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

John Wiedenhoeft, Eric Brugel and Alexander Schliep

PLOS Computational Biology, 2016, vol. 12, issue 5, 1-28

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.

Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1004871

DOI: 10.1371/journal.pcbi.1004871

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