转录组数据不足以控制监管网络推断中的错误发现.
Eric Kernfeld1, Rebecca Keener1, Patrick Cahan2
1Department of Biomedical Engineering, Johns Hopkins University, 3400 N. Charles Street, Wyman Park Building, Suite 400 West, Baltimore, MD 21218, USA.
Cell systems
|August 22, 2024
概括
从基因表达数据推断基因调节网络容易出现错误. 一种新的方法,Model-X knockoffs,通过控制错误发现和考虑复杂的生物因素,如间接调节和非线性效应,提高了准确性.
科学领域:
- 计算生物学 计算生物学
- 基因组学就是基因组学.
- 系统生物学 系统生物学
背景情况:
- 从转录基因数据推断的转录调节网络 (TRN) 经常产生高的假阳性率.
- 现有的方法难以区分直接与间接调节,非线性效应和未测量的混变量.
研究的目的:
- 引入和评估在TRN推断中用于FDR控制的X模仿框架.
- 通过计算间接效应,非线性和共变量来解决以前方法的局限性.
主要方法:
- 模型-X模仿统计框架应用于转录组数据的应用.
- 根据不完整的黄金标准对FDR估计程序的调整.
- 与ChIP-seq和其他黄金标准进行基准测试.
主要成果:
- 模型X仿制框架有效控制FDR,同时适应间接影响和非线性剂量反应.
- 观察到的FDR在与黄金标准比较时超过报告的FDR,突出显示了未测量的混的影响.
- 该方法在推断因果TRN方面表现出更高的准确性.
结论:
- 模型X模仿提供了一个强大的方法来提高因果TRN推断的可靠性.
- 不测量的混仍然是TRN推断的一个重大挑战,需要进一步的方法开发.
- 这项研究为了解基因调节机制提供了更准确的框架.
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