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Review Article

Open Access

Visualization of Biomedical Data

  • Seán I. O'Donoghue1,2,3,Benedetta Frida Baldi2,Susan J. Clark2,Aaron E. Darling4,James M. Hogan5,Sandeep Kaur6,Lena Maier-Hein7,Davis J. McCarthy8,9,William J. Moore10,Esther Stenau7,Jason R. Swedlow10,Jenny Vuong1 andJames B. Procter10
  • 1Data61, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Eveleigh NSW 2015, Australia; email:[email protected]2Genomics and Epigenetics Division, Garvan Institute of Medical Research, Sydney NSW 2010, Australia3School of Biotechnology and Biomolecular Sciences, University of New South Wales (UNSW), Kensington NSW 2033, Australia4The ithree Institute, University of Technology Sydney, Ultimo NSW 2007, Australia5School of Electrical Engineering and Computer Science, Queensland University of Technology, Brisbane QLD, 4000, Australia6School of Computer Science and Engineering, University of New South Wales (UNSW), Kensington NSW 2033, Australia7Division of Computer Assisted Medical Interventions (CAMI), German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany8European Bioinformatics Institute (EBI), European Molecular Biology Laboratory (EMBL), Wellcome Genome Campus, Hinxton CB10 1SD, United Kingdom9St. Vincent's Institute of Medical Research, Fitzroy VIC 3065, Australia10School of Life Sciences, University of Dundee, Dundee DD1 5EH, United Kingdom
  • Vol. 1:275-304(Volume publication date July 2018)
  • First published as a Review in Advance onMay 16, 2018
  • Copyright © 2018 Seán I. O'Donoghue et al.
    This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. See credit lines of images or other third party material in this article for license information.

  • Seán I. O'Donoghue, Benedetta Frida Baldi, Susan J. Clark, Aaron E. Darling, James M. Hogan, Sandeep Kaur, Lena Maier-Hein, Davis J. McCarthy, William J. Moore, Esther Stenau, Jason R. Swedlow, Jenny Vuong, James B. Procter. 2018. Visualization of Biomedical Data.Annual Review Biomedical Data Science.1:275-304.https://doi.org/10.1146/annurev-biodatasci-080917-013424

Abstract

The rapid increase in volume and complexity of biomedical data requires changes in research, communication, 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 help address this difficult challenge. We then survey how visualization is being used in a selection of emerging biomedical research areas, including three-dimensional genomics, single-cell RNA sequencing (RNA-seq), the protein structure universe, phosphoproteomics, augmented reality–assisted surgery, and metagenomics. While specific research areas need highly tailored visualizations, there are common challenges that can be addressed with general methods and strategies. Also common, however, are poor visualization practices. We outline ongoing initiatives aimed at improving visualization practices in biomedical research via better tools, peer-to-peer learning, and interdisciplinary collaboration with computer scientists, science communicators, and graphic designers. These changes are revolutionizing how we see and think about our data.

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