alexa Non-Parametric Bayesian Modelling of Digital Gene Expression Data
ISSN: 0974-7230

Journal of Computer Science & Systems Biology
Open Access

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Research Article

Non-Parametric Bayesian Modelling of Digital Gene Expression Data

Dimitrios V Vavoulis* and Julian Gough

Department of Computer Science, University of Bristol, Bristol, United Kingdom

*Corresponding Author:
Dimitrios V Vavoulis
Department of Computer Science
University of Bristol
Bristol, United Kingdom
Tel: +44 (0)117 331573
E-mail: [email protected]

Received Date: October 20, 2013; Accepted Date: November 18, 2013; Published Date: November 25, 2013

Citation: Vavoulis DV, Gough J (2013) Non-Parametric Bayesian Modelling of Digital Gene Expression Data. J Comput Sci Syst Biol 7:001-009. doi: 10.4172/jcsb.1000131

Copyright: © 2013 Vavoulis DV, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.



Next-generation sequencing technologies provide a revolutionary tool for generating gene expression data. Starting with a fixed RNA sample, they construct a library of millions of differentially abundant short sequence tags or “reads”, which constitute a fundamentally discrete measure of the level of gene expression. A common limitation in experiments using these technologies is the low number or even absence of biological replicates, which complicates the statistical analysis of digital gene expression data. Analysis of this type of data has often been based on modified tests originally devised for analysing microarrays; both these and even de novo methods for the analysis of RNA-seq data are plagued by the common problem of low replication. We propose a novel, non-parametric Bayesian approach for the analysis of digital gene expression data. We begin with a hierarchical model for modelling over-dispersed count data and a blocked Gibbs sampling algorithm for inferring the posterior distribution of model parameters conditional on these counts. The algorithm compensates for the problem of low numbers of biological replicates by clustering together genes with tag counts that are likely sampled from a common distribution and using this augmented sample for estimating the parameters of this distribution. The number of clusters is not decided a priori, but it is inferred along with the remaining model parameters. We demonstrate the ability of this approach to model biological data with high fidelity by applying the algorithm on a public dataset obtained from cancerous and non-cancerous neural tissues. Source code implementing the methodology presented in this paper takes the form of the Python Package DGEclust, which is freely available at the following link:


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