Developing novel machine learning approaches to studying cell development

开发新的机器学习方法来研究细胞发育

• 批准号: 2326879
• 负责人: Xiaobo Zhou
• 金额: $ 51.57万
• 依托单位: The University of Texas Health Science Center at Houston
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2026-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2326879&HistoricalAwards=false
• 关键词: Developing; novel; machine; learning; approaches

The overall goal of this research is to develop novel machine learning (ML) methods to accurately characterize the dynamic process and states of a cell cycle, to model the lineage commitment along differentiation process, and to predict the key elements that regulate cell cycle and differentiation processes with single-cell multi-omics data. This will lead to insights into the mechanisms coupling between cell cycle differentiation for cell growth and development. The tools will significantly improve our understanding of stem cells, germinal cells and tissue development and functions. The ML methods can be modified and applied to other general problems in biology. Graduate and undergraduate students will work under this project and gain experience in doing leading-edge research. The project will develop two ML approaches: integrated sinusoidal and piecewise autoencoder (SPAE) for cell cycle estimate and cell development study, and cell cycle-aware domain separation network (CADSN) to study the dynamic coupling of cell cycle and differentiation. SPAE employs a distinct sinusoidal autoencoder to characterize the circular process in the cell cycle and piecewise autoencoder to capture the inherent nonlinear data structure in high-dimensional space, formed by single cells sampled from various stages of a periodic process. This model can also characterize the connections between the cell cycle and cell development while it effectively unfolds the circular manifold onto a non-linear space to obtain precise pseudo-time. Secondly, CADSN is proposed for multi-omics single-cell data integration and label transfer. An autoencoder-based cell-cycle-aware domain separation network is proposed to predict and remove cell cycle effects from the integrated multi-omics single-cell data while keeping the cell type-specific heterogeneity. This is the first computational model to study the cell-cycle effect in the integrated analysis for multi-omics single-cell data. Finally, some of the inferred results will be experimentally validated by using the team’s established models. Software prototypes and the gene biomarkers regulating cell cycle and differentiation will be made publicly available to the research community via a project website at https:/ccsm.uth.edu/NSF-SSL.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.

本研究的总体目标是开发新的机器学习（ML）方法，以准确表征细胞周期的动态过程和状态，模拟分化过程中的谱系承诺，并利用单细胞多组学数据预测调节细胞周期和分化过程的关键因素。这将有助于深入了解细胞周期分化与细胞生长发育之间的耦合机制。这些工具将显著提高我们对干细胞、生发细胞和组织发育和功能的理解。机器学习方法可以修改并应用于生物学中的其他一般问题。研究生和本科生将在该项目下工作，并获得从事前沿研究的经验。该项目将开发两种机器学习方法：用于细胞周期估计和细胞发育研究的集成正弦和分段自编码器（SPAE），以及用于研究细胞周期和分化动态耦合的细胞周期感知域分离网络（CADSN）。SPAE采用独特的正弦自编码器来表征细胞周期中的循环过程，并采用分段自编码器来捕获高维空间中固有的非线性数据结构，这些数据结构是由从周期过程的各个阶段采样的单个细胞形成的。该模型还可以表征细胞周期和细胞发育之间的联系，同时它有效地将圆形流形展开到非线性空间以获得精确的伪时间。其次，提出了多组学单细胞数据集成和标签传输的CADSN。提出了一种基于自编码器的细胞周期感知域分离网络，在保持细胞类型特异性异质性的同时，预测和去除多组学单细胞数据中的细胞周期效应。这是第一个在多组学单细胞数据综合分析中研究细胞周期效应的计算模型。最后，一些推断结果将通过使用该团队建立的模型进行实验验证。软件原型和调节细胞周期和分化的基因生物标志物将通过项目网站（https:/ccsm.uth.edu/NSF-SSL.This）向研究界公开。该奖项反映了美国国家科学基金会的法定使命，并通过基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。