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Continuous-index hidden Markov modelling of array CGH copy number data
Lund University.
Lund University.
Örebro University, Department of Business, Economics, Statistics and Informatics.
2007 (English)In: Bioinformatics, ISSN 1367-4803, E-ISSN 1460-2059, Vol. 23, no 8, 1006-1014 p.Article in journal (Refereed) Published
Abstract [en]

Motivation: In recent years, a range of techniques for analysis and segmentation of array comparative genomic hybridization (aCGH) data have been proposed. For array designs in which clones are of unequal lengths, are unevenly spaced or overlap, the discrete-index view typically adopted by such methods may be questionable or 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 dataset 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. (Lai,W.R. et al. (2005) Comparative analysis of algorithms for identifying amplifications and deletions in array CGH data. Bioinformatics, 21, 3763–3370.) giving result similar to theirs but with certain features highlighted in the continuous-index setting

Place, publisher, year, edition, pages
2007. Vol. 23, no 8, 1006-1014 p.
National Category
Probability Theory and Statistics
Research subject
Statistics
Identifiers
URN: urn:nbn:se:oru:diva-5715DOI: 10.1093/bioinformatics/btm059OAI: oai:DiVA.org:oru-5715DiVA: diva2:173945
Available from: 2009-02-18 Created: 2009-02-18 Last updated: 2010-11-01Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
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