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Omnibus testing and gene filtration in microarray data analysis

机译：微阵列数据分析中的综合测试和基因过滤

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摘要

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,l) 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.
机译：当同时进行成千上万的测试以检测微阵列分析中差异表达的基因时，如果不进行多重性调整，则I型错误的数目可能会很大。但是，由于规模大，传统的调整方法需要非常严格的显着性水平才能进行个别测试，从而降低了检测变化的能力。在这项工作中，我们描述了如何将两种综合测试与基因过滤过程结合使用，以规避由于大规模测试而带来的困难。这两个综合测试，即D检验和修正似然比检验（MLRT），可用于调查是否从Uniform（0，l）分布中产生了P值的集合，或者Uniform（0,1 ）的分布更容易受到另一Beta分布的污染。在前一种情况下，可以将注意力集中在基因组的较小部分上。在后一种情况下，污染模型的参数估计为多重比较提供了参考框架。与似然比检验（LRT）不同，D检验和MLRT在无污染的零假设下均享有简单的极限分布，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。案例研究说明了综合测试和过滤。

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