Statistical methods for gene regulatory analysis and single cell genomics

基因调控分析和单细胞基因组学的统计方法

• 批准号: 10439652
• 负责人: Wing H. WONG
• 金额: $ 37.67万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-22 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10439652
• 关键词: 3-Dimensional; ATAC-seq; Biological; Biological Assay; Biological Process; Cells; Chromatin; Complex; Coupled; Coupling; DNase I hypersensitive sites sequencing; Data; Data Set; Development; Disease; Gene Expression; Gene Expression Profiling; Genomics; Goals; Hi-C; Joints; Methodology; Methods; Modeling; Molecular Conformation; Population; Population Heterogeneity; Regulator Genes; Research; Sampling; Statistical Methods; Time; base; experimental study; genome-wide; interest; single cell analysis; single-cell RNA sequencing; transcriptome sequencing

PROJECT SUMMARY This project has three major components. Analysis of matched accessibility, expression and 3D contact data in diverse contexts In this component we will develop statistical methods for modeling gene regulatory relations based on the joint analysis of gene expression data (from RNA-seq), chromatin accessibility data (from ATAC-seq or DNase-seq) and 3D interaction data (from Hi-C or HiChIP) from diverse cellular contexts. The focus will on “matched data sets” where the different types of omics assays are performed on the same or closely matched cellular contexts. The methodology should be applicable to cases when some data types are missing in a substantial subset of contexts. The methodology should also be able to incorporate a large variety of non-context dependent data. Analysis of matched omics data in time courses In this component we will develop an approach to the modeling of time course data that is capable of utilizing prior information provided by a general regulatory model learned from diverse contexts. The emphasis will be on experiments where expression, accessibility and 3D contact data are generated at each time points to study a specific biological processes. Joint analysis of multiple types of single cell omics data In this component we will develop statistical methods for the joint analysis of single cell omics data on expression, accessibility, and 3D contact. Specifically, we are interested in the situation where multiple types of single cell omics data are generated from the same heterogeneous population of cells. We will develop statistical methods to resolve the population into relevant subpopulations and to infer subpopulation-specific gene regulatory relations. We will also develop methods to handle the case when 3D contact data is from bulk sample.

项目摘要 该项目具有三个主要组成部分。 分析潜水员环境中匹配的可访问性，表达和3D联系数据 在此组件中，我们将开发用于建模基因调节关系的统计方法 关于基因表达数据的联合分析（来自RNA-Seq），染色质可及性数据（来自 来自Divers Cellular 上下文。重点将放在不同类型的OMICS分析的“匹配数据集”上 在相同或紧密匹配的蜂窝情况下进行。该方法应该是 适用于在大量上下文中缺少某些数据类型的情况。这 方法学还应该能够合并各种非上文依赖性数据。 分析匹配的OMIC数据在时间课程中 在此组件中，我们将开发一种能够建模的方法 使用从潜水员环境中学到的一般监管模型提供的先前信息。 重点将放在表达，可访问性和3D联系数据的实验上 在每个时间点生成以研究特定的生物学过程。 多种类型的单细胞OMIC数据的联合分析 在此组件中，我们将开发统计方法，用于单元格数据的联合分析 关于表达，可访问性和3D接触。具体来说，我们对 从相同的异质种群中生成多种类型的单细胞磁磁数据数据 细胞。我们将开发统计方法，将人群解决为相关的亚群 并推断亚群特异性基因调节关系。我们还将开发方法 当3D接触数据来自批量样本时处理案例。