Visual analytics at the atlas scale for multimodal and spatial single-cell data

Abstract

Scientific measurements at the resolution of individual cells – single-cell experiments – are central to biology because the cell is the fundamental unit of life. In the past decade, advancements in sequencing and bioimaging technologies have enabled cellular measurements to be made at high-throughput, forging the field of single-cell biology. Single-cell experiments are now being applied in large scale, driven by concerted efforts from funding institutions and consortia worldwide. The resulting datasets are being compiled into single-cell atlas resources: collections of cellular- resolution maps intended to summarize and communicate single-cell data through hierarchical and spatial organization and inclusion of multiple biosamples, tissue types, organs, and/or organisms from one or more experimental conditions. Single-cell atlases are intended to facilitate downstream usage in biology and medicine via their establishment as gold-standard sets of measurements that can serve as common points of reference. Current information visualization systems are not equipped to adequately deal with the scale, complexity, and heterogeneity of single-cell atlas data, nor are they tailored to the needs of target user audiences. This thesis investigates how visual analytics systems can be employed for interactive visualization of multimodal and spatial single-cell datasets, addressing challenges at the scale of individual experiments to whole atlases. The first chapter provides an overview of the landscape of single-cell data visualizations, including interactive systems. The second chapter builds on preliminary work on a framework for interactive data visualization for single-cell data, including for spatial, imaging, and multimodal data. The third chapter builds upon this framework to develop a system tailored to cross-experiment comparisons, for example between single-cell data from case and control groups, informed through interviews with individuals from its intended audience of biologists, pathologists, and clinicians. The fourth chapter explores how algorithms for identification of spatial domains with relevance to biological function can be integrated with interactive visualizations. The fifth chapter explores how to integrate chromatin accessibility measurements into the existing framework for single-cell data visualization. By combining and extending concepts from bioinformatics, information visualization, human-computer interaction, and software engineering, this thesis pioneers approaches for exploring, understanding, and communicating foundational single-cell atlas resources.