Statistical Methods for Gene Regulatory Analysis From Single Cell Genomics Data

单细胞基因组数据基因调控分析的统计方法

• 批准号: 10728206
• 负责人: Robert R. H Anholt
• 金额: $ 10.84万
• 依托单位: CLEMSON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-06 至 2026-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10728206
• 关键词: Statistical; Methods; Gene; Regulatory; Analysis

Gene regulatory networks (GRNs) provide information on the cis-regulatory elements controlling contextspecific expression of target genes, as well as the transcription factors acting on these elements. Understanding the dynamics of gene regulation is fundamental for understanding how cells undergo specialization for different functions, despite having the same genome; how cells respond to different environments by modulating gene expression; and how non-coding genetic variants cause diseases. Inference of GRNs from genomics data is a systematic approach to study gene regulation. However, the accuracy of such inference is limited if the cellular context under interest is a heterogenous mixture. The development of single cell genomics technologies can fill this gap by providing high-resolution GRNs. Therefore, there is a compelling need for efficient statistical methods to infer GRNs from single cell genomics data. The long-term goal of this project is to obtain a mechanistic understanding of how noncoding genetic variants affect cellular context-dependent GRNs and influence phenotypes. Single cell transcriptomic (scRNA-seq) and chromatin accessibility (scATAC-seq) data provide information on different cellular features, i.e., gene expression and active regulatory element location, respectively. Integration of these two types of data will provide more accurate information on gene regulation. In Specific Aim 1, we will extend our initial studies inferring subpopulation-dependent GRNs from unpaired scRNA-seq and scATAC-seq data (supported by a COBRE in Human Genetics Pilot Project since 02/01/2022) by benchmarking existing methods for integrative analysis of unpaired scRNA-seq and scATAC-seq data to build an optimized pipeline for unpaired data analysis. We will develop a statistical method to infer subpopulation-specific GRNs and analyze large-scale published datasets to build a database of GRNs for hundreds of cellular contexts. In Specific Aim 2, we will develop statistical methods for comparative gene regulatory analysis based on single cell genomics data. The comparison of GRNs between samples from diseased versus healthy patients or between two different treatments is an important scientific problem. Thus, an efficient computational method for comparative gene regulatory analysis based on different types of single cell genomics data is needed. In Specific Aim 3, we will develop a method and software to infer cell type specific GRNs from sc-multiome data. This method and software would have a significant and broad impact by providing a detailed view of how trans- and cis-regulatory elements work together to affect gene expression in a cell type-specific manner.

基因调控网络 (GRN) 提供有关控制特定环境的顺式调控元件的信息 目标基因的表达，以及作用于这些元件的转录因子。 了解基因调控的动态是了解细胞如何经历的基础 尽管具有相同的基因组，但针对不同功能的专门化；细胞如何应对不同的 通过调节基因表达的环境；以及非编码遗传变异如何导致疾病。 从基因组数据推断 GRN 是研究基因调控的系统方法。然而， 如果感兴趣的细胞环境是异质混合物，则这种推断的准确性受到限制。这 单细胞基因组学技术的发展可以通过提供高分辨率的 GRN 来填补这一空白。 因此，迫切需要有效的统计方法来从单细胞推断 GRN 基因组学数据。该项目的长期目标是获得对非编码如何进行机械理解 遗传变异影响细胞环境依赖性 GRN 并影响表型。单细胞 转录组 (scRNA-seq) 和染色质可及性 (scATAC-seq) 数据提供有关不同 细胞特征，即基因表达和活性调控元件位置。整合 这两类数据将提供更准确的基因调控信息。在具体目标 1 中，我们 将扩展我们的初步研究，从未配对的 scRNA-seq 推断亚群依赖性 GRN 和 scATAC-seq 数据（自 2022 年 2 月 1 日起由 COBRE 人类遗传学试点项目支持） 对未配对的 scRNA-seq 和 scATAC-seq 数据进行综合分析的现有方法进行基准测试 构建用于不配对数据分析的优化管道。我们将开发一种统计方法来推断 亚群体特定的 GRN 并分析大规模已发布的数据集，以构建 GRN 数据库 数百个细胞环境。在具体目标2中，我们将开发比较基因的统计方法 基于单细胞基因组学数据的监管分析。样本之间GRN的比较 患病患者与健康患者或两种不同治疗方法之间的比较是一个重要的科学问题。 因此，一种基于不同类型的比较基因调控分析的有效计算方法 需要单细胞基因组学数据。在具体目标 3 中，我们将开发一种方法和软件来推断 来自 sc-multiome 数据的细胞类型特异性 GRN。该方法和软件将具有重大意义 通过提供反式和顺式监管要素如何共同影响的详细视图来产生广泛的影响 基因以细胞类型特异性的方式表达。