XCluSim: a visual analytics tool for interactively comparing multiple clustering results of bioinformatics data

BMC Bioinformatics

Research

Young-Joon Cho¹  Jinwook Seo²  Sehi L'Yi²  Bongkyung Ko²  DongHwa Shin²  Bohyoung Kim³  Jaeyong Lee⁴ 

[1] ChunLab, Inc., Seoul National University, 151-742, Seoul, Korea;Department of Computer Science and Engineering & Institute of Computer Technology & Bioinformatics Institute, Seoul National University, 151-744, Seoul, Korea;Department of Radiology, Seoul National University Bundang Hospital, 463-707, Gyeonggi-do, Korea;Department of Statistics, Seoul National University, 151-742, Seoul, Korea;

关键词: Cluster Analysis;    Multiple Clustering Results;    Visualization Technique;    Gene Expression;   

DOI  :  10.1186/1471-2105-16-S11-S5

来源: Springer

【 摘 要 】

BackgroundThough cluster analysis has become a routine analytic task for bioinformatics research, it is still arduous for researchers to assess the quality of a clustering result. To select the best clustering method and its parameters for a dataset, researchers have to run multiple clustering algorithms and compare them. However, such a comparison task with multiple clustering results is cognitively demanding and laborious.ResultsIn this paper, we present XCluSim, a visual analytics tool that enables users to interactively compare multiple clustering results based on the Visual Information Seeking Mantra. We build a taxonomy for categorizing existing techniques of clustering results visualization in terms of the Gestalt principles of grouping. Using the taxonomy, we choose the most appropriate interactive visualizations for presenting individual clustering results from different types of clustering algorithms. The efficacy of XCluSim is shown through case studies with a bioinformatician.ConclusionsCompared to other relevant tools, XCluSim enables users to compare multiple clustering results in a more scalable manner. Moreover, XCluSim supports diverse clustering algorithms and dedicated visualizations and interactions for different types of clustering results, allowing more effective exploration of details on demand. Through case studies with a bioinformatics researcher, we received positive feedback on the functionalities of XCluSim, including its ability to help identify stably clustered items across multiple clustering results.

【 授权许可】

Unknown   
© L'Yi et al. 2015. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.