Visual Analytics of Genomic and Cancer Data: A Systematic Review

Abstract

Visual analytics and visualisation can leverage the human perceptual system to interpret and uncover hidden patterns in big data. The advent of next-generation sequencing technologies has allowed the rapid production of massive amounts of genomic data and created a corresponding need for new tools and methods for visualising and interpreting these data. Visualising genomic data requires not only simply plotting of data but should also offer a decision or a choice about what the message should be conveyed in the particular plot; which methodologies should be used to represent the results must provide an easy, clear, and accurate way to the clinicians, experts, or researchers to interact with the data. Genomic data visual analytics is rapidly evolving in parallel with advances in high-throughput technologies such as artificial intelligence (AI) and virtual reality (VR). Personalised medicine requires new genomic visualisation tools, which can efficiently extract knowledge from the genomic data and speed up expert decisions about the best treatment of individual patient’s needs. However, meaningful visual analytics of such large genomic data remains a serious challenge. This article provides a comprehensive systematic review and discussion on the tools, methods, and trends for visual analytics of cancer-related genomic data. We reviewed methods for genomic data visualisation including traditional approaches such as scatter plots, heatmaps, coordinates, and networks, as well as emerging technologies using AI and VR. We also demonstrate the development of genomic data visualisation tools over time and analyse the evolution of visualising genomic data.

Tasks, Techniques, and Tools for Genomic Data Visualization

“…Node-link diagrams are used to visualize functional relationships between different entities, such as genes. Qu et al [QLN*19] also review visualization methods for oncogenomic data, such as scatterplots, networks, heatmaps, clusters and the combination of machine learning and visualization. Moreover, they discuss future trends in this field.…”

Analysis of sputum microbial diversity in patients with different histological types of lung cancer

“…The binary-Jaccard algorithm was used for fungal beta diversity analysis. Based on the appeal of the two distance matrices, Non-MetricMulti-Dimensional Scaling (NMDS) [33] , Heatmap [34] , PERMANOVA [35] and Unweighted Pair-group Method with Arithmetic Mean (UPGMA) [36] are used for multi-dimensional beta diversity analysis.…”

Explaining artificial intelligence with visual analytics in healthcare

“…Despite the diverging terminology, a lot of interesting work has been presented in different healthcare areas, for example, population health services (Chishtie et al, 2020), prevention of disease outbreaks (Preim & Lawonn, 2020), and cancer-related genomics (Qu et al, 2019). In biomedics, Sturm et al (2015) categorized existing work on interactivity level versus analysis type and visualization technique, and Turkay et al (2014) classified visual analysis tools by their analytical task and integration of computational methods.…”