Revealing cell-level gene regulation through integration of single-cell multi-omics measurements

摘要 先进的单细胞测序技术使我们能够在单细胞水平上推断基因调控。我们 建议开发计算方法以克服阐明单细胞基因调控的障碍 决议。我们首次提出了一种基于比对的计算框架来整合单细胞多组学 测量。基于比对的计算框架可以有效地处理单元类型不平衡 问题，并且对超参数更健壮。此外，我们还整合了集成的单细胞多组学 用于推断转录调控、末端调控的测量和高级机器学习算法 单细胞水平上的转录后调控。我们希望开发出计算方法 更好地了解基因调控，为疾病的诊断、治疗和治疗奠定坚实的基础 预防。

Summary Advanced single-cell sequencing techniques have enabled us to infer gene regulation at the single-cell level. We propose to develop computational methods to overcome obstacles for elucidating gene regulation at single-cell resolution. We first present an alignment-based computational framework to integrate single-cell multi-omics measurements. The alignment-based computational framework can effectively handle the cell type imbalance problem and is more robust to hyperparameters. Furthermore, we incorporate the integrated single-cell multi-omics measurements and advanced machine learning algorithms to infer transcriptional regulation, distal regulatory elements, and post-transcriptional regulation at the single-cell level. We expect to develop computational methods to better understand gene regulation, which would lay a solid foundation for disease diagnosis, treatment, and prevention.