A latent class model with hidden markov dependence for array CGH data

Stacia M. Desantis, E. Andrés Houseman, Brent A. Coull, David N. Louis, Gayatry Mohapatra, Rebecca A. Betensky

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)1296-1305
Number of pages10
Issue number4
StatePublished - Dec 2009


  • Array CGH
  • Hidden Markov Model
  • Latent class

ASJC Scopus subject areas

  • Statistics and Probability
  • General Biochemistry, Genetics and Molecular Biology
  • General Immunology and Microbiology
  • General Agricultural and Biological Sciences
  • Applied Mathematics


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