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

Shirit Dvorkin, Reut Levi, Yoram Louzoun

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


Recent advances in T cell repertoire (TCR) sequencing allow for the characterization of repertoire properties, as well as the frequency and sharing of specific TCR. However, there is no efficient measure for the local density of a given TCR. TCRs are often described either through their Complementary Determining region 3 (CDR3) sequences, or theirV/J usage, or their clone size. We here show that the local repertoire density can be estimated using a combined representation of these components through distance conserving autoencoders and Kernel Density Estimates (KDE). We present ELATE-an Encoder-based LocAl Tcr dEnsity and show that the resulting density of a sample can be used as a novel measure to study repertoire properties. The cross-density between two samples can be used as a similarity matrix to fully characterize samples from the same host. Finally, the same projection in combination with machine learning algorithms can be used to predict TCR-peptide binding through the local density of known TCRs binding a specific target.

Original languageEnglish
Article numbere1009225
JournalPLoS Computational Biology
Issue number7
StatePublished - Jul 2021

Bibliographical note

Publisher Copyright:
© 2021 Dvorkin et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


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