使用RM-ASCA使用复杂时间动态的高维代谢学数据的分析
Balázs Erdős1, Johan A Westerhuis2, Michiel E Adriaens1
1Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, The Netherlands.
PLoS computational biology
|June 23, 2023
概括
从纵向研究中分析复杂的生物"omics"数据是具有挑战性的. 本研究在重复测量ANOVA同时组件分析+ (RM-ASCA+) 框架内引入了一种定量纵向模型,以有效量化多变量结果中的动态.
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.
- 系统生物学 系统生物学
背景情况:
- 从纵向干预研究中获得的生物"omics"数据,由于高度的维度和复杂的相互关系,存在重大分析挑战.
- 量化微妙的动态差异,在营养研究中至关重要,是特别困难的传统方法.
- 现有的方法难以捕捉复杂的依赖结构和重复测量数据中常见的多变量结果.
研究的目的:
- 为了证明RM-ASCA+框架内定量纵向模型的实用性,用于分析频繁采样的纵向"omics"数据.
- 为系统量化和总结纵向研究中的多变量结果提供一种方法.
- 在复杂的生物数据集中考虑主体内部的依赖结构.
主要方法:
- 应用重复测量ANOVA同时组件分析+ (RM-ASCA+) 框架.
- 线性混合模型与时间变量的多项式和分线基础扩展的集成.
- 利用模拟研究和现实世界代谢学数据集进行验证.
主要成果:
- 拟议的方法有效地捕捉了频繁采样的纵向数据中的动态,具有多变量结果.
- 使用线性混合模型成功量化了非线性动态,在RM-ASCA+中使用了基底扩展的线性混合模型.
- 该方法提供了一种方便和可解释的方式来分析复杂的纵向"omics"数据.
结论:
- 通过线性混合模型增强的RM-ASCA+框架,为分析纵向"omics"数据提供了强大的解决方案.
- 这种方法有助于系统量化和总结多变量结果,同时尊重数据依赖性.
- 这些发现可以从复杂的生物干预研究中获得更有意义的见解.
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