在基于约束的差异代谢流量分析中为错误发现进行调整
Bruno G Galuzzi1, Luca Milazzo2, Chiara Damiani3
1Department of Biotechnology and Biosciences, University of Milano-Bicocca, Piazza dell'Ateneo Nuovo, 1, Milan, 20126, Italy; Institute of Molecular Bioimaging and Physiology (IBFM), Segrate, 20054, Italy; SYSBIO Centre of Systems Biology/ ISBE.IT, Milan, Italy.
Journal of biomedical informatics
|January 25, 2024
概括
识别代谢流差异是疾病研究的关键. 这项研究表明,一种新的基于角的采样方法减少了代谢网络分析中的错误发现,提高了准确性.
科学领域:
- 系统生物学 系统生物学
- 代谢网络分析代谢网络分析
- 计算生物学 计算生物学
背景情况:
- 在多因素疾病中表征代谢变化需要确定患者队列之间的显著流量差异.
- 基于约束的代谢网络模型用于估计代谢流动,随机抽样成为网络比较的普遍.
研究的目的:
- 在代谢网络分析中研究不同采样策略对虚假发现率 (FDR) 的影响.
- 开发和评估一种新的采样策略,以便进行更强大,更可重复的代谢网络比较.
主要方法:
- 对比了各种采样算法 (撞击和跑步与基于角落的) 和代谢网络模型的参数.
- 评估算法访问基于约束区域的所有顶点的能力.
- 利用基于Kullback-Leibler分歧的实证零分布的统计测试来纠正错误的发现.
主要成果:
- 在小规模的代谢网络中,即使在大样本中,也可能出现高错误发现率.
- 一个有效地纠正错误发现的统计测试.
- 与撞击逃跑策略相比,拟议的基于角的算法显示出更高的效率和更低的FDR.
结论:
- 采样策略在代谢网络分析中显著影响FDR.
- 新的基于角的算法提供了更强大的方法来识别代谢变化.
- 这项研究为更可重复的代谢网络分析提供了指导方针.
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