TY - JOUR
T1 - Bayesian multivariate Poisson abundance models for T-cell receptor data
AU - Greene, Joshua
AU - Birtwistle, Marc R.
AU - Ignatowicz, Leszek
AU - Rempala, Grzegorz A.
N1 - Funding Information:
The research was partially funded by the NIH under Grant R01CA152158 to GAR. The authors would like to thank the members of the Ignatowicz's research laboratory for providing TCR data for the analysis and for helpful discussions. We would also like to thank the reviewers and the associate editor for their comments and improvement suggestions made on the earlier drafts of the manuscript.
PY - 2013/6/7
Y1 - 2013/6/7
N2 - 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.
AB - 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.
KW - Lognormal distribution
KW - MAP estimation
KW - Poisson abundance models
KW - Species diversity estimation
KW - T-cell antigen receptors
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U2 - 10.1016/j.jtbi.2013.02.009
DO - 10.1016/j.jtbi.2013.02.009
M3 - Article
C2 - 23467198
AN - SCOPUS:84875247634
SN - 0022-5193
VL - 326
SP - 1
EP - 10
JO - Journal of Theoretical Biology
JF - Journal of Theoretical Biology
ER -