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)将提供专业知识来管理、建模和分析 杜克组织测绘中心(TMC)，从而提供衰老细胞特征和衰老组织图 细胞转移到CODCC。这将通过务实和创新地执行授权的AIMS-DATA实现 处理、数据分析、地图构建和财团协调。数据处理团队将 负责在Microsoft Azure上实施将处理数据的云本地平台 根据公平(可查找、可访问、可互操作和可重复使用)的指导方针。团队将协调 使用BioSpecimen Core记录潜在的混淆变量，如种族、性别、活着的或身体的 组织起源；与生物分析核心公司在处理特定组织的最佳管道方面的专业知识 分析数据，并与数据分析团队合作，确保以可互操作的格式收集数据 与下游分析。数据分析团队将负责描述衰老的特征 考虑到衰老细胞的异质性和过渡到 衰老的状态。该团队将使用迭代策略来识别衰老细胞、识别和扩展 相关联的标记，并根据生物背景来表征功能签名 衰老的细胞。该团队将使用有机化合物来初步表征动态签名，使用 这些假定的特征可以识别正常组织(包括生物液)中罕见的衰老细胞，并提炼 通过根据签名元素在衰老过程中出现的程度重新加权签名元素来推定签名 正常组织中的细胞。地图构建小组将负责开发空间地图 使用先进的计算生物学方法，创新的张量分析，在正常组织中发现衰老细胞 方法和现代深度学习架构。该团队将整合来自空间分析的数据 (多路免疫组织化学图像、维西姆空间转录和Cartana原位测序) 和单细胞分析(联合scRNA-seq和scatac-seq)，以建立预测 正常肺、心脏、肌肉和骨骼肌衰老细胞的转录组、表观基因组和分泌组 皮肤。该团队还将开发一个仪表板工具，该工具与Azure交互以实现地图可视化，并评估 使用交叉验证的这些地图的准确性、来自公共资源库的数据集以及由 其他TMC。财团协调小组将负责使用术语对所有数据集进行注释 从NIH公共数据元素存储库和OBO Foundry本体中，为数据和 元数据捕获、可重现分析实践的定义，包括容器和工作流的使用 编排脚本，以及将数据、模型、管道和组织图转换为可互操作的格式 正在上传到CODCC。该团队还将领导与其他感兴趣的各方的合作开发 来自Sennet联盟的衰老细胞本体论。