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

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 Complementar...

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Autores principales:	Shirit Dvorkin, Reut Levi, Yoram Louzoun
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/c03dd12f3c5d405295964248bfb699b8
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id	oai:doaj.org-article:c03dd12f3c5d405295964248bfb699b8
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spelling	oai:doaj.org-article:c03dd12f3c5d405295964248bfb699b82021-12-02T19:57:22ZAutoencoder based local T cell repertoire density can be used to classify samples and T cell receptors.1553-734X1553-735810.1371/journal.pcbi.1009225https://doaj.org/article/c03dd12f3c5d405295964248bfb699b82021-07-01T00:00:00Zhttps://doi.org/10.1371/journal.pcbi.1009225https://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Recent 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.Shirit DvorkinReut LeviYoram LouzounPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 17, Iss 7, p e1009225 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Shirit Dvorkin Reut Levi Yoram Louzoun Autoencoder based local T cell repertoire density can be used to classify samples and T cell receptors.
description	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.
format	article
author	Shirit Dvorkin Reut Levi Yoram Louzoun
author_facet	Shirit Dvorkin Reut Levi Yoram Louzoun
author_sort	Shirit Dvorkin
title	Autoencoder based local T cell repertoire density can be used to classify samples and T cell receptors.
title_short	Autoencoder based local T cell repertoire density can be used to classify samples and T cell receptors.
title_full	Autoencoder based local T cell repertoire density can be used to classify samples and T cell receptors.
title_fullStr	Autoencoder based local T cell repertoire density can be used to classify samples and T cell receptors.
title_full_unstemmed	Autoencoder based local T cell repertoire density can be used to classify samples and T cell receptors.
title_sort	autoencoder based local t cell repertoire density can be used to classify samples and t cell receptors.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/c03dd12f3c5d405295964248bfb699b8
work_keys_str_mv	AT shiritdvorkin autoencoderbasedlocaltcellrepertoiredensitycanbeusedtoclassifysamplesandtcellreceptors AT reutlevi autoencoderbasedlocaltcellrepertoiredensitycanbeusedtoclassifysamplesandtcellreceptors AT yoramlouzoun autoencoderbasedlocaltcellrepertoiredensitycanbeusedtoclassifysamplesandtcellreceptors
_version_	1718375864915197952

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

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