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 流式细胞术是一种数据丰富的单细胞测量技术，在基础细胞学中起着关键作用。 研究和临床诊断。目前用现代数据库制作的数据集的数量和维度 仪器的数量级比过去大。现场的自动分析方法 在过去的五年里取得了很大的进步。这些工具可用于执行自动化细胞群 识别，但基础设施，方法和数据标准尚不存在，以整合和比较 非标准化的大型流式细胞术数据集可在公共存储库中获得。该提案将发展 数据标准，软件基础设施和计算方法，使研究人员能够利用大型 大量的公共细胞计数数据，以整合，重新分析，并从中得出新的生物学见解 数据集。该项目的影响将是为研究人员提供可用于弥合差距的工具 从孤立的单一实验或研究中得出的推论，到从大型数据集中得出的见解， 交叉研究分析和多中心试验。