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Review
. 2018 Jul;284(1):148-166.
doi: 10.1111/imr.12664.

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

Branden J Olson  1 , Frederick A Matsen 4th  1
Affiliations
Review

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

Branden J Olson et al. Immunol Rev. 2018 Jul.

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 datasets. 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.

Keywords: Bayesian inference; high-throughput sequencing; likelihood models; repertoire analysis.

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Conflict of interest statement

Conflict of interest

We declare no conflict of interest.

Figures

Figure 1:
Figure 1:
Immune repertoires are hierarchically structured, here illustrated by the hierarchy for B cell receptor sequences. We benefit by considering the whole hierarchy that contributes to our observable sequences rather than one sequence at a time. For example, by considering all the reads at once one can infer a personal germline set, which then informs the per-read annotation. By learning lots of personal germline sets one can infer population-level germline trends.
Figure 2:
Figure 2:
Human height and immune receptor formation can both be modeled using probabilistic methods. Right panel modified (with permission) from (4).
Figure 3:
Figure 3:
Statisticians and biologists have typically approached adaptive immune receptor research in two parallel tracks. Statisticians (upper path) treat data as given and perform model criticism based on numerical estimates of how well models fit the data. Biologists (lower path) formulate their models mechanistically and generate data in targeted experiments to directly test their model. An integrated approach (dashed arrows) has biologists using parameter estimates to formulate mechanistic models, and statisticians using the results of targeted experiments to formulate statistical models. Ideally, the distinction between these two classes of models would evaporate, although mechanistic models are not always readily fit using statistical means.
See this image and copyright information in PMC

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