Data Analysis Core

数据分析核心

• 批准号: 10689782
• 负责人: Cliburn C Chan
• 金额: $ 78.13万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-30 至 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10689782
• 关键词: Age; Architecture; Bioinformatics; Biological; Biological Assay; Biological Markers; Cadaver; Catalogs; Cell Aging; Cells; Cellular Assay; Common Data Element; Computational Biology; Computer software; Confounding Factors (Epidemiology); Data; Data Analyses; Data Provenance; Data Science; Data Set; Development; Documentation; Elements; Emerging Technologies; Ensure; Evaluation; FAIR principles; Foundations; Generations; Guidelines; Heart; Heterogeneity; High-Throughput Nucleotide Sequencing; Human; Image; Image Analysis; Immunohistochemistry; Immunology; Individual; Lead; Leadership; Maps; Metadata; Methods; Modeling; Modernization; Muscle; Normal tissue morphology; Ontology; Organoids; Policies; Population; Procedures; Process; Race; Reproducibility; Resolution; Skin; Specific qualifier value; Specimen; Structure of parenchyma of lung; System; Time; Tissues; United States National Institutes of Health; Update; Validation; Visualization; biomarker signature; cell type; comparative; computerized data processing; dashboard; data integration; data sharing; data standards; deep learning; digital pathology; epigenome; experience; flexibility; high dimensionality; high throughput screening; in situ sequencing; innovation; interest; interoperability; mathematical model; meetings; member; method development; multidimensional data; multiple omics; public repository; repository; senescence; sex; simulation; single cell analysis; single-cell RNA sequencing; statistics; tissue mapping; tool; transcriptome; transcriptomics

Data Analyses Core: Abstract The Data Analysis Core (DAC) will provide the expertise to manage, model, and analyze data generated by the Duke Tissue Mapping Center (TMC), so as to deliver senescent cell signatures and tissue maps of senescent cells to the CODCC. This will be achieved by pragmatic and innovative execution of the mandated aims – Data Processing, Data Analysis, Map Construction and Consortium Coordination. The Data Processing team will be responsible for the implementation of a cloud native platform on Microsoft Azure that will process data according to FAIR (Findable, Accessible, Interoperable and Reusable) guidelines. The team will coordinate with the Biospecimen Core to document potential confounding variables such as race, sex, live or cadaveric tissue origin; with the Biological Analysis Core for their expertise in optimal pipelines for processing specific assay data, and with the Data Analysis team to ensure the data is collected in a format that is interoperable with downstream analysis. The Data Analysis team will be responsible for the characterization of senescent cell signatures that takes into account the heterogeneity of senescent cells and the dynamics of transitioning to the senescent state. The team will use an iterative strategy to identify senescent cells, identify and expand associated markers, and characterize the functional signature conditional on the biological context of the senescent cell. The team will make use of organoids for initial characterization of the dynamic signature, using these putative signatures to identify rare senescent cells in normal tissue (including biofluids), and refine the putative signature by re-weighting signature elements based on the extent to which they occur in senescent cells in normal tissue. The Map Construction team will be responsible for the development of spatial maps of senescent cells in normal tissue using advanced computational biology methods, innovative tensor analysis approaches and modern deep learning architectures. The team will integrate data from spatial assays (multiplexed immunohistochemistry images, Visium spatial transcriptomics, and Cartana in-situ sequencing) and single cell assays (combined scRNA-seq and scATAC-seq) to build spatial maps predictive of the transcriptome, epigenome and secretome of senescent cells in normal tissue from lung, heart, muscle and skin. The team will also develop a dashboard tool that interfaces with Azure for map visualization, and evaluate the accuracy of these maps using cross-validation, data sets from public repositories, and maps constructed by other TMCs. The Consortium Coordination team will be responsible for annotation of all data sets using terms from NIH Common Data Elements Repository and OBO Foundry ontologies, creation of policies for data and metadata capture, definition of practices for reproducible analysis including use of containers and workflow orchestration scripts, and conversion of data, models, pipelines and tissue maps to interoperable formats for uploading to the CODCC. The team will also lead the collaborative development, with other interested parties from the SenNet consortium, of a Senescent Cell Ontology.

数据分析核心：摘要 数据分析核心（DAC）将为管理，建模和分析由 杜克组织映射中心（TMC），以提供感官的细胞特征和感官的组织图 细胞到CODCC。这将通过务实和创新的执行授权目标来实现 - 数据 处理，数据分析，地图构建和财团协调。数据处理团队将是 负责在Microsoft Azure上实现云本机平台，该平台将处理数据 根据公平（可访问，可访问，可互操作和可重复使用的）指南。团队将协调 使用生物循环核心来记录潜在的混杂变量，例如种族，性别，现场或尸体 组织起源；具有生物分析核心的核心 测定数据，并与数据分析团队确保以可互操作的格式收集数据 通过下游分析。数据分析团队将负责Senscent的表征 考虑到感官细胞的异质性以及过渡到的动力学的细胞签名 感觉状态。团队将使用迭代策略来识别感觉细胞，识别和扩展 相关标记，并在生物环境中表征有条件的功能签名 感觉细胞。该团队将利用类器官进行动态签名的初始表征 这些假定的特征是鉴定正常组织中稀有的感觉细胞（包括生物流体），并完善 根据Senscent中发生的程度，通过重新加权签名元素来定义签名 正常组织中的细胞。地图建筑团队将负责开发的空间地图 使用先进的计算生物学方法，正常组织中的感觉细胞，创新的张量分析 方法和现代深度学习体系结构。团队将从空间测定中整合数据 （多路复用免疫组织化学图像，空间转录组学和Cartana原位测序） 以及单细胞测定（合并的scrna-seq和scatac-seq），以构建空间图的预测 肺，心脏，肌肉和 皮肤。该团队还将开发一个仪表板工具，该工具与Azure接口以进行地图可视化，并评估 这些地图使用交叉验证，公共存储库的数据集以及由 其他TMC。财团协调团队将负责使用条款的所有数据集注释 从NIH通用数据元素存储库和OBO铸造本体论，创建数据和 元数据捕获，可再现分析实践的定义，包括使用容器和工作流程 编排脚本以及数据，模型，管道和组织地图的转换为可互操作的格式 上传到CODCC。该团队还将与其他有趣的政党领导协作发展 来自Sennet财团，衰老细胞本体论。