Stahlbom Emilia, Molin Jesper, Lundstrom Claes, Ynnerman Anders
IEEE Trans Vis Comput Graph. 2025 Apr;31(4):2211-2222. doi: 10.1109/TVCG.2024.3385118. Epub 2025 Feb 27.
Genomics is at the core of precision medicine, and there are high expectations on genomics-enabled improvement of patient outcomes in the years to come. Around the world, initiatives to increase the use of DNA sequencing in clinical routine are being deployed, such as the use of broad panels in the standard care for oncology patients. Such a development comes at the cost of increased demands on throughput in genomic data analysis. In this paper, we use the task of copy number variant (CNV) analysis as a context for exploring visualization concepts for clinical genomics. CNV calls are generated algorithmically, but time-consuming manual intervention is needed to separate relevant findings from irrelevant ones in the resulting large call candidate lists. We present a visualization environment, named Copycat, to support this review task in a clinical scenario. Key components are a scatter-glyph plot replacing the traditional list visualization, and a glyph representation designed for at-a-glance relevance assessments. Moreover, we present results from a formative evaluation of the prototype by domain specialists, from which we elicit insights to guide both prototype improvements and visualization for clinical genomics in general.
基因组学是精准医学的核心，人们对未来几年通过基因组学改善患者治疗效果寄予厚望。在全球范围内，正在开展一些举措以增加DNA测序在临床常规中的应用，例如在肿瘤患者的标准护理中使用广泛的基因检测 panel。这种发展是以对基因组数据分析通量的更高要求为代价的。在本文中，我们将拷贝数变异（CNV）分析任务作为探索临床基因组学可视化概念的背景。CNV调用是通过算法生成的，但需要耗时的人工干预才能从生成的大量调用候选列表中分离出相关发现与无关发现。我们提出了一个名为Copycat的可视化环境，以在临床场景中支持这项审查任务。关键组件包括取代传统列表可视化的散点图，以及为一目了然的相关性评估而设计的图形表示。此外，我们展示了领域专家对该原型进行的形成性评估结果，从中我们获得了一些见解，以指导原型改进以及一般临床基因组学的可视化。
