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.

Which drug is most promising for a cancer patient? A new microscopy-based approach for measuring the mass of individual cancer cells treated with different drugs promises to answer this question in only a few hours. However, the analysis pipeline for extracting data from these images is still far from complete automation: human intervention is necessary for quality control for preprocessing steps such as segmentation, adjusting filters, removing noise, and analyzing the result. To address this workflow, we developed Loon, a visualization tool for analyzing drug screening data based on quantitative phase microscopy imaging. Loon visualizes both derived data such as growth rates and imaging data. Since the images are collected automatically at a large scale, manual inspection of images and segmentations is infeasible. However, reviewing representative samples of cells is essential, both for quality control and for data analysis. We introduce a new approach for choosing and visualizing representative exemplar cells that retain a close connection to the low-level data. By tightly integrating the derived data visualization capabilities with the novel exemplar visualization and providing selection and filtering capabilities, Loon is well suited for making decisions about which drugs are suitable for a specific patient.