Dean Nema, Raftery Adrian E
Department of Statistics, University of Washington, Box 354322, Seattle, WA 98195-4322, USA.
BMC Bioinformatics. 2005 Jul 12;6:173. doi: 10.1186/1471-2105-6-173.
One of the primary tasks in analysing gene expression data is finding genes that are differentially expressed in different samples. Multiple testing issues due to the thousands of tests run make some of the more popular methods for doing this problematic.
We propose a simple method, Normal Uniform Differential Gene Expression (NUDGE) detection for finding differentially expressed genes in cDNA microarrays. The method uses a simple univariate normal-uniform mixture model, in combination with new normalization methods for spread as well as mean that extend the lowess normalization of Dudoit, Yang, Callow and Speed (2002) 1. It takes account of multiple testing, and gives probabilities of differential expression as part of its output. It can be applied to either single-slide or replicated experiments, and it is very fast. Three datasets are analyzed using NUDGE, and the results are compared to those given by other popular methods: unadjusted and Bonferroni-adjusted t tests, Significance Analysis of Microarrays (SAM), and Empirical Bayes for microarrays (EBarrays) with both Gamma-Gamma and Lognormal-Normal models.
The method gives a high probability of differential expression to genes known/suspected a priori to be differentially expressed and a low probability to the others. In terms of known false positives and false negatives, the method outperforms all multiple-replicate methods except for the Gamma-Gamma EBarrays method to which it offers comparable results with the added advantages of greater simplicity, speed, fewer assumptions and applicability to the single replicate case. An R package called nudge to implement the methods in this paper will be made available soon at http://www.bioconductor.org.
分析基因表达数据的主要任务之一是找到在不同样本中差异表达的基因。由于要进行数千次测试，多重检验问题使得一些较常用的方法存在问题。
我们提出了一种简单的方法——正态均匀分布差异基因表达（NUDGE）检测法，用于在cDNA微阵列中寻找差异表达基因。该方法使用简单的单变量正态-均匀混合模型，并结合新的用于离散度和均值的归一化方法，扩展了Dudoit、Yang、Callow和Speed（2002）的局部加权散点平滑法（lowess）归一化。它考虑了多重检验，并将差异表达的概率作为输出的一部分。它可应用于单张芯片或重复实验，且速度非常快。使用NUDGE分析了三个数据集，并将结果与其他常用方法的结果进行了比较：未调整和经Bonferroni调整的t检验、微阵列显著性分析（SAM）以及采用伽马-伽马模型和对数正态-正态模型的微阵列经验贝叶斯法（EBarrays）。
对于先验已知/怀疑有差异表达的基因，该方法给出高的差异表达概率，而对于其他基因则给出低概率。就已知的假阳性和假阴性而言，该方法优于所有多重重复方法，除了伽马-伽马EBarrays方法，该方法与之相比结果相当，且具有更简单、速度更快、假设更少以及适用于单重复情况等额外优势。一个名为nudge的R包将很快在http://www.bioconductor.org上提供，用于实现本文中的方法。
