ROBUST HYPERPARAMETER ESTIMATION PROTECTS AGAINST HYPERVARIABLE GENES AND IMPROVES POWER TO DETECT DIFFERENTIAL EXPRESSION.
ROBUST HYPERPARAMETER ESTIMATION PROTECTS AGAINST HYPERVARIABLE GENES AND IMPROVES POWER TO DETECT DIFFERENTIAL EXPRESSION.
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DOI：
10.1214/16-aoas920
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发表时间：
2016-06
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通讯作者：
Smyth GK
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作者：
Phipson B;Lee S;Majewski IJ;Alexander WS;Smyth GK
基因组研究中最常见的分析任务之一是识别不同实验条件下的差异表达基因。经验性贝叶斯（EB）统计检验使用适度的基因方差已经非常有效的用于这一目的，特别是当生物重复样本的数量很少。然而，EB程序可能会受到少数差异非常大或非常小的基因的严重影响。本文通过对超参数估计过程的鲁棒性改进了差分表达式检验。鲁棒性过程降低了先验分布对异常基因的信息量，而增加了对其他基因的信息量。这种效应有双重好处：减少了高变异基因被错误地识别为DE的机会，同时增加了基因主体的统计能力。鲁棒EB算法速度快，数值稳定。该程序允许测试统计量的精确小样本零分布，并在没有异常基因存在时精确地减少到原始EB程序。仿真结果表明，在不存在异常值基因的情况下，鲁棒化后的测试结果与原始测试结果相似，而在存在异常值基因的情况下，鲁棒性更强。本文包括案例研究，其中鲁棒方法正确识别和降低与隐藏协变量相关的基因，并检测更多可能与实验条件科学相关的基因。新程序在limma软件包中实现，该软件包可从Bioconductor存储库免费获得。
One of the most common analysis tasks in genomic research is to identify genes that are differentially expressed (DE) between experimental conditions. Empirical Bayes (EB) statistical tests using moderated genewise variances have been very effective for this purpose, especially when the number of biological replicate samples is small. The EB procedures can however be heavily influenced by a small number of genes with very large or very small variances. This article improves the differential expression tests by robustifying the hyperparameter estimation procedure. The robust procedure has the effect of decreasing the informativeness of the prior distribution for outlier genes while increasing its informativeness for other genes. This effect has the double benefit of reducing the chance that hypervariable genes will be spuriously identified as DE while increasing statistical power for the main body of genes. The robust EB algorithm is fast and numerically stable. The procedure allows exact small-sample null distributions for the test statistics and reduces exactly to the original EB procedure when no outlier genes are present. Simulations show that the robustified tests have similar performance to the original tests in the absence of outlier genes but have greater power and robustness when outliers are present. The article includes case studies for which the robust method correctly identifies and downweights genes associated with hidden covariates and detects more genes likely to be scientifically relevant to the experimental conditions. The new procedure is implemented in the limma software package freely available from the Bioconductor repository.