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Bioinformatics Advance Access published online on February 19, 2007
Bioinformatics, doi:10.1093/bioinformatics/btm059
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© The Author (2007). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Continuous-index hidden Markov modelling of array CGH copy number data

Susann Stjernqvist a,*, Tobias Rydén a, Martin Sköld a and Johan Staaf a

aCentre for Mathematical Sciences, Lund University, Box 118, 221 00 Lund, Sweden, bDepartment of Oncology, University Hospital, 221 85 Lund, Sweden

*to whom correspondence should be addressed. Susann Stjernqvist, E-mail: susann.stjernqvist{at}matstat.lu.se


  Abstract

Motivation: In recent years a range of techniques for analysis and segmentation of array comparative genomic hybridisation (aCGH) data has been proposed. For array designs in which clones are of unequal lengths, are unevenly spaced or overlap, the discreteindex view typically adopted by such methods may be questionableor improved.

Results: We describe a continuous-index hidden Markov model for aCGH data as well as a Monte Carlo EM algorithm to estimate its parameters. It is shown that for a data-set from the BT-474 cell line analysed on 32K BAC tiling microarrays, this model yields considerably better model fit in terms of lag-1 residual autocorrelations compared to a discrete-index HMM, and it is also shown how to use the model for e.g. estimation of change points on the base-pair scale and for estimation of conditional state probabilities across the genome. In addition the model is applied to the Glioblastoma Multiforme data used in the comparative study by Lai et al. (2005), giving result similar to theirs but with certain features highlighted in the continuous-index setting.

Associate Editor: Keith Crandall

Received on November 8, 2006; revised on December 20, 2006; accepted on February 12, 2007

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