2dGBH:二维组Benjaminini-Hochberg程序用于对基因组数据的双向多重测试中的错误发现率控制
Lu Yang1,2, Pei Wang3, Jun Chen1,2
1Division of Computational Biology, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN 55905, United States.
Bioinformatics (Oxford, England)
|January 20, 2024
概括
一种新的方法,2D组Benjaminini-Hochberg (2dGBH),利用双向数据结构在奥米克学研究. 这种方法提高了在基因和细胞类型分析中的多重测试的统计能力.
科学领域:
- 生物信息学是一种生物信息学.
- 统计遗传学 统计遗传学
- 计算生物学 计算生物学
背景情况:
- 奥米克技术产生复杂的数据集与固有的双向分组结构,如基因和单细胞数据中的细胞类型.
- 传统的多重测试方法往往无法充分利用这种双向结构,这可能导致统计能力降低.
研究的目的:
- 为了引入一种新的统计程序,2D组Benjaminini-Hochberg (2dGBH),旨在利用omics数据中的双向分组.
- 扩展现有的单向适应性方法的多重测试能力.
主要方法:
- 2D组Benjaminini-Hochberg (2dGBH) 程序的开发. 这是一个很好的方法.
- 使用模拟数据集进行验证,以在受控条件下评估性能.
- 应用到现实世界的omics数据集,以评估实际效用.
主要成果:
- 2dGBH程序在各种生物相关场景中有效控制了错误发现率 (FDR).
- 2dGBH与标准的Benjaminini-Hochberg (BH) 和q-value方法相比,显示出更高的统计能力.
- 拟议的方法比传统的单向自适应程序更强大.
结论:
- 2dGBH程序为具有双向结构的omics数据中的多重测试提供了一种强大而稳健的方法.
- 这种方法通过更好地利用复杂的数据关系,提高了在单细胞基因组学等领域的发现潜力.
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