Variable scaling is an important data pre-processing step in NMR metabolomics, especially for biomarkers identification. It aims to make the subsequent multivariate analysis more reliable and easier by highlighting the biomarkers-related variables, and reducing the contamination of the noise and irrelevant variables. A new scaling method is proposed in this paper. The proposed method adjusts the weight of variables by their significance and stabilities in order to enhance the variable probably related to signature metabolites. Both of simulated dataset and real metabolomic dataset are used to estimate the performance of the proposed method. Comparing with Unit variance (UV), Variable stability (VAST) and Level scaling (LS) methods, the new scaling method would be robust to preserve molecular information of NMR spectra, improving the predictive ability of multivariate statistical model and making the results of subsequent analysis more interpretable. Therefore, the method proposed herein is more suitable for biomarker identification.
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