Developing new computational tools for spatial transcriptomics data

项目摘要 空间转录学是一项突破性的新技术，可以测量基因活性。 在绘制活动发生的位置的同时，研究组织样本中的活性。它承诺促进 我们对基本表型和疾病背后的空间异质性的理解，例如 神经退行性疾病和癌症。然而，生物信息学基础设施的发展 而计算工具已经严重落后于技术进步。缺乏适当的 计算方法提出了当前的数据分析障碍，这意味着fi可能会阻碍生物学 调查。这项提案的首要目标是解决一些最紧迫的问题-- 支持fi和解释空间转录数据面临的重大挑战，包括1)缺乏健壮性 跨多种技术平台的具有空间表达模式的基因的同源fi阳离子，2)缺乏 用于识别组织结构、微环境以及组织发育轨迹的工具， 以及3)缺乏可跨多个样本联合分析空间转录数据的工具 多个数据源。在提案中，我们将致力于以下目标：目标1.发展非对抗性-- 用于识别具有空间表达模式的基因的度量工具。目标2.培养空间意识 用于检测组织上的结构和发育轨迹的降维工具。目标3. 开发用于跨多个样本的空间转录分析的综合关联工具 数据集。所有方法都将在用户友好的软件中实施，并分发给SCI- Entific社区。所有目标的成功实现将极大地增加空间的力量 转录分析，并促进这些尖端技术在翻译中的应用。 国家和临床研究。

Project Summary Spatial transcriptomics is a groundbreaking new technology that allows measurement of gene ac- tivity in a tissue sample while mapping where the activity is occurring. It holds the promise to facilitate our understanding of spatial heterogeneity underlying essential phenotypes and diseases, such as neurodegenerative diseases and cancer. However, the development of bioinformatics infrastructures and computational tools has fallen seriously behind the technological advances. The lack of proper computational approaches presents current data analysis barriers that significantly hinder biological investigations. The overarching goal of this proposal is to address some of the most pressing ana- lytic challenges facing profiling and interpreting spatial transcriptomics data, including 1) lack of robust identification of genes with spatial expression patterns across a variety of technical platforms, 2) lack of tools to identify structures, microenvironments as well as developmental trajectory on the tissue, and 3) lack of tools that can jointly analyze spatial transcriptomic data across multiple samples and multiple data sources. In the proposal, we will work on the following aims: Aim 1. Develop nonpara- metric tools for identifying genes with spatial expression patterns. Aim 2. Develop spatially aware dimension reduction tools for detecting structures and developmental trajectories on the tissue. Aim 3. Develop integrative association tools for spatial transcriptomic analysis across multiple samples and datasets. All the methods will be implemented in user-friendly software and disseminated to the sci- entific community. Successful achievement of all aims will dramatically increase the power of spatial transcriptomics analysis, and facilitate the application of these cutting-edge technologies to transla- tional and clinical studies.