Friedman, J. H., Hastie, T. J. & Tibshirani, R. J. (2008). Sparse inverse covariance estimation with the graphical lasso. Biostatistics 9, 432-41.
17. Guo, J., Levina, E., Michailidis, G. & Zhu, J. (2011). Joint estimation of multiple graphical models. Biometrika 98, 1-15.
18. Hauser, A. & B̈uhlmann, P. (2014). Jointly interventional and observational data: Estimation of interventional Markov equivalence classes of directed acyclic graphs. J. R. Statist. Soc. B. To appear.
19. Huang, J., Ma, S., Xie, H. & Zhang, C-H. (2009). A group bridge approach for variable selection. Biometrika 96, 339-55.
20. Hudson, N. J., Reverter, A. & Dalrymple, B. P. (2009). A differential wiring analysis of expression data correctly identifies the gene containing the causal mutation. PLoS Comp. Biol. 5, e1000382.
21. Ideker, T. & Krogan, N. (2012). Differential network biology. Molec. Syst. Biol. 8, 565.
22. Johnstone, R. W., Frew, A. J. & Smyth, M. J. (2008). The TRAIL apoptotic pathway in cancer onset, progression and therapy. Nature Rev. Cancer 8, 782-98.
23. Kalisch, M. & B̈uhlmann, P. (2007). Estimating high-dimensional directed acyclic graphs with the PC-Algorithm. J. Mach. Learn. Res. 8, 613-36. (Pubitemid 46473523)
24. Kanehisa, M., Goto, S., Sato, Y., Furumichi, M. & Tanabe, M. (2012). KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res. 40, D109-14.
25. Kipps, E., Tan, D. S. P. & Kaye, S. B. (2013). Meeting the challenge of ascites in ovarian cancer: New avenues for therapy and research. Nature Rev. Cancer, 273-82.
26. Lauritzen, S. L. (1996). Graphical Models. Oxford: Oxford University Press.
27. Li, K-C., Palotie, A., Yuan, S., Bronnikov, D., Chen, D., Wei, X., Choi, O-W., Saarela, J. & Peltonen, L. (2007). Finding disease candidate genes by liquid association. Genome Biol. 8, R205.
28. Li, X., Zhao, T., Wang, L., Yuan, X. & Liu, H. (2013). flare: Family of Lasso Regression. R package version 1.0.0.
29. Lounici, K. (2008). Sup-norm convergence rate and sign concentration property of lasso and Dantzig estimators. Electron. J. Statist. 2, 90-102.
30. Maathuis, M. H., Kalisch, M. & B̈uhlmann, P. (2009). Estimating high-dimensional intervention effects from observational data. Ann. Statist. 37, 3133-64.
31. Marchini, S., Fruscio, R., Clivio, L., Beltrame, L., Porcu, L., Nerini, I. F., Cavalieri, D., Chiorino, G., Cattoretti, G., Mangioni, C., Milani, R., Torri, V., Romualdi, C., Zambelli, A., Romano, M., Signorelli, M., Di GIANDOMENICO, S. & Dincalci, M. (2013). Resistance to platinum-based chemotherapy is associated with epithelial to mesenchymal transition in epithelial ovarian cancer. Eur. J. Cancer 49, 520-30.
32. Markowetz, F. & Spang, R. (2007). Inferring cellular networks-A review. BMC Bioinformatics 8, S5.
33. Meinshausen, N. & B̈uhlmann, P. (2006). High-dimensional graphs and variable selection with the lasso. Ann. Statist. 34, 1436-62. (Pubitemid 44231168)
34. Mohan, K., Chung, M., Han, S., Witten, D., Lee, S-I. & Fazel, M. (2012). Structured learning of Gaussian graphical models. In Adv. Neural Info. Proces. Syst. 25. New York: Curran Associates, pp. 629-37.
35. Newman, M. E. (2003). The structure and function of complex networks. SIAM Rev. 45, 167-256.
36. Ogata, H., Goto, S., Sato, K., Fujibuchi, W., Bono, H. & Kanehisa, M. (1999). KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 27, 29-34. (Pubitemid 29209394)
37. Pang, H., Liu, H. & Vanderbei, R. (2013). fastclime: A Fast Solver for Parameterized-p Problems and Constrained-1 Minimization Approach to Sparse Precision Matrix Estimation. R package version 1.2.3.
38. Petrucci, E., Pasquini, L., Bernabei, M., Saulle, E., Biffoni, M., Accarpio, F., Sibio, S., Di Giorgio, A., Di Donato, V., Casorelli, A., Benedetti-Panici, P. & Testa, U. (2012). A small molecule SMAC mimic LBW242 potentiates TRAIL-And anticancer drug-mediated cell death of ovarian cancer cells. PloS One 7, e35073.
39. R DEVELOPMENT CORE TEAM (2014). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0. http://www.R-project.org.
40. Ravikumar, P. K., Raskutti, G., Wainwright, M. J. & Yu, B. (2008). Model selection in Gaussian graphical models: High-dimensional consistency of-1-regularized MLE. In Adv. Neural Info. Proces. Syst. 21. New York: Curran Associates, pp. 1329-36.
41. Tibshirani, R. J., Saunders, M., Rosset, S., Zhu, J. & Knight, K. (2005). Sparsity and smoothness via the fused lasso. J. R. Statist. Soc. B 67, 91-108. (Pubitemid 40167043)
42. Tothill, R. W., Tinker, A. V., George, J., Brown, R., Fox, S. B., Lade, S., Johnson, D. S., Trivett, M. K., Etemadmoghadam, D., Locandro, B., Traficante, N., Fereday, S., Hung, J. A., Chiew, Y-E., Haviv, I., Australian OVARIAN CANCER STUDY GROUP, GERTIG, D., Defazio, A. & Bowtell, D. D. L. (2008). Novel molecular subtypes of serous and endometrioid ovarian cancer linked to clinical outcome. Clin. Cancer Res. 14, 5198-208.
43. Vucic, D. & Fairbrother, W. J. (2007). The inhibitor of apoptosis proteins as therapeutic targets in cancer. Clin. Cancer Res. 13, 5995-6000. (Pubitemid 350075054)
44. Wang, S., Nan, B., Zhu, N. & Zhu, J. (2009). Hierarchically penalized Cox regression with grouped variables. Biometrika 96, 307-22.
45. Yagita, H., Takeda, K., Hayakawa, Y., Smyth, M. J. & OKUMURA, K. (2004). TRAIL and its receptors as targets for cancer therapy. Cancer Sci. 95, 777-83. (Pubitemid 39545582)
46. Yuan, M. (2010). Sparse inverse covariance matrix estimation via linear programming. J. Mach. Learn. Res. 11, 2261-86.
47. Yuan, M. & Lin, Y. (2006). Model selection and estimation in regression with grouped variables. J. R. Statist. Soc. B 68, 49-67. (Pubitemid 43415335)
48. Zhao, P. & Yu, B. (2006). On model selection consistency of lasso. J.Mach. Learn. Res. 7, 2541-63. (Pubitemid 44866738)