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

Area of Science:

• Immunoinformatics
• Computational Biology
• Statistical Modeling

Background:

• Probabilistic modeling is crucial for statistical analysis of complex datasets.
• Bayesian inference provides a framework for parameter estimation in probabilistic models.
• Adaptive immune receptor repertoire data presents unique analytical challenges.

Purpose of the Study:

• To review and motivate the application of probabilistic modeling to adaptive immune receptor repertoire data.
• To explore current progress and future prospects in this field.
• To highlight opportunities for Bayesian approaches in immune sequence analysis.

Main Methods:

• Review of probabilistic modeling techniques relevant to immune repertoire analysis.
• Discussion of Bayesian inference for parameter and object estimation.
• Exploration of methods for handling continuous and discrete data in immune sequences.

Main Results:

• Probabilistic models provide a coherent description of immune receptor data generation.
• Bayesian inference enables parameter estimation and understanding of data agreement.
• Opportunities exist for probabilistic modeling across various aspects of immune repertoire analysis.

Conclusions:

• Probabilistic modeling, especially Bayesian methods, holds significant promise for adaptive immune receptor repertoire analysis.
• Ancestral sequence estimation for B-cell receptor lineages presents a key area for future progress.
• Addressing uncertainty in germline genotype, rearrangement, and lineage development is critical.