推断与近似的当地错误发现率的推断
Rajesh Karmakar1, Ruth Heller1, Saharon Rosset1
1Department of Statistics and Operations Research, Tel Aviv University, Tel Aviv 69978, Israel.
Biometrics
|April 12, 2025
概括
本研究引入了一种使用邻近局部错误发现率 (locFDR_N) 进行大规模多重测试的新方法,以提高依赖测试统计中的功率. 该方法通过考虑局部依赖性来增强统计能力,在模拟和遗传研究中表现优于传统方法.
科学领域:
- 统计 统计 统计 统计
- 生物信息学是一种生物信息学.
- 遗传学 是一个遗传学.
背景情况:
- 埃弗朗的2组模型是大规模多重测试的标准,假设独立的测试统计数据.
- 局部边际错误发现率 (locFDR) 控制错误发现,但不考虑依赖性.
- 在现实的设置中,依赖测试统计数据可以增加功率,但计算通常在计算上是不可避免的.
研究的目的:
- 通过计算依赖性测试统计数据,开发一种计算可行的方法来增加大规模多重测试中的功率.
- 引入和验证社区本地错误发现率 (locFDR_N),以改善统计决策.
- 在遗传关联研究中证明拟议方法的实际实用性.
主要方法:
- 建议使用 locFDR_N,给定在 N 邻近的测试统计数据中,零假设的概率.
- 在N-邻居导向决策中拒绝小locFDR_N的已被证明的最佳性,显示功率随N.增加.
- 评估了相对于N的计算复杂性,建议选择最大可行的邻里.
主要成果:
- locFDR_N方法在现有的实用方法上提供了实质性的权力增长,即使在小的N社区.
- 电力随着N社区的大小而增加,平衡计算可行性.
- 模拟证实了拟议方法的卓越性能.
结论:
- locFDR_N方法为使用依赖数据进行大规模多重测试提供了强大而实用的方法.
- 该方法在现实世界基因组范围的高度关联研究中显示出了显著的实用性.
- 这种方法为研究人员处理复杂,依赖的数据集提供了有价值的工具.
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