DiffMAP-GP: Continuous 2D diffusion maps from particle trajectories without data binning using Gaussian processes
Diffusion coefficients often vary across regions, such as cellular membranes, and quantifying their variation can provide valuable insight into local membrane properties such as composition and stiffness. Toward quantifying diffusion coefficient spatial maps and uncertainties from particle tracks, w...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2025-03-01
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| Series: | Biophysical Reports |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667074724000533 |
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| author | Vishesh Kumar J. Shepard Bryan, IV Alex Rojewski Carlo Manzo Steve Pressé |
| author_facet | Vishesh Kumar J. Shepard Bryan, IV Alex Rojewski Carlo Manzo Steve Pressé |
| author_sort | Vishesh Kumar |
| collection | DOAJ |
| description | Diffusion coefficients often vary across regions, such as cellular membranes, and quantifying their variation can provide valuable insight into local membrane properties such as composition and stiffness. Toward quantifying diffusion coefficient spatial maps and uncertainties from particle tracks, we develop a Bayesian framework (DiffMAP-GP) by placing Gaussian process (GP) priors on the family of candidate maps. For sake of computational efficiency, we leverage inducing point methods on GPs arising from the mathematical structure of the data giving rise to nonconjugate likelihood-prior pairs. We analyze both synthetic data, where ground truth is known, as well as data drawn from live-cell single-molecule imaging of membrane proteins. The resulting tool provides an unsupervised method to rigorously map diffusion coefficients continuously across membranes without data binning. |
| format | Article |
| id | doaj-art-7a442fe6e6da4f7abf730f3e876d3f5c |
| institution | Kabale University |
| issn | 2667-0747 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Biophysical Reports |
| spelling | doaj-art-7a442fe6e6da4f7abf730f3e876d3f5c2025-01-26T05:05:17ZengElsevierBiophysical Reports2667-07472025-03-0151100194DiffMAP-GP: Continuous 2D diffusion maps from particle trajectories without data binning using Gaussian processesVishesh Kumar0J. Shepard Bryan, IV1Alex Rojewski2Carlo Manzo3Steve Pressé4Center for Biological Physics, Arizona State University, Tempe, Arizona; Department of Physics, Arizona State University, Tempe, ArizonaCenter for Biological Physics, Arizona State University, Tempe, Arizona; Department of Physics, Arizona State University, Tempe, ArizonaCenter for Biological Physics, Arizona State University, Tempe, Arizona; Department of Physics, Arizona State University, Tempe, ArizonaFacultat de Ciències, Tecnologia i Enginyeries, Universitat de Vic – Universitat Central de Catalunya (UVic-UCC), Barcelona, Spain; Institut de Recerca i Innovació en Ciències de la Vida i de la Salut a la Catalunya Central (IRIS-CC), Barcelona, SpainCenter for Biological Physics, Arizona State University, Tempe, Arizona; Department of Physics, Arizona State University, Tempe, Arizona; School of Molecular Sciences, Arizona State University, Tempe, Arizona; Corresponding authorDiffusion coefficients often vary across regions, such as cellular membranes, and quantifying their variation can provide valuable insight into local membrane properties such as composition and stiffness. Toward quantifying diffusion coefficient spatial maps and uncertainties from particle tracks, we develop a Bayesian framework (DiffMAP-GP) by placing Gaussian process (GP) priors on the family of candidate maps. For sake of computational efficiency, we leverage inducing point methods on GPs arising from the mathematical structure of the data giving rise to nonconjugate likelihood-prior pairs. We analyze both synthetic data, where ground truth is known, as well as data drawn from live-cell single-molecule imaging of membrane proteins. The resulting tool provides an unsupervised method to rigorously map diffusion coefficients continuously across membranes without data binning.http://www.sciencedirect.com/science/article/pii/S2667074724000533 |
| spellingShingle | Vishesh Kumar J. Shepard Bryan, IV Alex Rojewski Carlo Manzo Steve Pressé DiffMAP-GP: Continuous 2D diffusion maps from particle trajectories without data binning using Gaussian processes Biophysical Reports |
| title | DiffMAP-GP: Continuous 2D diffusion maps from particle trajectories without data binning using Gaussian processes |
| title_full | DiffMAP-GP: Continuous 2D diffusion maps from particle trajectories without data binning using Gaussian processes |
| title_fullStr | DiffMAP-GP: Continuous 2D diffusion maps from particle trajectories without data binning using Gaussian processes |
| title_full_unstemmed | DiffMAP-GP: Continuous 2D diffusion maps from particle trajectories without data binning using Gaussian processes |
| title_short | DiffMAP-GP: Continuous 2D diffusion maps from particle trajectories without data binning using Gaussian processes |
| title_sort | diffmap gp continuous 2d diffusion maps from particle trajectories without data binning using gaussian processes |
| url | http://www.sciencedirect.com/science/article/pii/S2667074724000533 |
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