Davey H M, Jones A, Shaw A D, Kell D B
Institute of Biological Sciences, University of Wales, Aberystwyth, Ceredigion, United Kingdom.
Cytometry. 1999 Feb 1;35(2):162-8. doi: 10.1002/(sici)1097-0320(19990201)35:2<162::aid-cyto8>3.0.co;2-u.
When exploited fully, flow cytometry can be used to provide multiparametric data for each cell in the sample of interest. While this makes flow cytometry a powerful technique for discriminating between different cell types, the data can be difficult to interpret. Traditionally, dual-parameter plots are used to visualize flow cytometric data, and for a data set consisting of seven parameters, one should examine 21 of these plots. A more efficient method is to reduce the dimensionality of the data (e.g., using unsupervised methods such as principal components analysis) so that fewer graphs need to be examined, or to use supervised multivariate data analysis methods to give a prediction of the identity of the analyzed particles.
We collected multiparametric data sets for microbiological samples stained with six cocktails of fluorescent stains. Multivariate data analysis methods were explored as a means of microbial detection and identification.
We show that while all cocktails and all methods gave good accuracy of predictions (>94%), careful selection of both the stains and the analysis method could improve this figure (to > 99% accuracy), even in a data set that was not used in the formation of the supervised multivariate calibration model.
Flow cytometry provides a rapid method of obtaining multiparametric data for distinguishing between microorganisms. Multivariate data analysis methods have an important role to play in extracting the information from the data obtained. Artificial neural networks proved to be the most suitable method of data analysis.
充分利用时，流式细胞术可用于为感兴趣的样本中的每个细胞提供多参数数据。虽然这使流式细胞术成为区分不同细胞类型的强大技术，但数据可能难以解释。传统上，双参数图用于可视化流式细胞术数据，对于由七个参数组成的数据集，需要检查其中的21个图。一种更有效的方法是降低数据的维度（例如，使用主成分分析等无监督方法），以便只需检查更少的图，或者使用有监督的多变量数据分析方法来预测被分析颗粒的身份。
我们收集了用六种荧光染料混合物染色的微生物样本的多参数数据集。探索了多变量数据分析方法作为微生物检测和鉴定的手段。
我们表明，虽然所有混合物和所有方法都给出了良好的预测准确性（>94%），但仔细选择染料和分析方法可以提高这一数字（至>99%的准确性），即使在未用于构建有监督多变量校准模型的数据集中也是如此。
流式细胞术提供了一种快速获取多参数数据以区分微生物的方法。多变量数据分析方法在从所获得的数据中提取信息方面发挥着重要作用。事实证明，人工神经网络是最合适的数据分析方法。
