Developing novel machine learning approaches to studying cell development

本研究的总体目标是开发新的机器学习（ML）方法，以准确地描述细胞周期的动态过程和状态，建模的谱系承诺沿着分化过程，并预测调控细胞周期和分化过程的关键因素与单细胞多组学数据。这将导致对细胞生长和发育的细胞周期分化之间的耦合机制的见解。这些工具将大大提高我们对干细胞、生殖细胞和组织发育和功能的理解。 ML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这个项目下工作，并获得做前沿研究的经验。该项目将开发两种ML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动编码器（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采用一个独特的正弦自编码器来表征细胞周期中的循环过程，分段自编码器来捕获高维空间中固有的非线性数据结构，该数据结构由从周期过程的各个阶段采样的单个细胞形成。该模型还可以描述细胞周期和细胞发育之间的联系，同时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，CADSN被提出用于多组学单细胞数据集成和标签传递。提出了一种基于自动编码器的细胞周期感知域分离网络，用于预测和去除整合的多组学单细胞数据中的细胞周期效应，同时保持细胞类型特异性异质性。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，一些推断的结果将通过使用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：/ www.example.com向研究界公开提供ccsm.uth.edu/NSF-SSL.This奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。

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.