Autoencoder based local T cell repertoire density can be used to classify samples and T cell receptors

Shirit Dvorkin, Reut Levi, Yoram Louzoun

Abstract: Author summary T cell repertoires contain a vast amount of information on the donors, and can be used to characterize the donor, and apply machine learning algorithms on such repertoires. A limiting factor in the analysis of such repertoire is the lack of a good representation of the T cell receptors. We here propose an autoencoder, named ELATE to present receptors as real vectors. We show that this encoder can be used to characterize both full donors and specific receptors using either supervised or unsupervised methods.