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

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

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Detail(s)

Original languageEnglish
Article numberbbad038
Journal / PublicationBriefings in Bioinformatics
Volume24
Issue number2
Online published8 Feb 2023
Publication statusPublished - Mar 2023

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 largely outperforms state-of-the-art methods in estimating the probability distribution of a TCR repertoire, boosting the average 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: profiling TCR repertoire probabilistically, 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 for deciphering complex immunogenomic repertoires.

Research Area(s)

  • T-cell receptor repertoires, deep neural networks, probabilistic inference, immunoinformatics, SPECIFICITY, ANTIGEN