A Latent Class Model with Hidden Markov Dependence for Array CGH Data |
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Authors: | Stacia M. DeSantis E. Andrés Houseman Brent A. Coull David N. Louis Gayatry Mohapatra Rebecca A. Betensky |
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Affiliation: | 1. Department of Biostatistics, Bioinformatics, and Epidemiology, Medical University of South Carolina, 135 Cannon Street, Suite 303, Charleston, South Carolina 29403, U.S.A.;2. Center for Environmental Health and Technology, The Warren Alpert Medical School of Brown University and Department of Biostatistics, Harvard School of Public Health, 121 South Main Street, Room 217, Providence, Rhode Island 02903, U.S.A.;3. Department of Biostatistics, Harvard School of Public Health, 655 Huntington Avenue, Boston, Massachusetts 02115, U.S.A.;4. Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Building 149 13th Street, Charlestown, Massachusetts 02129, U.S.A.;5. Department of Pathology, Massachusetts General Hospital, Building 149 13th Street, Room 7015, Charlestown, Massachusetts 02129, U.S.A. |
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Abstract: | 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. |
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Keywords: | Array CGH Hidden Markov Model Latent class |
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