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DynaVenn: web-based computation of the most significant overlap between ordered sets.
BMC Bioinformatics ( IF 3 ) Pub Date : 2019-12-30 , DOI: 10.1186/s12859-019-3320-5
Jérémy Amand 1 , Tobias Fehlmann 1 , Christina Backes 1 , Andreas Keller 1
Affiliation  

BACKGROUND In many research disciplines, ordered lists are compared. One example is to compare a subset of all significant genes or proteins in a primary study to those in a replication study. Often, the top of the lists are compared using Venn diagrams, ore more precisely Euler diagrams (set diagrams showing logical relations between a finite collection of different sets). If different cohort sizes, different techniques or algorithms for evaluation were applied, a direct comparison of significant genes with a fixed threshold can however be misleading and approaches comparing lists would be more appropriate. RESULTS We developed DynaVenn, a web-based tool that incrementally creates all possible subsets from two or three ordered lists and computes for each combination a p-value for the overlap. Respectively, dynamic Venn diagrams are generated as graphical representations. Additionally an animation is generated showing how the most significant overlap is reached by backtracking. We demonstrate the improved performance of DynaVenn over an arbitrary cut-off approach on an Alzheimer's Disease biomarker set. CONCLUSION DynaVenn combines the calculation of the most significant overlap of different cohorts with an intuitive visualization of the results. It is freely available as a web service at http://www.ccb.uni-saarland.de/dynavenn.

中文翻译:

DynaVenn:基于网络计算有序集之间最重要的重叠。

背景技术在许多研究学科中,对有序列表进行比较。一个例子是将初步研究中所有重要基因或蛋白质的子集与复制研究中的所有重要基因或蛋白质的子集进行比较。通常,列表的顶部使用维恩图进行比较,或更准确地说是欧拉图(显示不同集合的有限集合之间的逻辑关系的集合图)。然而,如果应用不同的队列规模、不同的评估技术或算法,则将重要基因与固定阈值进行直接比较可能会产生误导,而比较列表的方法会更合适。结果我们开发了 DynaVenn,这是一种基于 Web 的工具,可以从两个或三个有序列表中增量创建所有可能的子集,并为每个组合计算重叠的 p 值。分别生成动态维恩图作为图形表示。此外,还会生成一个动画,显示如何通过回溯达到最显着的重叠。我们证明了 DynaVenn 相对于阿尔茨海默病生物标志物集的任意截止方法的性能改进。结论 DynaVenn 将不同群组最显着重叠的计算与结果的直观可视化结合起来。它可以作为网络服务免费提供:http://www.ccb.uni-saarland.de/dynavenn。
更新日期:2019-12-31
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