多次测量与潜伏轨迹的稀疏正规相关性分析
Nuria Senar1, Aeilko H Zwinderman1, Michel H Hof1
1Department of Epidemiology & Data Science, Amsterdam School of Public Health, Amsterdam UMC, Amsterdam, The Netherlands.
Biometrical journal. Biometrische Zeitschrift
|October 30, 2025
概括
这项研究引入了一种新的稀疏法定相关性分析 (CCA) 方法,用于分析高维欧米数据中的重复测量. 这种新的方法有效地建模了时间动态,提供了可解释的纵向轨迹,并减少了计算时间.
科学领域:
- 多变量统计学 多变量统计学
- 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:
- 规范相关性分析 (CCA) 通过识别观察到的特征之间的相关性来整合高维的奥米克数据集.
- 标准CCA需要独立的观察,限制其使用重复或纵向测量.
- 现有的CCA扩展用于重复测量对于高维数据和纵向分析是不理想的.
研究的目的:
- 开发一种稀疏CCA的新型扩展,其中包含时间动态,用于分析高维纵向数据.
- 为了解决标准CCA在处理相关重复测量的局限性.
- 为了提高可解释性和计算效率的CCA在奥米克研究.
主要方法:
- 提出了一种新的稀疏CCA扩展,使用纵向模型在潜变量水平上结合时间动态.
- 针对固定的稀疏度级别实施了$\ell _0$的罚款,提高了可解释性和计算效率.
- 通过将模型配合低维潜变量来估计纵向轨迹,利用集群数据结构.
主要成果:
- 新的CCA方法有效地处理重复测量,并将时间动态纳入高维数据集.
- 该方法提供了可解释的纵向轨迹,揭示了共享的潜在机制.
- 与高维分析的现有方法相比,显著减少了计算时间.
结论:
- 拟议的CCA方法提供了一个高效和可解释的解决方案,用于分析高维的纵向奥米克数据,并进行重复测量.
- 这种方法可以估计对集群数据的测量中的正规相关性,捕捉时间动态.
- 这种方法适用于稀疏和不规则地观察到的数据,正如人类微生物组项目的数据所示.
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