  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.
