A latent class model with hidden Markov dependence for array CGH data.
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.
DeSantis
Stacia M
SM
Department of Biostatistics, Bioinformatics, and Epidemiology, Medical University of South Carolina, 135 Cannon Street, Suite 303, Charleston, South Carolina 29403, USA. sdesanti@hsph.harvard.edu
Houseman
E Andrés
EA
Coull
Brent A
BA
Louis
David N
DN
Mohapatra
Gayatry
G
Betensky
Rebecca A
RA
D001499:Bayes Theorem; D001699:Biometry / Q000379:methods*; D001932:Brain Neoplasms / Q000235:genetics; D016000:Cluster Analysis; D055028:Comparative Genomic Hybridization / Q000706:statistics & numerical data*; D004273:DNA, Neoplasm / Q000235:genetics; D018628:Gene Dosage; D005910:Glioma / Q000235:genetics; D006801:Humans; D015233:Models, Statistical; D009010:Monte Carlo Method
D004273:DNA, Neoplasm