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Bayesian hidden Markov model for DNA sequence segmentation: A prior sensitivity analysis

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  • Nur, Darfiana
  • Allingham, David
  • Rousseau, Judith
  • Mengersen, Kerrie L.
  • McVinish, Ross

Abstract

The sensitivity to the specification of the prior in a hidden Markov model describing homogeneous segments of DNA sequences is considered. An intron from the chimpanzee [alpha]-fetoprotein gene, which plays an important role in embryonic development in mammals, is analysed. Three main aims are considered: (i) to assess the sensitivity to prior specification in Bayesian hidden Markov models for DNA sequence segmentation; (ii) to examine the impact of replacing the standard Dirichlet prior with a mixture Dirichlet prior; and (iii) to propose and illustrate a more comprehensive approach to sensitivity analysis, using importance sampling. It is obtained that (i) the posterior estimates obtained under a Bayesian hidden Markov model are indeed sensitive to the specification of the prior distributions; (ii) compared with the standard Dirichlet prior, the mixture Dirichlet prior is more flexible, less sensitive to the choice of hyperparameters and less constraining in the analysis, thus improving posterior estimates; and (iii) importance sampling was computationally feasible, fast and effective in allowing a richer sensitivity analysis.

Suggested Citation

  • Nur, Darfiana & Allingham, David & Rousseau, Judith & Mengersen, Kerrie L. & McVinish, Ross, 2009. "Bayesian hidden Markov model for DNA sequence segmentation: A prior sensitivity analysis," Computational Statistics & Data Analysis, Elsevier, vol. 53(5), pages 1873-1882, March.
    • Handle: RePEc:eee:csdana:v:53:y:2009:i:5:p:1873-1882
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    References listed on IDEAS

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    1. Richard J. Boys & Daniel A. Henderson, 2004. "A Bayesian Approach to DNA Sequence Segmentation," Biometrics, The International Biometric Society, vol. 60(3), pages 573-581, September.
    2. R. J. Boys & D. A. Henderson & D. J. Wilkinson, 2000. "Detecting homogeneous segments in DNA sequences by using hidden Markov models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 49(2), pages 269-285.
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