通过单变量或多变量分析方法寻找差异代谢物一直是临床代谢组学研究的主流策略。然而，由于代谢物和代谢通路存在多种正/负反馈调节作用，并且疾病本身也是一个动态发展的过程，基于单代谢物的差异分析策略已经很难满足临床样本的分析需求。代谢系统的复杂性、多面性以及疾病特征的动态性是当下临床代谢组学研究所面临的新挑战。本项目拟通过探索适合临床代谢组学的生物信息学分析方法，基于代谢物之间的相关关系设计共变化网络的分析策略，用于疾病的代谢特征提取。本项目的预期成果不仅有助于深入了解代谢机制，而且为临床代谢组学研究提供关键技术支持，具有重要理论研究和实际应用价值。

Looking for differential metabolites through univariate or multivariate statistical analysis has been the main strategy in clinical metabolomics researches. However, since there are various cis-/trans- regulations within metabolites and metabolic pathways, and the disease itself is a dynamic developing process, difference analysis strategy based on individual metabolite has been limited in clinical analysis. The complex and multi-view metabolic system and disease’s dynamic features are new challenges for current researches in clinical metabolomics. The present project intends to explore bioinformatics methods suitable for clinical metabolomics. Covariation networks will be designed based on the correlation between metabolites. Meanwhile, manual annotation method depending on subjective experience will be changed. Convenient and effective annotation technologies and causal analysis methods will be developed to infer abnormal metabolism. Expected outcomes of this project will not only help deeply understand the metabolic mechanisms, but also provide key technical support for clinical metabolomics, which has important theoretical and practical value.