  High-throughput sequencing of B- and T-cell receptors makes it possible to
track immune repertoires across time, in different tissues, and in acute and
chronic diseases or in healthy individuals. However, quantitative comparison
between repertoires is confounded by variability in the read count of each
receptor clonotype due to sampling, library preparation, and expression noise.
Here, we present a general Bayesian approach to disentangle repertoire
variations from these stochastic effects. Using replicate experiments, we first
show how to learn the natural variability of read counts by inferring the
distributions of clone sizes as well as an explicit noise model relating true
frequencies of clones to their read count. We then use that null model as a
baseline to infer a model of clonal expansion from two repertoire time points
taken before and after an immune challenge. Applying our approach to yellow
fever vaccination as a model of acute infection in humans, we identify
candidate clones participating in the response.
