Bayesian robust inference for differential gene expression in microarrays with multiple samples
Bayesian robust inference for differential gene expression in microarrays with multiple samples
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DOI：
10.1111/j.1541-0420.2005.00397.x
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发表时间：
2006-03-01
期刊：
影响因子：
1.9
通讯作者：
Bumgarner, RE
中科院分区：
文献类型：
--
作者：
Gottardo, R;Raftery, AE;Bumgarner, RE
我们研究了利用基因表达微阵列技术鉴定不同条件下差异表达基因的问题。由于实验过程涉及许多步骤，从杂交到图像分析，cDNA微阵列数据经常包含离群值。例如，由于表面上的划痕或灰尘、玻璃中的缺陷或阵列生产中的缺陷，可能出现所有偏离的数据值。我们开发了一个强大的贝叶斯层次模型的差异表达测试。使用t分布明确地对误差进行建模，该t分布考虑了离群值。该模型包括一个可交换的方差先验，它允许不同的基因方差，但仍然缩小极端的经验方差。我们的模型可以用于测试多个样本之间的差异表达基因，并且当有三个或更多个样本时，它可以区分不同的可能差异表达模式。参数估计是使用一种新版本的马尔可夫链蒙特卡罗，这是适当的模型时，把质量的子空间的完整的参数空间。使用两个公开的基因表达数据集的方法进行说明。我们比较我们的方法与其他六个基线和常用的技术，即t检验，Bonferroni调整的t检验，微阵列（SAM）的显著性分析，埃夫隆的经验贝叶斯，和EBarrays在其对数正态和伽马-伽马形式。在一项使用艾滋病毒数据的实验中，根据重复之间的一致性和不一致性，我们的方法比这些替代方法表现得更好。
We consider the problem of identifying differentially expressed genes tinder different conditions using gene expression microarrays. Because of the many steps involved in the experimental process, from hybridization to image analysis, cDNA microarray data often contain outliers. For example, ail outlying data value could occur because of scratches or dust on the surface, imperfections in the glass, or imperfections in the array production. We develop a robust Bayesian hierarchical model for testing for differential expression. Errors are modeled explicitly using a t-distribution, which accounts for outliers. The model includes an exchangeable prior for the variances, which allows different variances for the genes but still shrinks extreme empirical variances. Our model can be used for testing for differentially expressed genes among multiple samples, and it can distinguish between the different possible patterns of differential expression when there are three or more samples. Parameter estimation is carried out using a novel version of Markov chain Monte Carlo that is appropriate when the model puts mass on subspaces of the full parameter space. The method is illustrated using two publicly available gene expression data sets. We compare our method to six other baseline and commonly used techniques, namely the t-test, the Bonferroni-adjusted t-test, significance analysis of microarrays (SAM), Efron's empirical Bayes, and EBarrays in both its lognormal-normal and gamma-gamma forms. In an experiment with HIV data, our method performed better than these alternatives, on the basis of between-replicate agreement and disagreement.