Sticky hidden Markov modeling of comparative genomic hybridization

Published

Journal Article

We develop a sticky hidden Markov model (HMM) with a Dirichlet distribution (DD) prior, motivated by the problem of analyzing comparative genomic hybridization (CGH) data. As formulated the sticky DD-HMM prior is employed to infer the number of states in an HMM, while also imposing state persistence. The form of the proposed hierarchical model allows efficient variational Bayesian (VB) inference, of interest for large-scale CGH problems. We compare alternative formulations of the sticky HMM, while also examining the relative efficacy of VB and Markov chain Monte Carlo (MCMC) inference. To validate the formulation, example results are presented for an illustrative synthesized data set and our main applicationCGH, for which we consider data for breast cancer. For the latter, we also make comparisons and partially validate the CGH analysis through factor analysis of associated (but distinct) gene-expression data. © 2010 IEEE.

Full Text

Duke Authors

Cited Authors

  • Du, L; Chen, M; Lucas, J; Carin, L

Published Date

  • October 1, 2010

Published In

Volume / Issue

  • 58 / 10

Start / End Page

  • 5353 - 5368

International Standard Serial Number (ISSN)

  • 1053-587X

Digital Object Identifier (DOI)

  • 10.1109/TSP.2010.2053033

Citation Source

  • Scopus