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.

中文翻译：

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基于网络推理性能的糖组学数据标准化方法的系统评估。


与所有其他高通量技术一样，糖组学测量也会因实验条件的波动而受到技术变化的影响。从数据中去除这种非生物信号称为标准化。与其他组学数据类型相反，迄今为止尚未发表对糖组学数据标准化选项的系统评估。在本文中，我们采用创新方法评估糖组数据不同标准化策略的质量。先前已经表明，从糖组学数据推断的高斯图形模型（GGM）能够以数据驱动的方式识别聚糖合成途径中的酶促步骤。基于这一发现，我们根据从各自标准化数据推断的 GGM 重建糖基化途径中已知合成反应的程度来量化给定标准化方法的质量。因此，该方法利用了生物学的善良衡量标准。我们分析了跨三个实验平台应用于六个大规模糖组学队列的 23 种不同的归一化组合：液相色谱 - 电喷雾电离 - 质谱 (LC-ESI-MS)、超高效液相色谱荧光检测 (UHPLC-FLD) 和基质辅助激光解吸电离 – 傅里叶变换离子回旋共振 – 质谱 (MALDI-FTICR-MS)。根据我们的结果，我们建议使用“概率商”方法对聚糖数据进行标准化，然后进行对数转换，无论测量平台如何。 这一建议得到了另一项分析的进一步支持，其中我们根据标准化方法与年龄的统计关联对标准化方法进行了排名，年龄是已知与糖组学测量相关的因素。