A Latent Class Model with Hidden Markov Dependence for Array CGH Data
Article first published online: 7 APR 2009
© 2009, The International Biometric Society
Volume 65, Issue 4, pages 1296–1305, December 2009
How to Cite
DeSantis, S. M., Houseman, E. A., Coull, B. A., Louis, D. N., Mohapatra, G. and Betensky, R. A. (2009), A Latent Class Model with Hidden Markov Dependence for Array CGH Data. Biometrics, 65: 1296–1305. doi: 10.1111/j.1541-0420.2009.01226.x
- Issue published online: 23 NOV 2009
- Article first published online: 7 APR 2009
- Received July 2007. Revised November 2008. Accepted November 2008.
- Array CGH;
- Hidden Markov Model;
- Latent class
Summary Array CGH is a high-throughput technique designed to detect genomic alterations linked to the development and progression of cancer. The technique yields fluorescence ratios that characterize DNA copy number change in tumor versus healthy cells. Classification of tumors based on aCGH profiles is of scientific interest but the analysis of these data is complicated by the large number of highly correlated measures. In this article, we develop a supervised Bayesian latent class approach for classification that relies on a hidden Markov model to account for the dependence in the intensity ratios. Supervision means that classification is guided by a clinical endpoint. Posterior inferences are made about class-specific copy number gains and losses. We demonstrate our technique on a study of brain tumors, for which our approach is capable of identifying subsets of tumors with different genomic profiles, and differentiates classes by survival much better than unsupervised methods.