Probabilistic quotient’s work & pharmacokinetics’ contribution: countering size effect in metabolic time series measurements

Abstract

Metabolomic time course analyses of biofluids are highly relevant for clinical diagnostics. However, some sampling methods suffer from unknown sample sizes commonly known as size effects, which prevents absolute quantification of biomarkers. Recently, studies have developed mathematical post acquisition normalization methods to overcome these problems either by exploiting already known pharmacokinetic information or with statistics. Here we present an improved normalization method, MIX, that unifies the advantages of both approaches. It combines two normalization terms, one representing pharmacokinetic normalization (PKM) and one representing a popular statistical approach (PQN). To test the performance of MIX, we generated synthetic data closely resembling real finger sweat metabolome measurements. MIX was subsequently used for normalization of the synthetic data and we were able to show that it overcomes weaknesses of the two normalization strategies applied separately. Moreover, we validate our results by using real finger sweat metabolome data from literature. There, we were able to demonstrate that MIX is more robust than the normalization strategy originally used for the data set. In conclusion, the MIX method improves the reliability and robustness of biomarker measurements in finger sweat and other biofluids. Moreover, it has potential to pave the way for quantitative biomarker discovery and hypothesis generation from metabolic time course data.