GaGa：一个精简灵活的差异表达分析模型
GaGa: A parsimonious and flexible model for differential expression analysis
摘要：
分层模型是一个强大的工具，用于高通量数据，具有少量到中等数量的重复，因为它们允许跨信息单元（例如基因）共享信息。我们提出了两个这样的模型，并显示其在微阵列差异表达应用的敏感性增加。我们建立在gamma-gamma分层模型介绍了Kendziorski等人。Med.22（2003）3899- 3914]和Newton等人[Biostatistics 5（2004）155- 176]，通过解决可能阻碍其性能和更广泛使用的重要限制。这些模型用少量的超参数粗略地描述了数千个基因的表达。这使得它们易于解释和分析。第一个模型是一个简单的扩展，它大大提高了拟合，几乎没有增加复杂性。我们提出了第二个扩展，使用混合的Γ分布，以进一步提高拟合，在增加计算负担的代价。我们得到几个近似，显着降低计算成本。我们发现，我们的模型优于原来制定的模型，以及其他一些流行的方法进行差异表达分析。对于高通量实验中经常遇到的小样本量，性能的提高尤其明显。我们的方法在免费提供的Bioconductor gaga包中实现。
英文摘要：
Hierarchical models are a powerful tool for high-throughput data with a small to moderate number of replicates, as they allow sharing information across units of information, for example, genes. We propose two such models and show its increased sensitivity in microarray differential expression applications. We build on the gamma--gamma hierarchical model introduced by Kendziorski et al. [Statist. Med. 22 (2003) 3899--3914] and Newton et al. [Biostatistics 5 (2004) 155--176], by addressing important limitations that may have hampered its performance and its more widespread use. The models parsimoniously describe the expression of thousands of genes with a small number of hyper-parameters. This makes them easy to interpret and analytically tractable. The first model is a simple extension that improves the fit substantially with almost no increase in complexity. We propose a second extension that uses a mixture of gamma distributions to further improve the fit, at the expense of increased computational burden. We derive several approximations that significantly reduce the computational cost. We find that our models outperform the original formulation of the model, as well as some other popular methods for differential expression analysis. The improved performance is specially noticeable for the small sample sizes commonly encountered in high-throughput experiments. Our methods are implemented in the freely available Bioconductor gaga package.
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