Improved variational Bayes inference for transcript expression estimation

Papastamoulis, Panagiotis and Hensman, James and Glaus, Peter and Rattray, Magnus (2014) Improved variational Bayes inference for transcript expression estimation. Statistical Applications in Genetics and Molecular Biology, 13 (2). pp. 203-216. ISSN 2194-6302

Full text not available from this repository.

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.

Item Type:
Journal Article
Journal or Publication Title:
Statistical Applications in Genetics and Molecular Biology
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/2600/2605
Subjects:
ID Code:
84330
Deposited By:
Deposited On:
25 Jan 2017 13:32
Refereed?:
Yes
Published?:
Published
Last Modified:
01 Jan 2020 09:44