Title: The Bayesian optimist's guide to adaptive immune receptor repertoire analysis,
Abstract: Probabilistic modeling is fundamental to the statistical analysis of complex
data. In addition to forming a coherent description of the data-generating
process, probabilistic models enable parameter inference about given data sets.
This procedure is well-developed in the Bayesian perspective, in which one
infers probability distributions describing to what extent various possible
parameters agree with the data. In this paper we motivate and review
probabilistic modeling for adaptive immune receptor repertoire data then
describe progress and prospects for future work, from germline haplotyping to
adaptive immune system deployment across tissues. The relevant quantities in
immune sequence analysis include not only continuous parameters such as gene
use frequency, but also discrete objects such as B cell clusters and lineages.
Throughout this review, we unravel the many opportunities for probabilistic
modeling in adaptive immune receptor analysis, including settings for which the
Bayesian approach holds substantial promise (especially if one is optimistic
about new computational methods). From our perspective the greatest prospects
for progress in probabilistic modeling for repertoires concern ancestral
sequence estimation for B cell receptor lineages, including uncertainty from
germline genotype, rearrangement, and lineage development.