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BMCBI
2010

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

8 years 8 months ago
baySeq: Empirical Bayesian methods for identifying differential expression in sequence count data
Background: High 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. Results: We 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. Conclusions: Our method performs at least as well, and often better, than existing methods for analyses of pairwise differential expression in both real and simulated...
Thomas J. Hardcastle, Krystyna A. Kelly
Added 08 Dec 2010
Updated 08 Dec 2010
Type Journal
Year 2010
Where BMCBI
Authors Thomas J. Hardcastle, Krystyna A. Kelly
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