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，建模和分析由 杜克组织映射中心（TMC），以便递送衰老细胞特征和衰老细胞的组织图。 细胞的CODCC。这将通过务实和创新地执行规定的目标来实现-数据 数据处理、数据分析、地图绘制和联合体协调。数据处理团队将 负责在Microsoft Azure上实施云原生平台， 符合FAIR（Findable，Interoperable，Interoperable and Reusable）标准。该小组将协调 使用生物样本核心记录潜在混杂变量，如人种、性别、活体或尸体 组织来源;与生物分析核心的专业知识，在最佳管道处理特定的 分析数据，并与数据分析团队合作，以确保以可互操作的格式收集数据 下游分析。数据分析团队将负责衰老的表征 考虑到衰老细胞的异质性和向衰老细胞转变的动力学， 衰老状态该团队将使用迭代策略来识别衰老细胞， 相关的标记物，并以生物学背景为条件表征功能签名。 衰老细胞该团队将利用类器官对动态特征进行初步表征， 这些推定的签名，以确定罕见的衰老细胞在正常组织（包括生物流体），并完善 通过基于它们在衰老过程中出现的程度对特征元素进行重新加权， 正常组织中的细胞。地图绘制小组将负责绘制 使用先进的计算生物学方法，创新的张量分析， 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 （多重免疫组织化学图像，Visium空间转录组学和Cartana原位测序） 和单细胞测定（组合的scRNA-seq和scATAC-seq），以构建预测细胞凋亡的空间图。 来自肺、心脏、肌肉的正常组织中衰老细胞的转录组、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接口以实现地图可视化，并评估 这些地图的准确性，使用交叉验证，数据集从公共存储库，和地图构建 其他TMCs联合体协调小组将负责使用术语注释所有数据集 从NIH公共数据元素存储库和OBO Foundry本体，为数据和 元数据捕获，定义可重复分析的实践，包括使用容器和工作流程 协调脚本，以及将数据、模型、管道和组织图转换为可互操作的格式， 上传到CODCC该团队还将领导与其他相关方的合作开发 来自SenNet联盟的衰老细胞本体论。