Datasets with large heterogeneity in data density are prone to result in visualizations with unutilized whitespace and overplotting. Sequential data, such as in genomics or electronic health records, often has such density distributions, containing long empty stretches between data-rich regions. For these data types, the exact distance is not necessarily the central feature, and plotting along a linear axis causes occlusion of the actual datapoints. In this work, we evaluate content-driven axis compression for minimizing whitespace and reducing occlusion while maintaining distance information. The axis is compressed based on the density of the data, and it requires minimal a priori knowledge of the data distribution. We present a framework for reasoning about the benefits and drawbacks of using axis compression. The framework includes reasoning about the separability of items and clusters, suggests careful consideration of how the position visual channel is utilized, and highlights the influence of the usage context for which the visualization is designed. We conducted a study to evaluate the strengths and weaknesses of axis compression and found that it leads to improved item visibility while maintaining high accuracy, also for tasks targeted at understanding the data distribution.

Genomics is at the core of precision medicine, and there are high expectations on genomics-enabled improvement of patient outcomes in the years to come. Around the world, initiatives to increase the use of DNA sequencing in clinical routine are being deployed, such as the use of broad panels in the standard care for oncology patients. Such a development comes at the cost of increased demands on throughput in genomic data analysis. In this paper, we use the task of copy number variant (CNV) analysis as a context for exploring visualization concepts for clinical genomics. CNV calls are generated algorithmically, but time-consuming manual intervention is needed to separate relevant findings from irrelevant ones in the resulting large call candidate lists. We present a visualization environment, named Copycat, to support this review task in a clinical scenario. Key components are a scatter-glyph plot replacing the traditional list visualization, and a glyph representation designed for at-a-glance relevance assessments. Moreover, we present results from a formative evaluation of the prototype by domain specialists, from which we elicit insights to guide both prototype improvements and visualization for clinical genomics in general.

Abstract Visual representations based on circular shapes are frequently used in visualization applications. One example are circos plots within bioinformatics, which bend graphs into a wheel of information with connective lines running through the center like spokes. The results are aesthetically appealing and impressive visualizations that fit long data sequences into a small quadratic space. However, the authors' experiences are that when asked, a visualization researcher would generally advise against making visualizations with radial layouts. Upon reviewing the literature we found that there is evidence that circular layouts are preferable in some cases, but we found no clear evidence for what layout is preferable for matrices and connective data in particular, which both are common data types in circos plots. In this work, we thus performed a user study to compare circular and linear layouts. The tasks are inspired by genomics data, but our results generalize to many other application areas, involving comparison and connective data. To build the prototype we utilized Gosling, a grammar for visualizing genomics data. We contribute empirical evidence on the suitedness of linear versus circular layouts, adding to the specific and general knowledge concerning perception of circular graphs. In addition, we contribute a case study evaluation of the grammar Gosling as a rapid prototyping language, confirming its utility and providing guidance on suitable areas for future development.