Big Flow Cytometry Data: Data Standards, Integration and Analysis

流式细胞术大数据：数据标准、集成与分析

• 批准号: 9311431
• 负责人: Greg Finak
• 金额: $ 35.79万
• 依托单位: FRED HUTCHINSON CANCER RESEARCH CENTER
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-20 至 2022-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9311431
• 关键词: Address; Adoption; Advisory Committees; Anus; Archives; Area; Basic Science; Bioconductor; Bioinformatics; Biological; Biological Assay; Cells; Collection; Communities; Complex; Computer software; Computing Methodologies; Cytometry; Data; Data Analyses; Data Analytics; Data Files; Data Set; Databases; Development; Dimensions; Disease; Environment; Flow Cytometry; Foundations; Genes; Goals; Heterogeneity; Immune System Diseases; Immunologic Monitoring; Industry; Informatics; International; Knock-out; Knowledge; Learning; Manuals; Measurable; Measurement; Measures; Meta-Analysis; Metadata; Methods; Modernization; Mouse Strains; Multicenter Trials; Mus; Output; Phenotype; Play; Population; Procedures; Protocols documentation; Reagent; Research; Research Infrastructure; Research Personnel; Retrieval; Role; Societies; Software Tools; Standardization; Supervision; Technology; Testing; Validation; Work; base; body system; cancer diagnosis; clinical diagnostics; community based evaluation; computerized tools; data exchange; data integration; experimental study; high dimensionality; human disease; insight; instrument; instrumentation; mammalian genome; novel; operation; phenotypic data; repository; research and development; software development; statistics; tool; vaccine development

PROJECT SUMMARY Flow cytometry is a single-cell measurement technology that is data-rich and plays a critical role in basic research and clinical diagnostics. The volume and dimensionality of data sets currently produced with modern instrumentation is orders of magnitude greater than in the past. Automated analysis methods in the field have made great progress in the past five years. The tools are available to perform automated cell population identification, but the infrastructure, methods and data standards do not yet exist to integrate and compare non-standardized big flow cytometry data sets available in public repositories. This proposal will develop the data standards, software infrastructure and computational methods to enable researchers to leverage the large amount of public cytometry data in order to integrate, re-analyze, and draw novel biological insights from these data sets. The impact of this project will be to provide researchers with tools that can be used to bridge the gap between inference from isolated single experiments or studies, to insights drawn from large data sets from cross-study analysis and multi-center trials.

项目摘要 流式细胞仪是一种单细胞测量技术，它是数据丰富的，并且在基本中起着至关重要的作用 研究和临床诊断。现代生产的数据集的数量和维度 仪器比过去大的数量级。该领域的自动分析方法具有 在过去的五年中取得了长足的进步。这些工具可用于执行自动细胞种群 识别，但是基础架构，方法和数据标准尚不存在以集成和比较 公共存储库中可用的非标准化大流式细胞仪数据集。该建议将发展 数据标准，软件基础架构和计算方法，使研究人员能够利用大型 为了整合，重新分析并从中融合，重新分析和吸引新颖的生物学见解 数据集。该项目的影响是为研究人员提供可用于弥合差距的工具 从孤立的单个实验或研究到从大型数据集中得出的见解之间的推论之间 跨研究分析和多中心试验。