ArticleMetabolites2020
Systematic Evaluation of Normalization Methods for Glycomics Data Based on Performance of Network Inference.
Article in Metabolites, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Navigating the glycomics landscape with CE-MS: advances in sample preparation and analytical strategies.The Analyst · 2026Review
- Collection and Lipidomic Analysis of Murine Knee Synovium and Infrapatellar Fat Pad.Methods and protocols · 2026Article
- A genome-wide association study in 10,000 individuals links plasma N-glycome to liver disease and anti-inflammatory proteins.Nature communications · 2025Article
- NEU1-Mediated Extracellular Vesicle Glycosylation in Alzheimer's Disease: Mechanistic Insights into Intercellular Communication and Therapeutic Targeting.Pharmaceuticals (Basel, Switzerland) · 2025Review
- Machine learning framework to extract the biomarker potential of plasma IgG N-glycans towards disease risk stratification.Computational and structural biotechnology journal · 2024Article
- The Omega-6 Lipid pathway shift is associated with neutrophil influx and structural lung damage in early cystic fibrosis lung disease.Clinical & translational immunology · 2024Article
- Multinozzle Emitter for Improved Negative Mode Analysis of Reduced NativeAnalytical chemistry · 2024Article
- Workflow for Evaluating Normalization Tools for Omics Data Using Supervised and Unsupervised Machine Learning.Journal of the American Society for Mass Spectrometry · 2023Article
- Association of Arachidonic Acid-Derived Lipid Mediators With Disease Severity in Patients With Relapsing and Progressive Multiple Sclerosis.Neurology · 2023Article
- High-Throughput Glycomic Methods.Chemical reviews · 2022Review
- Heat Stress of Algal Partner Hinders Colonization Success and Alters the Algal Cell Surface Glycome in a Cnidarian-Algal Symbiosis.Microbiology spectrum · 2022Article
- Correcting for sparsity and interdependence in glycomics by accounting for glycan biosynthesis.Nature communications · 2021Article
- The local-balanced model for improved machine learning outcomes on mass spectrometry data sets and other instrumental data.Analytical and bioanalytical chemistry · 2021Article
- Intergenerational Metabolomic Analysis of Mothers with a History of Gestational Diabetes Mellitus and Their Offspring.International journal of molecular sciences · 2020Article
- Mass spectrometry in clinical glycomics: The path from biomarker identification to clinical implementation.Clinical mass spectrometry (Del Mar, Calif.) · 2020Review
Corrections and comments
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Authors and funding
17 authors.
Funding
Abstract
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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