代谢组学数据分析方法及在糖尿病研究中的应用

A New Data Processing Method for Metabonomic and Its Application in a Study of Diabetes

对NMR波谱数据的统计分析是基于NMR代谢组学研究的关键问题之一.鉴于NMR波谱信号可以近似为样品中各种成分谱信号的线性叠加,本文将非负矩阵分解(NMF)方法引入基于NMR代谢组学的数据处理中,并与代谢组学中常用的统计方法——主成分分析(PCA)进行比较.通过NMF和PCA两种方法对健康志愿者与2型糖尿患者血液和尿液的NMR谱图的统计分析,对所获取的特征代谢物进行比较和验证,并探讨了PCA方法可能存在的不足之处及其原因;阐明了NMF方法是基于NMR的代谢组学研究中较理想的数据分析方法.最后,讨论了基于NMR代谢组学在糖尿病研究中的前景.

Multivariate statistical methods are frequently used in nuclear magnetic resonance （NMR）-based metabonomic researches to analyze NMR spectra of biofluids. Based on the fact that the NMR spectrum of a given sample are a sum of the NMR signals from all constituting ingredients, we developed a non-negative matrix factorization （NMF） method, capable of finding parts-based and linear representations of non-negative data, for analyzing the data acquired in NMR-based metabonomic studies. Detail comparisons were made between the NMF method and the commonly use principal component analysis （PCA） method by employing the two methods to discriminate the urine and serum spectra of type-2 diabetic patients from those of the healthy controls. It was shown that, compared to the PCA method, the NMF method is a more effective and ac- curate method for processing NMR spectra acquired in the metabonomic studies, partially due to its unique features such as the non-negative constraints and part-based representation. The disadvantages of the PCA method were also analyzed and discussed.