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Deep generative selection models of T and B cell receptor repertoires with soNNia

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Isacchini,  Giulio
Max Planck Research Group Statistical physics of evolving systems, Max Planck Institute for Dynamics and Self-Organization, Max Planck Society;

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Nourmohammad,  Armita
Max Planck Research Group Statistical physics of evolving systems, Max Planck Institute for Dynamics and Self-Organization, Max Planck Society;

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Citation

Isacchini, G., Walczak, A. M., Mora, T., & Nourmohammad, A. (2021). Deep generative selection models of T and B cell receptor repertoires with soNNia. Proceedings of the National Academy of Sciences, 118(14): e2023141118. doi:10.1073/pnas.2023141118.


Cite as: http://hdl.handle.net/21.11116/0000-0008-85DA-C
Abstract
Subclasses of lymphocytes carry different functional roles to work together and produce an immune response and lasting immunity. Additionally to these functional roles, T and B cell lymphocytes rely on the diversity of their receptor chains to recognize different pathogens. The lymphocyte subclasses emerge from common ancestors generated with the same diversity of receptors during selection processes. Here, we leverage biophysical models of receptor generation with machine learning models of selection to identify specific sequence features characteristic of functional lymphocyte repertoires and subrepertoires. Specifically, using only repertoire-level sequence information, we classify CD4+ and CD8+ T cells, find correlations between receptor chains arising during selection, and identify T cell subsets that are targets of pathogenic epitopes. We also show examples of when simple linear classifiers do as well as more complex machine learning methods.