baySeq: Empirical Bayesian methods for identifying differential expression in sequence count data

BMC Bioinformatics

Research Article

Krystyna A Kelly¹  Thomas J Hardcastle¹ 

[1] Department of Plant Sciences, University of Cambridge, Downing Street, Cambridge, UK;

关键词: Posterior Probability;    Negative Binomial Distribution;    Library Size;    Complex Experimental Design;    Empirical Bayesian Approach;   

DOI  :  10.1186/1471-2105-11-422

 received in 2010-04-30, accepted in 2010-08-10,  发布年份 2010

来源: Springer

【 摘 要 】

BackgroundHigh throughput sequencing has become an important technology for studying expression levels in many types of genomic, and particularly transcriptomic, data. One key way of analysing such data is to look for elements of the data which display particular patterns of differential expression in order to take these forward for further analysis and validation.ResultsWe propose a framework for defining patterns of differential expression and develop a novel algorithm, baySeq, which uses an empirical Bayes approach to detect these patterns of differential expression within a set of sequencing samples. The method assumes a negative binomial distribution for the data and derives an empirically determined prior distribution from the entire dataset. We examine the performance of the method on real and simulated data.ConclusionsOur method performs at least as well, and often better, than existing methods for analyses of pairwise differential expression in both real and simulated data. When we compare methods for the analysis of data from experimental designs involving multiple sample groups, our method again shows substantial gains in performance. We believe that this approach thus represents an important step forward for the analysis of count data from sequencing experiments.

【 授权许可】

CC BY   
© Hardcastle and Kelly; licensee BioMed Central Ltd. 2010