构成对构成回归分析用于多omics集成的元基因组数据
Nicholas Rios1, Yuke Shi2, Jun Chen3
1Department of Statistics, George Mason University, Fairfax, VA 22030, United States.
Bioinformatics (Oxford, England)
|July 12, 2025
概括
我们介绍了一种新的构成对构成 (COC) 回归方法,用于分析高维构成数据,有效地处理零,而无需逻辑比转换. 这种方法在多学科研究中显示出卓越的表现.
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:
- 在生物医学研究中,组合数据分析至关重要,特别是在下一代测序方面.
- 现有的回归方法在高维组合数据上扎,特别是当零存在时.
- 现有方法中常见的逻辑比转换无法适应在测序数据中经常发现的零值.
研究的目的:
- 开发一种可靠的回归方法,用于分析两个高维组成数据集之间的关联.
- 解决现有方法在处理零值组成数据方面的局限性.
- 为多omics组成数据分析提供一个工具.
主要方法:
- 提出一种新的构成对构成 (COC) 回归分析.
- 使用处罚估计方程来处理高维度.
- 为拟议的COC回归模型开发推理程序.
- 避免了逻辑比转换,使得包括零在内的组成数据的直接分析成为可能.
主要成果:
- 拟议的COC回归方法有效地分析高维组成数据.
- 该方法成功处理含有零的数据集,这是下一代测序中常见的问题.
- 通过广泛的数值模拟和现实世界的案例研究,证明了卓越的性能.
结论:
- 构成对构成 (COC) 回归为高维构成数据分析提供了一个强大的新工具.
- COC回归克服了现有方法的关键局限性,特别是在处理零值时.
- 这种方法非常适合研究生物医学研究中的多omics组合数据中的关联.
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