CAREER: Mining biological functions from single cell multi-omics data

生物功能活动包括转录调节、代谢和信号转导等细胞内功能，以及细胞间相互作用等细胞间活动。随着单细胞多组学(scMulti-seq)生物技术的出现，研究人员可以在细胞分辨率上研究复杂生物系统的生物学功能。SCM-SEQ数据和多个研究对象的综合分析产生了丰富的信息，能够表征物种或组织特定的生物功能，同时也对如何识别和提取具有生物意义的数据模式提出了巨大的挑战。尽管人们已经做了大量的工作来解释单细胞多组学数据中的数据模式，但现有的方法大多集中在完全数据驱动的无监督学习上，而没有考虑到丰富的现有知识。此外，根据生物功能类型的不同，它们在scMulti-seq数据中的基本数学表示形式也不同。这需要系统生物学模型和机器学习概念，以从scMulti-seq数据中定位真正的生物功能。从scMulti-seq数据中研究生物功能的第一个挑战是获得与真实生物功能相对应的数据模式，并为特定的生物机制和途径开发适当的计算模型。第二个挑战在于对不同物种、组织类型和实验条件的研究的知识表示和共享的困难。迫切需要整合来自不同数据源的知识，以优化生物功能建模，以便所学知识可以用于研究其他生物系统或数据类型，并促进新假说的产生。PI的长期职业目标是开发数学公式和计算方法，以从多组学数据中模拟生物功能。该项目将开发新的数学模型和先进的计算框架，通过将scMulti-seq数据与来自独立数据集或实验的特定背景和一般知识相结合，来优化生物功能的挖掘。国际和平研究所的研究团队将通过以下三个目标实现这些目标。首先，将开发一个新的子空间表示模型来识别转录调控和功能基因模块。该方法将通过一种新的局部低阶矩阵检测方法来检测基因共调控模块，并通过元学习框架来优化结果解释。其次，PI的研究团队将开发一种新的图形神经网络结构来估计通量携带网络的细胞功能活动，并开发一种图形数据聚类方法来识别具有不同功能状态和不同路径的细胞组。第三，将构建一个知识图来表示从scMulti-seq数据派生的生物功能，这使得从文献数据派生的独立知识和新的生物学假说的发展成为可能。该项目预计将提供新的计算工具，能够有效地从各种不同的数据集探索生物功能，并通过最大限度地利用现有的scMulti-seq和文献数据以及对新的生物学假设和机制进行推理，为单个数据集的功能解释提供新的能力。在教育方面，这些科学发现，包括开发的方法和生物知识，将无缝地整合到一个在线教育知识库中，供大规模公众参与，还将导致针对高中、本科生和研究生的新的基于项目的跨学科培训。该项目的结果可以在：https://zcslab.github.io/.This奖反映了国家科学基金会的法定使命，并已被认为值得支持，通过使用基金会的智力价值和更广泛的影响审查标准进行评估。

Biological functional activities include intracellular functions such as transcriptional regulation, metabolism, and signaling transduction, and intercellular activities such as cell-cell interactions. With the advent of single cell multi-omics (scMulti-seq) biotechnology, researchers can study the biological functions of a complex biological system at the cellular resolution. The integrative analysis of scMulti-seq data and multiple study objects produces a wealth of rich information that enables the characterization of species or tissue specific biological functions, and at the same time, poses great challenge on how to identify and extract biologically meaningful data patterns. Though substantial amount of efforts has been made to interpret data patterns in single cell multi omics data, most of the existing methods focused on unsupervised learning in a completely data driven manner without considering the rich existing knowledge. In addition, depending on the types of biological functions, their underlying mathematical representation forms are different in scMulti-seq data. This calls for systems biology models and machine learning concepts to target true biological functions from scMulti-seq data. The first challenge to study biological functions from scMulti-seq data is to derive the data patterns that correspond to true biological functions and develop proper computational models for specific biological mechanisms and pathways. The second challenge lies in the difficulty of knowledge representation and sharing across the studies for different species, tissue types and experimental conditions. There remains an urgent need to integrate knowledge derived from disparate data sources to optimize the biological functional modeling, such that the learned knowledge could be utilized to study other biological systems or data types and promote the generation of new hypotheses. The PI’s long-term career goal is to develop mathematical formulations and computational methods to model biological functions from multi-omics data. This project will develop new mathematical models and an advanced computational framework to optimize the mining of biological functions, by integrating scMulti-seq data with context specific and general knowledge derived from independent data sets or experiments. The PI's research team will achieve the goals through the following three objectives. First, a novel subspace representation model will be developed to identify transcriptional regulation and functional gene modules. The proposed method will be empowered by a novel local low-rank matrix detection method to detect gene co regulation modules and a meta-learning framework to optimize results interpretation. Second, the PI's research team will develop a new graph neural network architecture to estimate cell-wise functional activities for flux carrying networks and a graph data clustering method to identify cell groups with varied functional states and distinct pathways. Thirdly, a knowledge graph will be constructed to represent the biological functions derived from scMulti-seq data, which enables the integration of independent knowledge derived from literature data and development of new biological hypotheses. The project is expected to deliver novel computational tools that can effectively explore biological functions from a wide range of heterogeneous datasets, and it could provide new capabilities for functional interpretation of individual data sets by maximizing the utilization of existing scMulti-seq and literature data, and reasoning of new biological hypotheses and mechanisms. Educationally, the scientific discoveries, including developed methods and biological knowledge, will be seamlessly integrated into an online educational knowledge base for large-scale public engagement, and will also lead to new project-based interdisciplinary training for high school, undergraduate and graduate students. The results of this project can be found at: https://zcslab.github.io/.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.