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Annual Review of Biomedical Data Science

Volume 1, 2018

Visualization of Biomedical Data

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.

Keyword(s): cell biology, data visualization, metagenomics, molecular biology, multivariate data, tissue imaging
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/content/journals/10.1146/annurev-biodatasci-080917-013424
2018-07-20
2024-04-19
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/content/journals/10.1146/annurev-biodatasci-080917-013424
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Visualization of Biomedical Data
Annual Review of Biomedical Data Science 1, 275 (2018); https://doi.org/10.1146/annurev-biodatasci-080917-013424
/content/journals/10.1146/annurev-biodatasci-080917-013424
/content/journals/10.1146/annurev-biodatasci-080917-013424
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