Department of Plant Sciences, University of Cambridge, Downing Street, Cambridge, UK.
BMC Bioinformatics. 2010 Aug 10;11:422. doi: 10.1186/1471-2105-11-422.
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.
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.
Our 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.
高通量测序已成为研究基因组和特别是转录组数据中表达水平的重要技术。分析此类数据的一种关键方法是寻找数据中显示特定差异表达模式的元素，以便将这些元素进一步用于进一步的分析和验证。
我们提出了一种定义差异表达模式的框架，并开发了一种新的算法 baySeq，该算法使用经验贝叶斯方法在一组测序样本中检测这些差异表达模式。该方法假设数据服从负二项分布，并从整个数据集推导出经验确定的先验分布。我们在真实数据和模拟数据上检查了该方法的性能。
我们的方法在真实和模拟数据中分析成对差异表达时的性能至少与现有方法一样好，并且通常更好。当我们比较涉及多个样本组的实验设计数据的分析方法时，我们的方法再次显示出性能的显著提高。我们相信，这种方法代表了测序实验中计数数据分析的重要进展。
