Integrative Visualization of Spatiotemporal Tumor Atlases

项目概要 随着新的高通量技术的兴起，使得生物分子的测量成为可能。 在空间组织背景下的单细胞水平上，新兴的人类肿瘤图谱有望阐明这一作用 癌症中的细胞相互作用。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 进入驱动肿瘤发生并最终指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 很关键。然而，这些工具的设计必须能够解决几个挑战：大量的功能 例如需要可视化的细胞或分子，需要连接不同的模式，例如 基因组和成像数据、多个时间点和组织规模的存在，以及 许多测定产生的数据量。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群体。因此，我们建议创建一个 基于网络的人类肿瘤图谱综合可视化框架。我们的工作将遵循 以用户为中心的设计流程，我们将与癌症研究界的用户合作 引出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 适合目标用户群体的可视化工具。鉴于多样性和规模 用于构建肿瘤图谱的分析生成的数据集，我们将设计这个框架，使其可扩展并且 从头开始可扩展。我们为该项目提出三个不同的目标。一个目标是开发一种 基于网络的模块化工具包，用于多模式空间单细胞癌症数据集的可视化分析。这个工具包 还可以通过 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该工具包创建的任何可视化连接到以下状态： 艺术计算分析技术，补充了由 可视化。我们工作的另一个目标是设计和实施新的比较方法 以及时空肿瘤图谱数据集的纵向可视化。最后，我们还将构建一个基于网络的 平台将允许任何癌症研究人员创建自己的多模式交互式可视化 通过图形用户界面的时空肿瘤图谱数据集。用户还可以分享链接 与其他研究人员和公众一起进行这些可视化。

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.