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

群体规模 scRNA-seq 数据共表达网络分析的统计方法

• 批准号: 10740240
• 负责人: Sunduz Keles
• 金额: $ 40.76万
• 依托单位: UNIVERSITY OF WISCONSIN-MADISON
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-05 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10740240
• 关键词: Address; Adopted; Benchmarking; Biological; Biological Process; Cells; Cluster Analysis; Collaborations; Computer Analysis; Computer software; Data; Data Analyses; Data Set; Detection; Development; Gene Cluster; Genes; Genetic Transcription; Genotype; Hematopoietic System; Immune system; Individual; Link; Measurement; Methodology; Methods; Modeling; Morphologic artifacts; Nature; Partner in relationship; Pathway Analysis; Phenotype; Population; Population Analysis; Population Dynamics; Positioning Attribute; Publications; Regulatory Pathway; Research; Severities; Shapes; Statistical Methods; Technology; Time; Validation; Variant; Virus; Work; cell type; detection method; differential expression; flexibility; gene function; gene network; innovation; insight; interest; method development; multiple datasets; novel; programs; response; simulation; single-cell RNA sequencing; tool; transcriptome sequencing

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.

项目概要 基因共表达网络分析是检测潜在基因的关键推理工具 聚类和差异表达的标准工作流程不可见的关系 分析。这种网络方法在批量 RNA-seq 分析中发挥了重要作用，以链接 基因与生物过程。尽管在方法上取得了显着的进步 scRNA-seq 分析的开发，目前还没有既定的最佳实践 从 scRNA-seq 数据构建强大的基因共表达网络。随着宽 scRNA-seq 技术、群体规模 scRNA-seq 数据集的可用性 多个主题和时间点/扰动正在出现。虽然眼前的 这些数据集的分析侧重于聚类和差异的标准分析 表达，利用 scRNA-seq 在共表达网络水平上的力量 解锁基因的潜力，这些基因汇聚成关键的被破坏的监管途径。 网络水平变异，当与表型变异相关时（例如， 对病毒的反应），可以揭示重要的生物学见解。这样的进步呈现 构建个性化动态共表达网络并识别动态 基因模块通过考虑网络的个体化性质作为 群体规模 scRNA-seq 分析的下一个关键挑战。该提案将 应对这些挑战有两个目标。目标 1 将开发一种去偏见方法 从 scRNA-seq 数据估计基因-基因相关性，并防止低风险 测序深度、数据稀疏性和不同数量的细胞并检测相关性 否则会因技术限制而变得模糊。目标2将创新正规化 谱聚类方法，将基因的共表达网络作为输入 主题和时间/扰动水平并推断动态基因模块。这两个目标都将是 通过方法论的发展、理论的结合来完成 分析、数据驱动模拟、计算分析和实验验证。 该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，并且具有独特的定位 分析群体规模的 scRNA-seq 数据。