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Isacchini G, Sethna Z, Elhanati Y, Nourmohammad A, Walczak AM, Mora T. Generative models of T-cell receptor sequences. Phys Rev E 2021; 101:062414. [PMID: 32688532 DOI: 10.1103/physreve.101.062414] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2020] [Accepted: 05/14/2020] [Indexed: 01/16/2023]
Abstract
T-cell receptors (TCR) are key proteins of the adaptive immune system, generated randomly in each individual, whose diversity underlies our ability to recognize infections and malignancies. Modeling the distribution of TCR sequences is of key importance for immunology and medical applications. Here, we compare two inference methods trained on high-throughput sequencing data: a knowledge-guided approach, which accounts for the details of sequence generation, supplemented by a physics-inspired model of selection; and a knowledge-free variational autoencoder based on deep artificial neural networks. We show that the knowledge-guided model outperforms the deep network approach at predicting TCR probabilities, while being more interpretable, at a lower computational cost.
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Affiliation(s)
- Giulio Isacchini
- Max Planck Institute for Dynamics and Self-organization, Am Faßberg 17, 37077 Göttingen, Germany.,Laboratoire de Physique de l'École Normale Supérieure (PSL University), CNRS, Sorbonne Université, and Université de Paris, 75005 Paris, France
| | - Zachary Sethna
- Memorial Sloan Kettering Cancer Center, New York, New York 10065, USA
| | - Yuval Elhanati
- Memorial Sloan Kettering Cancer Center, New York, New York 10065, USA
| | - Armita Nourmohammad
- Max Planck Institute for Dynamics and Self-organization, Am Faßberg 17, 37077 Göttingen, Germany.,Department of Physics, University of Washington, 3910 15th Avenue Northeast, Seattle, Washington 98195, USA.,Fred Hutchinson cancer Research Center, 1100 Fairview ave N, Seattle, Washington 98109, USA
| | - Aleksandra M Walczak
- Laboratoire de Physique de l'École Normale Supérieure (PSL University), CNRS, Sorbonne Université, and Université de Paris, 75005 Paris, France
| | - Thierry Mora
- Laboratoire de Physique de l'École Normale Supérieure (PSL University), CNRS, Sorbonne Université, and Université de Paris, 75005 Paris, France
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Abry P, Pustelnik N, Roux S, Jensen P, Flandrin P, Gribonval R, Lucas CG, Guichard É, Borgnat P, Garnier N. Spatial and temporal regularization to estimate COVID-19 reproduction number R(t): Promoting piecewise smoothness via convex optimization. PLoS One 2020; 15:e0237901. [PMID: 32817697 PMCID: PMC7444593 DOI: 10.1371/journal.pone.0237901] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Accepted: 08/05/2020] [Indexed: 11/18/2022] Open
Abstract
Among the different indicators that quantify the spread of an epidemic such as the on-going COVID-19, stands first the reproduction number which measures how many people can be contaminated by an infected person. In order to permit the monitoring of the evolution of this number, a new estimation procedure is proposed here, assuming a well-accepted model for current incidence data, based on past observations. The novelty of the proposed approach is twofold: 1) the estimation of the reproduction number is achieved by convex optimization within a proximal-based inverse problem formulation, with constraints aimed at promoting piecewise smoothness; 2) the approach is developed in a multivariate setting, allowing for the simultaneous handling of multiple time series attached to different geographical regions, together with a spatial (graph-based) regularization of their evolutions in time. The effectiveness of the approach is first supported by simulations, and two main applications to real COVID-19 data are then discussed. The first one refers to the comparative evolution of the reproduction number for a number of countries, while the second one focuses on French departments and their joint analysis, leading to dynamic maps revealing the temporal co-evolution of their reproduction numbers.
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Affiliation(s)
- Patrice Abry
- Université de Lyon, ENS de Lyon, CNRS, Laboratoire de Physique, Lyon, France
| | - Nelly Pustelnik
- Université de Lyon, ENS de Lyon, CNRS, Laboratoire de Physique, Lyon, France
| | - Stéphane Roux
- Université de Lyon, ENS de Lyon, CNRS, Laboratoire de Physique, Lyon, France
| | - Pablo Jensen
- Université de Lyon, ENS de Lyon, CNRS, Laboratoire de Physique, Lyon, France
- Université de Lyon, ENS de Lyon, CNRS, Inst. Systèmes Complexes, Lyon, France
| | - Patrick Flandrin
- Université de Lyon, ENS de Lyon, CNRS, Laboratoire de Physique, Lyon, France
| | - Rémi Gribonval
- Univ Lyon, Inria, CNRS, ENS de Lyon, UCB Lyon 1, LIP UMR 5668, Lyon, France
| | | | - Éric Guichard
- Université de Lyon, ENS de Lyon, CNRS, Inst. Systèmes Complexes, Lyon, France
- Université de Lyon, ENS de Lyon, CNRS, Laboratoire Triangle, Lyon, France
| | - Pierre Borgnat
- Université de Lyon, ENS de Lyon, CNRS, Laboratoire de Physique, Lyon, France
- Université de Lyon, ENS de Lyon, CNRS, Inst. Systèmes Complexes, Lyon, France
| | - Nicolas Garnier
- Université de Lyon, ENS de Lyon, CNRS, Laboratoire de Physique, Lyon, France
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