Boddy L, Wilkins M F, Morris C W
Cardiff School of Biosciences, Cardiff University, Cardiff, United Kingdom.
Cytometry. 2001 Jul 1;44(3):195-209. doi: 10.1002/1097-0320(20010701)44:3<195::aid-cyto1112>3.0.co;2-h.
Analytical flow cytometry (AFC), by quantifying sometimes more than 10 optical parameters on cells at rates of approximately 10(3) cells/s, rapidly generates vast quantities of multidimensional data, which provides a considerable challenge for data analysis. We review the application of multivariate data analysis and pattern recognition techniques to flow cytometry.
Approaches were divided into two broad types depending on whether the aim was identification or clustering. Multivariate statistical approaches, supervised artificial neural networks (ANNs), problems of overlapping character distributions, unbounded data sets, missing parameters, scaling up, and estimating proportions of different types of cells comprised the first category. Classic clustering methods, fuzzy clustering, and unsupervised ANNs comprised the second category. We demonstrate the state of the art by using AFC data on marine phytoplankton populations.
Information held within the large quantities of data generated by AFC was tractable using ANNs, but for field studies the problem of obtaining suitable training data needs to be resolved, and coping with an almost infinite number of cell categories needs further research.
分析流式细胞术（AFC）以大约10³个细胞/秒的速率对细胞上有时超过10个光学参数进行量化，迅速生成大量多维数据，这给数据分析带来了相当大的挑战。我们综述了多变量数据分析和模式识别技术在流式细胞术中的应用。
根据目的是识别还是聚类，方法分为两大类。多变量统计方法、有监督人工神经网络（ANN）、特征分布重叠问题、无界数据集、缺失参数、扩大规模以及估计不同类型细胞的比例属于第一类。经典聚类方法、模糊聚类和无监督ANN属于第二类。我们通过使用关于海洋浮游植物种群的AFC数据来展示当前的技术水平。
使用人工神经网络可以处理AFC产生的大量数据中的信息，但对于实地研究，需要解决获取合适训练数据的问题，并且应对几乎无限数量的细胞类别还需要进一步研究。
