The Statistical and Computational Analysis of Flow Cytometry Data

流式细胞术数据的统计和计算分析

• 批准号: 8068069
• 负责人: Raphael Gottardo
• 金额: $ 5.14万
• 依托单位: FRED HUTCHINSON CANCER RESEARCH CENTER
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-05-01 至 2012-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8068069
• 关键词: AIDS/HIV problem; Address; Antibodies; Antigens; Area; Basic Science; Biological; Cancer Vaccines; Cations; Cells; Characteristics; Clinical; Clinical Research; Clinical Trials; Collaborations; Computer Analysis; Computer software; Computing Methodologies; Cytometry; Data; Data Analyses; Data Set; Data Sources; Development; Disease; Ensure; Event; Flow Cytometry; Future; Genomics; HIV; Hypersensitivity; Imagery; Immune response; Immunity; Intervention; Lasers; Machine Learning; Malignant Neoplasms; Manuals; Medical; Methods; Names; Noise; Patients; Play; Population; Process; Reagent; Research Infrastructure; Research Personnel; Role; Rosa; Sampling; Shapes; Software Tools; Staining method; Stains; Statistical Methods; Surface; Technology; Transplantation; Vaccine Research; Variant; Work; graft vs host disease; human disease; innate immune function; innovation; instrument; instrumentation; leukemia/lymphoma; longitudinal analysis; particle; public health relevance; sound; tool

DESCRIPTION (provided by applicant): Flow cytometry is a data-rich technology that plays a critical role in basic research and clinical therapy for a variety of human diseases. Recent technological developments have greatly increased the areas of application and data throughput, and corresponding innovative analysis methods are needed. In order to be able to take advantage of these new capabilities researchers need access to high quality analysis tools that will help to identify subpopulations of cells with particular characteristics. The methods we are proposing include advanced methods for machine learning and visualization. We will apply our methods to a number of different scenarios such as the analysis of longitudinal data, and the analysis of data arising from clinical studies. PUBLIC HEALTH RELEVANCE: The aims of this project are to provide statistical and computational methods for the analysis of flow cytometry data. The impact of these tools will be to provide better, more reliable, tools for the analysis of flow cytometry data. The domain of application spans all diseases, but current applications are focused on HIV disease and cancer.

描述（由申请人提供）：流式细胞术是一项数据丰富的技术，在多种人类疾病的基础研究和临床治疗中发挥着关键作用。最近的技术发展大大增加了应用领域和数据吞吐量，需要相应的创新分析方法。为了能够利用这些新功能，研究人员需要获得高质量的分析工具，这将有助于识别具有特定特征的细胞亚群。我们提出的方法包括机器学习和可视化的高级方法。我们将把我们的方法应用于许多不同的情况，如纵向数据分析和临床研究数据分析。公共卫生相关性：该项目的目的是为流式细胞术数据的分析提供统计和计算方法。这些工具的影响将是为流式细胞术数据的分析提供更好、更可靠的工具。应用领域涵盖所有疾病，但目前的应用主要集中在艾滋病和癌症上。