AIRIVA: A Deep Generative Model of Adaptive Immune Repertoires
This work addresses the problem of improving diagnostic models for infectious diseases by handling repertoire diversity and systematic biases, though it appears incremental as it builds on existing generative methods for immunomics.
The authors tackled the challenge of disentangling disease-specific T-cell receptor (TCR) signals from systematic effects in adaptive immune repertoires, presenting AIRIVA, a generative model that learns low-dimensional representations and successfully identifies disease-associated TCRs in COVID-19 and Herpes Simplex Virus case-studies.
Recent advances in immunomics have shown that T-cell receptor (TCR) signatures can accurately predict active or recent infection by leveraging the high specificity of TCR binding to disease antigens. However, the extreme diversity of the adaptive immune repertoire presents challenges in reliably identifying disease-specific TCRs. Population genetics and sequencing depth can also have strong systematic effects on repertoires, which requires careful consideration when developing diagnostic models. We present an Adaptive Immune Repertoire-Invariant Variational Autoencoder (AIRIVA), a generative model that learns a low-dimensional, interpretable, and compositional representation of TCR repertoires to disentangle such systematic effects in repertoires. We apply AIRIVA to two infectious disease case-studies: COVID-19 (natural infection and vaccination) and the Herpes Simplex Virus (HSV-1 and HSV-2), and empirically show that we can disentangle the individual disease signals. We further demonstrate AIRIVA's capability to: learn from unlabelled samples; generate in-silico TCR repertoires by intervening on the latent factors; and identify disease-associated TCRs validated using TCR annotations from external assay data.