注意在具有强烈内相关性的数据集中出现反直觉的错误发现水平
Chakravarthi Kanduri1,2, Maria Mamica3,4, Emilie Willoch Olstad4,5
1Scientific Computing and Machine Learning Section, Department of Informatics, University of Oslo, Oslo, Norway. skanduri@uio.no.
Genome biology
|August 18, 2025
概括
控制错误发现率 (FDR) 的Benjaminini-Hochberg (BH) 方法可以在具有特征依赖性的omics数据中夸大意义. 研究人员应使用合成零数据等方法,以避免大量的错误阳性结果.
科学领域:
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
- 统计遗传学 统计遗传学
背景情况:
- 本杰明-霍赫伯格 (BH) 程序被广泛用于控制高维奥米学研究中的错误发现率 (FDR).
- 准确的统计推断对于识别复杂数据集中的真实生物信号至关重要.
研究的目的:
- 调查BH方法在以实质性特征依赖性为特征的奥米克数据集中的性能.
- 为了突出标准的FDR控制方法在依赖下潜在的陷.
- 为强大的多重测试推替代策略.
主要方法:
- 分析模拟和真实的奥米克数据集,具有不同程度的特征相关性.
- 对本杰明尼-霍赫伯格 (BH) FDR控制程序的评估.
- 合成零数据 (负控制) 方法的应用.
主要成果:
- 证明BH方法在存在强烈特征依赖的情况下可以产生大量假阳性.
- 在依赖数据结构下识别虚假发现的反直觉通货膨胀.
- 合成零数据的验证作为检测和减轻这些问题的工具.
结论:
- 像BH这样的标准FDR控制方法在具有显著特征依赖性的omics数据集中可能不可靠.
- 建议研究人员采用先进的多重测试策略,包括使用合成零数据.
- 仔细考虑数据依赖性至关重要,以尽量减少误导性结果,并确保omics发现的有效性.
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