Statistical Learning and Inference for Single-Cell RNA Sequencing

单细胞基因组学是一种新兴的技术，已成为了解细胞多样性和细胞活动的不可或缺的工具。在各种单细胞测序技术中，单细胞RNA测序（scRNA-seq），同时测量每个细胞内的数万个RNA，是最成熟和最广泛使用的技术。该项目旨在为scRNA-seq开发新的分析工具，具有明确和一致的统计框架，以提供可靠的不确定性量化和推断。与此同时，新工具将保留现有基于算法的方法的可扩展性和用户友好性。PI将专注于为深度学习等机器学习框架构建概率模型，并解决scRNA-seq中生物随机性和技术噪声的新挑战。PI将开发用于分析scRNA-seq数据的开源软件，以帮助科学家了解细胞发育，基因调控机制以及常见疾病的细胞类型特异性特征。由于该项目的跨学科特点，它还将培养统计学内外的研究生和本科生成为应用统计学和计算生物学快速发展领域的未来科学家。PI计划专注于单细胞数据分析所特有的三个研究问题：轨迹推断，细胞类型反卷积和基因-基因共表达/共爆发。对于轨迹推断，PI将分层混合模型纳入深度神经网络，以推断来自多个来源的细胞共享的轨迹。在细胞类型去卷积问题中，scRNA-seq数据用作参考以估计批量样品中的细胞类型比例，PI将在没有参数假设的情况下推导估计的细胞类型比例的渐近有效置信区间，并考虑三个主要的不确定性膨胀因素：技术噪声、个体间的生物异质性和基因间的依赖性。最后，在基因-基因共表达/共爆发分析中，PI将从噪声观测数据中估计真实的基因-基因相关性和共爆发模式，并设计可扩展的多测试框架，该框架可以有效地找到共表达或共爆发的基因对。PI还旨在将共表达和共爆发信号与三维基因组结构中的增强子-启动子接触联系起来，以了解转录调控的因果机制。该奖项反映了NSF的法定使命，并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，被认为值得支持。

Single-cell genomics is an emerging technique that has become an indispensable tool for understanding cellular diversity and cell activities. Among the various single-cell sequencing technologies, single-cell RNA sequencing (scRNA-seq), simultaneously measuring tens of thousands of RNAs inside each individual cell, is the most mature and widely used technology. This project aims to develop new analytical tools for scRNA-seq with explicit and coherent statistical frameworks to provide reliable uncertainty quantification and inference. At the same time, the new tool will retain the scalability and user-friendly features in existing algorithmic-based methods. The PI will focus on building probabilistic models for machine learning frameworks such as deep learning and address new challenges to account for biological randomness and technical noise in scRNA-seq. The PI will develop open-source software for analyzing scRNA-seq data to help scientists understand cell development, the mechanism of gene regulation, and cell-type-specific features of common diseases. Because of the interdisciplinary feature of this project, it will also train both graduate and undergraduate students within and outside statistics to become future scientists in the fast-evolving area of applied statistics and computational biology.The PI plans to focus on three research problems that are unique to the analysis of single-cell data: trajectory inference, cell type deconvolution, and gene-gene co-expression / co-bursting. For trajectory inference, the PI will incorporate a hierarchical mixture model into a deep neural network to infer trajectories shared by cells from multiple sources. In the cell type deconvolution problem where scRNA-seq data are used as references to estimate cell type proportions in bulk samples, the PI will derive asymptotically valid confidence intervals of the estimated cell type proportions without parametric assumptions and account for three major uncertainty-inflation factors: the technical noise, biological heterogeneity across individuals, and dependence across genes. Finally, in the gene-gene co-expression / co-bursting analysis, the PI will estimate the true gene-gene correlation and co-bursting pattern from noisy observed data and design a scalable multiple testing framework that can efficiently find gene pairs that are co-expressed or co-bursted. The PI also aims to link the co-expression and co-bursting signals with the enhancer-promoter contacts in the three-dimensional genome structure to understand causal mechanisms of transcriptional regulation.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.