Visualization of Biomedical Data

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.

Glossary for public health surveillance in the age of data science

“…However, with the growth in available data and the improvement in statistical tools, data exploration through visualisation has gained importance for surveillance and monitoring activities. The field has evolved with contributions of computer science merging scientific visualisation, information visualisation and visual analytics, making visualisation an important part of surveillance data analyses 22 ; it is a powerful tool to understand complex multilayer data, which are not easily captured by simple indicators. It has a major impact on how temporal and spatial analyses are conducted and reported.…”

“…One major change is the possibility of tailoring visualisation surveillance output to users’ needs through interactive data visualisation. 22…”

Temporal ordering of omics and multiomic events inferred from time-series data

“…These ordered events provide insight into the causal flow of information underlying transitions in cellular state. These insights, in turn, have the potential to lead to fundamental advances in biology and biomedical sciences-however, realizing this potential requires improving the analysis methods used, which are often inadequate with existing data sets 1,2 , especially from multiomics experiments.…”

“…When analyzing data from such experiments, a common first step is to partition the (typically) tens of thousands of abundance time profiles found into tens of clusters, each with similar temporal changes that are then visualized in a single profile plot 1,2 (Fig. 1).…”

FluxVisualizer, a Software to Visualize Fluxes through Metabolic Networks

“…The study of genome-scale metabolic models has grown strongly in recent years. This has stimulated the development of visualization software of large models of metabolism [1][2][3] for reviews. At the same time, new methods of studying metabolic fluxes have emerged which lead to the enumeration of EFMs, Flux Balance Analysis (FBA) [4,5] for reviews alongside more traditional methods such as dynamical systems and Metabolic Control Analysis [6][7][8] using the rate functions of the metabolic steps [9,10].…”