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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：
Sunduz Keles
金额：
$ 40.76万
依托单位：
UNIVERSITY OF WISCONSIN-MADISON
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：
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

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