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

基本信息

  • 批准号:
    10740240
  • 负责人:
  • 金额:
    $ 40.76万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 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

项目摘要

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、人口规模 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。

项目成果

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Sunduz Keles其他文献

Sunduz Keles的其他文献

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{{ truncateString('Sunduz Keles', 18)}}的其他基金

Functionally relevant mapping of human GWAS SNPs on model organisms
人类 GWAS SNP 在模式生物上的功能相关图谱
  • 批准号:
    10056966
  • 财政年份:
    2020
  • 资助金额:
    $ 40.76万
  • 项目类别:
Statistical Power Calculations for ChIP-seq experiments
ChIP-seq 实验的统计功效计算
  • 批准号:
    8284083
  • 财政年份:
    2012
  • 资助金额:
    $ 40.76万
  • 项目类别:
High dimensional statistical data modeling and integration for studying regulatory variation
用于研究监管变化的高维统计数据建模和集成
  • 批准号:
    10413927
  • 财政年份:
    2007
  • 资助金额:
    $ 40.76万
  • 项目类别:
Statistical Analysis Methods and Software for ChIP-seq Data
ChIP-seq 数据的统计分析方法和软件
  • 批准号:
    8605900
  • 财政年份:
    2007
  • 资助金额:
    $ 40.76万
  • 项目类别:
Statistical Analysis Methods and Software for ChIP-seq Data
ChIP-seq 数据的统计分析方法和软件
  • 批准号:
    8785690
  • 财政年份:
    2007
  • 资助金额:
    $ 40.76万
  • 项目类别:
Statistical Methods for the Analysis of ChlP-chip Data
ChlP 芯片数据分析的统计方法
  • 批准号:
    7253510
  • 财政年份:
    2007
  • 资助金额:
    $ 40.76万
  • 项目类别:
Statistical Analysis Methods and Software for ChIP-seq Data
ChIP-seq 数据的统计分析方法和软件
  • 批准号:
    8370723
  • 财政年份:
    2007
  • 资助金额:
    $ 40.76万
  • 项目类别:
Statistical Methods for the Analysis of ChlP-chip Data
ChlP 芯片数据分析的统计方法
  • 批准号:
    7799293
  • 财政年份:
    2007
  • 资助金额:
    $ 40.76万
  • 项目类别:
High dimensional statistical data integration for studying regulatory variation
用于研究监管变化的高维统计数据集成
  • 批准号:
    9344668
  • 财政年份:
    2007
  • 资助金额:
    $ 40.76万
  • 项目类别:
High dimensional statistical data modeling and integration for studying regulatory variation
用于研究监管变化的高维统计数据建模和集成
  • 批准号:
    10610872
  • 财政年份:
    2007
  • 资助金额:
    $ 40.76万
  • 项目类别:

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