When flow cytometric data on mixtures of cell populations are collected from samples under different experimental conditions, computational methods are needed (a) to classify the samples into similar groups, and (b) to characterize the changes within the corresponding populations due to the different conditions. Manual inspection has been used in the past to study such changes, but high-dimensional experiments necessitate developing new computational approaches to this problem. A robust solution to this problem is to construct distinct templates to summarize all samples from a class, and then to compare these templates to study the changes across classes or conditions.