Bayesian multivariate Poisson abundance models for T-cell receptor data

Joshua Greene, Marc R. Birtwistle, Leszek Ignatowicz, Grzegorz A. Rempala

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

A major feature of an adaptive immune system is its ability to generate B- and T-cell clones capable of recognizing and neutralizing specific antigens. These clones recognize antigens with the help of the surface molecules, called antigen receptors, acquired individually during the clonal development process. In order to ensure a response to a broad range of antigens, the number of different receptor molecules is extremely large, resulting in a huge clonal diversity of both B- and T-cell receptor populations and making their experimental comparisons statistically challenging. To facilitate such comparisons, we propose a flexible parametric model of multivariate count data and illustrate its use in a simultaneous analysis of multiple antigen receptor populations derived from mammalian T-cells. The model relies on a representation of the observed receptor counts as a multivariate Poisson abundance mixture (m PAM). A Bayesian parameter fitting procedure is proposed, based on the complete posterior likelihood, rather than the conditional one used typically in similar settings. The new procedure is shown to be considerably more efficient than its conditional counterpart (as measured by the Fisher information) in the regions of m PAM parameter space relevant to model T-cell data.

Original languageEnglish (US)
Pages (from-to)1-10
Number of pages10
JournalJournal of Theoretical Biology
Volume326
DOIs
StatePublished - Jun 7 2013

Keywords

  • Lognormal distribution
  • MAP estimation
  • Poisson abundance models
  • Species diversity estimation
  • T-cell antigen receptors

ASJC Scopus subject areas

  • Statistics and Probability
  • Modeling and Simulation
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Agricultural and Biological Sciences(all)
  • Applied Mathematics

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