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In most of the recent immunological literature the differences across antigenreceptor populations are examined via non-parametric statistical measures ofspecies overlap and diversity borrowed from ecological studies. While thisapproach is robust in a wide range of situations, it seems to provide littleinsight into the underlying clonal size distribution and the overall mechanismdifferentiating the receptor populations. As a possible alternative, thecurrent paper presents a parametric method which adjusts for the dataunder-sampling as well as provides a unifying approach to simultaneouscomparison of multiple receptor groups by means of the modern statistical toolsof unsupervised learning. The parametric model is based on a flexiblemultivariate Poisson-lognormal distribution and is seen to be a naturalgeneralization of the univariate Poisson-lognormal models used in ecologicalstudies of biodiversity patterns. The procedure for evaluating model's fit isdescribed along with the public domain software developed to perform thenecessary diagnostics. The model-driven analysis is seen to compare favorablyvis a vis traditional methods when applied to the data from T-cell receptors intransgenic mice populations.

Model for Diversity Analysis of Antigen Receptor Repertoires