Non-negative principal component analysis for NMR-based metabolomic data analysis
Proton nuclear magnetic resonance (H-1-NMR) spectroscopy is one of the major analytical platforms used in metabolomics. The data acquired from NMR experiments are frequently processed using multivariate statistical methods such as principal component analysis (PCA) and partial least squares (PLS) to extract biologically meaningful information from complex spectra. Conventionally, these methods produce components with both positive and negative loadings, which contradict with the non-negativity of Fourier-transformed NMR spectra. In recent years, there is an increasing interest in incorporating non-negative constraints into multivariate methods. In the current study, a non-negative principal component analysis (NPCA) algorithm was introduced for the analysis of NMR-based metabolomic data. Using a simulated dataset, we showed that NPCA could reveal interesting local features in multivariate dataset, which are hidden in conventional PCA model. Notably, simulated peaks arising from a single compound were extracted by a same component in NPCA model. The current results also highlighted NPCA to be less susceptible to noise as compared to PCA. Furthermore, a supervised version of NPCA (sNPCA) was developed for class discrimination analysis, and it was used to identify urinary metabolites that distinguished hyperthyroid patients from healthy volunteers. Our results demonstrated that both NPCA and sNPCA could produce easily interpretable results and provide additional information to that of conventional projection methods. (C) 2012 Elsevier B.V. All rights reserved.
non-negative principal component analysis nmr-based metabolomics local feature representation k+1 rule multivariate data analysis