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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Veröffentlicht in:PLoS computational biology 2021-07, Vol.17 (7), p.e1009225-e1009225
Hauptverfasser: Dvorkin, Shirit, Levi, Reut, Louzoun, Yoram
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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.
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subjects Accuracy
Algorithms
Analysis
Binding
Biology and Life Sciences
Cloning
Coders
Complementarity-determining region 3
Datasets
Density
Engineering and Technology
Forecasting
Genes
Genetic aspects
Identification and classification
Learning algorithms
Lymphocytes
Lymphocytes T
Machine learning
Medicine and Health Sciences
Methods
Neural networks
Peptides
Physical Sciences
Properties
Research and Analysis Methods
T cell receptors
T cells
T-cell receptor
title Autoencoder based local T cell repertoire density can be used to classify samples and T cell receptors
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