Normalization of large-scale mass spectrometry-based metabolic profiling experiments

Abstract

Combining liquid chromatography mass spectrometry (LC-MS) based metabolomics experiments that were collected over a longer period of time remains problematic due to systematic variability between LC-MS measurements. Until now, most normalization methods for LC-MS data are model-driven based on internal standards or intermediate quality control runs, where an external model is extrapolated to the dataset of interest. In the first part of this manuscript, we evaluate several existing data-driven normalization approaches on LC-MS metabolomics experiments, which do not require the use of internal standards. According to variability measures, each normalization method performs relatively well, showing that the use of any normalization method will greatly improve data-analysis originating from multiple experimental runs . In the second part, we apply cyclic-Loess normalization to a Leishmania sample. This normalization method allows to remove the systematic variability between two measurement blocks over time and maintains the differential metabolites. In conclusion, normalization allows for pooling datasets from different measurement blocks over time and increases the statistical power of the analysis, hence paving the way to increase the scale of LC-MS metabolomics experiments. From our investigation, we recommend data-driven normalization methods over model-driven normalization methods if only a few internal standards were used. Moreover, data-driven normalization methods are the best option to normalize datasets from untargeted LC-MS experiments.