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.
项目总结
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
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$ 40.76万 - 项目类别:
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Statistical Analysis Methods and Software for ChIP-seq Data
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8605900 - 财政年份:2007
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Statistical Analysis Methods and Software for ChIP-seq Data
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用于研究监管变化的高维统计数据集成
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