Single Neuron Analyzer for Multi-modal, Cross-dataset (Epi)genomic Cell Type Datasets

用于多模式、跨数据集（表观）基因组细胞类型数据集的单神经元分析仪

基本信息

批准号：
9795063
负责人：
ERAN A MUKAMEL
金额：
$ 122.75万
依托单位：
UNIVERSITY OF CALIFORNIA, SAN DIEGO
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-09-18 至 2023-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9795063
关键词：
ATAC-seq Area Atlases BRAIN initiative Biological Biological Assay Brain Brain region Cell Nucleus Cells Censuses Characteristics Chromatin Cluster Analysis Communities Computer Analysis Computer software DNA DNA Methylation Data Data Set Databases Diffusion Dimensions Enhancers Feedback Gene Expression Gene Expression Profile Genes Genomic Segment Genomics Goals Graph Human Imagery Individual Information Systems Institutes Joints Label Laboratories Learning Link Machine Learning Measures Methods Modality Molecular Molecular Analysis Molecular Genetics Molecular Profiling Mus Neurons Neurosciences Neurosciences Research Nucleic Acid Regulatory Sequences Privatization Receiver Operating Characteristics Regulation Reporting Reproducibility Research Personnel Resolution Resources Sampling System Systems Integration Technology Validation Work base brain cell cell type cloud based computerized tools computing resources data integration epigenetic regulation epigenome epigenomics experimental study human reference genome methylome molecular scale multimodality neural circuit single cell sequencing single cell technology transcriptome transcriptome sequencing transcriptomics web-accessible

项目摘要

Project Summary/Abstract Our project will create a computational resource, the Single Neuron Analyzer, to support the neuroscience community’s efforts to build a reproducible, comprehensive, data-driven atlas of brain cell types. Laboratories in the BRAIN Initiative Cell Census Network and others are generating large-scale molecular datasets from multiple regions of the mouse and human brain using single cell sequencing technology. These datasets include single cell and single nucleus transcriptomes (RNA-Seq), as well as single nucleus DNA methylomes (mC-Seq) and chromatin accessibility (ATAC-Seq). Each data type provides complementary information about the molecular identity of brain cells: transcriptomes directly measure gene expression, while epigenomic data indicates both gene expression levels and the activity of intergenic regulatory regions such as enhancers. However, there is no computational resource for integrating these data from these multiple modalities and for statistically validating the reliability and reproducibility of the cell types defined based on each dataset. The Single Neuron Analyzer will work within the framework of the Single Cell Portal, which provides horizontally-scalable, highly performant solutions that allow researchers to efficiently scale with the growing size of datasets as the technology for single cell sequencing advances. In Aim 1, we will use machine learning and cross-validation to study the reproducibility of cell types defined by researchers based on one or more datasets. The Single Neuron Analyzer will allow users to compute a quantitative score, corresponding to the area under the receiver operating characteristic (AUROC), which quantifies the degree to which cell type labels can be predicted based on independent data such as experimental replicates or complementary molecular assays. Aim 2 will build a data integration system that can jointly analyze single cells profiled by different technologies and modalities, including transcriptomic and epigenomic data. We will take advantage of the reliable correlation of gene expression with low gene body DNA methylation and high chromatin accessibility, to link cells measured in one modality with their closest matching neighbors in the other two modalities. The resulting neighbor graphs will be used to impute the missing data, followed by joint cluster analysis and low-dimensional projection of the integrated dataset. Following joint analysis, the system will provide a variety of visualizations and downloadable reports about key markers for each cell type. By combining transcriptomic and epigenomic information, the system will predict cell type specific genes as well as putative enhancers. Single Neuron Analyzer will offer researchers across the neuroscience community a resource for rigorous multi-modal molecular analysis of neuronal cell types, helping to advance the goal of comprehensively understanding the brain’s cellular parts list.

项目概要/摘要我们的项目将创建一个计算资源，即单神经元分析器，以支持神经科学社区努力建立一个可重复的、全面的、数据驱动的脑细胞类型图谱。 BRAIN Initiative 细胞普查网络和其他人正在生成大规模分子数据集这些数据集使用单细胞测序技术对小鼠和人类大脑的多个区域进行了分析。包括单细胞和单核转录组 (RNA-Seq)，以及单核 DNA 甲基化组 (mC-Seq) 和染色质可及性 (ATAC-Seq) 每种数据类型都提供有关的补充信息。脑细胞的分子身份：转录组直接测量基因表达，而表观基因组数据指示基因表达水平和基因间调节区域（例如增强子）的活性。然而，没有计算资源来整合这些多种模式的数据，也没有计算资源。统计验证基于每个数据集定义的细胞类型的可靠性和再现性。单神经元分析仪将在单细胞门户的框架内工作，该门户提供水平可扩展的高性能解决方案，使研究人员能够随着不断增长的数据而有效地扩展随着单细胞测序技术的进步，数据集的大小在目标 1 中，我们将使用机器学习。和交叉验证，以研究研究人员基于一种或多种定义的细胞类型的再现性单神经元分析器将允许用户计算对应于的定量分数。接收器操作特性 (AUROC) 下的面积，量化细胞类型标记的程度可以根据独立数据（例如实验重复或互补分子）进行预测目标 2 将建立一个数据集成系统，可以联合分析不同类型的单细胞。我们将利用技术和模式，包括转录组和表观基因组数据。基因表达与低基因体 DNA 甲基化和高染色质可及性的可靠相关性，将一种模式中测量的细胞与其他两种模式中最接近的匹配邻居联系起来。生成的邻居图将用于估算丢失的数据，然后进行联合聚类分析和联合分析后，系统将提供各种集成数据集的低维投影。通过结合转录组，获得有关每种细胞类型关键标记的可视化和可下载报告。和表观基因组信息，系统将预测细胞类型特定基因以及假定的增强子。单神经元分析仪将为整个神经科学界的研究人员提供严格的资源神经细胞类型的多模式分子分析，有助于推进全面的目标了解大脑的细胞部分列表。