Qu Zhonglin, Lau Chng Wei, Nguyen Quang Vinh, Zhou Yi, Catchpoole Daniel R
School of Computing, Engineering and Mathematics, Western Sydney University, Penrith, NSW, Australia.
The MARCS Institute, Western Sydney University, Penrith, NSW, Australia.
Cancer Inform. 2019 Mar 13;18:1176935119835546. doi: 10.1177/1176935119835546. eCollection 2019.
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.
可视化分析与可视化能够利用人类感知系统来解读和揭示大数据中隐藏的模式。新一代测序技术的出现使得海量基因组数据得以快速产生，相应地也催生了对可视化和解读这些数据的新工具及方法的需求。对基因组数据进行可视化不仅需要简单地绘制数据，还应就特定图表中应传达何种信息提供决策或选择；必须以简单、清晰且准确的方式向临床医生、专家或研究人员提供用于表示结果应采用哪些方法，以便他们与数据进行交互。随着人工智能（AI）和虚拟现实（VR）等高通量技术的进步，基因组数据可视化分析也在迅速发展。个性化医疗需要新的基因组可视化工具，这些工具能够从基因组数据中高效提取知识，并加快专家针对个体患者需求做出最佳治疗决策的速度。然而，对如此庞大的基因组数据进行有意义的可视化分析仍然是一项严峻的挑战。本文对癌症相关基因组数据的可视化分析工具、方法及趋势进行了全面系统的综述与讨论。我们回顾了基因组数据可视化方法，包括散点图、热图、坐标和网络等传统方法，以及使用人工智能和虚拟现实的新兴技术。我们还展示了基因组数据可视化工具随时间的发展，并分析了基因组数据可视化的演变过程。
