title: Hierarchical Bayesian mixture modelling for antigen-specific T-cell subtyping in combinatorially encoded flow cytometry studies.
abstract: Novel uses of automated flow cytometry technology for measuring levels of protein markers on thousands to millions of cells are promoting increasing need for relevant, customized Bayesian mixture modelling approaches in many areas of biomedical research and application. In studies of immune profiling in many biological areas, traditional flow cytometry measures relative levels of abundance of marker proteins using fluorescently labeled tags that identify specific markers by a single-color. One specific and important recent development in this area is the use of combinatorial marker assays in which each marker is targeted with a probe that is labeled with two or more fluorescent tags. The use of several colors enables the identification of, in principle, combinatorially increasingly numbers of subtypes of cells, each identified by a subset of colors. This represents a major advance in the ability to characterize variation in immune responses involving larger numbers of functionally differentiated cell subtypes. We describe novel classes of Markov chain Monte Carlo methods for model fitting that exploit distributed GPU (graphics processing unit) implementation. We discuss issues of cellular subtype identification in this novel, general model framework, and provide a detailed example using simulated data. We then describe application to a data set from an experimental study of antigen-specific T-cell subtyping using combinatorially encoded assays in human blood samples. Summary comments discuss broader questions in applications in immunology, and aspects of statistical computation.
authors: Department of Statistical Science, Duke University, Durham, NC, 27708-0251, USA. lin@stat.duke.edu
authors: Lin
authors: L
authors: Lin
authors: Cliburn
authors: C
authors: Chan
authors: Sine R
authors: SR
authors: Hadrup
authors: Thomas M
authors: TM
authors: Froesig
authors: Quanli
authors: Q
authors: Wang
authors: Mike
authors: M
authors: West
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D000943:Antigens, Differentiation / Q000378:metabolism; D001499:Bayes Theorem; D003198:Computer Simulation; D005434:Flow Cytometry / Q000379:methods*; D006801:Humans; D016130:Immunophenotyping / Q000379:methods*; D007610:Kallikreins / Q000737:chemistry / Q000378:metabolism; D008390:Markov Chains; D008954:Models, Biological; D009010:Monte Carlo Method; D016011:Normal Distribution; D010641:Phenotype; D017430:Prostate-Specific Antigen / Q000737:chemistry / Q000378:metabolism; D013601:T-Lymphocytes / Q000378:metabolism*
chemical_list: D000943:Antigens, Differentiation; C549870:KLK3 protein, human; D007610:Kallikreins; D017430:Prostate-Specific Antigen
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