Data Analysis Core

项目总结 中西部小鼠衰老细胞组织图谱中心的数据分析核心(DAC) (SNCS)将建立广泛的计算资源，以满足中心的所有信息学和数据分析 需要。DAC MPI是基因组信息学和生物信息学建模专家王金华，他的 在成功建立和领导华盛顿大学中心资助的信息学核心方面有长期的记录 和亚历山大·米沙林，他是专门研究单细胞数据和集成的高级研究员 西北大学的基因组学。DAC还包括跨物种比较基因组学的专家， 转录分析、基因途径建模、遗传生物标记物选择、蛋白质组学/代谢组学数据 分析和工具开发，细胞成像的深度神经网络建模，以及统计规划和质量 控制措施和统计假设检验。发改委的总体目标是执行多尺度和 对收集的数据进行多模式分析(用于SNC鉴定、SNC生物标志物新发现、SNC空间 模式发现和SNC蜂窝状态动力学建模)，并为Sennet联盟做好准备 组织和数据协调中心(CODCC)，用于构建小鼠SNC4D地图集。DAC MPIS将与耶鲁大学和UMN人类TMC DAC中心密切合作，提供信息并帮助推进正在进行的 在人体组织中进行的分析。值得注意的是，DAC MPI Wang与耶鲁大学和 担任UMN人类DAC董事十多年。选择小鼠组织(肝、肺、骨骼肌和 脂肪)范围内的年龄、品系和扰动，将用整体和单细胞图谱进行分析 由MM-TMC生物分析核心(BAC)进行空间分析。DAC将负责数据 从BAC摄取、映射到可互操作和可搜索的本体、注释、整理和分析。 我们将1)构建或使用数据存储、搜索、检索、分析和多组学数据的最佳实践工具 联合嵌入；2)创建包括已知和新的全面的小鼠SNC生物标记物集合 生物标志物；以及3)建立一个交叉比较程序，将小鼠和人类的SNC分析联系起来。在……里面 与Sennet联盟合作，发改委将建立基准，为标准运作做出贡献 程序和标准制定，并准备数据集并与CODCC共享，以使小鼠 SNC 4D地图集。DAC将利用尖端信息学、高性能计算、专家教师和 MM-TMC机构的高级数据分析、数据存储和管理能力。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 针对财团的需求进行了微调，包括：1)数据质量指标、本体和数据元素；2) 用于可视化、分割和注释的成像和组学数据分析工具的集成；3)SOP； 4)公共数据元素；5)网络公共数据共享策略。

PROJECT SUMMARY The Data Analysis Core (DAC) of the Midwest Murine Tissue Mapping Center (MM-TMC) of Senescent Cells (SnCs) will build upon extensive computational resources to meet all the Center’s informatics and data analytics needs. The DAC MPIs are Jinhua Wang, an expert in genome informatics and bioinformatics modeling with a long track record in successfully building and leading informatics cores for center grants at the University of Minnesota (UMN), and Alexander Misharin, a senior researcher specializing in single cell data and integrative genomics at Northwestern University. The DAC also includes experts in cross-species comparative genomics, transcriptomic analysis, gene pathway modeling, genetic biomarker selection, proteomics/metabolomics data analysis and tool development, deep neural network modeling of cellular imaging, and statistical planning, quality control measures, and statistical hypothesis testing. The overall goal of the DAC is to perform multi-scale and multi-modality analysis of the collected data (for SnC identification, novel SnC biomarker discovery, SnC spatial pattern discovery, and SnC cellular states dynamics modeling) and prepare it for the SenNet Consortium Organization and Data Coordinating Center (CODCC) for the construction of a murine SnC 4D Atlas. The DAC MPIs will work closely with Yale and UMN human TMC DAC centers to inform and help advance the ongoing analysis carried out in human tissues. Notably, DAC MPI Wang has had productive collaborations with Yale and UMN human DAC directors for more than a decade. Select murine tissues (liver, lung, skeletal muscle, and adipose) over a range of ages, strains, and perturbations will be analyzed with both bulk and single cell profiling and spatial analysis by the MM-TMC Biological Analysis Core (BAC). The DAC will be responsible for data ingestion from the BAC, mapping to interoperable and searchable ontologies, annotation, curation, and analysis. We will 1) build or use the best practice tools for data storage, search, retrieval, analysis, and multi-omics data joint embedding; 2) create a comprehensive murine SnC biomarker set, including both known and novel biomarkers; and 3) establish a cross-comparison procedure to bridge murine and human SnC analyses. In collaboration with the SenNet Consortium, the DAC will establish benchmarks, contribute to standard operating procedures and standards development, and prepare and share datasets with the CODCC to enable a murine SnC 4D atlas. The DAC will leverage cutting-edge informatics, high performance computing, expert faculty, and advanced data analytics, data storage and management capabilities at MM-TMC institutions. 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 including: 1) data quality metrics, ontologies, and data elements; 2) integration of imaging and omics data analytical tools for visualization, segmentation, and annotation; 3) SOPs; 4) Common Data Elements; and 5) a network public data sharing policy.