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

Details

• Alternative Title:
  Stat Appl Genet Mol Biol
• Personal Author:
  Lin, Lin ; Chan, Cliburn ; Hadrup, Sine R. ; Froesig, Thomas M. ; Wang, Quanli ; West, Mike
• Description:
  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.
• Subjects:
  Algorithms Amino Acid Sequence Antigens, Differentiation Article Bayes Theorem Computer Simulation Dirichlet Process Mixtures Flow Cytometry GPU Computing Hierarchical Model Humans Immune Profiling Immune Response Biomarkers Immunophenotyping Kallikreins Large Data Sets Markov Chain Monte Carlo Markov Chains Massive Mixture Models Models, Biological Monte Carlo Method Multimers Normal Distribution Phenotype Posterior Simulation Prostate-Specific Antigen Relabeling T-cell Subtyping T-Lymphocytes

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• Source:
  Stat Appl Genet Mol Biol. 12(3):309-331.
• Pubmed ID:
  23629459
• Pubmed Central ID:
  PMC4155753
• Document Type:
  Journal Article
• Funding:
  RC1 AI086032/AI/NIAID NIH HHS/United States ; AI086032/AI/NIAID NIH HHS/United States ; P50 GM081883/GM/NIGMS NIH HHS/United States ; P50-GM081883/GM/NIGMS NIH HHS/United States ; U58 DP006031/DP/NCCDPHP CDC HHS/United States
• Volume:
  12
• Issue:
  3
• Collection(s):
  CDC Public Access
• Main Document Checksum:
  urn:sha256:54595327eed0843e904ae217574da3bd01df8a4c9b4e02083b12514591512ab7
• Download URL:
  https://stacks.cdc.gov/view/cdc/41981/cdc_41981_DS1.pdf
• File Type:
  [PDF - 2.86 MB ]