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: 10728209
• 负责人: Robert R. H Anholt
• 金额: $ 26.09万
• 依托单位: CLEMSON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-02-10 至 2026-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10728209
• 关键词: Affect; Benchmarking; Cells; Centers of Research Excellence; Chromatin; Computer software; Computing Methodologies; Data; Data Analyses; Data Set; Databases; Development; Disease; Elements; Environment; Gene Expression; Gene Expression Regulation; Genes; Genome; Genomics; Goals; Human Genetics; Location; Methods; Multiomic Data; Patients; Phenotype; Pilot Projects; Publishing; Regulator Genes; Regulatory Element; Resolution; Sampling; Statistical Methods; Technology; Untranslated RNA; Work; cell type; comparative; gene regulatory network; genetic variant; genomic data; interest; single cell analysis; single-cell RNA sequencing; transcription factor; transcriptomics

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.

基因调节网络（GRNS）提供有关控制上下文特定的顺式调节元件的信息 靶基因的表达以及作用于这些元素的转录因子。 了解基因调节的动力学对于理解细胞如何进行是基础 尽管具有相同的基因组，但针对不同功能的专业化；细胞如何响应不同的 通过调节基因表达来环境；以及非编码遗传变异如何引起疾病。 基因组数据中GRN的推断是一种系统研究基因调节的方法。但是， 如果感兴趣的细胞环境是异源混合物，则这种推断的准确性是有限的。这 单细胞基因组学技术的开发可以通过提供高分辨率GRN来填补这一空白。 因此，需要有效的统计方法从单个细胞中推断出GRN 基因组学数据。该项目的长期目标是获得对不编码的机械理解 遗传变异会影响细胞上下文依赖性GRN并影响表型。单细胞 转录组（SCRNA-SEQ）和染色质可及性（SCATAC-SEQ）数据提供了有关不同的信息 细胞特征，即基因表达和主动调节元件位置。整合 这两种类型的数据将提供有关基因调节的更准确信息。在特定目标1中，我们 将扩展我们的初始研究，从未配对的scrna-seq和 SCATAC-SEQ数据（自2012年2月1日以来由人类遗传试验项目的毛绒支持） 基准测试现有方法，用于整合未配对的scrna-seq和scatac-seq数据的方法 构建优化的管道，以进行未配对的数据分析。我们将开发一种统计方法来推断 亚群特定的GRNS并分析大规模发布的数据集，以构建一个GRN数据库 数百个细胞环境。在特定目标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调节分析。样品之间的GRN的比较 患者与健康患者或两种不同治疗之间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节分析的有效计算方法 需要单细胞基因组学数据。在特定目标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。此方法和软件将具有重要的 通过提供对跨性别和顺式调节元素如何共同影响影响的广泛影响 基因表达以细胞类型特异性方式。