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We consider the use of the Joint Clustering and Matching (JCM) procedure forthe supervised classification of a flow cytometric sample with respect to anumber of predefined classes of such samples. The JCM procedure has beenproposed as a method for the unsupervised classification of cells within asample into a number of clusters and in the case of multiple samples, thematching of these clusters across the samples. The two tasks of clustering andmatching of the clusters are performed simultaneously within the JCM framework.In this paper, we consider the case where there is a number of distinct classesof samples whose class of origin is known, and the problem is to classify a newsample of unknown class of origin to one of these predefined classes. Forexample, the different classes might correspond to the types of a particulardisease or to the various health outcomes of a patient subsequent to a courseof treatment. We show and demonstrate on some real datasets how the JCMprocedure can be used to carry out this supervised classification task. Amixture distribution is used to model the distribution of the expressions of afixed set of markers for each cell in a sample with the components in themixture model corresponding to the various populations of cells in thecomposition of the sample. For each class of samples, a class template isformed by the adoption of random-effects terms to model the inter-samplevariation within a class. The classification of a new unclassified sample isundertaken by assigning the unclassified sample to the class that minimizes theKullback-Leibler distance between its fitted mixture density and each classdensity provided by the class templates.

Supervised Classification of Flow Cytometric Samples via the Joint Clustering and Matching (JCM) Procedure