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Article Dans Une Revue Proceedings of the National Academy of Sciences of the United States of America Année : 2021

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

Résumé

Subclasses of lymphocytes carry different functional roles to work together to 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-cells 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.
Subclasses of lymphocytes carry different functional roles to work together to 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-cells 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.
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Dates et versions

hal-02991468 , version 1 (06-11-2020)
hal-02991468 , version 2 (17-02-2022)

Licence

Paternité - Pas d'utilisation commerciale - Pas de modification

Identifiants

Citer

Giulio Isacchini, Aleksandra M Walczak, Thierry Mora, Armita Nourmohammad. Deep generative selection models of T and B cell receptor repertoires with soNNia. Proceedings of the National Academy of Sciences of the United States of America, 2021, 118 (14), pp.e2023141118. ⟨10.1101/2020.11.05.370346⟩. ⟨hal-02991468v2⟩
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