IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v53y2009i5p1873-1882.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-9473(08)00336-8
    Download Restriction: Full text for ScienceDirect subscribers only.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wolfgang P. Lehrach & Dirk Husmeier, 2009. "Segmenting bacterial and viral DNA sequence alignments with a trans‐dimensional phylogenetic factorial hidden Markov model," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 58(3), pages 307-327, July.
    2. Hilary S. Booth & Conrad J. Burden & John H. Maindonald & Lucia Santoso & Matthew J. Wakefield & Susan R. Wilson, 2005. "Discussion of “A Bayesian Approach to DNA Sequence Segmentation”," Biometrics, The International Biometric Society, vol. 61(2), pages 635-637, June.
    3. Gong Chen & Qing Zhou, 2010. "Heterogeneity in DNA Multiple Alignments: Modeling, Inference, and Applications in Motif Finding," Biometrics, The International Biometric Society, vol. 66(3), pages 694-704, September.
    4. R. J. Boys & D. A. Henderson, 2005. "Discussion of "A Bayesian Approach to DNA Sequence Segmentation "," Biometrics, The International Biometric Society, vol. 61(2), pages 637-639, June.
    5. Andreas C. Georgiou & Alexandra Papadopoulou & Pavlos Kolias & Haris Palikrousis & Evanthia Farmakioti, 2021. "On State Occupancies, First Passage Times and Duration in Non-Homogeneous Semi-Markov Chains," Mathematics, MDPI, vol. 9(15), pages 1-17, July.
    6. Nasroallah Abdelaziz & Elkimakh Karima, 2017. "HMM with emission process resulting from a special combination of independent Markovian emissions," Monte Carlo Methods and Applications, De Gruyter, vol. 23(4), pages 287-306, December.
    7. Elkimakh Karima & Nasroallah Abdelaziz, 2020. "Hidden Markov Model with Markovian emission," Monte Carlo Methods and Applications, De Gruyter, vol. 26(4), pages 303-313, December.
    8. Luigi Spezia, 2010. "Bayesian analysis of multivariate Gaussian hidden Markov models with an unknown number of regimes," Journal of Time Series Analysis, Wiley Blackwell, vol. 31(1), pages 1-11, January.
    9. Husmeier Dirk & Mantzaris Alexander V., 2008. "Addressing the Shortcomings of Three Recent Bayesian Methods for Detecting Interspecific Recombination in DNA Sequence Alignments," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 7(1), pages 1-41, November.
    10. Rolando de la Cruz & Cristian Meza & Nicolás Narria & Claudio Fuentes, 2022. "A Bayesian Change Point Analysis of the USD/CLP Series in Chile from 2018 to 2020: Understanding the Impact of Social Protests and the COVID-19 Pandemic," Mathematics, MDPI, vol. 10(18), pages 1-15, September.
    11. Castro, Bruno M. & Lemes, Renan B. & Cesar, Jonatas & Hünemeier, Tábita & Leonardi, Florencia, 2018. "A model selection approach for multiple sequence segmentation and dimensionality reduction," Journal of Multivariate Analysis, Elsevier, vol. 167(C), pages 319-330.
    12. Wilkinson, Darren J & KH Yeung, Stephen, 2004. "A sparse matrix approach to Bayesian computation in large linear models," Computational Statistics & Data Analysis, Elsevier, vol. 44(3), pages 493-516, January.
    13. 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.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:csdana:v:53:y:2009:i:5:p:1873-1882. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/csda .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.