Visualization of Biomedical Data

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1 Data61, Commonwealth Science and Technology Organisation (CSIRO), Eveleigh, New South Wales, Australia
2 Genomics and Epigenetics Division, The Garvan Institute for Medical Research, Sydney, New South Wales, Australia
3 School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, Australia
4 The ithree Institute, University of Technology Sydney, Sydney, New South Wales, Australia
5 School of Electrical Engineering and Computer Science, Queensland University of Technology, Brisbane, Queensland, Australia
6 School of Computer Science and Engineering, University of New South Wales, Sydney, Australia
7 Division of Computer Assisted Medical Interventions, German Cancer Research Centre (DKFZ), Heidelberg, Germany
8 European Bioinformatics Institute, European Molecular Biology Laboratory (EMBL-EBI), Cambridge, United Kingdom
9 St. Vincent's Institute of Medical Research, Fitzroy, Victoria, Australia
10 School of Life Sciences, University of Dundee, Dundee, United Kingdom
DOI
10.7287/peerj.preprints.26896v1
Published
Accepted
Subject Areas
Computational Biology, Science and Medical Education, Human-Computer Interaction, Data Science
Keywords
Cell biology, Data visualization, Multivariate data, Molecular biology, Metagenomics, Tissue imaging
Copyright
© 2018 O'Donoghue et al.
Licence
This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
Cite this article
O'Donoghue SI, Baldi BF, Clark SJ, Darling AE, Hogan JM, Kaur S, Maier-Hein L, McCarthy DJ, Moore WJ, Stenau E, Swedlow JR, Vuong J, Procter JB. 2018. Visualization of Biomedical Data. PeerJ Preprints 6:e26896v1

Abstract

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.

Author Comment

This work was accepted for publication in the inaugural issue of Annual Review of Biomedical Data.