AggreSet: Rich and Scalable Set Exploration using Visualizations of Element Aggregations

Yalçın, Mehmet Adil; Elmqvist, Niklas; Bederson, Benjamin B.

doi:10.1109/tvcg.2015.2467051

Cited by 22 publications

(11 citation statements)

References 28 publications

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“…This fact has fostered a recent interest in the visualization community to advance visualization and processing techniques for engineering tensor fields [Carpendale, 2008]. Designing effective visualizations for engineering tensor fields is a multifaceted problem, which includes factors like visual intuitiveness [Franklin, 2015], scalability [Yalcin et al, 2016], interactivity [Boy et al, 2016], merging detail and context [Tax et al, 2015], integrating modeling and simulation [Nowke et al, 2015], and overcoming terminology barriers [Thielea et al, 2015], among many others. All this complexity usually results in trade-offs among different visualization strategies.…”

Section: Related Workmentioning

confidence: 99%

Tangible visual analytics: the integration of tangible interactions and computational techniques for biological data visualization and modelling with experts in the loop

Manshaei¹

2021

Preprint

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Understanding and interpreting the inherently uncertain nature of complex biological systems, as well as the time to an event in these systems, are notable challenges in the field of bioinformatics. Overcoming these challenges could potentially lead to scientific discoveries, for example paving the path for the design of new drugs to target specific diseases such as cancer, or helping to apply more effective treatment for these diseases. In general, reverse engineering of these types of biological systems using online datasets is difficult. In particular, finding a unique solution to these systems is hard due to their complexity and the small sample size of datasets. This remains an unsolved problem due to such uncertainty, and the often intractable solution space of these systems. The term"uncertainty" describes the application-based margin of significance, validity, and efficiency of inferred or predictive models in their ability to extract characteristic properties and features describing the observed state of a given biological system. In this work, uncertainties within two specific bioinformatics domains are considered, namely "gene regulatory network reconstruction" (in which gene interactions/relationships within a biological entity are inferred from gene expression data); and "cancer survivorship prediction" (in which patient survival rates are predicted based on clinical factors and treatment outcomes). One approach to reduce uncertainty is to apply different constraints that have particular relevance to each application domain. In gene network reconstruction for instance, the consideration of constraints such as sparsity, stability and modularity, can informand reduce uncertainty in the inferred reconstructions. While in cancer survival prediction, there is uncertainty in determining which clinical features (or feature aggregates) can improve associated prediction models. The inherent lack of understanding of how, why and when such constraints should be applied, however, prompts the need for a radically new approach. In this dissertation, a new approach is thus considered to aid human expert users in understanding and exploring inherent uncertainties associated with these two bioinformatics domains. Specifically, a novel set of tools is introduced and developed to assist in evidence gathering, constraint definition, and refinement of models toward the discovery of better solutions. This dissertation employs computational approaches, including convex optimization and feature selection/aggregation, in order to increase the chances of finding a unique solution. These approaches are realized through three novel interactive tools that employ tangible interaction in combination with graphical visualization to enable experts to query and manipulate the data. Tangible interaction provides physical embodiments of data and computational functions in support of learning and collaboration. Using these approaches, the dissertation demonstrates: (1) a modified stability constraint for reconstructing gene regulatory network that shows improvement in accuracy of predicted networks, (2) a novel modularity constraint (neighbor norm) for extracting available structures in the data which is validated with Laplacian eigenvalue spectrum, and (3) a hybrid method for estimating overall survival and inferring effective prognosis factors for patients with advanced prostate cancer that improves the accuracy of survival analysis.

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Section: Related Workmentioning

confidence: 99%

Tangible visual analytics: the integration of tangible interactions and computational techniques for biological data visualization and modelling with experts in the loop

Manshaei¹

2021

Preprint

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“…settyped data). AggreSet [33] is another approach for set-typed data. It creates aggregations for set intersections, set pairs and set degrees, and represents the cardinality of each aggregation.…”

Section: # Taskmentioning

confidence: 99%

“…In the literature, existing approaches, including UpSet [4] and AggreSet [33], rely on aggregation (or grouping) rather than clustering: they provide predefined aggregations, such as "grouping intersections by degree", "grouping intersections by set" or "grouping intersections by pair of elements", or allows the user to create his own aggregates. Aggregation gives a more general view of the data.…”

Section: Intersection Clusteringmentioning

confidence: 99%

RainBio: Proportional Visualization of Large Sets in Biology

Lamy

Tsopra

2020

IEEE Trans. Visual. Comput. Graphics

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Set visualization is a well-known task in information visualization. In biology, it is used for comparing visually sets of genes or proteins, typically using Venn diagrams. However, limitations of the Venn diagram are well-known: they are limited to 6 sets and difficult to read above 4. Many other set visualization techniques have been proposed, but they have never been widely used in biology. In this paper, we introduce RainBio, a technique for visualizing sets in biology and aimed at providing a global overview showing the size of the main intersections, in a proportional way, and the similarities between sets. We adapt rainbow boxes, a technique for visualizing small datasets, to the visualization of larger sets, using element aggregation and intersection clustering. We present the application of RainBio to three datasets, with 5, 6 and 12 sets. We also describe a small user study comparing RainBio with Venn diagrams, involving 30 students in biology. Results showed that RainBio led to significantly fewer errors on 6-set dataset, and that the majority of students preferred RainBio. RainBio is proposed as a web-based tool for up to 15 sets.

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“…[15,9] In particular, as regards variants such as Bubble Sets [11,29] and LineSets, [1] these techniques are unlike linear (and mosaic) diagrams, which display only abstract set relations, in that they "require the existence of embedded items", as pointed out by Rodgers et al [31] Similarly, this study does not compare mosaics to the many interactive set visualization systems proposed in the burgeoning literature on this topic. The reader is referred to these works and to the literature review below for comparisons of interactive systems in terms of their design features [42] and task taxonomies. [4] While empirical studies of interactive versions of mosaic (and indeed linear diagrams) are of great practical interest for future work, comparisons of this kind lie beyond our scope here.…”

Section: Introductionmentioning

confidence: 99%

“…As such, aggregation-based techniques are more suitable for representing relationships between sets with large number of elements, where it would be impractical to show all the relationships between those elements. Examples of these techniques include AggreSet, [42] Radial Sets, [2] and PowerSets. [5] 6.…”

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confidence: 99%

A comparison of linear and mosaic diagrams for set visualization

Luz

Masoodian

2018

Information Visualization

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Linear diagrams have been shown to compare favourably to better known forms of set visualization, such as Venn and Euler diagrams, in supporting non-interactive assessment of set relationships. Recent studies that compared several variants of linear diagrams have demonstrated that users perform best at tasks involving identification of intersections, disjointness and subsets when using a horizontally drawn linear diagram with thin lines representing sets and employing vertical lines as guide lines. The essential visual task the user needs to perform in order to interpret this kind of diagram is vertical alignment of parallel lines and detection of overlaps. Space-filling mosaic diagrams which support this same visual task have been used in other applications, such as the visualization of schedules of activities, where they have been shown to be superior to linear Gantt charts. In this article, we present an experimental comparison of linear and mosaic diagrams for visualization of set relationships, in terms of accuracy, time-to-answer and subjective ratings of perceived task difficulty. The findings show that the two visualizations are largely similar with respect to these measures, suggesting that the choice of one or the other may be solely guided by other visual design considerations. Mosaic diagrams might be more suitable, for instance, in cases where miniature diagrams representing overviews of relations in different collections of sets are required, such as in small-multiples displays.

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AggreSet: Rich and Scalable Set Exploration using Visualizations of Element Aggregations

Cited by 22 publications

References 28 publications

Tangible visual analytics: the integration of tangible interactions and computational techniques for biological data visualization and modelling with experts in the loop

Tangible visual analytics: the integration of tangible interactions and computational techniques for biological data visualization and modelling with experts in the loop

RainBio: Proportional Visualization of Large Sets in Biology

A comparison of linear and mosaic diagrams for set visualization

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