Core D: Data Analysis Core

项目摘要-核心D（数据分析核心） 数据分析核心（DAC）将建立在可用的计算基础设施上，以满足以下所有需求： 耶鲁SenNet TMC的高级数据分析和信息学。它还将为数据存储提供支持 元数据的管理和引用、基于网络的数据可移植和查询、严格的 统计分析，以完成中心的使命。DAC将由Yuval Kluger博士领导，他提供 生物信息学和多维数据的计算分析，包括测序 和成像数据。他在方法开发和协作方面有着广泛的记录， 研究分析主要NIH联盟的基因组学和蛋白质组学技术产生的数据。Co-I Gershkovich负责耶鲁临床病理学数据基础设施和管理。他将提供突出的 在建立管道、框架和门户网站方面的专业知识， 分析来自包括组学和图像在内的多种模态的数据。他将开发一个工作流程跟踪系统 用于标本采集、注释、分析数据和共享。DAC将开发管道来分析数据 来自生物分析核心（BAC）中的4种测定模式。具体来说，DAC将执行密钥 用于（1）数据处理的功能-建立一组多尺度和多模态数据处理管道，以及 质量保证协议，（2）数据分析-建立一个综合的单细胞和空间数据分析 工作流程和生物标志物特征推断，（3）图谱构建-构建组织分子和细胞 通过图像配准和多组学共同参考绘制地图，以及（4）联盟协调-与 CODCC和其他TMC开发通用数据库、可视化和查询。DAC将生成 人类淋巴器官细胞衰老的分子图谱和细胞图谱，定义了细胞类型特异性 衰老生物标志物，解剖衰老细胞异质性，并描绘与衰老的相互作用- 相关的组织环境。它还将汇集数据标准，促进传播，并整合我们的 现有的或新生成的地图集和SenNet和其他NIH联盟的地图数据。这些都是高度 有价值的下一代生物医学数据资源，用于基础，翻译和临床的广泛社区 research.

Project Summary – Core D (Data Analysis Core) The Data Analysis Core (DAC) will build on available computational infrastructure to fulfill all the needs for advanced data analysis and informatics of the Yale SenNet TMC. It will also provide support for data storage and sharing, metadata management and referencing, web-based data portable and query, and rigorous statistical analyses to accomplish the center’s missions. The DAC will be led by Dr. Yuval Kluger, who provides prominent expertise bioinformatics and computational analysis of multi-dimensional data including sequencing and imaging data. He has an extensive track record of methodology development and collaborative work in studies analyzing data generated by genomics and proteomics technologies in major NIH consortia. Co-I Gershkovich is directing Yale Clinical Pathology data infrastructure and management. He will provide prominent expertise in building pipelines, frameworks and portals for integrating biospecimen metadata with biological analysis data from multiple modalities including omics and images. He will develop a workflow tracking system for specimen collection, annotation, analytic data, and sharing. The DAC will develop pipelines to analyze data from 4 assay modalities in the Biological Analysis Core (BAC). Specifically, the DAC will carry out the key functions for (1) data processing – to set up a set of multiscale and multimodal data processing pipelines and quality assurance protocols, (2) data analysis – to establish an integrated single-cell & spatial data analysis workflow and biomarker signature inference, (3) map construction – to construct tissue molecular and cellular maps via image co-registration and multi-omics co-referencing, and (4) consortium coordination – to work with the CODCC and other TMCs to develop common database, visualization, and query. The DAC will generate the Molecular Maps and Cellular Maps of cellular senescence in human lymphoid organs, define cell type-specific senescence biomarkers, dissect senescent cell heterogeneity, and delineate the interactions with senescence- associated tissue environments. It will also assemble data standards, facilitate dissemination, and integrate our data with existing or newly generated atlases and maps from SenNet and other NIH Consortia. These are highly valuable next-generation biomedical data resources for a broad community of basic, translational, and clinical research.