BaySeq: empirical Bayesian methods for identifying differential expression in sequence count data.

ABSTRACT:

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 performa

SUBMITTER: Hardcastle TJ 

PROVIDER: S-EPMC2928208 | biostudies-literature | 2010 Aug

REPOSITORIES: biostudies-literature