An efficient Monte Carlo approach to assessing statistical significance in genomic studies
An efficient Monte Carlo approach to assessing statistical significance in genomic studies
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DOI：
10.1093/bioinformatics/bti053
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发表时间：
2005-03-15
期刊：
影响因子：
5.8
通讯作者：
Lin, DY
中科院分区：
文献类型：
--
作者：
Lin, DY
动机：多假设检验是基因组研究中常见的问题，特别是在微阵列实验和全基因组关联研究中。未能解释多重比较的影响将导致大量的假阳性结果。Bonferroni修正和Holm的降序过程过于保守，而排列测试耗时且仅限于简单的问题。结果：我们开发了一种有效的蒙特卡罗方法来近似检验统计量沿基因组的联合分布。然后，我们使用蒙特卡罗分布来评估错误控制的常用标准，例如家庭错误率和正错误发现率。这种方法适用于任何数据结构和测试统计。对仿真和实际数据的应用表明，该方法提供了精确的误差控制，并且比Bonferroni和Holm方法强大得多，特别是当测试统计量高度相关时。
Motivation: Multiple hypothesis testing is a common problem in genome research, particularly in microarray experiments and genomewide association studies. Failure to account for the effects of multiple comparisons would result in an abundance of false positive results. The Bonferroni correction and Holm's step-down procedure are overly conservative, whereas the permutation test is time-consuming and is restricted to simple problems.Results: We developed an efficient Monte Carlo approach to approximating the joint distribution of the test statistics along the genome. We then used the Monte Carlo distribution to evaluate the commonly used criteria for error control, such as familywise error rates and positive false discovery rates. This approach is applicable to any data structures and test statistics. Applications to simulated and real data demonstrate that the proposed approach provides accurate error control, and can be substantially more powerful than the Bonferroni and Holm methods, especially when the test statistics are highly correlated.