{"title":"评估三维空间连接性的二维视觉编码","authors":"B. Baldi, Jenny Vuong, Seán I. O’Donoghue","doi":"10.3389/fbinf.2023.1232671","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":null,"pages":null},"PeriodicalIF":2.8000,"publicationDate":"2024-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Assessing 2D visual encoding of 3D spatial connectivity\",\"authors\":\"B. Baldi, Jenny Vuong, Seán I. O’Donoghue\",\"doi\":\"10.3389/fbinf.2023.1232671\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":73066,\"journal\":{\"name\":\"Frontiers in bioinformatics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.8000,\"publicationDate\":\"2024-01-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Frontiers in bioinformatics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3389/fbinf.2023.1232671\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"MATHEMATICAL & COMPUTATIONAL BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers in bioinformatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3389/fbinf.2023.1232671","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATHEMATICAL & COMPUTATIONAL BIOLOGY","Score":null,"Total":0}
引用次数: 0
摘要
介绍:在对复杂数据进行可视化时,所选择的布局方法会在很大程度上影响识别异常值、发现不正确的建模假设或识别意外模式的能力。此外,可视化布局在与同行交流结果时也能起到至关重要的作用:在本文中,我们比较了三种可视化布局--邻接矩阵、半矩阵布局和圆形布局--在可视化空间连通性数据(如染色质构象捕获实验得出的接触)方面的效果。为了评估这些可视化布局,我们进行了一项由亚马逊 Mechanical Turk 的 150 名参与者组成的研究,以及第二项由 30 名生物医学研究科学家组成的专家研究:结果:Mechanical Turk 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:我们的结论是,对于空间接触相对较少的较小数据集,圆形布局可能是可视化的良好默认选择,而对于较大的数据集,半矩阵布局可能是更好的选择。我们的研究结果还展示了如何利用众包方法来确定哪种可视化布局最适合应对生物信息学中的特定数据挑战。
Assessing 2D visual encoding of 3D spatial connectivity
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.