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 journalArticle

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, 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
Volume1
Early online date14 May 2018
DOIs
Publication statusPublished - 20 Jul 2018

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Keywords

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

Cite this

O'Donoghue, S. I., Baldi, B. F., Clark, S. J., Darling, A. E., Hogan, J. M., Kaur, S., ... Procter, J. (2018). Visualization of Biomedical Data. Annual Review of Biomedical Data Science, 1. https://doi.org/10.1146/annurev-biodatasci-080917-013424
O'Donoghue, Sean I. ; Baldi, Benedetta Frida ; Clark, Susan J. ; Darling, Aaron E. ; Hogan, James M. ; Kaur, Sandeep ; Maier-Hein, Lena ; McCarthy, Davis J. ; Moore, William ; Stenau, Esther ; Swedlow, Jason ; Vuong, Jenny ; Procter, James. / Visualization of Biomedical Data. In: Annual Review of Biomedical Data Science. 2018 ; Vol. 1.
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O'Donoghue, SI, Baldi, BF, Clark, SJ, Darling, AE, Hogan, JM, Kaur, S, Maier-Hein, L, McCarthy, DJ, Moore, W, Stenau, E, Swedlow, J, Vuong, J & Procter, J 2018, 'Visualization of Biomedical Data', Annual Review of Biomedical Data Science, vol. 1. https://doi.org/10.1146/annurev-biodatasci-080917-013424

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

In: Annual Review of Biomedical Data Science, Vol. 1, 20.07.2018.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Visualization of Biomedical Data

AU - O'Donoghue, Sean I.

AU - Baldi, Benedetta Frida

AU - Clark, Susan J.

AU - Darling, Aaron E.

AU - Hogan, James M.

AU - Kaur, Sandeep

AU - Maier-Hein, Lena

AU - McCarthy, Davis J.

AU - Moore, William

AU - Stenau, Esther

AU - Swedlow, Jason

AU - Vuong, Jenny

AU - Procter, James

N1 - Funding: BBSRC BB/L020742/1. JR Swedlow and WJ Moore are supported by the Wellcome Trust (Ref: 202908/Z/16/Z) and the BBSRC (Ref: BB/P027032/1).

PY - 2018/7/20

Y1 - 2018/7/20

N2 - 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.

AB - 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.

KW - Data visualization

KW - Multivariate data

KW - Molecular biology

KW - Cell biology

KW - Tissue imaging

KW - Metagenomics

U2 - 10.1146/annurev-biodatasci-080917-013424

DO - 10.1146/annurev-biodatasci-080917-013424

M3 - Article

VL - 1

JO - Annual Review of Biomedical Data Science

JF - Annual Review of Biomedical Data Science

SN - 2574-3414

ER -

O'Donoghue SI, Baldi BF, Clark SJ, Darling AE, Hogan JM, Kaur S et al. Visualization of Biomedical Data. Annual Review of Biomedical Data Science. 2018 Jul 20;1. https://doi.org/10.1146/annurev-biodatasci-080917-013424