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.
Highlights
The launch of this Annual Reviews journal is driven by the rapid increase in volume and complexity of biomedical data, requiring changes in research, communication, and clinical practices [1]
Many of the commonly used tailored tools provide powerful features that make it easy to create visualizations that are dramatic or aesthetically appealing but where the underlying scientifically meaning becomes obscured. This can be useful when creating impactful artwork, but it undermines the goals of data visualization in research, which are always clarity and insight
For many of the data challenges faced in biomedical research [20,21,22,23,24], tailored visualization methods have already been invented and implemented into working tools
Summary
The launch of this Annual Reviews journal is driven by the rapid increase in volume and complexity of biomedical data, requiring changes in research, communication, and clinical practices [1]. Many of the commonly used tailored tools provide powerful features that make it easy to create visualizations that are dramatic or aesthetically appealing but where the underlying scientifically meaning becomes obscured This can be useful when creating impactful artwork (e.g., a cover figure), but it undermines the goals of data visualization in research, which are always clarity and insight. For many of the data challenges faced in biomedical research [20,21,22,23,24], tailored visualization methods have already been invented and implemented into working tools These tools often use interactivity [25, 26] to facilitate combined exploration and integration of raw data, data derived via analysis, and additional supporting evidence. These data are not just limited to raw DNA sequences but include an increasing spectrum of additional information that can be obtained www.annualreviews.org Visualization of Biomedical Data 281
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