Omnibus testing and gene filtration in microarray data analysis
Omnibus testing and gene filtration in microarray data analysis
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DOI：
10.1080/02664760701683528
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发表时间：
2008-01-01
影响因子：
1.5
通讯作者：
Charnigo, Richard
中科院分区：
文献类型：
--
作者：
Dai, Hongying;Charnigo, Richard
在微阵列分析中，当同时进行数千次检测以检测差异表达的基因时，如果不进行多重性调整，I型错误的数量可能是巨大的。然而，由于大规模，传统的调整方法需要非常stringen显著性水平的个人测试，这产生低功率检测变化。在这项工作中，我们描述了两个综合测试可以结合使用的基因过滤过程，以规避由于大规模的测试的困难。这两个综合检验，即D检验和修正似然比检验（MLRT），可以用来研究P值的集合是否来自均匀（0，1）分布，或者均匀（0，1）分布是否被另一个Beta分布污染更合适。在前一种情况下，可以将注意力集中在基因组的一小部分;在后一种情况下，污染模型的参数估计值为多重比较提供了一个参考框架。与似然比检验（LRT）不同，D检验和MLRT在无污染的零假设下都具有简单的极限分布，因此可以从标准表中获得临界值。仿真研究表明，D-检验和MLRT优于AIC，BIC和Kolmogorov-Smirnov检验的上级。一个案例研究说明了综合测试和过滤。
When thousands of tests are performed simultaneously to detect differentially expressed genes in microarray analysis, the number of Type I errors can be immense if a multiplicity adjustment is not made. However, due to the large scale, traditional adjustment methods require very stringen significance levels for individual tests, which yield low power for detecting alterations. In this work, we describe how two omnibus tests can be used in conjunction with a gene filtration process to circumvent difficulties due to the large scale of testing. These two omnibus tests, the D-test and the modified likelihood ratio test (MLRT), can be used to investigate whether a collection of P-values has arisen from the Uniform(0,1) distribution or whether the Uniform(0,1) distribution contaminated by another Beta distribution is more appropriate. In the former case, attention can be directed to a smaller part of the genome; in the latter event, parameter estimates for the contamination model provide a frame of reference for multiple comparisons. Unlike the likelihood ratio test (LRT), both the D-test and MLRT enjoy simple limiting distributions under the null hypothesis of no contamination, so critical values can be obtained from standard tables. Simulation studies demonstrate that the D-test and MLRT are superior to the AIC, BIC, and Kolmogorov-Smirnov test. A case study illustrates omnibus testing and filtration.