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
- 来源:
- 关键词:AddressAdoptedBenchmarkingBiologicalBiological ProcessCellsCluster AnalysisCollaborationsComputer AnalysisComputer softwareDataData AnalysesData SetDetectionDevelopmentGene ClusterGenesGenetic TranscriptionGenotypeHematopoietic SystemImmune systemIndividualLinkMeasurementMethodologyMethodsModelingMorphologic artifactsNaturePartner in relationshipPathway AnalysisPhenotypePopulationPopulation AnalysisPopulation DynamicsPositioning AttributePublicationsRegulatory PathwayResearchSeveritiesShapesStatistical MethodsTechnologyTimeValidationVariantVirusWorkcell typedetection methoddifferential expressionflexibilitygene functiongene networkinnovationinsightinterestmethod developmentmultiple datasetsnovelprogramsresponsesimulationsingle-cell RNA sequencingtooltranscriptome sequencing
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
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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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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