Sample description of type 2 diabetes mellitus rats metabonomics data by using multivariate data mapping methods

摘要

Metabonomics is widely used in the study of Type 2 Diabetes Mellitus, it is important to extract information from the metabonomics data. In this article we introduced four mapping methods for sample description. Type 2 Diabetes mellitus Model was made on rats, and Rosiglitazone was used as positive drug. Data obtained by using UPLC-Q-TOF/MS method were classified into three groups (normal control group, DM group, Treatment group). R software was used for Principal Component Analysis (PCA), Sammon mapping, Kruskal-Wallis mapping (isoMDS) and Independent Component Analysis (ICA). The results showed that isoMDS and PCA methods got better values than the rest methods, meanwhile ICA plot did not show any useful information. These results demonstrate the capacity of multivariate data mapping methods in metabonomics data.