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.

项目摘要/摘要 美国国立卫生研究院共同基金支持单细胞基因组数据的产生、管理和共享 从数百万个细胞到几个大型国际财团，目标是建立一个全面的 人体内多个器官中健康细胞的参考。我们将使用单细胞/核RNA- 来自共同基金资助的人类生物分子图谱计划(HuBMAP)的测序(scRNA-seq)数据 和基因类型-组织表达(GTEx)联盟，以原型细胞类型协调协议 构建跨联合体单元普查元图集。HuBMAP联盟提供器官特异性细胞 GTEx提供了一个完整的跨器官单细胞图谱。我们的团队已经 开发并广泛验证的生物标记物计算算法，NS-Forest和FR-Match 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，提供卓越的分类 性能，同时保持生物应用的高可解释性和可解释性。以特定的目标 1.数据集的选择和准备将采用严格的数据质量控制方法。NS-森林 然后将使用算法来确定器官特定细胞的特征的最佳生物标志物组合 HuBMAP的个体器官类型和GTEx的跨器官细胞类型。在具体目标2中，我们将重点关注 人肺作为样本器官，通过开发一种 使用我们经过验证和基准的FR-Match算法和 HuBMAP-LONG、GTEx肺子集和其他公开可用的人类肺细胞图谱(HLCA)数据集。我们 我会将FR-Match与另外两种流行的细胞类型方法Azimuth和CellTypist进行比较并进行基准测试 使用所有方法进行匹配并验证匹配结果。我们还将成立一个领域专家小组进行审查 并利用领域知识和社区文献信息对小区类型协调结果进行验证 批准。我们将构建一个策略来捕获样本元数据、解剖结构信息、细胞类型 将基于命名和生物标记的定义转化为元地图集的本体表示，并填充 内容进入临时细胞本体论。在具体目标3中，我们将把我们的成果传播给Key 利益相关者社区，包括HuBMAP解剖结构、细胞类型和生物标记物(ASCT+B) 工作组和GTEx多基因单细胞查询平台。我们将介绍该项目并参与其中 在共同基金数据生态系统春季会议上与社区接触并征求反馈意见。 除试点阶段外，在该项目中建立的小区类型协调框架一般可 适用于跨共同基金和其他数据资源的单元格类型数据集的集成。