Normal uniform mixture differential gene expression detection for cDNA microarrays.
Normal uniform mixture differential gene expression detection for cDNA microarrays.
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DOI：
10.1186/1471-2105-6-173
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发表时间：
2005-07-12
影响因子：
3
通讯作者：
Raftery AE
中科院分区：
文献类型：
--
作者：
Dean N;Raftery AE
分析基因表达数据的主要任务之一是找到在不同样品中差异表达的基因。由于运行了数千个测试而导致的多个测试问题使得一些更流行的方法存在问题。我们提出了一个简单的方法，正常均匀差异基因表达（NUDGE）检测发现差异表达基因的cDNA微阵列。该方法使用一个简单的单变量正态-均匀混合模型，结合新的标准化方法，扩展了Dudoit，Yang，Callow和Speed（2002）的lowess标准化。它考虑到多重测试，并给出差分表达式的概率作为其输出的一部分。它可以应用于单载玻片或重复实验，而且速度非常快。使用NUDGE分析了三个数据集，并将结果与其他流行方法：未调整和Bonferroni调整的t检验，微阵列的显著性分析（SAM）和经验贝叶斯微阵列（EBarrays）与Gamma-Gamma和对数正态-正态模型进行了比较。该方法对先验已知/怀疑差异表达的基因给出了高的差异表达概率，而对其他基因给出了低的概率。在已知的假阳性和假阴性方面，该方法优于所有多次重复的方法，除了伽马-伽马EBarrays方法，它提供了可比的结果，具有更大的简单性，速度，更少的假设和适用于单次重复的情况下的附加优势。一个名为nudge的R包将很快在上提供，以实现本文中的方法。
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). 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 .
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DOI：
10.1111/1467-9868.00346
发表时间：
2002-01-01
影响因子：
5.8
作者：
Storey, JD
通讯作者：
Storey, JD
DOI：
10.1073/pnas.091062498
发表时间：
2001-04-24
影响因子：
11.1
作者：
Tusher, VG;Tibshirani, R;Chu, G
通讯作者：
Chu, G
影响因子：
4.8
作者：
Long, AD;Mangalam, HJ;Baldi, P
通讯作者：
Baldi, P
影响因子：
1.7
作者：
Newton, MA;Kendziorski, CM;Tsui, KW
通讯作者：
Tsui, KW
DOI：
10.1111/j.1369-7412.2004.05695.x
发表时间：
2004-01-01
影响因子：
5.8
作者：
Cox, DR
通讯作者：
Cox, DR