HIGH-RESOLUTION MULTIPARAMETER CELL ANALYSIS AND SORTING

高分辨率多参数细胞分析和分类

• 批准号: 2179438
• 负责人: JAMES F. LEARY
• 金额: $ 25.25万
• 依托单位: UNIVERSITY OF ROCHESTER
• 依托单位国家: 美国
• 财政年份: 1988
• 资助国家: 美国
• 起止时间: 1988-04-01 至 1994-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/2179438
• 关键词: artificial intelligence; bioengineering /biomedical engineering; cell population study; computer assisted diagnosis; computer data analysis; computer graphics /printing; computer program /software; flow cytometry; gel electrophoresis; image processing; method development; personal computers; polymerase chain reaction; single cell analysis; statistics /biometry; time resolved data

Our proposed multivariate statistical analysis and sorting methods are based on what we perceive to be some of the more fundamental problems in multiparameter flow cytometry and cell sorting. The ability of experimenters in both the basic research and clinical laboratories to acquire complex multiparameter flow cytometric data has far out-stripped most currently available data analysis methods which deal with data at the histogram or bivariate display level. Listmode reprocessing and use of color to "map" the data through various histogram or bivariate displays is useful but inadequate for at least three reasons. First, the selection of regions is usually done by arbitrarily chosen rectilinear or bit-map boundaries. Second, even if all possible bivariate displays are viewed one does not see all of the data since these projections are at fixed orientations to the actual multidimensional data. Expecting to see all of the multidimensional data with bivariate displays is analogous to trying to see 2 dimensional data with the separate histograms. Third, non-flow cytometric (FCM) data is usually not included in the overall analysis (and never directly in the sorting process) of flow cytometry data in terms of statistical analyses of the combined FCM and non-FCM data. To address these problems we first attempt to view this complex data in as high a dimensionality as possible through use of projection pursuit analyses (e.g. principal components, Friedman-Tukey) and our own "local multivariate dimensionality" analyses and the use of our unique autostereo scopic display and 3D mouse. Second, we attempt to address the question of proper selection of analysis and sort boundaries based on rigorous multivariate statistical techniques rather than arbitrarily constructed rectilinear or bit-map boundaries. Third, we attempt to directly combine other non-flow cytometric information about the cells of interest (e.g. patient information, physicians' diagnoses) with flow cytometric variables using recursive partitioning techniques. In addition, we attempt to address the problem of high-resolution cell sorting beyond rectilinear and bit-map sorting by using the preceding statistical methods to allow real-time sorting of cells based on sophisticated statistics and expert systems criteria using our special "flexible" sorting system. Lastly, we propose implementing the preceding techniques in hardware, software and optics that will allow for direct sorting and subsequent analyses of cells to confirm the utility of these multivariate techniques for a variety of important biological and clinical applications.

我们建议的多元统计分析和排序方法包括 基于我们认为的一些更根本的问题， 多参数流式细胞术和细胞分选。的能力 基础研究和临床实验室的实验人员 获取复杂的多参数流式细胞仪数据已远远超出 目前可用的大多数数据分析方法处理以下数据 直方图或双变量显示级别。列表模式重新处理和使用 通过不同的直方图或二变量对数据进行“映射” Display是有用的，但至少有三个原因是不够的。首先， 区域的选择通常通过任意选择直线或 位图边界。第二，即使所有可能的双变量显示都是 查看时不会看到所有数据，因为这些预测位于 固定指向实际多维数据的方向。期待看到 具有双变量显示的所有多维数据类似于 尝试使用独立的直方图查看二维数据。第三, 非流式细胞仪(FCM)数据通常不包括在总体 流式细胞术的分析(在分选过程中从不直接) 结合FCM和非FCM的统计分析数据 数据。为了解决这些问题，我们首先尝试查看这个综合体 通过使用投影，使数据具有尽可能高的维度 追踪分析(例如主成分、弗里德曼-图基)和我们自己的 “局部多变量维度”的分析及我国独有的 自动立体显示和3D鼠标。第二，我们试图解决 的分析和排序边界的适当选择问题 严格的多元统计技术，而不是武断的 构造的直线或位图边界。第三，我们试图 直接结合关于细胞的其他非流式细胞术信息 感兴趣(例如，患者信息、医生的诊断)与流 使用递归分区技术的细胞学变量。在……里面 此外，我们还尝试解决高分辨率单元的问题 在直线排序和位图排序之外使用前面的 统计方法，允许基于以下各项对单元格进行实时排序 复杂的统计数据和专家系统标准使用我们的特殊 “灵活”的分拣系统。最后，我们建议实施上述 硬件、软件和光学方面的技术将允许直接 对细胞进行分类和后续分析，以确认这些方法的实用性 多变量技术用于各种重要的生物学和 临床应用。