Cell type harmonization of single cell data in HuBMAP and GTEx

HuBMAP 和 GTEx 中单细胞数据的细胞类型协调

• 批准号: 10777089
• 负责人: Yun Zhang
• 金额: $ 39万
• 依托单位: J. CRAIG VENTER INSTITUTE, INC.
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-20 至 2024-09-19
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10777089
• 关键词: 3-Dimensional; ATAC-seq; Algorithms; Anatomy; Atlases; BRAIN initiative; Benchmarking; Biological; Biological Assay; Biological Markers; Brain; Cell Nucleus; Cell Surface Proteins; Cells; Censuses; Characteristics; Classification; Collection; Communities; Computational algorithm; Data; Data Set; Dimensions; Disease; Ensure; Feedback; Freezing; Funding; Gene Expression; Generations; Genes; Genome; Genotype; Genotype-Tissue Expression Project; Goals; Graph; Health; Human; Human BioMolecular Atlas Program; Human body; Individual; International; Knowledge; Literature; Lung; Machine Learning; Metadata; Methods; Modeling; Morphology; Nomenclature; Ontology; Organ; Organism; Pattern; Performance; Phase; Phenotype; Physiological; Pilot Projects; Play; Preparation; Procedures; Property; Proteins; Protocols documentation; Quality Control; Research; Resolution; Resources; Role; Sampling; Semantics; Taxonomy; Technology; Time; Tissue Banks; Tissue atlas; Tissues; Trees; United States National Institutes of Health; biomarker identification; body system; cell type; community engagement; data ecosystem; data harmonization; data integration; data portal; data quality; data resource; data standards; design; feature selection; forest; genomic data; human tissue; machine learning algorithm; meetings; novel; programs; protein biomarkers; prototype; random forest; single cell analysis; single cell technology; single nucleus RNA-sequencing; single-cell RNA sequencing; specific biomarkers; synergism; tissue mapping; transcriptome sequencing; transcriptomics; working group

Project Summary/Abstract The NIH Common Fund have supported the generation, management, and sharing of single cell genomic data from millions of cells through several large international consortia with the goal of building a comprehensive reference of healthy cells across multiple organs in the human body. We will use single cell/nucleus RNA- sequencing (scRNA-seq) data from the Common Fund-supported Human BioMolecular Atlas Program (HuBMAP) and Genotype-Tissue Expression (GTEx) consortia to prototype a cell type harmonization protocol for constructing a cross-consortia cell census meta-atlas. The HuBMAP consortium provides organ-specific cell atlases for multiple organs, while GTEx provides an integrated cross-organ single cell atlas. Our group has developed and extensively validated computational algorithms, NS-Forest and FR-Match, for biomarker identification and robust cell type matching using scRNA-seq data. Our algorithms utilize Random Forest machine learning and minimum spanning tree graphical modeling, which provide superior classification performance while maintaining high explainability and interpretability for biological applications. In Specific Aim 1, rigorous data quality control approaches will be applied for dataset selection and preparation. The NS-Forest algorithm will then be used to identify optimal biomarker combinations for characterization of organ-specific cell types of individual organs in HuBMAP and cross-organ cell types in GTEx. In Specific Aim 2, we will focus on human lung, as an exemplar organ, to prototype the assembly of a cross-consortia meta-atlas by developing a robust cell type harmonization approach using our validated and benchmarked FR-Match algorithm and HuBMAP-Lung, GTEx lung subset, and other publicly available Human Lung Cell Atlas (HLCA) datasets. We will compare and benchmark FR-Match with two other popular methods, Azimuth and CellTypist, for cell type matching and validate the matching results using all methods. We will also form a domain expert panel to review and validate the cell type harmonization results using domain knowledge and literature information for community approval. We will build a strategy for capturing sample metadata, anatomic structure information, cell type nomenclature and biomarker-based definitions into an ontological representation for the meta-atlas and populate the contents into the Provisional Cell Ontology. In Specific Aim 3, we will disseminate our results to key stakeholder communities, including the HuBMAP Anatomical Structures, Cell Types and Biomarkers (ASCT+B) Working Group and the GTEx Multi-Gene Single Cell Query platform. We will present the project and participate in the Common Fund Data Ecosystem Spring Meeting for engaging the community and soliciting feedback. Beyond the pilot phase, the cell type harmonization framework established in this project can be generally applicable to integrate single cell-based cell type datasets across Common Fund and other data resources.

项目总结/摘要 NIH共同基金支持单细胞基因组数据的生成、管理和共享 通过几个大型国际财团，从数百万个细胞中， 健康细胞在人体多个器官中的参考。我们将使用单细胞/核RNA- 来自共同基金支持的人类生物分子图谱计划（HuBMAP）的scRNA-seq数据 和基因型-组织表达（GTEx）财团，以原型细胞类型协调协议， 构建跨联盟细胞普查元图谱。HuBMAP联盟提供器官特异性细胞 GTEx提供了多个器官的图谱，而GTEx提供了一个集成的跨器官单细胞图谱。我们集团 开发并广泛验证了生物标志物的计算算法NS-森林和FR-匹配 使用scRNA-seq数据进行鉴定和稳健的细胞类型匹配。我们的算法利用随机森林 机器学习和最小生成树图形建模，提供上级分类 性能，同时保持生物学应用的高度可解释性和可解释性。具体目标 1、严格的数据质量控制方法将应用于数据集的选择和准备。NS森林 然后将使用算法来鉴定用于表征器官特异性细胞的最佳生物标志物组合 HuBMAP中的单个器官类型和GTEx中的跨器官细胞类型。在具体目标2中，我们将重点关注 人肺，作为一个示范性的器官，原型的跨财团元图谱的组装，通过开发一个 使用我们经过验证和基准测试的FR-Match算法的强大细胞类型协调方法， HuBMAP-Lung、GTEx肺亚组和其他公开可用的人类肺细胞图谱（HLCA）数据集。我们 我将比较和基准FR-Match与其他两个流行的方法，方位角和CellTypist，为细胞类型 匹配并验证所有方法的匹配结果。我们还将成立一个领域专家小组， 并使用领域知识和文献信息验证小区类型协调结果 批准我们将建立一个捕获样本元数据，解剖结构信息，细胞类型， 命名法和基于标记的定义转换为元地图集的本体表示，并填充 将内容添加到临时细胞本体中。在具体目标3中，我们将把我们的成果传播给关键部门， 利益相关者社区，包括HuBMAP解剖结构，细胞类型和生物标志物（ASCT+B） GTEx Multi-Gene Single Cell Query平台。我们将展示该项目并参与 共同基金数据生态系统春季会议，以吸引社区参与并征求反馈意见。 在试验阶段之后，本项目中建立的细胞类型协调框架通常可以 适用于跨共同基金和其他数据资源整合基于单细胞的细胞类型数据集。