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: 10001015
• 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 2023-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10001015
• 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; 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 该项目有三个主要组成部分。 分析不同情境下匹配的可达性、表达和3D接触数据 在这个部分中，我们将开发基于基因调控关系建模的统计方法。 基因表达数据（来自RNA-seq）、染色质可及性数据（来自 ATAC-seq或DNase-seq）和来自不同细胞的3D相互作用数据（来自Hi-C或HiChIP）。 contexts.重点将放在“匹配的数据集”，其中不同类型的组学分析是 在相同或紧密匹配的细胞环境中进行。方法应该是 适用于某些数据类型在大量上下文子集中缺失的情况。的 该方法还应当能够结合大量的非上下文相关的数据。 时间进程中匹配组学数据的分析 在本组件中，我们将开发一种建模时间过程数据的方法， 利用从不同背景中学习到的一般监管模型提供的先验信息。 重点将放在表达、可访问性和3D接触数据的实验上 在每个时间点生成，以研究特定的生物过程。 多种类型单细胞组学数据的联合分析 在这一部分中，我们将开发用于单细胞组学数据联合分析的统计方法 关于表达、可访问性和3D接触。具体来说，我们感兴趣的情况是， 多种类型的单细胞组学数据是从相同的异质细胞群体产生的 细胞我们将开发统计方法，将人口分解为相关的亚群 并推断亚群特异性基因调控关系。我们还将制定方法， 处理3D接触数据来自批量样品的情况。