title: Continuous-index hidden Markov modelling of array CGH copy number data.
abstract: BACKGROUND
  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.
authors.affiliation: Centre for Mathematical Sciences, Lund University, Lund, Sweden. susann.stjernqvist@matstat.lu.se
authors.forename: Susann
authors.initials: S
authors.lastname: Stjernqvist
authors.forename: Tobias
authors.initials: T
authors.lastname: Rydén
authors.forename: Martin
authors.initials: M
authors.lastname: Sköld
authors.forename: Johan
authors.initials: J
authors.lastname: Staaf
mesh_terms: D001185:Artificial Intelligence; D002874:Chromosome Mapping / Q000379:methods*; D003198:Computer Simulation; D030541:Databases, Genetic; D018628:Gene Dosage / Q000235:genetics*; D008390:Markov Chains; D008957:Models, Genetic; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D016415:Sequence Alignment / Q000379:methods; D017422:Sequence Analysis, DNA / Q000379:methods*