在代谢时间过程数据-A非线性B-Spline混合效应方法中分离系统偏差和噪声的综合模型
Kathy Sharon Isaac1, Stanislav Sokolenko1
1Department of Process Engineering and Applied Science, Dalhousie University, Halifax, Canada.
Biotechnology and bioengineering
|May 9, 2025
概括
这项研究引入了一种新的方法来纠正代谢时间过程数据中的系统偏差. 非线性B-spline混合效应模型准确地纠正偏差,改善代谢物量化,并使更广泛的研究应用成为可能.
科学领域:
- 代谢学 代谢学 代谢学
- 系统生物学 系统生物学
- 生物统计学 生物统计学
背景情况:
- 代谢时间过程样本允许同时检测多种代谢物.
- 个体代谢物分析可以将测量噪声与系统偏差混为一谈.
- 系统偏差影响样本内的所有代谢物.
研究的目的:
- 开发一种方法来识别和纠正代谢时间过程数据中的系统偏差.
- 在时间过程实验中提高代谢物量化的准确性.
- 为跨研究领域的偏见纠正提供一个可通用的模型.
主要方法:
- 制定了一个非线性B-spline混合效应模型,用于同时测试所有检测到的代谢物.
- 该模型应用于真实细胞培养物的代谢数据.
- 验证使用模拟的时间过程数据与受控噪声和偏差进行.
主要成果:
- 拟议的模型成功地估计和纠正了代谢时间过程数据中的系统偏差.
- 实现了3%-10%的系统偏差的准确校正,平均在0.5%以内.
- 开发了一个R包,以促进对偏差纠正模型的采用.
结论:
- 非线性B-spline混合效应模型提供了一种强大的方法来纠正代谢时间过程数据中的系统偏差.
- 这种方法提高了代谢物量化的可靠性.
- 该模型的通用性将其实用性扩展到细胞培养代谢学之外.
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