Mildau Kevin, Ehlers Henry, Meisenburg Mara, Del Pup Elena, Koetsier Robert A, Torres Ortega Laura Rosina, de Jonge Niek F, Singh Kumar Saurabh, Ferreira Dora, Othibeng Kgalaletso, Tugizimana Fidele, Huber Florian, van der Hooft Justin J J
Bioinformatics Group, Wageningen University & Research, Wageningen, The Netherlands.
Visualization Group, Institute of Visual Computing and Human-Centered Technology, TU Wien, Vienna, Austria.
Nat Prod Rep. 2024 Dec 2. doi: 10.1039/d4np00039k.
Covering: 2014 to 2023 for metabolomics, 2002 to 2023 for information visualizationLC-MS/MS-based untargeted metabolomics is a rapidly developing research field spawning increasing numbers of computational metabolomics tools assisting researchers with their complex data processing, analysis, and interpretation tasks. In this article, we review the entire untargeted metabolomics workflow from the perspective of information visualization, visual analytics and visual data integration. Data visualization is a crucial step at every stage of the metabolomics workflow, where it provides core components of data inspection, evaluation, and sharing capabilities. However, due to the large number of available data analysis tools and corresponding visualization components, it is hard for both users and developers to get an overview of what is already available and which tools are suitable for their analysis. In addition, there is little cross-pollination between the fields of data visualization and metabolomics, leaving visual tools to be designed in a secondary and mostly fashion. With this review, we aim to bridge the gap between the fields of untargeted metabolomics and data visualization. First, we introduce data visualization to the untargeted metabolomics field as a topic worthy of its own dedicated research, and provide a primer on cutting-edge visualization research into data visualization for both researchers as well as developers active in metabolomics. We extend this primer with a discussion of best practices for data visualization as they have emerged from data visualization studies. Second, we provide a practical roadmap to the visual tool landscape and its use within the untargeted metabolomics field. Here, for several computational analysis stages within the untargeted metabolomics workflow, we provide an overview of commonly used visual strategies with practical examples. In this context, we will also outline promising areas for further research and development. We end the review with a set of recommendations for developers and users on how to make the best use of visualizations for more effective and transparent communication of results.
代谢组学为2014年至2023年，信息可视化则为2002年至2023年
基于液相色谱-串联质谱的非靶向代谢组学是一个快速发展的研究领域，催生了越来越多的计算代谢组学工具，以协助研究人员完成复杂的数据处理、分析和解释任务。在本文中，我们从信息可视化、视觉分析和视觉数据集成的角度回顾了整个非靶向代谢组学工作流程。数据可视化是代谢组学工作流程每个阶段的关键步骤，它提供了数据检查、评估和共享功能的核心组件。然而，由于可用的数据分析工具和相应的可视化组件数量众多，用户和开发人员都很难全面了解现有工具以及哪些工具适合他们的分析。此外，数据可视化领域和代谢组学领域之间几乎没有交叉融合，导致可视化工具大多是在次要阶段设计的。通过本次综述，我们旨在弥合非靶向代谢组学领域和数据可视化领域之间的差距。首先，我们将数据可视化引入非靶向代谢组学领域，将其作为一个值得专门研究的课题，并为活跃于代谢组学领域的研究人员和开发人员提供有关数据可视化前沿研究的入门知识。我们通过讨论数据可视化研究中出现的数据可视化最佳实践来扩展这一入门知识。其次，我们提供了一份实用的路线图，介绍可视化工具的概况及其在非靶向代谢组学领域的应用。在此，对于非靶向代谢组学工作流程中的几个计算分析阶段，我们通过实际示例概述了常用的可视化策略。在此背景下，我们还将概述有前景的进一步研究和开发领域。我们在综述结尾为开发人员和用户提供了一系列建议，说明如何更好地利用可视化来更有效地、更透明地传达结果。
Zotero 插件
