A neural network approach to determining cellular viability

Determination of cellular viability is a frequent goal of flow cytometry assays, and most published methods for creating boundaries that separate live, apoptotic, and dead cells are based on heuristics. We describe a method of determining these boundaries by training neural networks to learn the intensity patterns of a subset of cells with known viability, and then produce decision boundaries based on the networks measure of similarity. Five networks were studied and a radial basis perceptron was found to be the most accurate. We have shown that these neural networks provide an objective rationale for classification using all available data.