CLASSIFIERS FOR HIGH RESOLUTION CELL SORTING

尽管能够收集复杂的多参数列表模式 数据，直线或位图单元格排序边界仍然是 通常以相当任意的方式手动选择。通常 实验者在绘制边界之前进行视觉聚类， 没有成功分类的统计预测。 复杂的多参数数据的可视化也是困难的。一种方式 处理可视化问题的方法是查看前三个 主成分的数据，并使用这些信息来估计 数量和近似质心的“指导”聚类分析。集群 然后，将使用成员资格概率来作出排序决定。 处理在 任意“视觉分类”的基础是应用统计学 细胞分类的方法，例如用贝叶斯进行判别分析 决策边界。将计算判别函数， 决策边界将被用来排序细胞的基础上， 判别函数分数，将通过以下方式实时计算 硬件和/或软件查找表。错误分类的成本将 也包括在细胞分选决策中。 对于开发的所有分类器系统，分类器性能将 通过ROC（“受试者工作特征”）分析测量， 真阳性和假阳性。为了实现这一点，我们将使用 一个定义良好的数据系统和模型单元系统， 可以对照“标记的”参数检查分类器的正确性。 所有分选的模型细胞可以通过PCR（聚合酶链反应）明确地鉴定。 链反应）或FISH（荧光原位杂交）。 而该建议的主要重点是开发实时细胞 除了用于细胞分选的分类器之外，许多技术也可以被 被其他研究人员用于传统列表模式的离线分析 流式细胞术数据。 因此，这些技术中的许多应该证明 重要的是其他研究人员，即使他们无法执行 复杂的细胞分选。 证明这些新技术对许多问题的重要性 在生物学和医学中，我们将尝试应用这些新技术， 几个重要的应用包括：（1）高分辨率排序 用于产前诊断的来自人母血的单个胎儿细胞;（2） 癌基因、抑癌基因、转移基因、 罕见人类转移性乳腺癌中的多药耐药基因 从外周血和骨髓中分离细胞， 富集或高分辨率细胞分选;和（3）骨髓净化 转移性细胞，以允许在乳房中进行自体移植 接受大剂量化疗的癌症患者。

Despite the capability to collect sophisticated multiparameter listmode data, both rectilinear or bit-map cell sorting boundaries still are usually chosen manually and in a rather arbitrary fashion. Usually the experimenter performs visual clustering prior to drawing boundaries which have no statistical prediction of successful classification. Visualization of complex multiparameter data is also difficult. One way to deal with the visualization problem is to view the first three principal components of the data and use this information to estimate the number and approximate centroids for "guided" cluster analysis. Cluster membership probabilities will then be used to make sort decisions. Another way to deal with the problem of placing sort boundaries on the basis of arbitrary "visual classifications" is to apply statistical methods of classifying cells, e.g. discriminant analysis with Bayes decision boundaries. Discriminant functions will be calculated and Bayes decision boundaries will be used to sort cells on the basis of discriminant function scores which will be calculated in real-time by hardware and/or software lookup tables. A cost of misclassification will also be included in the cell sorting decision. For all classifier systems developed, classifier performance will be measured through ROC ("receiver operating characteristics") analyses of true-positives and false-positives. To accomplish this we will use a well-defined system of data and model cell systems whereby all classifiers can be checked for correctness against "tagged" parameters. All sorted model cells can be unequivocally identified by PCR (polymerase chain reaction) or by FISH (fluorescence in-situ hybridization). While the main focus of the proposal is to develop real-time cell classifiers useful for cell sorting, many of the techniques can also be used by other researchers for off-line analysis of conventional listmode flow cytometry data. Hence many of these techniques should prove important to other researchers even if they are unable to perform the sophisticated cell sorting described in this proposal. To demonstrate the importance of these new techniques to many problems in biology and medicine we will attempt to apply these new techniques to several important applications including: (1) high-resolution sorting of single fetal cells from human maternal blood for prenatal diagnosis; (2) molecular characterizations of oncogene, tumor suppresser, metastatic, and multi-drug resistance genes in rare human metastatic breast cancer cells isolated from peripheral blood and bone marrow by high-speed enrichment or high-resolution cell sorting; and (3) bone marrow purging of metastatic cells to allow for autologous transplantations in breast cancer patients undergoing high-dose chemotherapy.