Data-Analysis-Core

项目摘要 明尼苏达州衰老细胞组织图谱中心的数据分析核心(DAC) 是由生物医学数据科学和生物信息学建模专家康斯坦丁·阿维利斯共同执导的 成功领导大型信息学核心的长期记录；高级生物信息学家王金华 专门研究单细胞数据和整合基因组学；以及数据管理专家史蒂夫·约翰逊， 数据质量和信息学服务，以及协作科学。DAC还包括因果关系和 预测建模(Kummerfeld博士)、组学成像(Pengo博士)、细胞动力学和细胞建模 统计规划、质量控制措施和统计假设检验(Dr.Odde)。 DAC的总体目标是为以下方面建立MN TMC 4D SNC地图集的最后一步 将交付的健康人体脂肪、肝脏、骨骼肌和卵巢组织(以及所有支持 数据)提供给Sennet联合体组织和数据协调中心(CODCC)，用于建设 人类4D SNC地图集。将对不同年龄段的健康人体组织进行散装和单个分析 MN TMC生物分析核心(BAC)的细胞表征和时空分析 由Biospecimen Core(BSP)提供。DAC将负责从BSP和BAC获取数据， 映射到可互操作和可搜索的本体、注释、整理和分析。它将建立数据存储， 搜索、检索、分析和可视化工具，并将人类样本链接到一组丰富的未识别的健康 来自相应电子健康记录的元数据。在与Sennet财团的合作下，DAC将 建立基准，促进标准操作程序和标准开发，并最终 准备数据集并与CODCC共享，以实现最终的健康老化的4D人类SNC图谱。DAC将 利用尖端信息学、高性能计算、专家教师和高级数据存储以及 明尼苏达大学(UMN)的管理能力。它将使用现有的数据/元数据标准， 确保可重复性和可用性的软件工具和分析方法。DAC将部署本体和 在高通量组学和数据捕获、协调、 转让、安全和分析，这些都与创建SNC地图集的任务密切相关。DAC还将 与其他TMC和CODCC密切合作，开发和实施定制的Sennet范围标准 针对联合体的需求进行了微调；数据质量指标、本体和数据元素；集成 成像和组学数据.可视化、分割和注释的分析工具.标准操作规程；通用数据 元素(CDE)；以及网络的公共数据共享政策。发审会最终将进行初步研究 与梅奥诊所(LeBrasseur博士和Mielke博士)合作，这将全面说明 所得数据可用于构建功能性SNC图谱和建立一组SNC生物标记物。

Project Summary The Data Analysis Core (DAC) of the Minnesota Tissue Mapping Center (MN TMC) of Senescent Cells (SnCs) is co-directed by Constantin Aliferis, an expert in biomedical data science and bioinformatics modeling with a long track record in successfully leading large-scale informatics cores; Jinhua Wang, a senior bioinformaticist specializing in single cell data and integrative genomics; and Steve Johnson, an expert in data management, data quality and informatics services, and collaborative science. The DAC also includes experts in causal and predictive modeling (Dr. Kummerfeld), omics imaging (Dr. Pengo), modeling of cell dynamics and cell movement (Dr. Odde), and statistical planning, quality control measures, and statistical hypothesis testing (Dr. Guan).The overall goal of the DAC is to be the final step in the construction of a MN TMC 4D SnC atlas for healthy human adipose, liver, skeletal muscle, and ovarian tissues to be delivered (along with all supporting data) to the SenNet Consortium Organization and Data Coordinating Center (CODCC) for the construction of a human 4D SnC Atlas. Healthy human tissues over a range of ages will be analyzed with both bulk and single cell characterization and spatio-temporal analysis by the MN TMC Biological Analysis Core (BAC) using samples provided by the Biospecimen Core (BSP). The DAC will be responsible for data ingestion from BSP and BAC, mapping to interoperable and searchable ontologies, annotation, curation, and analysis. It will build data storage, search, retrieval, analysis, and visualization tools and link human specimens to a rich set of de-identified health metadata from corresponding electronic health records. In collaboration with the SenNet consortium, DAC will establish benchmarks, contribute to standard operating procedures and standards development, and ultimately prepare and share datasets with the CODCC to enable a final 4D human SnC atlas with healthy aging. DAC will leverage cutting-edge informatics, high performance computing, expert faculty, and advanced data storage and management capabilities at the University of Minnesota (UMN). It will use existing data/metadata standards, software tools, and analysis methods that ensure reproducibility and usability. DAC will deploy ontology and analytic standards widely accepted in the fields of high throughput omics and data capture, harmonization, transfer, security, and analysis and that are germane to the task of creating an atlas of SnCs. The DAC will also work closely with the other TMCs and the CODCC to develop and implement customized SenNet-wide standards fine-tuned to the needs of the consortium; data quality metrics, ontologies, and data elements; integration of imaging and omics data; analytical tools for visualization, segmentation, and annotation; SOPs; Common Data Elements (CDEs); and the network's public data sharing policy. The DAC will finally conduct a preliminary study in collaboration with Mayo Clinic (Drs. LeBrasseur and Mielke) that will comprehensively illustrate how the resulting data can be utilized to build a functional SnC atlas and establish a set of SnC biomarkers.