A continuous-index hidden Markov jump process for modeling DNA copy number data
2009 (English)In: Biostatistics, ISSN 1465-4644, E-ISSN 1468-4357, Vol. 10, no 4, 773-778 p.Article in journal (Refereed) Published
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.
Place, publisher, year, edition, pages
2009. Vol. 10, no 4, 773-778 p.
Array CGH, DNA copy number variation, Markov jump process, MCMC
Probability Theory and Statistics
IdentifiersURN: urn:nbn:se:kth:diva-61179DOI: 10.1093/biostatistics/kxp030ISI: 000269735000014PubMedID: 19628640OAI: oai:DiVA.org:kth-61179DiVA: diva2:478676
QC 201201172012-01-162012-01-162012-01-17Bibliographically approved