几种模式识别方法在大鼠高脂血症代谢组学研究中的应用

Application of several pattern recognition methods in metabonomic research of rat hyperlipidemia

目的:探讨不同的模式识别方法在分析大鼠高脂血症代谢组学实验数据中的应用。方法:采用高脂饲料喂养的方法,建立大鼠高脂血症模型。同时,以GC/MS为技术基础的代谢组学研究大鼠高脂血症的发病过程,并对图谱进行分析,将分离得到内源性代谢物,分别应用主成分分析(PCA)、软独立建模分类法(SIMCA)、非线性映射(NLM)3种模式识别方法对数据进行分析。结果:SIMCA和NLM在样本分类的效果上优于PCA,且NLM可表现出样本的经时变化规律;而PCA和SIMCA能更好地解释与疾病相关的内源性代谢物的信息,3种方法所得的结果可以互相印证和补充。结论:在代谢组学研究中,可以运用多种模式识别方法对数据进行分析处理,以得到更可靠和全面的信息。

Aim:To investigate the application of several pattern recognition methods in metabonomic research of rat hyperlipide.Methods:The hyperlipidemia rat model was developed successfully by feeding with high fat diet for 4 weeks.During this period,the course of disease was studied by GC-MS-based metabonomic approach,and there are some endogenous metabolites identified in the plasma sample by chromatogram.Then,these endogenous metabolites were processed by three different pattern recognitions respectively,includin...