Saraiya Purvi, North Chris, Duca Karen
Department of Computer Science, Virginia Tech, Blacksburg, VA 24061, USA.
IEEE Trans Vis Comput Graph. 2005 Jul-Aug;11(4):443-56. doi: 10.1109/TVCG.2005.53.
High-throughput experiments, such as gene expression microarrays in the life sciences, result in very large data sets. In response, a wide variety of visualization tools have been created to facilitate data analysis. A primary purpose of these tools is to provide biologically relevant insight into the data. Typically, visualizations are evaluated in controlled studies that measure user performance on predetermined tasks or using heuristics and expert reviews. To evaluate and rank bioinformatics visualizations based on real-world data analysis scenarios, we developed a more relevant evaluation method that focuses on data insight. This paper presents several characteristics of insight that enabled us to recognize and quantify it in open-ended user tests. Using these characteristics, we evaluated five microarray visualization tools on the amount and types of insight they provide and the time it takes to acquire it. The results of the study guide biologists in selecting a visualization tool based on the type of their microarray data, visualization designers on the key role of user interaction techniques, and evaluators on a new approach for evaluating the effectiveness of visualizations for providing insight. Though we used the method to analyze bioinformatics visualizations, it can be applied to other domains.
高通量实验，比如生命科学中的基因表达微阵列实验，会产生非常庞大的数据集。作为回应，人们创建了各种各样的可视化工具来辅助数据分析。这些工具的一个主要目的是为数据提供与生物学相关的见解。通常，可视化工具是在对照研究中进行评估的，这些研究通过测量用户在预定任务上的表现，或者使用启发式方法和专家评审来进行。为了基于实际数据分析场景对生物信息学可视化工具进行评估和排序，我们开发了一种更具相关性的评估方法，该方法侧重于数据洞察。本文介绍了洞察的几个特征，这些特征使我们能够在开放式用户测试中识别并量化洞察。利用这些特征，我们评估了五种微阵列可视化工具，评估内容包括它们提供的洞察的数量和类型，以及获取这些洞察所需的时间。该研究结果可指导生物学家根据其微阵列数据的类型选择可视化工具，指导可视化设计师了解用户交互技术的关键作用，也为评估人员提供了一种评估可视化工具提供洞察有效性的新方法。虽然我们使用该方法来分析生物信息学可视化工具，但它也可应用于其他领域。
