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

Publication ,  Journal Article
Lin, L; Chan, C; Hadrup, SR; Froesig, TM; Wang, Q; West, M
Published in: Stat Appl Genet Mol Biol
June 2013

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.

Duke Scholars

Published In

Stat Appl Genet Mol Biol

DOI

EISSN

1544-6115

Publication Date

June 2013

Volume

12

Issue

3

Start / End Page

309 / 331

Location

Germany

Related Subject Headings

  • T-Lymphocytes
  • Prostate-Specific Antigen
  • Phenotype
  • Normal Distribution
  • Monte Carlo Method
  • Models, Biological
  • Markov Chains
  • Kallikreins
  • Immunophenotyping
  • Humans
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Lin, L., Chan, C., Hadrup, S. R., Froesig, T. M., Wang, Q., & West, M. (2013). Hierarchical Bayesian mixture modelling for antigen-specific T-cell subtyping in combinatorially encoded flow cytometry studies. Stat Appl Genet Mol Biol, 12(3), 309–331. https://doi.org/10.1515/sagmb-2012-0001
Lin, Lin, Cliburn Chan, Sine R. Hadrup, Thomas M. Froesig, Quanli Wang, and Mike West. “Hierarchical Bayesian mixture modelling for antigen-specific T-cell subtyping in combinatorially encoded flow cytometry studies.Stat Appl Genet Mol Biol 12, no. 3 (June 2013): 309–31. https://doi.org/10.1515/sagmb-2012-0001.
Lin L, Chan C, Hadrup SR, Froesig TM, Wang Q, West M. Hierarchical Bayesian mixture modelling for antigen-specific T-cell subtyping in combinatorially encoded flow cytometry studies. Stat Appl Genet Mol Biol. 2013 Jun;12(3):309–31.
Lin, Lin, et al. “Hierarchical Bayesian mixture modelling for antigen-specific T-cell subtyping in combinatorially encoded flow cytometry studies.Stat Appl Genet Mol Biol, vol. 12, no. 3, June 2013, pp. 309–31. Pubmed, doi:10.1515/sagmb-2012-0001.
Lin L, Chan C, Hadrup SR, Froesig TM, Wang Q, West M. Hierarchical Bayesian mixture modelling for antigen-specific T-cell subtyping in combinatorially encoded flow cytometry studies. Stat Appl Genet Mol Biol. 2013 Jun;12(3):309–331.
Journal cover image

Published In

Stat Appl Genet Mol Biol

DOI

EISSN

1544-6115

Publication Date

June 2013

Volume

12

Issue

3

Start / End Page

309 / 331

Location

Germany

Related Subject Headings

  • T-Lymphocytes
  • Prostate-Specific Antigen
  • Phenotype
  • Normal Distribution
  • Monte Carlo Method
  • Models, Biological
  • Markov Chains
  • Kallikreins
  • Immunophenotyping
  • Humans