Improved variational Bayes inference for transcript expression estimation

Papastamoulis Panagiotis (), Hensman James, Glaus Peter and Rattray Magnus
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Papastamoulis Panagiotis: University of Manchester, Michael Smith Building, Oxford Road, Manchester, M13 9PT, UK
Hensman James: University of Sheffield, The Sheffield Institute for Translational Neuroscience, 385A Glossop Road, Sheffield, S10 2HQ, UK
Glaus Peter: University of Manchester, Michael Smith Building, Oxford Road, Manchester, M13 9PT, UK
Rattray Magnus: University of Manchester, Michael Smith Building, Oxford Road, Manchester, M13 9PT, UK

Statistical Applications in Genetics and Molecular Biology, 2014, vol. 13, issue 2, 203-216

Abstract: RNA-seq studies allow for the quantification of transcript expression by aligning millions of short reads to a reference genome. However, transcripts share much of their sequence, so that many reads map to more than one place and their origin remains uncertain. This problem can be dealt using mixtures of distributions and transcript expression reduces to estimating the weights of the mixture. In this paper, variational Bayesian (VB) techniques are used in order to approximate the posterior distribution of transcript expression. VB has previously been shown to be more computationally efficient for this problem than Markov chain Monte Carlo. VB methodology can precisely estimate the posterior means, but leads to variance underestimation. For this reason, a novel approach is introduced which integrates the latent allocation variables out of the VB approximation. It is shown that this modification leads to a better marginal likelihood bound and improved estimate of the posterior variance. A set of simulation studies and application to real RNA-seq datasets highlight the improved performance of the proposed method.

Keywords: Kullback-Leibler divergence; marginal likelihood bound; generalized Dirichlet distribution; BitSeq; mixture model (search for similar items in EconPapers)
Date: 2014
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DOI: 10.1515/sagmb-2013-0054

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