Abstract
MotivationData visualization is an important tool for exploring and communicating findings from genomic and healthcare datasets. Yet, without a systematic way of organizing and describing the design space of data visualizations, researchers may not be aware of the breadth of possible visualization design choices or how to distinguish between good and bad options.ResultsWe have developed a method that systematically surveys data visualizations using the analysis of both text and images. Our method supports the construction of a visualization design space that is explorable along two axes: why the visualization was created and how it was constructed. We applied our method to a corpus of scientific research articles from infectious disease genomic epidemiology and derived a Genomic Epidemiology Visualization Typology (GEViT) that describes how visualizations were created from a series of chart types, combinations and enhancements. We have also implemented an online gallery that allows others to explore our resulting design space of visualizations. Our results have important implications for visualization design and for researchers intending to develop or use data visualization tools. Finally, the method that we introduce is extensible to constructing visualizations design spaces across other research areas.Availability and implementationOur browsable gallery is available at http://gevit.net and all project code can be found at https://github.com/amcrisan/gevitAnalysisRelease.Supplementary information Supplementary data are available at Bioinformatics online.
Highlights
Genome sequencing is becoming an integral part of modern infectious disease diagnostics (Pankhurst et al, 2016) and epidemiology (Faria et al, 2016; Quick et al, 2016)
Areas of the design space that are currently underused. This methodological contribution can be applied to visualization design spaces in domains beyond public health genomic epidemiology; here we describe its application in a specific domain as an additional contribution
We present the Genomic Epidemiology Visualization Typology (GEViT), and we provide a web-based platform for exploring GEViT that researchers, bioinformaticians, and software developers can use to inform their own genomic epidemiology data visualization practice
Summary
Genome sequencing is becoming an integral part of modern infectious disease diagnostics (Pankhurst et al, 2016) and epidemiology (Faria et al, 2016; Quick et al, 2016). When genomic and/or phylogenetic data are combined with clinical and epidemiologic data routinely generated by public health laboratories and programs, the resulting analyses support a variety of public health professionals, including clinicians, epidemiologists, researchers, and policymakers, in their real-time decision-making around treatment, surveillance, and outbreak response. This new data-driven approach to public health introduces interpretability challenges – it is difficult to succinctly and accurately represent such multivariate and high-dimensional data, when many stakeholders do not routinely work with the genomic or phylogenetic data these analyses rely upon. As more and more visualization tools and libraries are being developed for genomic epidemiology, it is an appropriate moment at which to assess the type of visualizations being generated and used in public health genomic studies in order to inform the design of future visualization tools
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