Data management and analysis

摘要 疫苗利用我们对免疫系统的了解来预防传染病， 在历史上彻底改变了人类健康。然而，疫苗接种策略的巨大成功并不是 完整的和某些脆弱的人群，即使接受推荐的方案， 或低效的反应。临床医学和公共卫生的一个基本挑战是了解 这些人群中反应异质性的潜在机制，以便设计合理的 改进疫苗策略的方法。应对这一挑战将需要整合以下信息： 免疫系统的细胞和分子之间跨越时间和空间的复杂相互作用。的项目 在这项提案中，将对三个队列的疫苗接种反应进行系统水平的免疫分析， 易感患者人群：B细胞耗竭的自身免疫性患者（项目1）、老年和体弱个体 （项目2）和镰状细胞病患者（项目3）。多组学免疫分析核心将产生 来自循环免疫细胞的纵向样品的多组学数据沿着组织活检，以捕获免疫细胞。 皮肤中原位的早期反应和引流淋巴结中的峰值反应。数据管理和 由Steven Kleinstein博士领导的分析核心（DMAC）将支持数据库，生物信息学分析和 项目1、2和3的复杂数据分析需求。此外，核心小组将与项目调查人员合作， 实验设计和报告，并为项目人员提供软件工具培训环境 方法和结果解释的原则。具体而言，DMAC将： (1)为临床和研究数据提供安全的数据管理系统。 (2)将数据提交到NIAID ImmPort存储库和NIAID指定的其他公共门户网站，以确保 所有数据都是可查找、可访问、可互操作和可重用的（FAIR）。 (3)提供全面的生物统计分析支持和咨询。 (4)使用多元统计方法分析和整合队列范围的数据，以发现免疫 与脆弱人群和不同疫苗反应相关的特征。 (5)支持项目1-3的研究人员进行分析，以解决他们的科学问题。 (6)与Core C（多组学免疫分析）合作，对所有免疫分析数据进行预处理， 向管理系统提交原始数据和可计算数据，并进行系统级分析。 (7)整合项目间的数据，以检测疫苗反应的共同和独特特征。 (8)积极参加重债穷国数据管理和整合小组委员会。 (9)继续在重债穷国内努力制定和采用共同数据标准。

ABSTRACT Vaccines capitalize on our knowledge of the immune system to safeguard against infectious diseases and historically have revolutionized human health. However, the powerful success of vaccination strategies is not complete and certain vulnerable populations, even when receiving the recommended regimen, have ineffective or inefficient responses. A fundamental challenge in clinical medicine and public health is to understand the mechanisms underlying the heterogeneity of responses in these populations in order to design rational approaches to improve vaccine strategies. Addressing this challenge will require integration of information about the complex interactions among cells and molecules of the immune system across time and space. The projects in this proposal will carry out systems-level immune profiling of vaccination responses across three cohorts of vulnerable patient populations: autoimmune patients with B cell depletion (Project 1), aged and frail individuals (Project 2), and patients with sickle cell disease (Project 3). A multi-omics immune profiling core will generate multi-omics data from longitudinal samples of circulating immune cells along with tissue biopsies to capture the early response in situ in the skin and peak responses in draining lymph nodes. This Data Management and Analysis Core (DMAC), led by Dr. Steven Kleinstein, will support the database, bioinformatics analysis and complex data analysis needs of Projects 1, 2 and 3. In addition, the core will collaborate with project investigators on experimental design and reporting, and provide a training environment for project personnel on software tools and principles of methods and interpretation of results. Specifically, the DMAC will: (1) Provide a secure data management system for clinical and research data. (2) Submit data to the NIAID ImmPort repository and other public portals designated by NIAID, to ensure that all data are findable, accessible, interoperable, and reusable (FAIR). (3) Provide comprehensive biostatistical analysis support and consultation. (4) Analyze and integrate cohort-wide data using multivariate statistical approaches to discover immune signatures associated with vulnerable populations and differential vaccine responses. (5) Support investigators in Projects 1-3 by carrying out analyses to address their scientific questions. (6) Collaborate with Core C (Multi-omic Immune Profiling) on pre-processing of all immune profiling data, submission of raw and computable data into the management system, and systems-level analyses. (7) Integrate data across projects to detect common and unique signatures of vaccine responses. (8) Actively participate in the HIPC Data Management and Integration subcommittee. (9) Continue our efforts within HIPC towards the development and adoption of common data standards.