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首页> 外文会议>Conference on Optical Diagnostics of Living Cells Ⅴ, Jan 23-25, 2002, San Jose, USA >Advanced 'Real-Time' Classification Methods for Flow Cytometry Data Analysis; Cell Sorting
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Advanced 'Real-Time' Classification Methods for Flow Cytometry Data Analysis; Cell Sorting

机译：流式细胞术数据分析的高级“实时”分类方法；细胞分类

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While many flow cytometric data analysis and "discovery" methods have been developed, few of these have been applied to the problem of separating out purified cell subpopulations by cell sorting. The fundamental problem is that the data analysis techniques have been performed using relatively slow computational methods that take far more time than is allowed by the sort decision on a cell sorter (typically less than a millisecond). Thus cell sorting, which is really a form of "real-time data classification", is usually done with few, if any, multivariate statistical tools used either in the sort decision or in the evaluation of the correctness of the classification. We have developed new multivariate data analysis and "data discovery" methods that can be implemented for real-time data classification for cell sorting using linked lookup tables. One multivariate "data discovery" method, "subtractive clustering", has been used to find which clusters of cells are different between two or more files (cell samples) and to help guide analysis or sort boundaries for these cell subpopulations. Multivariate statistical methods (e.g. principal component analysis or discriminant function analysis) were implemented in linked lookup tables to establish analysis/sort boundaries that include "costs (or penalties) of misclassification. Costs of misclassification provided a measure of the quality of the analysis/sort boundary and were expressed in simple terms that describe the tradeoff between yield and purity.
机译：尽管已经开发了许多流式细胞术数据分析和“发现”方法，但是这些方法中很少有被应用于通过细胞分选来分离纯化的细胞亚群的问题。根本的问题是，数据分析技术是使用相对较慢的计算方法执行的，该方法花费的时间比单元格分类器上的分类决定所允许的时间要多得多（通常少于一毫秒）。因此，单元格排序实际上是“实时数据分类”的一种形式，通常使用很少（如果有的话）的多元统计工具来进行分类决策或分类正确性的评估。我们已经开发了新的多元数据分析和“数据发现”方法，可以使用链接的查找表对细胞分类进行实时数据分类。一种多元的“数据发现”方法，即“减法聚类”，已被用于发现两个或多个文件（细胞样本）之间哪些细胞簇不同，并有助于指导这些细胞亚群的分析或分类边界。在链接的查找表中实施了多元统计方法（例如主成分分析或判别函数分析）以建立分析/分类边界，其中包括“分类错误的成本（或罚款）。分类错误的成本提供了分析/分类质量的衡量标准边界，用简单的术语表示，描述了产量和纯度之间的折衷。

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