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Identification of antigen-specific patterns from high-dimensional sequencing data

Sun, Yuxin; (2020) Identification of antigen-specific patterns from high-dimensional sequencing data. Doctoral thesis (Ph.D), UCL (University College London). Green open access

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Abstract

T cells recognize antigens using a diverse set of antigen-specific T-cell receptors (TCRs) on the surface. This poses two challenges for studying TCRs that respond to a given antigen. First, the enormous diversity of the TCR repertoire creates an ultra-high dimensional feature space; second, TCRs that respond to an antigen are often correlated. This thesis aims to develop efficient machine learning algorithms concerning both problems for feature selection from high-dimensional feature spaces. Our research concerns two subproblems: identification of antigen-enriched sequence motifs within the CDR3 region of TCRs and antigen-enriched entire TCR sequences. We apply a string kernel and a Fisher kernel to represent subsequences and develop fast algorithms to learn antigen-specific subsequences from graph-represented features. Both fixed-length and varying-length subsequences from mouse samples are selected with high efficiency and accuracy. Our results also suggest that short subsequences are found at specific positions, which may correspond to the actual interacting regions between TCR and MHC-peptide complex. We further develop fast algorithms to solve exclusive group Lasso and provide a novel methodology to select entire TCR sequences that are relevant to specific antigens. Our solution concerns a notoriously difficult problem in feature selection to select highly correlated features. Experiments on synthetic data show good performance under various correlation settings. The proposed algorithms are also validated on real-world data to select a sparse set of entire TCRs with high accuracy.

Type: Thesis (Doctoral)
Qualification: Ph.D
Title: Identification of antigen-specific patterns from high-dimensional sequencing data
Event: UCL (University College London)
Open access status: An open access version is available from UCL Discovery
Language: English
Additional information: Copyright © The Author 2020. Original content in this thesis is licensed under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) Licence (https://creativecommons.org/licenses/by/4.0/). Any third-party copyright material present remains the property of its respective owner(s) and is licensed under its existing terms. Access may initially be restricted at the author’s request.
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Cancer Institute
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Cancer Institute > Research Department of Pathology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Infection and Immunity
URI: https://discovery.ucl.ac.uk/id/eprint/10108160
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