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局部样本凝聚力规范化:在代谢学数据中保持固有的生物学异质性

Fanjing Guo¹, Lingli Deng², Kian-Kai Cheng³

¹Department of Electronic Science, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen 361005, China.

Analytical chemistry

|December 23, 2025

概括

局部样本凝聚力规范化 (LSCN) 通过保持生物异质性来解决代谢学中的稀释效应. 与传统技术相比,这种新的方法提高了数据分析的可靠性.

科学领域:

代谢学代谢学代谢学
生物信息学是一种生物信息学.
数据科学数据科学数据科学

背景情况:

代谢学数据经常遭受稀释效应,混下游分析.
目前的规范化方法 (CSN,L2N,PQN,QT) 使用全球引用,未能保持生物异质性.
这种人造的统一性扭曲了生物数据结构,影响了分析结果.

研究的目的:

开发一种新的规范化方法,即局部样本凝聚力规范化 (LSCN),该方法可以纠正稀释效应,同时保持生物异质性.
为代谢学数据提供一个强大的,生物忠实的预处理框架.

主要方法:

在缩小尺寸的对相似性基础上,LSCN构建样本特定的邻居集.
规范化是在这些已识别的社区内本地进行的,以减轻技术偏差.
使用模拟和现实世界代谢学数据集与传统方法 (CSN,L2N,PQN,QT) 进行验证.

主要成果:

与传统方法相比,LSCN在保护异质性和生物信号方面表现优越.
提高了差异代谢物,相关性网络,途径丰富和分类准确性的识别.
有效地纠正稀释效应,产生更准确的正常化因子和减少组内变异.

结论:

LSCN提供了一种强大的,生物忠实的方法来进行代谢学数据预处理.
这种方法提高了下游分析的可靠性,通过准确地纠正稀释效应而不会失去生物变异.
在各种样本类型中,LSCN是推动代谢学研究的宝贵工具.

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