Deep autoregressive generative models capture the intrinsics embedded in T-cell receptor repertoires

Abstract

T-cell receptors (TCRs) play an essential role in the adaptive immune system. Probabilistic models for TCR repertoires can help decipher the underlying complex sequence patterns and provide novel insights into understanding the adaptive immune system. In this work, we develop TCRpeg, a deep autoregressive generative model to unravel the sequence patterns of TCR repertoires. TCRpeg outperforms state-of-the-art methods in estimating the probability distribution of a TCR repertoire, boosting the accuracy from 0.672 to 0.906 measured by the Pearson correlation coefficient. Furthermore, with promising performance in probability inference, TCRpeg improves on a range of TCR-related tasks: revealing TCR repertoire-level discrepancies, classifying antigen-specific TCRs, validating previously discovered TCR motifs, generating novel TCRs, and augmenting TCR data. Our results and analysis highlight the flexibility and capacity of TCRpeg to extract TCR sequence information, providing a novel approach to decipher complex immunogenomic repertoires.