Department of Chemistry and Molecular Biology, University of Gothenburg, 41390 Gothenburg, Sweden; Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, 41390 Gothenburg, Sweden.
Department of Chemistry and Molecular Biology, University of Gothenburg, 41390 Gothenburg, Sweden; Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, 41390 Gothenburg, Sweden.
Cell Rep Methods. 2023 Dec 18;3(12):100652. doi: 10.1016/j.crmeth.2023.100652. Epub 2023 Nov 21.
Glycomics, the comprehensive profiling of all glycan structures in samples, is rapidly expanding to enable insights into physiology and disease mechanisms. However, glycan structure complexity and glycomics data interpretation present challenges, especially for differential expression analysis. Here, we present a framework for differential glycomics expression analysis. Our methodology encompasses specialized and domain-informed methods for data normalization and imputation, glycan motif extraction and quantification, differential expression analysis, motif enrichment analysis, time series analysis, and meta-analytic capabilities, synthesizing results across multiple studies. All methods are integrated into our open-source glycowork package, facilitating performant workflows and user-friendly access. We demonstrate these methods using dedicated simulations and glycomics datasets of N-, O-, lipid-linked, and free glycans. Differential expression tests here focus on human datasets and cancer vs. healthy tissue comparisons. Our rigorous approach allows for robust, reliable, and comprehensive differential expression analyses in glycomics, contributing to advancing glycomics research and its translation to clinical and diagnostic applications.
糖组学是对样本中所有聚糖结构的全面分析，它正在迅速发展，以使我们能够深入了解生理和疾病机制。然而，聚糖结构的复杂性和糖组学数据分析带来了挑战，特别是在差异表达分析方面。在这里，我们提出了一个用于差异糖组学表达分析的框架。我们的方法包括专门的和领域知情的方法，用于数据标准化和插补、糖基 motif 提取和定量、差异表达分析、motif 富集分析、时间序列分析和荟萃分析功能，综合了多个研究的结果。所有方法都集成到我们的开源 glycowork 包中，便于高性能工作流程和用户友好的访问。我们使用专门的模拟和 N-、O-、脂联和游离聚糖的糖组学数据集来演示这些方法。这里的差异表达测试主要集中在人类数据集和癌症与健康组织的比较上。我们的严格方法允许在糖组学中进行稳健、可靠和全面的差异表达分析，有助于推进糖组学研究及其向临床和诊断应用的转化。
