Development and validation of an improved probabilistic quotient normalization method for LC/MS- and NMR-based metabonomic analysis

Abstract

Robust normalization is a prerequisite for reliable metabonomic analysis especially when intervention treatments cause drastic metabolomic changes or when spot urinary samples are employed without knowing the drinking water quantity. With the simulated and real datasets, here, we report a probabilistic quotient normalization method based on the mode-of-quotients (mPQN) which is suitable for metabonomic analysis of both NMR and LC–MS data with little and/or drastic metabolite changes. When applied to metabonomic analysis of both animal plasma samples and human urinary samples, this newly proposed method has clearly shown better robustness than all classical normalization methods especially when drastic changes of some metabolites occur.