Variational inference for coupled Hidden Markov Models applied to the joint detection of copy number variations

Xiaoqiang Wang; Emilie Lebarbier; Julie Aubert; Stéphane Robin

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arXiv · 1706.06742

Variational inference for coupled Hidden Markov Models applied to the joint detection of copy number variations

Xiaoqiang Wang·Emilie Lebarbier·Julie Aubert·Stéphane Robin

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Abstract

Hidden Markov models provide a natural statistical framework for the detection of the copy number variations (CNV) in genomics. In this paper, we consider a Hidden Markov Model involving several correlated hidden processes at the same time. When dealing with a large number of series, maximum likelihood inference (performed classically using the EM algorithm) becomes intractable. We thus propose an approximate inference algorithm based on a variational approach (VEM). A simulation study is performed to assess the performance of the proposed method and an application to the detection of structural variations in plant genomes is presented.

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Xiaoqiang Wang, Emilie Lebarbier, Julie Aubert, Stéphane Robin. 2017-06-21. Variational inference for coupled Hidden Markov Models applied to the joint detection of copy number variations. https://arxiv.org/abs/1706.06742

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