Pattern recognition in flow cytometry

Background: 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 provide s a considerable challenge for data analysis. We review the application of multivariate data analysis and pattern recognition techniques to flow cytom etry. Methods: Approaches were divided into two broad types depending on whether the aim was identification or clustering. Multivariate statistical approach es, supervised artificial neural networks (ANNs), problems of overlapping c haracter distributions, unbounded data sets, missing parameters, scaling up , and estimating proportions of different types of cells comprised the firs t 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. Results and Conclusions: information held within the large quantities of da ta generated by AFC was tractable using ANNs, but for field studies the pro blem of obtaining suitable training data needs to be resolved, and coping w ith an almost infinite number of cell categories needs further research. (C ) 2001 Wiley-Liss, Inc.