Next Article in Journal
Metabolic Signatures of 10 Processed and Non-processed Meat Products after In Vitro Digestion
Previous Article in Journal
Serum Metabolomic Alterations Associated with Cesium-137 Internal Emitter Delivered in Various Dose Rates
Article

Systematic Evaluation of Normalization Methods for Glycomics Data Based on Performance of Network Inference

1
Department of Physiology and Biophysics, Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY 10022, USA
2
Institute of Computational Biology, Helmholtz Zentrum München—German Research Center for Environmental Health, 85764 Neuherberg, Germany
3
Max Planck Institute for Psychiatry, 80804 Munich, Germany
4
Genos Glycoscience Research Laboratory, 10000 Zagreb, Croatia
5
Faculty of Pharmacy and Biochemistry, University of Zagreb, 10000 Zagreb, Croatia
6
Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, 3584 CH Utrecht, The Netherlands
7
Center for Proteomics and Metabolomics, Leiden University Medical Center, 2333 ZC Leiden, The Netherlands
8
Department of Clinical Chemistry and Laboratory Medicine, Leiden University Medical Center, 2333 ZC Leiden, The Netherlands
9
Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh EH8 9AG, UK
10
Medical School, University of Split, 21000 Split, Croatia
11
Gen-Info Ltd., 10000 Zagreb, Croatia
12
Medical Research Council Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh EH4 2XU, UK
13
Section of Molecular Epidemiology, Leiden University Medical Center, 2333 ZC Leiden, The Netherlands
14
Colon Cancer Genetics Group, Institute of Genetics and Molecular Medicine, University of Edinburgh and Medical Research Council Human Genetics Unit, Edinburgh EH8 9YL, UK
*
Author to whom correspondence should be addressed.
Metabolites 2020, 10(7), 271; https://doi.org/10.3390/metabo10070271
Received: 21 April 2020 / Revised: 29 May 2020 / Accepted: 4 June 2020 / Published: 2 July 2020
(This article belongs to the Section Bioinformatics and Data Analysis)
Glycomics measurements, like all other high-throughput technologies, are subject to technical variation due to fluctuations in the experimental conditions. The removal of this non-biological signal from the data is referred to as normalization. Contrary to other omics data types, a systematic evaluation of normalization options for glycomics data has not been published so far. In this paper, we assess the quality of different normalization strategies for glycomics data with an innovative approach. It has been shown previously that Gaussian Graphical Models (GGMs) inferred from glycomics data are able to identify enzymatic steps in the glycan synthesis pathways in a data-driven fashion. Based on this finding, here, we quantify the quality of a given normalization method according to how well a GGM inferred from the respective normalized data reconstructs known synthesis reactions in the glycosylation pathway. The method therefore exploits a biological measure of goodness. We analyzed 23 different normalization combinations applied to six large-scale glycomics cohorts across three experimental platforms: Liquid Chromatography-ElectroSpray Ionization-Mass Spectrometry (LC-ESI-MS), Ultra High Performance Liquid Chromatography with Fluorescence Detection (UHPLC-FLD), and Matrix Assisted Laser Desorption Ionization-Furier Transform Ion Cyclotron Resonance-Mass Spectrometry (MALDI-FTICR-MS). Based on our results, we recommend normalizing glycan data using the ‘Probabilistic Quotient’ method followed by log-transformation, irrespective of the measurement platform. This recommendation is further supported by an additional analysis, where we ranked normalization methods based on their statistical associations with age, a factor known to associate with glycomics measurements. View Full-Text
Keywords: glycomics; data normalization; gaussian graphical models glycomics; data normalization; gaussian graphical models
Show Figures

Figure 1

MDPI and ACS Style

Benedetti, E.; Gerstner, N.; Pučić-Baković, M.; Keser, T.; Reiding, K.R.; Ruhaak, L.R.; Štambuk, T.; Selman, M.H.J.; Rudan, I.; Polašek, O.; Hayward, C.; Beekman, M.; Slagboom, E.; Wuhrer, M.; Dunlop, M.G.; Lauc, G.; Krumsiek, J. Systematic Evaluation of Normalization Methods for Glycomics Data Based on Performance of Network Inference. Metabolites 2020, 10, 271. https://doi.org/10.3390/metabo10070271

AMA Style

Benedetti E, Gerstner N, Pučić-Baković M, Keser T, Reiding KR, Ruhaak LR, Štambuk T, Selman MHJ, Rudan I, Polašek O, Hayward C, Beekman M, Slagboom E, Wuhrer M, Dunlop MG, Lauc G, Krumsiek J. Systematic Evaluation of Normalization Methods for Glycomics Data Based on Performance of Network Inference. Metabolites. 2020; 10(7):271. https://doi.org/10.3390/metabo10070271

Chicago/Turabian Style

Benedetti, Elisa, Nathalie Gerstner, Maja Pučić-Baković, Toma Keser, Karli R. Reiding, L. R. Ruhaak, Tamara Štambuk, Maurice H.J. Selman, Igor Rudan, Ozren Polašek, Caroline Hayward, Marian Beekman, Eline Slagboom, Manfred Wuhrer, Malcolm G. Dunlop, Gordan Lauc, and Jan Krumsiek. 2020. "Systematic Evaluation of Normalization Methods for Glycomics Data Based on Performance of Network Inference" Metabolites 10, no. 7: 271. https://doi.org/10.3390/metabo10070271

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop