用双重机器学习分析多omics数据中的关联和更高阶效应
Julian Hecker1, Dmitry Prokopenko2, Georg Hahn3,4
1Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
bioRxiv : the preprint server for biology
|December 22, 2025
概括
本研究介绍了强大的OMICS方法论 (ROMY) 框架,用于整合的OMICS分析. 在复杂的生物数据中,ROMY提供了用于关联测试,方差分析和相互作用效应的强大方法.
科学领域:
- 生物统计学 生物统计学
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
背景情况:
- 综合性奥米克分析对于了解疾病机制和识别生物标志物至关重要.
- 这些分析面临的统计挑战是由于高维度,非标准数据和复杂的混效应.
研究的目的:
- 引入强大的OMICS方法论 (ROMY) 框架及其R包 (romy) 进行先进的多omics数据集成和分析.
- 提供强大而灵活的方法来进行关联测试,差异分析和对OMIC数据的相互作用效应.
主要方法:
- 强大的奥米克斯方法论 (ROMY) 框架的开发.
- 在一个名为"romy"的R包中实现ROMY.
- 利用理论统计和双重机器学习来实现稳定性和统计有效性.
主要成果:
- 罗米框架允许通过灵活的协变量调整进行强大的关联测试.
- 它允许对测量差异和共差的影响进行检查 (例如,共同表达,共同丰富).
- 罗米促进了严格的相互作用效应测试.
结论:
- 罗米框架为复杂的整合性奥米克分析提供了强大的解决方案.
- "romy" R包为研究人员提供了可访问的工具,以应用这些先进的统计方法.
- 这种方法提高了多omics数据解释的统计有效性和灵活性.
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