Faculty of Information Technology, Monash University, 3800, Clayton, Australia.
School of Computing, University of Kent, CT2 7NZ, Canterbury, UK.
BMC Bioinformatics. 2021 Apr 26;22(1):214. doi: 10.1186/s12859-021-04121-8.
BACKGROUND: Area-proportional Euler diagrams are frequently used to visualize data from Microarray experiments, but are also applied to a wide variety of other data from biosciences, social networks and other domains. RESULTS: This paper details Edeap, a new simple, scalable method for drawing area-proportional Euler diagrams with ellipses. We use a search-based technique optimizing a multi-criteria objective function that includes measures for both area accuracy and usability, and which can be extended to further user-defined criteria. The Edeap software is available for use on the web, and the code is open source. In addition to describing our system, we present the first extensive evaluation of software for producing area-proportional Euler diagrams, comparing Edeap to the current state-of-the-art; circle-based method, venneuler, and an alternative ellipse-based method, eulerr. CONCLUSIONS: Our evaluation-using data from the Gene Ontology database via GoMiner, Twitter data from the SNAP database, and randomly generated data sets-shows an ordering for accuracy (from best to worst) of eulerr, followed by Edeap and then venneuler. In terms of runtime, the results are reversed with venneuler being the fastest, followed by Edeap and finally eulerr. Regarding scalability, eulerr cannot draw non-trivial diagrams beyond 11 sets, whereas no such limitation is present in Edeap or venneuler, both of which draw diagrams up to the tested limit of 20 sets.
背景:区域比例欧拉图常用于可视化微阵列实验数据,但也广泛应用于生物科学、社交网络和其他领域的各种其他数据。
结果:本文详细介绍了 Edeap,这是一种新的简单、可扩展的方法,用于使用椭圆绘制区域比例欧拉图。我们使用基于搜索的技术来优化多标准目标函数,该函数包括面积准确性和可用性的度量标准,并且可以扩展到其他用户定义的标准。Edeap 软件可在网络上使用,并且代码是开源的。除了描述我们的系统外,我们还首次对用于生成区域比例欧拉图的软件进行了广泛评估,将 Edeap 与当前最先进的基于圆的方法 veneuler 以及另一种基于椭圆的方法 eulerr 进行了比较。
结论:我们的评估——使用 Gene Ontology 数据库中的数据(通过 GoMiner)、来自 SNAP 数据库的 Twitter 数据以及随机生成的数据集——显示了准确性的排序(从最好到最差):eulerr 之后是 Edeap,然后是 venneuler。在运行时方面,结果相反,venneuler 是最快的,其次是 Edeap,最后是 eulerr。关于可扩展性,eulerr 无法绘制超过 11 个数据集的非平凡图,而 Edeap 或 venneuler 则没有这种限制,这两者都可以绘制测试到的 20 个数据集的图。
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