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New directions in single cell genomics method development

单细胞基因组学方法开发的新方向

基本信息

批准号：
10732646
负责人：
Mengjie Chen
金额：
$ 35.21万
依托单位：
UNIVERSITY OF CHICAGO
依托单位国家：
美国
项目类别：
财政年份：
2017
资助国家：
美国
起止时间：
2017-08-01 至 2027-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10732646
关键词：
Achievement Address Area Bayesian Modeling Biological Biomedical Research Cells Cellular Assay Clinical Research Communities Complex Computer software Computing Methodologies Data Databases Development Disease Documentation Enhancers Exposure to Gene Expression Genes Genomics Goals Health Heterogeneity Human Immune response Joints Knowledge Maps Methods Microscopic Modeling Multiomic Data Nature Performance Population Procedures Research Resolution Sampling Signal Transduction Stimulus System Technology Testing Time Tissues Variant Work analytical tool bioinformatics infrastructure biological research cell type computerized tools conditioning differential expression experimental study falls flexibility frontier genetic architecture genomic data improved insight method development open source single cell analysis single cell technology single-cell RNA sequencing statistics tool trait transcription factor transcriptomics translational study user friendly software

项目摘要

PROJECT SUMMARY Single cell technologies, in particular single cell transcriptomics, have been applied to numerous areas in biological and biomedical research and become a powerful tool for complex tissue characterization. Despite its ever-growing throughput and complexity, the development of analytical tools for single cell genomics has fallen behind the technological advances. The overarching goal of this proposal is to address some of the most pressing analytic challenges facing profiling and interpreting single cell genomics data, including: 1) lack of differential expression analysis methods that properly account for within-sample cellular heterogeneity; 2) lack of cis-regulatory inference methods that leverage multi-omics data; and 3) lack of proper methods to perform eQTL mapping in population-scale scRNA-seq studies. In the proposal, we will work on the following aims: Aim 1. Develop a differential expression analysis framework that better resolves sample heterogeneity and combats false discoveries for single cell data. Aim 2. Develop Bayesian model selection methods that infer cis- regulatory relationships from multi-omics data. Aim 3. Develop eQTL mapping methods that accommodate multiple cell types and experimental conditions in population-scale scRNA-seq studies. All methods will be implemented in user-friendly software and disseminated to the scientific community. Successful achievement of Aims 1 and 2 will dramatically increase the power of routine single cell genomics analysis, facilitating the application of these cutting-edge technologies to translational and clinical studies. Successful achievement of Aim 3 will provide new ways to comprehensively characterize the genetic architecture underlying gene expression that is specific to both cell-type and experimental-condition, ultimately facilitating the understanding of common diseases and disease-related complex traits.

项目摘要单细胞技术，特别是单细胞转录组学，已经应用于许多领域，生物学和生物医学研究，并成为复杂组织表征的有力工具。尽管随着通量和复杂性的不断增长，单细胞基因组学分析工具的发展已经下降，技术进步的背后。本提案的总体目标是解决一些最重要的分析和解释单细胞基因组学数据面临的紧迫分析挑战，包括：1）缺乏差异表达分析方法，适当地解释样品内细胞异质性; 2）缺乏利用多组学数据的顺式调控推理方法; 3）缺乏适当的方法来执行群体规模scRNA-seq研究中的eQTL作图。在提案中，我们将努力实现以下目标： 1.开发差异表达分析框架，更好地解决样本异质性，单细胞数据的错误发现。目标2.开发贝叶斯模型选择方法，推断顺式- 从多组学数据的监管关系。目标3.开发eQTL作图方法，在群体规模的scRNA-seq研究中的多种细胞类型和实验条件。所有方法都将以方便用户的软件实施，并向科学界传播。成功实现目标1和目标2将显着提高常规单细胞基因组学分析的能力，促进将这些尖端技术应用于转化和临床研究。成功实现目的3将为全面表征基因的遗传结构提供新的方法表达是特定的细胞类型和实验条件，最终促进理解常见疾病和与疾病相关的复杂特征。