Novel Systems Biology Methods for the Cell-type-specific Regulatory Networks Reconstruction from scRNA-seq Data

从 scRNA-seq 数据重建细胞类型特异性调控网络的新系统生物学方法

• 批准号: 10579768
• 负责人: Haijun Gong
• 金额: $ 45.45万
• 依托单位: SAINT LOUIS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-21 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10579768
• 关键词: Advanced Development; Algorithms; Attenuated; Bayesian Modeling; Biological Process; Cell Differentiation process; Cells; Collaborations; Complex; Data; Data Analyses; Detection; Disease; Evolution; Gene Expression; Gene Expression Profiling; Genes; Genome; Heterogeneity; Individual; Intelligence; Malignant Neoplasms; Measures; Methods; Modeling; Modernization; Network-based; Pathogenesis; Patients; Pharmaceutical Preparations; Population; Resolution; Series; Structure; Systems Biology; Techniques; Technology; Time; Yeasts; base; cell type; computer studies; convolutional neural network; deep learning; detection method; disease heterogeneity; experience; experimental study; gene expression variation; high dimensionality; improved; long short term memory; novel; patient population; personalized medicine; public health relevance; reconstruction; recurrent neural network; single-cell RNA sequencing; targeted treatment; transcriptome; transcriptome sequencing

Project Summary/Abstract Complex diseases studies have revealed how specific cell types contribute to the evolution of different diseases. Many drugs are not effective to a large population of patients due to the cellular diversity. Modern single-cell RNA sequencing (scRNA-seq) technologies provide opportunities to detect and dissect the heterogeneity in cells, enable us to measure the gene expression level of thousands of individual cells in a single experiment. Comprehensive analysis of scRNA-seq data and correct reconstruction of cell-type-specific regulatory networks could help develop personalized and targeted treatment of some complex diseases for different patients. So, the scRNA-seq has more advantages than the traditional bulk RNA-seq. Most of genome and computational studies are based on the traditional bulk RNA sequencing data, which measure the average expression of the cell population, without examining the cell-type- specific expression profiles. There are several challenges in the scRNA-seq data analysis and cell-type-specific regulatory network reconstruction. The first challenge is, there are a large amount of missing values in the scRNA-seq data, which will attenuate the power and advantages of scRNA-seq, and make it difficult to correctly reconstruct a cell-type-specific network. We propose novel data-driven deep generative modeling methods to impute (estimate) the missing static and time-series scRNA-seq data without making certain distribution assumptions for the missing values. Some studies have revealed that the regulatory networks undergo systematic rewiring at different stages. It is of importance to know how many stages the cell has experienced, and when the stage transition starts to occur from the high- dimensional scRNA-seq data, which is the second challenge problem—change-points detection. We propose to develop an adversarial network-based method to identify the change-points without introducing model parameters. Another challenge is how to correctly reconstruct and intelligently validate the cell-type-specific regulatory networks from the scRNA-seq data, and identify key regulatory components that contribute to the network rewiring during stage transition. We propose to integrate the deep generative modeling methods and change-points detection algorithm with our weighted dynamic Bayesian network and Model Checking technique in a unified framework to reconstruct cell-type-specific regulatory networks. Our studies will improve our understanding of regulatory network dynamics, and provide a key to discovering the mechanisms underlying the pathogenesis of diseases.

项目总结/摘要 复杂疾病的研究揭示了特定细胞类型如何促进 不同的疾病。许多药物对大量患者无效， 细胞多样性现代单细胞RNA测序（scRNA-seq）技术提供了 检测和剖析细胞异质性的机会，使我们能够测量基因 在一个实验中成千上万的单个细胞的表达水平。综合分析 scRNA-seq数据和细胞类型特异性调控网络的正确重建可以 帮助开发个性化和针对性的治疗一些复杂的疾病， 患者因此，scRNA-seq比传统的bulk RNA-seq具有更多的优势。大部分 基因组和计算研究是基于传统的批量RNA测序数据， 其测量细胞群体的平均表达，而不检查细胞类型， 特异性表达谱。scRNA-seq数据分析中存在若干挑战， 细胞类型特异性调节网络重建。第一个挑战是， scRNA-seq数据中缺失值的数量，这将削弱功率， scRNA-seq的优点，并使其难以正确地重建细胞类型特异性 网络我们提出了新的数据驱动的深度生成建模方法来估算（估计） 缺失的静态和时间序列scRNA-seq数据没有进行一定的分布 缺失值的假设。一些研究表明， 在不同的阶段经历系统的重新连接。重要的是要知道有多少阶段 当细胞经历了，并且当阶段转变开始从高- 三维scRNA-seq数据，这是第二个挑战问题-变化点检测。 我们建议开发一种基于对抗网络的方法来识别变点 而不引入模型参数。另一个挑战是如何正确重建和 从scRNA-seq数据智能验证细胞类型特异性调控网络， 确定在阶段期间有助于网络重新布线的关键监管组件 过渡我们建议将深层生成式建模方法和变点方法相结合 基于加权动态贝叶斯网络和模型检测的检测算法 在一个统一框架中的技术，以重建细胞类型特异性调节网络。我们 研究将提高我们对监管网络动态的理解，并提供一个关键， 发现疾病的发病机制。