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In systems biomedicine, an experimenter encounters different potentialsources of variation in data such as individual samples, multiple experimentalconditions, and multi-variable network-level responses. In multiparametriccytometry, which is often used for analyzing patient samples, such issues arecritical. While computational methods can identify cell populations inindividual samples, without the ability to automatically match them acrosssamples, it is difficult to compare and characterize the populations in typicalexperiments, such as those responding to various stimulations or distinctive ofparticular patients or time-points, especially when there are many samples.Joint Clustering and Matching (JCM) is a multi-level framework for simultaneousmodeling and registration of populations across a cohort. JCM models everypopulation with a robust multivariate probability distribution. Simultaneously,JCM fits a random-effects model to construct an overall batch template -- usedfor registering populations across samples, and classifying new samples. Bytackling systems-level variation, JCM supports practical biomedicalapplications involving large cohorts.

Joint Modeling and Registration of Cell Populations in Cohorts of High-Dimensional Flow Cytometric Data