Deep learning-based prediction of the selection factors for quantifying selection in immune receptor repertoires

T cell selection is a vital process in which precursor T cells mature into functional cells. Accurately modelling and quantifying T cell selection utilizing high-throughput T cell receptor (TCR) sequencing data presents an important computational challenge in immunology. Statistical modelling of TCR repertoires allows the assessment of selection force through the selection factor that bridges the pre- and post-selection distributions. Current tools derive the principles underlying this selection factor through weakly supervised learning, limiting the effective use of available data. To overcome this shortcoming, we introduce TCRsep, a deep learning framework designed to directly learn the selection factor in a supervised training context. The performance and advantage of TCRsep were extensively validated across various scenarios using both simulated and real datasets. By applying TCRsep to over 1,500 repertoire samples, we elucidate the correlation between selection and repertoire diversities in aging, explore the stability and individuality of selection over short time frames, investigate the role of selection in defining TCR sharing profiles and demonstrate its efficiency in identifying candidate-disease-associated TCRs based on their sharing profiles. In particular, these identified TCRs were further utilized for diagnosing cytomegalovirus infection, achieving high predictive accuracy. In conclusion, TCRsep substantially improves the selection factor prediction and serves as a valuable discovery tool for clinical applications. © The Author(s), under exclusive licence to Springer Nature Limited
2025, corrected publication 2025