DMS/NIGMS 2: Advanced Statistical Methods for Spatially Resolved Transcriptomics Studies

DMS/NIGMS 2：空间分辨转录组学研究的高级统计方法

• 批准号: 10797593
• 负责人: Xiang Zhou
• 金额: $ 7.79万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 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/10797593
• 关键词: Administrative Supplement; Area; Award; Biology; Cells; Communities; Complex; Data; Development; Ensure; Equipment; Funding; Genes; Methods; Modeling; National Institute of General Medical Sciences; Neck; Parents; Pattern; Research; Statistical Methods; Structure; Technology; Tissues; cell type; computerized tools; novel; single-cell RNA sequencing; success; tool; transcriptomics; usability

Project Summary The recent emergence of various spatially resolved transcriptomic technologies have enabled the study of spatial transcriptomic landscape across a tissue section or within single cells, catalyzing new discoveries in many areas of biology. Despite the fast development of spatial transcriptomic technologies, however, statistical methods for analyzing spatial transcriptomic data are vastly underdeveloped. In the funded parent award, we proposed to develop a suite of novel statistical methods and computational tools (1) to model the spatial correlation structure in a computational effective way to rapidly identify genes with spatial expression patterns; (2) to incorporate reference single cell RNA sequencing data along with spatial correlation structure in spatial transcriptomics to enable accurate deconvolution of cell types on the tissue; and (3) to perform tissue segmentation and detect tissue regions and microenvironment in a de novo fashion. In this proposal to administrative supplements for equipment, we propose to use a single unit of GPU node to provide the necessary GPU computation support for this project, solve the computational bottle neck created by the increasingly large spatially resolved transcriptomics studies, and enhance the usability and applicability of the proposed statistical methods and computational tools and hence their impact in this emerging research field of spatially resolved transcriptomics studies. The proposed administrative supplement will ensure the successful development of the proposed statistical methods and computational tools in the parent award and the high impact of the developed methods to the scientific community.

项目摘要 最近出现的各种空间分辨转录组学技术使得能够研究 在组织切片或单细胞内的空间转录组景观，催化新的发现， 生物学的许多领域。尽管空间转录组学技术发展迅速，但是， 用于分析空间转录组数据的方法还远远不成熟。在资助父母奖中，我们 建议开发一套新的统计方法和计算工具（1）来模拟空间 以计算有效的方式快速识别具有空间表达模式的基因的相关结构; (2)为了将参考单细胞RNA测序数据沿着与空间相关性结构结合， 转录组学，以实现组织上细胞类型的准确去卷积;以及（3）进行组织 以从头开始的方式分割和检测组织区域和微环境。在这项建议中， 管理补充设备，我们建议使用一个GPU节点单元来提供 必要的GPU计算支持这个项目，解决了计算瓶颈造成的 越来越大的空间分辨转录组学研究，并提高了可用性和适用性， 提出的统计方法和计算工具，因此，他们在这一新兴的研究领域的影响， 空间分辨转录组学研究。拟议的行政补充将确保成功 在父母奖和高等教育中制定拟议的统计方法和计算工具， 所开发的方法对科学界的影响。