OS-PCA: Orthogonal Smoothed Principal Component Analysis Applied to Metabolome Data

Abstract

Principal component analysis (PCA) has been widely used in metabolomics. However, it is not always possible to detect phenotype-associated principal component (PC) scores. Previously, we proposed a smoothed PCA for samples acquired with a time course or rank order, but hypothesis testing to select significant metabolite candidates was not possible. Here, we modified the smoothed PCA as an orthogonal smoothed PCA (OS-PCA) so that statistical hypothesis testing in OS-PC loadings could be performed with the same PC projections provided by the smoothed PCA. Statistical hypothesis testing is especially useful in metabolomics because biological interpretations are made based on statistically significant metabolites. We applied the OS-PCA method to two real metabolome datasets, one for metabolic turnover analysis and the other for evaluating the taste of Japanese green tea. The OS-PCA successfully extracted similar PC scores as the smoothed PCA; these scores reflected the expected phenotypes. The significant metabolites that were selected using statistical hypothesis testing of OS-PC loading facilitated biological interpretations that were consistent with the results of our previous study. Our results suggest that OS-PCA combined with statistical hypothesis testing of OS-PC loading is a useful method for the analysis of metabolome data.