Visualization of Biomedical Data

Sean I. O'Donoghue (Lead / Corresponding author), Benedetta Frida Baldi, Susan J. Clark, Aaron E. Darling, James M. Hogan, Sandeep Kaur, Lena Maier-Hein, Davis J. McCarthy, William Moore, Esther Stenau, Jason Swedlow, Jenny Vuong, James Procter

Research output: Contribution to journalArticlepeer-review


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, however, common visualization challenges that can be addressed with general methods, and strategies, and challenges. Unfortunately, poor visualization practices are also common: ; however, there are strong 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.
Original languageEnglish
JournalAnnual Review of Biomedical Data Science
Early online date14 May 2018
Publication statusPublished - 20 Jul 2018


  • Data visualization
  • Multivariate data
  • Molecular biology
  • Cell biology
  • Tissue imaging
  • Metagenomics


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