Integrative Visualization of Spatiotemporal Tumor Atlases

时空肿瘤图谱的综合可视化

• 批准号: 10293808
• 负责人: Nils Gehlenborg
• 金额: $ 153.26万
• 依托单位: HARVARD MEDICAL SCHOOL
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-24 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10293808
• 关键词: 3-Dimensional; Address; Atlases; Biological Assay; Cells; Cellular Structures; Clinical; Clinical Data; Communication; Communities; Complement; Computer Analysis; Computer software; Computing Methodologies; Custom; Data; Data Set; Development; Ensure; Environment; Evaluation; General Population; Genomics; Human; Image; Link; Malignant Neoplasms; Maps; Measurement; Methods; Modality; Molecular; Online Systems; Process; Proteomics; Pythons; Research Personnel; Resolution; Role; Techniques; Tissues; Tumor Biology; Visual; Visualization; Visualization software; Work; anticancer research; base; cancer cell; cancer therapy; comparative; data visualization; design; flexibility; graphical user interface; high throughput technology; insight; iterative design; member; metabolomics; multimodal data; multimodality; novel; spatiotemporal; tool; transcriptomics; treatment strategy; tumor; tumorigenesis; user centered design

Project Summary With the rise of new high-throughput technologies that enable the measurement of biomolecules at the single-cell level within the spatial tissue context, emerging human tumor atlases promise to illuminate the role of cellular interactions in cancer. Spatiotemporal tumor atlases integrate molecular, cellular, and structural information, as well as clinical data. Interpretation of the resulting 2D and 3D maps will lead to new insights into molecular processes that drive tumorigenesis and eventually guide the development of new cancer treatment strategies. The development of specialized visual exploration tools to support this discovery process is critical. However, these tools must be designed to address several challenges: a large number of features such as cells or molecules that need to be visualized, the need to link across diverse modalities such as genomic and imaging data, the presence of multiple timepoints and organizational scales, as well as the volume of data generated by many assays. Furthermore, the cancer research community includes a broad spectrum of user audiences with varying data visualization needs. Therefore, we propose to create a framework for integrative, web-based visualization of human tumor atlases. Our work will be guided by user-centered design processes and we will be collaborating with users in the cancer research community to elicit user needs and to evaluate our visualization tools. This will allow us to design and implement visualization tools that are appropriate for the targeted user audiences. Given the diversity and size of the datasets generated by assays used to build tumor atlases, we will design this framework to be extensible and scalable from the ground up. We are proposing three distinct aims for this project. One aim is to develop a modular, web-based toolkit for visual analysis of multimodal spatial single-cell cancer datasets. This toolkit will also be available through R and Python APIs for integration into computational notebooks. This will enable data analysts and software developers to connect any visualizations created with the toolkit to state of the art computational analysis techniques, which complement the visual exploration supported by the visualizations. Another aim of our work is the design and implementation of novel methods for comparative and longitudinal visualization of spatiotemporal tumor atlas data sets. Finally, we will also build a web-based platform that will allow any cancer researcher to create their own interactive visualizations of multimodal spatiotemporal tumor atlas datasets through a graphical user interface. Users will also be able to share links to these visualizations with other researchers and the general public.

项目摘要 随着新的高通量技术的兴起，使得能够在生物分子的水平上测量生物分子， 在空间组织背景下的单细胞水平，新兴的人类肿瘤图谱有望阐明 癌症中细胞间相互作用。时空肿瘤图谱整合了分子、细胞和结构 信息，以及临床数据。对生成的2D和3D地图的解释将带来新的见解 转化为推动肿瘤发生并最终引导新癌症发展的分子过程 治疗策略。开发专门的视觉探索工具来支持这一发现过程 至关重要。但是，这些工具必须设计为解决以下几个挑战： 例如需要可视化的细胞或分子，需要连接不同的模态， 基因组和成像数据，多个时间点和组织规模的存在，以及 许多分析产生的大量数据。此外，癌症研究界包括广泛的 具有不同数据可视化需求的用户受众范围。因此，我们建议创建一个 框架的综合，基于网络的可视化人类肿瘤图谱。我们的工作将遵循以下原则： 以用户为中心的设计流程，我们将与癌症研究社区的用户合作， 了解用户需求并评估我们的可视化工具。这将使我们能够设计和实施 适合目标用户受众的可视化工具。鉴于其多样性和规模， 通过用于构建肿瘤图谱的测定所生成的数据集，我们将设计该框架以使其可扩展， 从地面向上扩展。我们为这个项目提出了三个不同的目标。一个目标是发展一个 模块化，基于网络的工具包，用于多模态空间单细胞癌症数据集的可视化分析。该工具包 也可以通过R和Python API集成到计算笔记本中。这将 使数据分析师和软件开发人员能够将使用工具包创建的任何可视化连接到 艺术计算分析技术，它补充了视觉探索的支持， 可视化。我们工作的另一个目的是设计和实施新的方法进行比较 以及时空肿瘤图谱数据集的纵向可视化。最后，我们还将建立一个基于Web的 该平台将允许任何癌症研究人员创建自己的多模态交互式可视化 时空肿瘤图谱数据集通过图形用户界面。用户还可以分享链接到 与其他研究人员和公众分享这些可视化。