HIGH-RESOLUTION MULTIPARAMETER CELL ANALYSIS AND SORTING

我们提出的多元统计分析和排序方法是 基于我们所认为的一些更基本的问题， 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， 获得复杂的多参数流式细胞术数据已经远远超过 大多数目前可用的数据分析方法， 直方图或双变量显示级别。 列表模式再处理和使用 通过各种直方图或双变量“映射”数据 显示器是有用的，但由于至少三个原因是不够的。 一是 区域的选择通常通过任意选择的直线或 位图边界。 其次，即使所有可能的二元显示都是 由于这些预测是在 固定方向到实际多维数据。 期待看到 所有具有双变量显示的多维数据类似于 试图用单独的直方图来查看二维数据。 第三、 非流式细胞术（FCM）数据通常不包括在总体 流式细胞术的分析（从不直接在分选过程中） 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复杂的 通过使用投影尽可能高维度的数据 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”分析和使用我们独特的 自动立体显示器和3D鼠标。 第二，我们试图解决 基于以下内容适当选择分析和分类边界的问题 严格的多元统计技术，而不是任意 构造的直线或位图边界。 第三，我们试图 直接联合收割机结合其他非流式细胞术的细胞信息， 感兴趣的（例如，患者信息、医生诊断） 使用递归分区技术的细胞计数变量。 在 此外，我们试图解决高分辨率细胞的问题， 排序超越直线和位图排序通过使用前面的 统计学方法允许基于 先进的统计和专家系统标准，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、软件和光学技术，将允许直接 细胞的分选和随后的分析以确认这些细胞的效用 多变量技术用于各种重要的生物学和 临床应用。

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.