简介:在可视化复杂数据时,选择的布局方法会极大地影响识别异常值的能力,发现错误的建模假设,或者识别意想不到的模式。此外,视觉布局可以在向同行传达结果方面发挥关键作用。方法:本文,我们比较了三种视觉布局的有效性——邻接矩阵,半矩阵布局,和圆形布局-用于可视化空间连通性数据,例如,来自染色质构象捕获实验的接触。为了评估这些视觉布局,我们进行了一项由150名来自亚马逊机械土耳其人的参与者组成的研究,以及由30位生物医学研究科学家组成的第二项专家研究。结果:MechanicalTurk研究发现圆形布局最准确、最直观,而专家研究发现,圆形和半矩阵布局比矩阵布局更准确。讨论:我们得出的结论是,圆形布局可能是一个很好的默认选择,用于可视化空间接触相对较少的较小数据集,while,对于较大的数据集,半矩阵布局可能是更好的选择。我们的结果还证明了如何使用众包方法来确定哪些视觉布局最适合解决生物信息学中的特定数据挑战。
Introduction: When visualizing complex data, the layout method chosen can greatly affect the ability to identify outliers, spot incorrect modeling assumptions, or recognize unexpected patterns. Additionally, visual layout can play a crucial role in communicating results to peers. Methods: In this paper, we compared the effectiveness of three visual layouts-the adjacency matrix, a half-matrix layout, and a circular layout-for visualizing spatial connectivity data, e.g., contacts derived from chromatin conformation capture experiments. To assess these visual layouts, we conducted a study comprising 150 participants from Amazon\'s Mechanical Turk, as well as a second expert study comprising 30 biomedical research scientists. Results: The Mechanical Turk study found that the circular layout was the most accurate and intuitive, while the expert study found that the circular and half-matrix layouts were more accurate than the matrix layout. Discussion: We concluded that the circular layout may be a good default choice for visualizing smaller datasets with relatively few spatial contacts, while, for larger datasets, the half- matrix layout may be a better choice. Our results also demonstrated how crowdsourcing methods could be used to determine which visual layouts are best for addressing specific data challenges in bioinformatics.