一个多omics框架的生存调解分析高维的蛋白质基因组数据的生存调解
Seungjun Ahn1,2, Weijia Fu1,2, Maaike van Gerwen3
1Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, U.S.A.
ArXiv
|March 31, 2025
概括
我们介绍SMAHP,这是一个新的生存调解分析方法,集成多omics数据用于高维暴露. SMAHP使用加速失效时间模型来识别影响生存结果的因果途径.
科学领域:
- 生物统计学 生物统计学
- 基因组学就是基因组学.
- 蛋白质组学是指蛋白质组学.
背景情况:
- 生存分析对于疾病进展等时间到事件结果至关重要.
- 目前用于生存分析的因果调解方法经常使用考克斯回归,限制了多omics数据的整合,忽视了相互作用.
- 这限制了从综合数据中利用全面的生物见解.
研究的目的:
- 提出SMAHP,一种用于生存介导分析的新方法.
- 同时处理高维曝光和介质.
- 将多学科数据集成到一个强大的统计框架中,以确定对生存结果的因果关系.
主要方法:
- 在多omics因果调解框架内引入加速失效时间 (AFT) 模型.
- 开发SMAHP用于同时分析高维暴露和介质.
- 通过模拟和应用到现实世界的蛋白质基因组数据来验证.
主要成果:
- 在模拟中,SMAHP表现出高的统计能力和有效的错误发现率 (FDR) 控制.
- 该方法成功地确定了影响头癌生存的基因-蛋白质调解途径.
- 这凸显了SMAHP在分析复杂的多omics生存数据中的实用性.
结论:
- SMAHP提供了一个强大的统计框架,用于多omics生存介导分析.
- 该方法有效地整合了高维数据,并确定了因果途径.
- SMAHP通过结合多omics互动,推进了时间到事件结果的分析.
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