错误发现率的确切积分公式和错误发现比例的方差
Rovshan G Sadygov1, Justin X Zhu1, Henock M Deberneh1
1Department of Biochemistry and Molecular Biology, The University of Texas Medical Branch, 301 University Blvd, Galveston, Texas 77555, United States.
Journal of proteome research
|May 29, 2024
概括
本研究提供了正假发现比例 (pFDR) 和多重假设测试中的差异的确切公式. 新的积分表达式提供了更好的准确性,特别是对于大规模数据分析中较小的假设集.
科学领域:
- 生物信息学和计算生物学
- 统计遗传学 统计遗传学
- 高通量欧米克数据分析数据分析
背景情况:
- 多重假设测试对于大规模的奥米克数据分析 (蛋白质组学,转录组学,代谢组学) 是至关重要的.
- 错误发现率 (FDR) 和积极的FDR (pFDR) 是标准的错误控制措施.
- 假发现比率 (FDP) 的预期 pFDR 经常被预期的比率所近似,但这种转换的条件尚不清楚.
研究的目的:
- 为期望 (pFDR) 和FDP的变量推导出精确的积分表达式.
- 调查pFDR常用的近似值的有效性和局限性.
- 为大规模的假设测试提供准确的错误估计方法.
主要方法:
- 对pFDR和FDP变异的精确积分表达式的导出.
- 对近似的分析 (预期比) 作为积分公式的局限性情况.
- 用特定数量的零假设计算pFDR的复杂度公式的开发.
- 在蛋白质组学中用于类鉴定的FDP差异的近似值.
主要成果:
- 成功地获得了pFDR和FDP变异的精确积分表达式.
- 广泛使用的预期近似比率是大样本大小的积分公式的具体情况.
- 模拟表明,对于少量的假设,积分表达式比近似更准确.
- 对于较大的样本大小,这两种方法的结果是可比的.
结论:
- 衍生的积分表达式为pFDR计算提供了更准确的框架,特别是当假设数量很小时.
- 这项工作阐明了精确的pFDR与其共同近似之间的关系,为实际应用提供了指导.
- 这些发现加强了大规模的奥米克研究中的错误控制,包括蛋白质组学数据分析.
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