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

项目摘要/摘要 我们的项目将创建一个计算资源，单神经元分析仪，以支持神经科学 社区努力建立一个可复制的、全面的、以数据为驱动的脑细胞类型图谱。实验室 在大脑倡议中，细胞普查网络和其他机构正在从 利用单细胞测序技术对小鼠和人脑的多个区域进行研究。这些数据集 包括单细胞和单核转录本(RNA-Seq)以及单核DNA甲基组 (MC-Seq)和染色质可及性(ATAC-Seq)。每种数据类型都提供了有关 脑细胞的分子同一性：转录本直接测量基因表达，而表观基因组数据 指示基因表达水平和基因间调节区(如增强子)的活性。 然而，没有计算资源来集成来自这些多个模态的这些数据，并且用于 从统计上验证基于每个数据集定义的细胞类型的可靠性和重复性。这个 Single Neuron Analyzer将在Single Cell门户的框架内工作，该门户提供 可横向扩展的高性能解决方案，使研究人员能够随着不断增长的 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 以及交叉验证，以研究研究人员基于一个或多个 数据集。单神经元分析仪将允许用户计算定量分数，对应于 接收器工作特性(AUROC)下的面积，它量化了小区类型标签的程度 可以根据实验复制或互补分子等独立数据进行预测 化验。AIM 2将建立一个数据集成系统，可以联合分析不同类型的单个细胞 技术和模式，包括转录组和表观基因组数据。我们将利用 基因表达与低基因体DNA甲基化和高染色质可及性的可靠关联， 将在一种模式下测量的细胞与在其他两种模式中最匹配的相邻细胞相链接。这个 生成的邻接图将被用来归因于缺失数据，随后是联合聚类分析和 集成数据集的低维投影。经过联合分析，该系统将提供各种 关于每种细胞类型的关键标记的可视化和可下载报告。通过将转录组合 和表观基因组信息，该系统将预测细胞类型特定的基因以及可能的增强子。 Single Neuron Analyzer将为神经科学界的研究人员提供严格的 神经细胞类型的多模式分子分析，有助于全面推进目标 了解大脑的细胞部件列表。

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.