Open AccessApplication of neural networks to flow cytometry data analysis and real‐time cell classification
Author(s) -
Frankel Donald S.,
Frankel Sheila L.,
Binder Brian J.,
Vogt Robert F.
Publication year - 1996
Publication title -
cytometry
Language(s) - English
Resource type - Journals
eISSN - 1097-0320
pISSN - 0196-4763
DOI - 10.1002/(sici)1097-0320(19960401)23:4<290::aid-cyto5>3.0.co;2-l
Subject(s) - bottleneck , artificial neural network , computer science , identification (biology) , nonlinear system , population , pattern recognition (psychology) , artificial intelligence , data mining , biological system , machine learning , biology , physics , demography , quantum mechanics , sociology , embedded system , botany
Conventional analysis of flow cytometric data requires that population identification be performed graphically after a sample has been run using two‐parameter scatter plots. As more parameters are measured, the number of possible two‐parameter plots increases geometrically, making data analysis increasingly cumbersome. Artificial Neural Systems (ANS), also known as neural networks, are a powerful and convenient method for overcoming this data bottleneck. ANS “learn” to make classifications using all of the measured parameters simultaneously. Mathematical models and programming expertise are not required. ANS are inherently parallel so that high processing speed can be achieved. Because ANS are nonlinear, curved class boundaries and other nonlinearities can emerge naturally. Here, we present biomedical and oceanographic data to demonstrate the useful properties of neural networks for processing and analyzing flow cytometry data. We show that ANS are equally useful for human leukocytes and marine plankton data. They can easily accommodate nonlinear variations in data, detect subtle changes in measurements, interpolate and classify cells they were not trained on, and analyze multiparameter cell data in real time. Real‐time classification of a mixture of six cyanobacteria strains was achieved with an average accuracy of 98%. © 1996 Wiley‐Liss, Inc.