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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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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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.</description><identifier>ISSN: 1553-7358</identifier><identifier>ISSN: 1553-734X</identifier><identifier>EISSN: 1553-7358</identifier><identifier>DOI: 10.1371/journal.pcbi.1009225</identifier><identifier>PMID: 34310600</identifier><language>eng</language><publisher>San Francisco: Public Library of Science</publisher><subject>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</subject><ispartof>PLoS computational biology, 2021-07, Vol.17 (7), p.e1009225-e1009225</ispartof><rights>COPYRIGHT 2021 Public Library of Science</rights><rights>2021 Dvorkin et al. 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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. 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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. 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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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