Statistical methods for co-expression network analysis of population-scale scRNA-seq data

项目摘要 基因共表达网络分析是一种重要的推理工具， 对聚类和差异表达的标准工作流不可见的关系 分析.这种网络方法有助于批量RNA-seq分析，以连接 基因与生物过程。尽管在方法上取得了显著的进步 对于scRNA-seq分析的开发，没有建立的最佳实践 从scRNA-seq数据构建稳健的基因共表达网络。与宽 scRNA-seq技术的可用性，人群规模的scRNA-seq数据集， 多个受试者和时间点/扰动正在出现。虽然即时 对这些数据集的分析集中在聚类和差分的标准分析上。 表达，在共表达网络水平上利用scRNA-seq的力量， 有可能解开基因聚合到关键中断的调控途径。 当与表型变异（例如，严重程度 对病毒的反应），可以揭示关键的生物学见解。这样的进步呈现出 构建个性化动态共表达网络， 基因模块，考虑到网络的个性化性质， 群体规模scRNA-seq分析的下一个关键挑战。这项建议会 通过两个目标应对这些挑战。Aim 1将开发一种消除偏见的方法， 从scRNA-seq数据中估计基因-基因相关性， 排序深度、数据稀疏性和变化的像元数，并检测相关性 否则会被技术限制所掩盖。目标2将创新一个正规化的 谱聚类方法，作为输入的基因共表达网络在 主题和时间/扰动水平，并推断动态基因模块。这两个目标将是 通过结合方法论的发展，理论 分析、数据驱动的模拟、计算分析和实验验证。 项目的成功完成将提供基础方法和软件， 适用于广泛的scRNA-seq数据集，并具有独特的定位， 分析群体规模的scRNA-seq数据。

Project Summary Gene co-expression network analysis is a key inference tool for detecting latent relationships invisible to standard workflows of clustering and differential expression analysis. Such a network approach was instrumental in bulk RNA-seq analysis to link genes with biological processes. Despite the remarkable progress in method development for scRNA-seq analysis, there are no established best practices for constructing robust gene co-expression networks from scRNA-seq data. With the wide availability of scRNA-seq technology, population-scale scRNA-seq datasets across multiple subjects and time points/perturbations are emerging. Although the immediate analyses of these datasets focus on the standard analysis of clustering and differential expression, leveraging the power of scRNA-seq at the co-expression network level has the potential to unlock genes converging into key disrupted regulatory pathways. Network-level variation, when associated with phenotypic variation (e.g., severity of response to virus), can reveal critical biological insights. Such an advancement presents constructing personalized dynamic co-expression networks and identifying dynamic gene modules by taking into account the individualized nature of the networks as the next critical challenge in population-scale scRNA-seq analysis. This proposal will address these challenges in two aims. Aim 1 will develop a de-biasing approach to estimate gene-gene correlations from scRNA-seq data with safeguards against low sequencing depth, data sparsity, and varying numbers of cells and detect correlations that are otherwise obscured by technical limitations. Aim 2 will innovate a regularized spectral clustering method that takes in as input co-expression networks of genes at the subject and time/perturbation levels and infers dynamic gene modules. Both aims will be accomplished through a combination of methodological development, theoretical analysis, data-driven simulation, computational analysis, and experimental validation. Successful completion of the project will deliver foundational methods and software that are applicable to a wide range of scRNA-seq datasets and are uniquely positioned for analyzing population-scale scRNA-seq data.