TourDino: A Support View for Confirming Patterns in Tabular Data

Eckelt, Klaus; Adelberger, Patrick; Zichner, Thomas; Wernitznig, Andreas; Streit, Marc

doi:10.2312/eurova.20191117

Cited by 3 publications

(2 citation statements)

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“…However, the visual comparison of cohorts is limited to one or two attributes that need to be manually selected by the user. Additionally, Coral integrates TourDino [10] for pairwise statistical comparisons of cohorts by a user-defined set of attributes. In previous work, we have analyzed cohort differences in low-dimensional embeddings to provide an overview of differences in high-dimensional space [11].…”

Section: Related Workmentioning

confidence: 99%

Kokiri: Random-Forest-Based Comparison and Characterization of Cohorts

Eckelt

Adelberger

Bauer

et al. 2022

Preprint

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We propose an interactive visual analytics approach for the characterization and comparison of patient subgroups (i.e., cohorts). Despite having the same disease and similar demographic characteristics, patients respond differently to therapy. One reason for this is the vast number of variables in the genome that influence the outcome. Nevertheless, most existing tools do not offer effective means to identify the most differing attributes or look at them in isolation, neglecting combinatorial effects. To fill this gap, we present Kokiri, a visual analytics approach that aims to separate cohorts based on user-selected data, ranks attributes by their importance to distinguish between cohorts, and visualizes the overlap and separability of the cohorts. Using our approach, users can additionally characterize the homogeneity and outliers of the cohort. To demonstrate the applicability of our approach, we integrated Kokiri in the Coral cohort analysis tool for the purpose of comparing and characterizing lung cancer patient cohorts.

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Section: Related Workmentioning

confidence: 99%

Kokiri: Random-Forest-Based Comparison and Characterization of Cohorts

Eckelt

Adelberger

Bauer

et al. 2022

Preprint

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“…However, this approach could easily lead to incorrect inferences, unless some precautions are taken 64,65 . In a follow-up project, we are working on a solution that supports such confirmatory analysis in a way that users can understand without being trained in statistics 66 .…”

Section: Case Study: Drug Target Discoverymentioning

confidence: 99%

Taggle: Combining overview and details in tabular data visualizations

Furmanová

Gratzl²,

Stitz

et al. 2019

Information Visualization

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Most tabular data visualization techniques focus on overviews, yet many practical analysis tasks are concerned with investigating individual items of interest. At the same time, relating an item to the rest of a potentially large table is important. In this work we present Taggle, a tabular visualization technique for exploring and presenting large and complex tables. Taggle takes an item-centric, spreadsheet-like approach, visualizing each row in the source data individually using visual encodings for the cells. At the same time, Taggle introduces data-driven aggregation of data subsets. The aggregation strategy is complemented by interaction methods tailored to answer specific analysis questions, such as sorting based on multiple columns and rich data selection and filtering capabilities. We demonstrate Taggle using a case study conducted by a domain expert on complex genomics data analysis for the purpose of drug discovery.

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