Face Recognition: Features Versus Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Clustering Validity Assessment: Finding the Optimal Partitioning of a Data Set
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Robust mixture modeling using multivariate skew t distributions
Statistics and Computing
Identifying rare cell populations in comparative flow cytometry
WABI'10 Proceedings of the 10th international conference on Algorithms in bioinformatics
Template-Based Continuous Speech Recognition
IEEE Transactions on Audio, Speech, and Language Processing
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We describe an algorithm to dynamically classify flow cytometry data samples into several classes based on their immunophenotypes. Flow cytometry data consists of fluorescence measurements of several proteins that characterize different cell types in blood or cultured cell lines. Each sample is initially clustered to identify the cell populations present in it. Using a combinatorial dissimilarity measure between cell populations in samples, we compute meta-clusters that correspond to the same cell population across samples. The collection of meta-clusters in a class of samples then describes a template for that class. We organize the samples into a template tree, and use it to classify new samples into existing classes or create a new class if needed. We dynamically update the templates and their statistical parameters as new samples are classified, so that the new information is reflected in the classes. We use our dynamic classification algorithm to classify T cells that on stimulation with an antibody show increased abundance of the proteins SLP-76 and ZAP-70. These proteins are involved in a platform that assembles signaling proteins in the immune response. We also use the algorithm to show that variation in an immune subsystem between individuals is a larger effect than variation in multiple samples from one individual.