Institute of Clinical Chemistry, Inselspital, Bern University Hospital, 3010 Bern, Switzerland.
Biomolecules. 2023 Jan 27;13(2):244. doi: 10.3390/biom13020244.
Over the past decades, pathway analysis has become one of the most commonly used approaches for the functional interpretation of metabolomics data. Although the approach is widely used, it is not well standardized and the impact of different methodologies on the functional outcome is not well understood. Using four publicly available datasets, we investigated two main aspects of topological pathway analysis, namely the consideration of non-human native enzymatic reactions (e.g., from microbiota) and the interconnectivity of individual pathways. The exclusion of non-human native reactions led to detached and poorly represented reaction networks and to loss of information. The consideration of connectivity between pathways led to better emphasis of certain central metabolites in the network; however, it occasionally overemphasized the hub compounds. We proposed and examined a penalization scheme to diminish the effect of such compounds in the pathway evaluation. In order to compare and assess the results between different methodologies, we also performed over-representation analysis of the same datasets. We believe that our findings will raise awareness on both the capabilities and shortcomings of the currently used pathway analysis practices in metabolomics. Additionally, it will provide insights on various methodologies and strategies that should be considered for the analysis and interpretation of metabolomics data.
在过去的几十年中，途径分析已成为代谢组学数据功能解释中最常用的方法之一。尽管该方法被广泛使用，但它尚未得到很好的标准化，并且不同方法对功能结果的影响也未得到很好的理解。我们使用了四个公开可用的数据集，研究了拓扑途径分析的两个主要方面，即考虑非人类天然酶反应（例如，来自微生物群）和个体途径的互联性。排除非人类天然反应会导致反应网络分离和代表性差，并导致信息丢失。考虑途径之间的连通性会导致网络中某些中心代谢物得到更好的强调；但是，它偶尔会过分强调枢纽化合物。我们提出并检查了一种惩罚方案，以减少途径评估中此类化合物的影响。为了在不同方法之间比较和评估结果，我们还对相同数据集执行了过表达分析。我们相信，我们的发现将提高人们对代谢组学中当前使用的途径分析实践的能力和局限性的认识。此外，它还将提供有关代谢组学数据分析和解释应考虑的各种方法和策略的见解。
