Aardvark: Composite Visualizations of Trees, Time-Series, and Images

Aardvark screenshot

Abstract

How do cancer cells grow, divide, proliferate and die? How do drugs influence these processes? These are difficult questions that we can attempt to answer with a combination of time-series microscopy experiments, classification algorithms, and data visualization. However, collecting this type of data and applying algorithms to segment and track cells and construct lineages of proliferation is error-prone; and identifying the errors can be challenging since it often requires cross-checking multiple data types. Similarly, analyzing and communicating the results necessitates synthesizing different data types into a single narrative. State-of-the-art visualization methods for such data use independent line charts, tree diagrams, and images in separate views. However, this spatial separation requires the viewer of these charts to combine the relevant pieces of data in memory. To simplify this challenging task, we describe design principles for weaving cell images, time-series data, and tree data into a cohesive visualization. Our design principles are based on choosing a primary data type that drives the layout and integrates the other data types into that layout. We then introduce Aardvark, a system that uses these principles to implement novel visualization techniques. Based on Aardvark, we demonstrate the utility of each of these approaches for discovery, communication, and data debugging in a series of case studies.

Citation

Devin Lange, Robert Judson-Torres, Thomas A. Zangle, Alexander Lex
Aardvark: Composite Visualizations of Trees, Time-Series, and Images
Preprint, doi:10.31219/osf.io/cdbm6, 2024.

BibTeX

@preprint{2024_preprint_aardvark,
  title = {Aardvark: Composite Visualizations of Trees, Time-Series, and Images},
  author = {Devin Lange and Robert Judson-Torres and Thomas A. Zangle and Alexander Lex},
  booktitle = {Preprint},
  doi = {10.31219/osf.io/cdbm6},
  year = {2024}
}

Acknowledgements

This work was supported by NIH R01CA276653 and NIH 3P30CA042014-34S6. The authors wish to thank Jack Wilburn for technical help, Koushik Roy, members of the Visualization Design Lab, the Scientific Computing and Imaging Institute, and the Zangle and Judson-Torres labs for their feedback and advice.