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

L’Yi, Sehi; Ko, Bongkyung; Shin, DongHwa; Cho, Young Joon; Lee, Jae Yong; Kim, Bohyoung; Seo, Jinwook

doi:10.1186/1471-2105-16-s11-s5

Cited by 35 publications

(19 citation statements)

References 35 publications

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“…The InVEx toolkit can be compared with visual analytics tools such as Clustervision [6], Clustrophile [7], XCluSim [8], however these tools don't provide 3D visualization, that may be useful for the exploration of spatial objects, and flexible Level-of-Detail mechanism, that is crucial for the analysis of big volumes of data. It should be noted, that the Level-of-Detail component in InVEx, in particular, uses Intel DAAL library, providing significant increase in the performance of the K-means clustering, in contrast with python/sklearn library.…”

Section: Resultsmentioning

confidence: 99%

Enhancements in Functionality of the Interactive Visual Explorer for ATLAS Computing Metadata

et al. 2020

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The development of the Interactive Visual Explorer (InVEx), a visual analytics tool for the computing metadata of the ATLAS experiment at LHC, includes research of various approaches for data handling both on server and client sides. InVEx is implemented as a web-based application which aims at the enhancing of analytical and visualization capabilities of the existing monitoring tools and facilitates the process of data analysis with the interactivity and human supervision. The current work is focused on the architecture enhancements of the InVEx application. First, we will describe the user-manageable data preparation stage for cluster analysis. Then, the Level-of-Detail approach for the interactive visual analysis will be presented. It starts with the low detailing, when all data records are grouped (by clustering algorithms or by categories) and aggregated. We provide users with means to look deeply into this data, incrementally increasing the level of detail. Finally, we demonstrate the development of data storage backend for InVEx, which is adapted for the Level-of-Detail method to keep all stages of data derivation sequence.

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Section: Resultsmentioning

confidence: 99%

Enhancements in Functionality of the Interactive Visual Explorer for ATLAS Computing Metadata

et al. 2020

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“…A tool that is more closely related to our work is XCluSim [27]. It focuses on visual exploration and validation of different clustering algorithms and the concordance or disconcordance among them.…”

Section: Related Workmentioning

confidence: 99%

Interactive Visual Exploration and Refinement of Cluster Assignments

Kern

Lex

Gehlenborg

et al. 2017

Preprint

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Background: With ever-increasing amounts of data produced in biology research, scientists are in need of efficient data analysis methods. Cluster analysis, combined with visualization of the results, is one such method that can be used to make sense of large data volumes. At the same time, cluster analysis is known to be imperfect and depends on the choice of algorithms, parameters, and distance measures. Most clustering algorithms don't properly account for ambiguity in the source data, as records are often assigned to discrete clusters, even if an assignment is unclear. While there are metrics and visualization techniques that allow analysts to compare clusterings or to judge cluster quality, there is no comprehensive method that allows analysts to evaluate, compare, and refine cluster assignments based on the source data, derived scores, and contextual data. Results: In this paper, we introduce a method that explicitly visualizes the quality of cluster assignments, allows comparisons of clustering results and enables analysts to manually curate and refine cluster assignments. Our methods are applicable to matrix data clustered with partitional, hierarchical, and fuzzy clustering algorithms. Furthermore, we enable analysts to explore clustering results in context of other data, for example, to observe whether a clustering of genomic data results in a meaningful differentiation in phenotypes. Conclusions: Our methods are integrated into Caleydo StratomeX, a popular, web-based, disease subtype analysis tool. We show in a usage scenario that our approach can reveal ambiguities in cluster assignments and produce improved clusterings that better differentiate genotypes and phenotypes.

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“…There have also been guides aimed at two dimensional color maps, such as by Bernard et al [BSM * 15] and the software ColorMap-Explorer by Steiger et al [SBT * 15]. Furthermore, several tools have been developed to assist users select appropriate colors for maps, such as Colorbrewer [HB03], and color maps for clusters of data, such as “i want hue” [Med16] and XCluSim [LKS * 15]. Such clustering can, however, cause perceptual issues, either due to the large number of clusters or their layout.…”

Section: Related Workmentioning

confidence: 99%