Hierarchical Bayesian mixture modelling for antigen-specific T-cell subtyping in combinatorially encoded flow cytometry studies

Lin Lin (), Chan Cliburn, Hadrup Sine R., Froesig Thomas M., Wang Quanli and West Mike
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Lin Lin: Department of Statistical Science, Duke University, Durham, NC, 27708-0251, USA
Chan Cliburn: Department of Biostatistics and Bioinformatics, Duke University Medical Center, 2424 Erwin Road, 11078 Hock Plaza, Durham, NC 27705-3858, USA
Hadrup Sine R.: Center for Cancer Immune Therapy, Department of Hematology, University Hospital Herlev, DK-2730 Herlev, Denmark
Froesig Thomas M.: Center for Cancer Immune Therapy, Department of Hematology, University Hospital Herlev, DK-2730 Herlev, Denmark
Wang Quanli: Department of Statistical Science, Duke University, Durham, NC, 27708-0251, USA
West Mike: Department of Statistical Science, Duke University, Durham, NC, 27708-0251, USA

Statistical Applications in Genetics and Molecular Biology, 2013, vol. 12, issue 3, 309-331

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.

Keywords: Dirichlet process mixtures; GPU computing; Hierarchical model; Immune profiling; Immune response biomarkers; Large data sets; Markov chain Monte Carlo; Massive mixture models; Multimers; Posterior simulation; Relabeling; T-cell subtyping (search for similar items in EconPapers)
Date: 2013
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DOI: 10.1515/sagmb-2012-0001

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