Significance testing for small microarray experiments
Significance testing for small microarray experiments
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DOI：
10.1002/sim.2109
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发表时间：
2005-08-15
影响因子：
2
通讯作者：
Olson, JM
中科院分区：
文献类型：
--
作者：
Kooperberg, C;Aragaki, A;Olson, JM
在微阵列实验中，当重复次数较少时，进行哪种显著性检验会显著影响结果。当在两个样本比较中，两个条件都少于，比方说，五个重复时，传统的检验统计需要极端的结果，然后在多重比较校正后，基因被认为是统计学上显著的差异表达。在文献中，已经提出了许多方法来规避这个问题。其中一些建议使用（经验）贝叶斯参数来缓和个别基因的方差估计。其他建议试图通过结合基因组或类似的实验来稳定这些方差估计。在本文中，我们比较了这些方法中的几种，无论是在数据集，两个实验条件是相同的，因此很少有统计学意义的差异表达的基因应被确定，并在实验中，两个条件不同。这使我们能够识别哪些方法是最强大的，而不会识别许多误报。我们的结论是，在平衡假阳性和真阳性的数量后，经验贝叶斯方法和结合实验的方法表现最好。标准t检验是较差的，当样本量很小时几乎没有功效。版权所有（c）2005年约翰威利父子有限公司。
Which significance test is carried out when the number of repeats is small in microarray experiments can dramatically influence the results. When in two sample comparisons both conditions have fewer than, say, five repeats traditional test statistics require extreme results, before a gene is considered statistically significant differentially expressed after a multiple comparisons correction. In the literature many approaches to circumvent this problem have been proposed. Some of these proposals use (empirical) Bayes arguments to moderate the variance estimates for individual genes. Other proposals try to stabilize these variance estimate by combining groups of genes or similar experiments. In this paper we compare several of these approaches, both on data sets where both experimental conditions are the same, and thus few statistically significant differentially expressed genes should be identified, and on experiments where both conditions do differ. This allows us to identify which approaches are most powerful without identifying many false positives. We conclude that after balancing the numbers of false positives and true positives an empirical Bayes approach and an approach which combines experiments perform best. Standard t-tests are inferior and offer almost no power when the sample size is small. Copyright (c) 2005 John Wiley & Sons, Ltd.