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Biostatistics Advance Access originally published online on July 23, 2009
Biostatistics 2009 10(4):773-778; doi:10.1093/biostatistics/kxp030
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© The Author 2009. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org.

A continuous-index hidden Markov jump process for modeling DNA copy number data

Susann Stjernqvist*
Tobias Rydén

Centre for Mathematical Sciences, Lund University, Box 118, 22100 Lund, Sweden susann.stjernqvist{at}matstat.lu.se

* To whom correspondence should be addressed.

The number of copies of DNA in human cells can be measured using array comparative genomic hybridization (aCGH), which provides intensity ratios of sample to reference DNA at genomic locations corresponding to probes on a microarray. In the present paper, we devise a statistical model, based on a latent continuous-index Markov jump process, that is aimed to capture certain features of aCGH data, including probes that are unevenly long, unevenly spaced, and overlapping. The model has a continuous state space, with 1 state representing a normal copy number of 2, and the rest of the states being either amplifications or deletions. We adopt a Bayesian approach and apply Markov chain Monte Carlo (MCMC) methods for estimating the parameters and the Markov process. The model can be applied to data from both tiling bacterial artificial chromosome arrays and oligonucleotide arrays. We also compare a model with normal distributed noise to a model with t-distributed noise, showing that the latter is more robust to outliers.

Keywords: Array CGH; DNA copy number variation; Markov jump process; MCMC

Received September 11, 2008; revised March 30, 2009; revised May 12, 2009; accepted for publication June 27, 2009.


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