The rapid increase in volume and complexity of biomedical data requires changes in research, communication, training, and clinical practices. This includes learning how to effectively integrate automated analysis with high-data-density visualizations that clearly express complex phenomena. In this review, we summarize key principles and resources from data visualization research that address this difficult challenge. We then survey how visualization is being used in a selection of emerging biomedical research areas, including: 3D genomics, single-cell RNA-seq, the protein structure universe, phosphoproteomics, augmented-reality surgery, and metagenomics. While specific areas need highly tailored visualization tools, there are common visualization challenges that can be addressed with general methods and strategies. Unfortunately, poor visualization practices are also common; however, there are good prospects for improvements and innovations that will revolutionize how we see and think about our data. We outline initiatives aimed at fostering these improvements via better tools, peer-to-peer learning, and interdisciplinary collaboration with computer scientists, science communicators, and graphic designers.
The proteome: structure, function and evolution