Deconvolution of the T cell immune response using multi-modal learning

T cells play a pivotal role in the adaptive immune system recognizing foreign antigens through their T-cell receptor (TCR). Although the specificity and affinity of the TCR to its cognate antigen determines the functionality, the phenotypic differentiation and thereby also the fate of the T cell remain poorly understood. Therefore, studying the transcriptional changes of T cells in the context of their TCRs is key to deeper insights into T-cell biology. To this end, we developed a multi-view Variational Autoencoder (mvTCR) to jointly embed transcriptomic and TCR sequence information at a single-cell level to better capture the phenotypic behavior of T cells. We evaluated mvTCR on two datasets showing a clear separation of the cell state and their functionality, thus, providing a more biologically informative representation than models using each modality individually.