Topological and Geometric Modeling and Computation of Structures and Functions in Single-Cell Omics Data

单细胞组学数据中结构和功能的拓扑和几何建模及计算

基本信息

批准号：
2151934
负责人：
Zixuan Cang
金额：
$ 37.44万
依托单位：
North Carolina State University
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2022
资助国家：
美国
起止时间：
2022-09-01 至 2025-08-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2151934&HistoricalAwards=false
关键词：
Topological Geometric Modeling Computation Structures

项目摘要

Different cells interact to maintain the functions of biological tissues. Recent single-cell technologies profile a tissue with unprecedented resolution and scale, for example, expression levels of thousands of genes in thousands of individual cells. Extracting biological insights from this data relies on structural representations, such as how to describe similarities between cells and what global shape the data presents. While numerous methods have been developed to perform various analysis tasks, this initial step of representing the structure of data is understudied. This project will develop new topological and geometric methods, a formal language of describing shapes, to investigate and characterize the structure of single-cell data. The structural characterizations will be linked to cell functions to reveal structure-function relationships. These methods will be integrated into the large collection of existing analysis tools for single-cell data to improve the reliability and robustness of the biological conclusions and predictions. Application of these tools will help to identify cells carrying critical functions and the properties of these cells. The methods will be implemented as publicly available open-source software packages. The research will promote interdisciplinary collaborations between biologists and mathematicians with an interest in advancing the structure-function relationship in single-cell data. This project will also provide training for students and underrepresented groups at the interface of advanced mathematics and modern biological data analysis.Numerous single-cell data analysis tools rely on structural representations with reduced dimensions, and the observations could be sensitive to the low-dimensional representation used. A systematic exploration of structural representations is thus needed to control the reliability and interpretability of downstream analysis results. Methods based on applied topology and geometry will be developed to extract low-dimensional structural characteristics from the high-dimensional single-cell omics data by scanning a wide range of scales and parameters. Methods will be developed to adapt to the application of single-cell omics data analysis, for example, local topological fingerprints and topology-guided optimal transport. An atlas of structural representations for a single-cell dataset with well-defined metrics quantifying the difference between structures will be assembled to provide a systematic way of representing the structures of single-cell omics data. A generally applicable pipeline of applying downstream analysis tools upon this structure atlas will be introduced and evaluated in various application cases. The systematic structural analysis method will be combined with machine learning to further address two important questions: establishment of structure-function relationships in single-cell datasets, such as identifying transition cells based on their local structures in the dataset, and integration of single-cell multi-omics datasets based on topological and geometric characterizations, especially for datasets without shared features. Efficient, stable, and accurate numerical methods and algorithms will be developed for these mathematical questions motivated by biological applications. The tools will be implemented to be easily usable by both computational and experimental scientists.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

不同的细胞相互作用以维持生物组织的功能。最近的单细胞技术介绍了具有前所未有的分辨率和尺度的组织，例如，成千上万个单个细胞中数千个基因的表达水平。从这些数据中提取生物学见解取决于结构表示，例如如何描述细胞之间的相似性以及数据呈现的全局形状。尽管已经开发了执行各种分析任务的许多方法，但表示代表数据结构的初步步骤已研究。该项目将开发新的拓扑和几何方法，这是描述形状的形式语言，以调查和表征单细胞数据的结构。结构表征将与细胞功能相关，以揭示结构功能关系。这些方法将集成到大量的单细胞数据分析工具中，以提高生物学结论和预测的可靠性和鲁棒性。这些工具的应用将有助于识别带有关键功能的单元格和这些单元的特性。这些方法将被实现为公开可用的开源软件包。这项研究将促进生物学家和数学家之间的跨学科合作，并有兴趣促进单细胞数据中的结构功能关系。该项目还将在高级数学和现代生物学数据分析的界面上为学生和代表性不足的群体提供培训。单个单细胞数据分析工具依赖于尺寸降低的结构表示工具，并且观察值可能对使用的低维表示敏感。因此，需要对结构表示的系统探索来控制下游分析结果的可靠性和解释性。基于应用拓扑和几何形状的方法将通过扫描广泛的尺度和参数来从高维单细胞组数据中提取低维结构特性。将开发方法以适应单细胞OMIC数据分析的应用，例如局部拓扑指纹和拓扑引导的最佳运输。将组装具有明确定义的指标的单细胞数据集的结构表示图地图集，将组装结构之间的差异，以提供一种系统的方式来表示单细胞OMICS数据的结构。将在各种应用程序中引入和评估在此结构地图集上应用下游分析工具的通常适用的管道。系统的结构分析方法将与机器学习相结合，以进一步解决两个重要问题：在单细胞数据集中建立结构功能关系，例如基于数据集中的本地结构识别过渡单元，以及基于单个多族数据集的集成基于单层数据集的集成，尤其是对于没有共享功能的拓扑和几何学特征。将针对由生物学应用激发的这些数学问题开发有效，稳定和准确的数值方法和算法。这些工具将被实施，以便于计算和实验科学家都可以很容易地使用。该奖项反映了NSF的法定任务，并且使用基金会的知识分子优点和更广泛的影响评估标准，被认为值得通过评估来获得支持。