Multivariate analysis of metabolomics data

Due to the huge number of samples, the complexity of the data information as well as the high degree of correlation between variables in the multidimensional data matrix of metabolomics information derived from NMR and MS methods, data information cannot be extracted using traditional univariate analysis method. Thus, the mining and refining of potential relevant information between metabolites from these massive data plays an important role in the subsequent finding of biomarker groups and the interpretation of biological significance. At the same time, the selection of appropriate data analysis methods is also crucial for the correct extraction of metabolomics information.