Uncertainty Visualization: Concepts, Methods, and Applications in Biological Data Visualization

Abstract

This paper provides an overview of uncertainty visualization in general, along with specific examples of applications in bioinformatics. Starting from a processing and interaction pipeline of visualization, components are discussed that are relevant for handling and visualizing uncertainty introduced with the original data and at later stages in the pipeline, which shows the importance of making the stages of the pipeline aware of uncertainty and allowing them to propagate uncertainty. We detail concepts and methods for visual mappings of uncertainty, distinguishing between explicit and implict representations of distributions, different ways to show summary statistics, and combined or hybrid visualizations. The basic concepts are illustrated for several examples of graph visualization under uncertainty. Finally, this review paper discusses implications for the visualization of biological data and future research directions.

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“…Moreover, these rules merely cover the tip of the iceberg, and we strongly encourage our readers to take a look at the references included in this paper to further improve their understanding. Other interesting matters that did not make it through the introductory cut include visual design validation [ 55 , 56 ], inclusion and accessibility matters [ 57 , 58 ], and visual representation of uncertainty [ 59 , 60 ], which are subfields on their own.…”

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“…The topic of uncertainty poses a longstanding challenge in visualization [5]. Survey papers [6]- [9] discuss the relevance of uncertainty for better decisionmaking, propose guidelines, and highlight challenges, such as uncertainty propagation, which we address in this paper as well. Related to propagation is the question of modeling uncertainty, where we use a probabilistic view in which uncertainty is expressed via random variables.…”

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“…Uncertainty in visualization is a broad topic covering numerous aspects of uncertain data and uncertain representation thereof [32], [33]. Sources of uncertainty comprise the data (e.g., inaccuracies), derived uncertainty from data transformation, and uncertainty that results from the visualization itself [34], [35]. A common focus of such techniques is on performance and user experience for confirmatory purposes.…”