Hierarchical Bayesian Mixture Modelling for Antigen-specific T-cell Subtyping in Combinatorially Encoded Flow Cytometry Studies

Authors: 
Lynn Lin, Cliburn Chan, Sine R. Hadrup, Quanli Wang, Mike West
Duke University

May 14 2012

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 Bayesian mixture models with hierarchical structure that reflects the biological context, design and nature of resulting data in this setting of combinatorial encoding. We develop Bayesian analysis involving structured priors, and computations using customized 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.
 
 

Research reported here was partially supported by grants from the U.S. National Science Foundation (DMS 1106516 of M.W.) and National Institutes of Health (P50-GM081883 of M.W., and RC1 AI086032 of C.C. & M.W.). Any opinions, findings and conclusions or recommendations expressed in this work are those of the authors and do not necessarily reflect the views of the NIH and/or NSF.

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

Manuscript: 

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BibTeX Citation: 

@ARTICLE{Lin2012,
  author = {L. Lin and C. Chan and S. R. Hadrup and T. M. Froesig and Q. Wang and M. West},
  title = {Hierarchical {B}ayesian mixture modelling for antigen-specific {T-}cell
    	       subtyping in combinatorially encoded flow cytometry studies},
  journal = {Statistical Applications in Genetics and Molecular Biology},
  year = {2013},
  note = {ISSN (Online, April 4th 2013) 1544-6115, ISSN (Print) 2194-6302},
  doi = {10.1515/sagmb-2012-0001},
  url = {http://stat.duke.edu/research/papers/2012-06}
}