The Bayesian optimist's guide to adaptive immune receptor repertoire analysis

TL;DR

This paper reviews probabilistic Bayesian modeling approaches for analyzing adaptive immune receptor repertoires, highlighting recent progress and future prospects in understanding immune sequence data and lineage inference.

Contribution

It provides a comprehensive overview of Bayesian methods applied to immune repertoire analysis and discusses new opportunities for probabilistic modeling in this field.

Findings

Bayesian models effectively describe gene use and lineage structures.

Probabilistic approaches can quantify uncertainty in immune sequence inference.

Future work includes ancestral sequence estimation and germline haplotyping.

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.