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

Abstract

<jats:p>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.</jats:p>

Intergenerational Metabolomic Analysis of Mothers with a History of Gestational Diabetes Mellitus and Their Offspring

“…Metabolite concentrations of mothers and their offspring were quotient normalized and log-transformed, as has been recommended for normalizing omics data [ 14 , 40 ]. To depict the metabolic response to glucose challenge, the area under the curve (AUC) was calculated for each maternal and offspring metabolite from metabolite concentrations obtained at 0, 30, and 120 min during the OGTT.…”

Heat Stress of Algal Partner Hinders Colonization Success and Alters the Algal Cell Surface Glycome in a Cnidarian-Algal Symbiosis

“…Absolute abundance was normalized by probabilistic quotient normalization (PQN) without prior total area normalization, as described by Benedetti et al ( 55 ). PQN is a robust method for normalizing mass spectrometric data and has explicitly proven useful for glycomics ( 55 – 57 ). First, a reference spectrum is generated by calculating the median value of each glycan’s absolute abundance from every sample.…”

High-Throughput Glycomic Methods

“…Only recently, a systematic evaluations of other normalization methods for glycomics data, in addition to total area normalization, have been reported. 132,133 5.1.3. Peak Assignment/Glycan Identification.…”

“…After manual or automatic integration, total area normalization is usually used to extract glycan amounts as relative percentage areas (%area) used for further analysis, followed by batch correction and statistical analysis. Only recently, a systematic evaluations of other normalization methods for glycomics data, in addition to total area normalization, have been reported. , …”