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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/15298

Title: Evaluation of normalization methods to pave the way towards large-scale LC-MS-based metabolomic profiling experiments
Authors: Ejigu, Bedilu Alamirie
Valkenborg, Dirk
Baggerman, Geert
Vanaerschot, Manu
Witters, Erwin
Dujardin, Jean-Claude
Burzykowski, Tomasz
Berg, Maya
Issue Date: 2013
Citation: OMICS-A JOURNAL OF INTEGRATIVE BIOLOGY, 17 (9), p. 473-485
Abstract: 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.
Notes: Reprint author: Ejigu, BA, Hasselt Univ, Interuniv Inst Biostat & Stat Bioinformat, Campus Diepenbeek,Bldg D, B-3590 Diepenbeek, Belgium. bedilu.ejigu@uhasselt.be
URI: http://hdl.handle.net/1942/15298
Link to publication: http://online.liebertpub.com/doi/abs/10.1089/omi.2013.0010
DOI: 10.1089/omi.2013.0010
ISI #: 000323820700003
ISSN: 1536-2310
Category: A1
Type: Journal Contribution
Validation: ecoom, 2014
Appears in Collections: Research publications

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