  In most of the recent immunological literature the differences across antigen
receptor populations are examined via non-parametric statistical measures of
species overlap and diversity borrowed from ecological studies. While this
approach is robust in a wide range of situations, it seems to provide little
insight into the underlying clonal size distribution and the overall mechanism
differentiating the receptor populations. As a possible alternative, the
current paper presents a parametric method which adjusts for the data
under-sampling as well as provides a unifying approach to simultaneous
comparison of multiple receptor groups by means of the modern statistical tools
of unsupervised learning. The parametric model is based on a flexible
multivariate Poisson-lognormal distribution and is seen to be a natural
generalization of the univariate Poisson-lognormal models used in ecological
studies of biodiversity patterns. The procedure for evaluating model's fit is
described along with the public domain software developed to perform the
necessary diagnostics. The model-driven analysis is seen to compare favorably
vis a vis traditional methods when applied to the data from T-cell receptors in
transgenic mice populations.
