权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Data Analysis Core

数据分析核心

基本信息

批准号：
10492754
负责人：
Cliburn C Chan
金额：
$ 77.71万
依托单位：
DUKE UNIVERSITY
依托单位国家：
美国
项目类别：
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/10492754
关键词：
Address 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 Docking Documentation Elements Emerging Technologies Ensure Evaluation FAIR principles Foundations Generations Guidelines Heterogeneity High-Throughput Nucleotide Sequencing Human Image Image Analysis Immunohistochemistry Immunology Individual Lead Leadership Maps Metadata Methods Modeling Modernization Myocardium 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 Weight base biomarker signature cell type comparative computerized data processing dashboard data integration data sharing data standards data tools 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

项目摘要

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