A Penalized Likelihood Approach for Bivariate Conditional Normal Models for Dynamic Co-expression Analysis

Biometrics

Volumn 67, Issue 1, 2011, Pages 299-308

  Chen, Jun ^a   Xie, Jichun ^a   Li, Hongzhe ^a  

^a UNIVERSITY OF PENNSYLVANIA   (United States)

Author keywords

Cyclic coordinate descent; EQTL; Gene regulation; Penalized likelihood; Variable selection

Indexed keywords

COMPUTATIONAL EFFICIENCY; ENZYMES; ITERATIVE METHODS; NUCLEOTIDES;

BIVARIATE; CO-EXPRESSION; CYCLIC COORDINATE DESCENTS; EQTL; EXPRESSION PATTERNS; GENE-REGULATION; NORMAL MODEL; PENALIZED LIKELIHOOD; PENALIZED-LIKELIHOOD; VARIABLES SELECTIONS;

GENE EXPRESSION;

ENZYME ACTIVITY; GENE EXPRESSION; POLYMORPHISM; PROTEIN; REGRESSION ANALYSIS;

EID: 79952587224     PISSN: 0006341X     EISSN: 15410420     Source Type: Journal    
DOI: 10.1111/j.1541-0420.2010.01413.x     Document Type: Article

References (15)

1. Baron, R. M. and Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic and statistical considerations. Journal of Personality and Social Psychology 51, 1173-1182.
2. Brem, R. and Kruglyak, L. (2005). The landscape of genetic complexity across 5, 700 gene expression traits in yeast. Proceedings of the National Academy of Sciences USA 102, 1572-1577.
3. Elion, E. A., Satterberg, B., and Kranz, J. E. (1993). FUS3 phosphorylates multiple components of the mating signal transduction cascade: Evidence for STE12 and FAR1. Molecular Biology of Cell 4, 495-510.
4. Fan, J. and Li, R. (2001). Variable selection via nonconcave penalized likelihood and its oracle properties. Journal of the American Statistical Association 96, 1348-1360.
5. Friedman, J., Tibshirani, R., and Hastie, T. (2008). Regularized paths for generalized linear models via coordinate descent. Technical report, Department of Statistics, Stanford University
6. Green, P. J. (1984). Iteratively reweighted least squares for maximum likelihood estimation, and some robust and resistant alternatives. Journal of the Royal Statistical Society, Series B 46, 149-192.
7. Ho, Y. Y., Cope, L., Louis, T. A., and Parmigiani, G. (2009). Generalized liquid association. Johns Hopkins University, Dept. of Biostatistics Working Papers Working Paper 183.
8. Li, K. C. (2002). Genome-wide co-expression dynamics: Theory and application. Proceedings of the National Academy of Sciences 99, 16875-16880.
9. Li, K. C., Liu, C.-T., Sun, W., Yuan, S., and Yu, T. (2004). A system for enhancing genome-wide co-expression dynamics study. Proceedings of the National Academy of Sciences 101, 15561-15566.
10. Li, K. C., Palotie, A., Yuan, S., Bronnikov, D., Chen, D., Wei, X., Choi, O., Saarela, J., and Peltonen, L. (2007). Finding disease candidate genes by liquid association. Genome Biology 8, R205.
11. MacKinnon, D. P., Fairchild, A. J., and Fritz, M. S. (2007). Mediation analysis. Annual Review of Psychology 58, 593-614.
12. Sun, W., Yuan, S., and Li, K. C. (2008). Trait-trait dynamic interaction: 2D-trait eQTL mapping for genetic variation study. BMC Genomics 9, 242.
13. Tibshirani, R. J. (1996). Regression shrinkage and selection via the LASSO. Journal of the Royal Statistical Society, Series B 58, 267-288.
14. Wu, T. T. and Lange, K. (2008). Coordinate descent algorithms for LASSO penalized regression. Annals of Applied Statistics 2, 224-244.
15. Zou, H. (2006). The adaptive lasso and its oracle properties. Journal of the American Statistical Association 101, 1418-1429.