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

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  • 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
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    References listed on IDEAS

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    1. 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.
    2. Edwin H. Cook Jr & Stephen W. Scherer, 2008. "Copy-number variations associated with neuropsychiatric conditions," Nature, Nature, vol. 455(7215), pages 919-923, October.
    3. 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.
    4. Chib, Siddhartha, 1996. "Calculating posterior distributions and modal estimates in Markov mixture models," Journal of Econometrics, Elsevier, vol. 75(1), pages 79-97, November.
    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.
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