IDEAS home Printed from https://ideas.repec.org/a/plo/pcbi00/1004871.html
   My bibliography  Save this article

Fast Bayesian Inference of Copy Number Variants using Hidden Markov Models with Wavelet Compression

Author

Listed:
  • John Wiedenhoeft
  • Eric Brugel
  • Alexander Schliep

Abstract

By integrating Haar wavelets with Hidden Markov Models, we achieve drastically reduced running times for Bayesian inference using Forward-Backward Gibbs sampling. We show that this improves detection of genomic copy number variants (CNV) in array CGH experiments compared to the state-of-the-art, including standard Gibbs sampling. The method concentrates computational effort on chromosomal segments which are difficult to call, by dynamically and adaptively recomputing consecutive blocks of observations likely to share a copy number. This makes routine diagnostic use and re-analysis of legacy data collections feasible; to this end, we also propose an effective automatic prior. An open source software implementation of our method is available at http://schlieplab.org/Software/HaMMLET/ (DOI: 10.5281/zenodo.46262). This paper was selected for oral presentation at RECOMB 2016, and an abstract is published in the conference proceedings.

Suggested Citation

  • John Wiedenhoeft & Eric Brugel & Alexander Schliep, 2016. "Fast Bayesian Inference of Copy Number Variants using Hidden Markov Models with Wavelet Compression," PLOS Computational Biology, Public Library of Science, vol. 12(5), pages 1-28, May.
    • Handle: RePEc:plo:pcbi00:1004871
    DOI: 10.1371/journal.pcbi.1004871
    as

    Download full text from publisher

    File URL: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004871
    Download Restriction: no
    File URL: https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1004871&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pcbi.1004871?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Chib, Siddhartha, 1996. "Calculating posterior distributions and modal estimates in Markov mixture models," Journal of Econometrics, Elsevier, vol. 75(1), pages 79-97, November.
    2. Fridlyand, Jane & Snijders, Antoine M. & Pinkel, Dan & Albertson, Donna G. & Jain, A.N.Ajay N., 2004. "Hidden Markov models approach to the analysis of array CGH data," Journal of Multivariate Analysis, Elsevier, vol. 90(1), pages 132-153, July.
    3. Edwin H. Cook Jr & Stephen W. Scherer, 2008. "Copy-number variations associated with neuropsychiatric conditions," Nature, Nature, vol. 455(7215), pages 919-923, October.
    4. Barry, D.A & Parlange, J.-Y & Li, L & Prommer, H & Cunningham, C.J & Stagnitti, F, 2000. "Analytical approximations for real values of the Lambert W-function," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 53(1), pages 95-103.
    5. J. Polzehl & V. G. Spokoiny, 2000. "Adaptive weights smoothing with applications to image restoration," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(2), pages 335-354.
    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. Billio, Monica & Casarin, Roberto & Osuntuyi, Anthony, 2016. "Efficient Gibbs sampling for Markov switching GARCH models," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 37-57.
    2. Xianguo HUANG & Roberto LEON-GONZALEZ & Somrasri YUPHO, 2013. "Financial Integration from a Time-Varying Cointegration Perspective," Asian Journal of Empirical Research, Asian Economic and Social Society, vol. 3(12), pages 1473-1487.
    3. Jia Liu & John M. Maheu & Yong Song, 2024. "Identification and forecasting of bull and bear markets using multivariate returns," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(5), pages 723-745, August.
    4. Jens Kolbe & Rainer Schulz & Martin Wersing & Axel Werwatz, 2012. "Location, Location, Location: Extracting Location Value from House Prices," Discussion Papers of DIW Berlin 1216, DIW Berlin, German Institute for Economic Research.
    5. Chan, Joshua C.C. & Santi, Caterina, 2021. "Speculative bubbles in present-value models: A Bayesian Markov-switching state space approach," Journal of Economic Dynamics and Control, Elsevier, vol. 127(C).
    6. Emilio Augusto Coelho-Barros & Jorge Alberto Achcar & Josmar Mazucheli, 2010. "Longitudinal Poisson modeling: an application for CD4 counting in HIV-infected patients," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(5), pages 865-880.
    7. Sylvia Frühwirth‐Schnatter & Sylvia Kaufmann, 2006. "How do changes in monetary policy affect bank lending? An analysis of Austrian bank data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(3), pages 275-305, April.
    8. Chib, Siddhartha, 1998. "Estimation and comparison of multiple change-point models," Journal of Econometrics, Elsevier, vol. 86(2), pages 221-241, June.
    9. Geffray, S. & Klutchnikoff, N. & Vimond, M., 2016. "Illumination problems in digital images. A statistical point of view," Journal of Multivariate Analysis, Elsevier, vol. 150(C), pages 191-213.
    10. Billé, AG & Salvioni, C. & Benedetti, R., 2015. "Spatial Heterogeneity In Production Functions Models," 150th Seminar, October 22-23, 2015, Edinburgh, Scotland 212662, European Association of Agricultural Economists.
    11. Wafo Tekam, Raoul Blaise & Kengne, Jacques & Djuidje Kenmoe, Germaine, 2019. "High frequency Colpitts’ oscillator: A simple configuration for chaos generation," Chaos, Solitons & Fractals, Elsevier, vol. 126(C), pages 351-360.
    12. Salvatore Fasola & Vito M. R. Muggeo & Helmut Küchenhoff, 2018. "A heuristic, iterative algorithm for change-point detection in abrupt change models," Computational Statistics, Springer, vol. 33(2), pages 997-1015, June.
    13. Francesco Bianchi & Leonardo Melosi, 2017. "Escaping the Great Recession," American Economic Review, American Economic Association, vol. 107(4), pages 1030-1058, April.
    14. Shu-Ping Shi & Yong Song, 2012. "Identifying Speculative Bubbles with an Infinite Hidden Markov Model," Working Paper series 26_12, Rimini Centre for Economic Analysis.
    15. Toni Beutler & Matthias Gubler & Simona Hauri & Sylvia Kaufmann, 2020. "Bank lending in Switzerland: Capturing cross-sectional heterogeneity and asymmetry over time," Working Papers 20.04, Swiss National Bank, Study Center Gerzensee.
    16. Sylvia Kaufmann, 2003. "The business cycle of European countries Bayesian clustering of country - individual IP growth series," Working Papers 83, Oesterreichische Nationalbank (Austrian Central Bank).
    17. Azar, Jose, 2009. "Electric Cars and Oil Prices," MPRA Paper 15538, University Library of Munich, Germany.
    18. Łukasz Kwiatkowski, 2011. "Bayesian Analysis of a Regime Switching In-Mean Effect for the Polish Stock Market," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 3(4), pages 187-219, December.
    19. Deschamps, Philippe J., 2012. "Bayesian estimation of generalized hyperbolic skewed student GARCH models," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3035-3054.
    20. Huixia Judy Wang & Jianhua Hu, 2011. "Identification of Differential Aberrations in Multiple-Sample Array CGH Studies," Biometrics, The International Biometric Society, vol. 67(2), pages 353-362, June.

    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:plo:pcbi00:1004871. 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: ploscompbiol (email available below). General contact details of provider: https://journals.plos.org/ploscompbiol/ .

    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.