van Eijnatten Abraham L, van Zon Luc, Manousou Eleni, Bikineeva Margarita, Wubs E R Jasper, van der Putten Wim H, Morriën Elly, Dutilh Bas E, Snoek L Basten
Theoretical Biology and Bioinformatics, Science4Life, Utrecht University (UU), Padualaan 8, 3584 CH Utrecht, The Netherlands.
Department of Terrestrial Ecology, The Netherlands Institute of Ecology (NIOO), Droevendaalsesteeg 10, 6708 PB Wageningen, The Netherlands.
ISME Commun. 2025 Feb 24;5(1):ycaf036. doi: 10.1093/ismeco/ycaf036. eCollection 2025 Jan.
Correlation networks are commonly used to explore microbiome data. In these networks, nodes are microbial taxa and edges represent correlations between their abundances. As clusters of correlating taxa (co-abundance clusters) often indicate a shared response to environmental drivers, network visualization contributes to the system understanding. Currently, most tools for creating and visualizing co-abundance networks from microbiome data either require the researcher to have coding skills or are not user-friendly, with high time expenditure and limited customizability. Furthermore, existing tools lack a focus on the association between environmental drivers and the structure of the microbiome, even though many edges in correlation networks can be understood through a shared association of two taxa with the environment. For these reasons, we developed SpeSpeNet (Species-Species Network, https://tbb.bio.uu.nl/SpeSpeNet), a practical and user-friendly R-shiny tool to construct and visualize correlation networks from taxonomic abundance tables. The details of data preprocessing, network construction, and visualization are automated, require no programming ability for the web version, and are highly customizable, including associations with user-provided environmental data. Here, we present the details of SpeSpeNet and demonstrate its utility using three case studies.
关联网络常用于探索微生物组数据。在这些网络中,节点是微生物分类群,边表示它们丰度之间的相关性。由于相关分类群的簇(共丰度簇)通常表明对环境驱动因素的共同反应,网络可视化有助于系统理解。目前,大多数用于从微生物组数据创建和可视化共丰度网络的工具要么要求研究人员具备编码技能,要么不便于用户使用,耗时较长且可定制性有限。此外,现有工具缺乏对环境驱动因素与微生物组结构之间关联的关注,尽管关联网络中的许多边可以通过两个分类群与环境的共同关联来理解。出于这些原因,我们开发了SpeSpeNet(物种 - 物种网络,https://tbb.bio.uu.nl/SpeSpeNet),这是一个实用且便于用户使用的R - shiny工具,用于从分类丰度表构建和可视化关联网络。数据预处理、网络构建和可视化的细节都是自动化的,网络版本无需编程能力,并且具有高度可定制性,包括与用户提供的环境数据的关联。在这里,我们介绍SpeSpeNet的详细信息,并通过三个案例研究展示其效用。