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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 10:271 (2020)
Verlagsversion Forschungsdaten DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
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.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Data Normalization ; Gaussian Graphical Models ; Glycomics; Compositional Data-analysis; Glycosylation; Allotypes; Discovery; Model
Sprache englisch
Veröffentlichungsjahr 2020
HGF-Berichtsjahr 2020
ISSN (print) / ISBN 2218-1989
e-ISSN 2218-1989
Zeitschrift Metabolites
Quellenangaben Band: 10, Heft: 7, Seiten: , Artikelnummer: 271 Supplement: ,
Verlag MDPI
Verlagsort St Alban-anlage 66, Ch-4052 Basel, Switzerland
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Computational Biology (ICB)
POF Topic(s) 30205 - Bioengineering and Digital Health
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-554100-001
G-503800-001
DOI 10.3390/metabo10070271
WOS ID WOS:000556424600001
Scopus ID 85090752872
PubMed ID 32630764
Erfassungsdatum 2020-07-09
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