Evaluation of normalization methods to pave the way towards large-scale LC-MS-based metabolomics profiling experimentsEvaluation of normalization methods to pave the way towards large-scale LC-MS-based metabolomics profiling experiments
Faculty of Sciences. Biology
Faculty of Pharmaceutical, Biomedical and Veterinary Sciences. Pharmacy
Faculty of Pharmaceutical, Biomedical and Veterinary Sciences . Biomedical Sciences
Systemic Physiological and Ecotoxicological Research (SPHERE)
Department of Biomedical Sciences - other
Medicinal Chemistry (UAMC)
Laboratory for Microbiology, Parasitology and Hygiene (LMPH)
Biomolecular & Analytical Mass Spectrometry (BAMS)
2013Larchmont, N.Y., 2013
Engineering sciences. Technology
OMICS: a journal of integrative biology. - Larchmont, N.Y.
17(2013):9, p. 473-485
University of Antwerp
Combining liquid chromatography-mass spectrometry (LC-MS)-based metabolomics experiments that were collected over a long 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 article, 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 the removal of 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.