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大规模复合假设测试程序用于OMIC数据分析

Annaïg De Walsche^1,2, Franck Gauthier², Nathalie Boissot³

¹Mathématiques et Informatique Appliquées Paris-Saclay, AgroParisTech, INRAE, Université Paris-Saclay, 91120 Palaiseau, France.

NAR genomics and bioinformatics

|September 8, 2025

概括

新的qch_copula方法有效地使用总结统计数据测试复合假设,提高可扩展性并准确地检测跨多个特征或omics级别的联合关联.

科学领域:

遗传学遗传学是一个
统计遗传学统计遗传学
生物信息学是一种生物信息学.

背景情况:

用总结统计数据复合假设测试对于识别复杂的特征关联至关重要.
现有的方法面临着可扩展性问题,并且由于未解决的特征依赖,难以控制假阳性.

研究的目的:

介绍 qch_copula,一种用于复合假设测试的新方法.
为了提高关节关联模式的检测,同时保持强大的I型错误控制.
提高大规模遗传和奥米克数据分析的可扩展性.

主要方法:

混合模型与配方函数的集成,以模拟特征或奥米克级别之间的依赖关系.
为复合假设计算严格定义的P值开发一个强大的框架.
与使用全面模拟的八种最先进的方法进行基准测试.

主要成果:

qch_copula证明了对I型错误率的有效控制.
与现有方法相比,该方法显著提高了联合协会模式的检测.
在EM算法中减少内存使用,可以分析多达20个特征和10^5-10^6个标记.

结论:

qch_copula提供了一个可扩展和准确的解决方案,用于在大型遗传和奥米克数据集中的复合假设测试.
该方法有效地捕捉了特征依赖性,从而提高了统计能力和可靠性.
在人类和植物遗传学中得到验证,qch_copula作为R包qch.

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