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Reconstructing cell cycle and disease progression using deep learning

Philipp Eulenberg, Niklas Köhler, Thomas Blasi, Andrew Filby, Paul Rees Orcid Logo, Anne E. Carpenter, Fabian J. Theis, F. Alexander Wolf

Nature Communications, Volume: 8, Issue: 1

Swansea University Author: Paul Rees Orcid Logo

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DOI (Published version): 10.1038/s41467-017-00623-3

Abstract

We show that deep convolutional neural networks combined with nonlinear dimension reduction enable reconstructing biological processes based on raw image data. We demonstrate this by reconstructing the cell cycle of Jurkat cells and disease progression in diabetic retinopathy. In further analysis of...

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Published in: Nature Communications
ISSN: 2041-1723 2041-1723
Published: 2017
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URI: https://cronfa.swan.ac.uk/Record/cronfa35476
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spelling 2021-01-14T13:14:28.3901487 v2 35476 2017-09-21 Reconstructing cell cycle and disease progression using deep learning 537a2fe031a796a3bde99679ee8c24f5 0000-0002-7715-6914 Paul Rees Paul Rees true false 2017-09-21 MEDE We show that deep convolutional neural networks combined with nonlinear dimension reduction enable reconstructing biological processes based on raw image data. We demonstrate this by reconstructing the cell cycle of Jurkat cells and disease progression in diabetic retinopathy. In further analysis of Jurkat cells, we detect and separate a subpopulation of dead cells in an unsupervised manner and, in classifying discrete cell cycle stages, we reach a sixfold reduction in error rate compared to a recent approach based on boosting on image features. In contrast to previous methods, deep learning based predictions are fast enough for on-the-fly analysis in an imaging flow cytometer. Journal Article Nature Communications 8 1 2041-1723 2041-1723 Cell division, Image processing, Machine learning 1 12 2017 2017-12-01 10.1038/s41467-017-00623-3 2018: This research is the first demonstration of deep learning applied to high throughput single cell image data, we demonstrate we can ‘learn’ cell cycle without using fluorescent cell markers and showed deep learning can define disease progression in an unsupervised manner. The work was undertaken as part of a BBSRC (BB/N005163/1) grant and also a USA NSF grant (Award Number 1458626 - Paul Rees co-investigator as visiting Professor at the Broad Institute of MIT and Harvard). It further led to another BBSRC (BB/P026818/1) grant which aims to make these tools open access. COLLEGE NANME Biomedical Engineering COLLEGE CODE MEDE Swansea University 2021-01-14T13:14:28.3901487 2017-09-21T14:37:52.7622143 Faculty of Science and Engineering School of Engineering and Applied Sciences - Biomedical Engineering Philipp Eulenberg 1 Niklas Köhler 2 Thomas Blasi 3 Andrew Filby 4 Paul Rees 0000-0002-7715-6914 5 Anne E. Carpenter 6 Fabian J. Theis 7 F. Alexander Wolf 8 0035476-21092017144020.pdf eulenberg2017.pdf 2017-09-21T14:40:20.2170000 Output 1493362 application/pdf Version of Record true 2017-09-21T00:00:00.0000000 Distributed under the terms of a Creative Commons Attribution (CC-BY) Licence. true eng https://creativecommons.org/licenses/by/4.0/
title Reconstructing cell cycle and disease progression using deep learning
spellingShingle Reconstructing cell cycle and disease progression using deep learning
Paul Rees
title_short Reconstructing cell cycle and disease progression using deep learning
title_full Reconstructing cell cycle and disease progression using deep learning
title_fullStr Reconstructing cell cycle and disease progression using deep learning
title_full_unstemmed Reconstructing cell cycle and disease progression using deep learning
title_sort Reconstructing cell cycle and disease progression using deep learning
author_id_str_mv 537a2fe031a796a3bde99679ee8c24f5
author_id_fullname_str_mv 537a2fe031a796a3bde99679ee8c24f5_***_Paul Rees
author Paul Rees
author2 Philipp Eulenberg
Niklas Köhler
Thomas Blasi
Andrew Filby
Paul Rees
Anne E. Carpenter
Fabian J. Theis
F. Alexander Wolf
format Journal article
container_title Nature Communications
container_volume 8
container_issue 1
publishDate 2017
institution Swansea University
issn 2041-1723
2041-1723
doi_str_mv 10.1038/s41467-017-00623-3
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Engineering and Applied Sciences - Biomedical Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Engineering and Applied Sciences - Biomedical Engineering
document_store_str 1
active_str 0
description We show that deep convolutional neural networks combined with nonlinear dimension reduction enable reconstructing biological processes based on raw image data. We demonstrate this by reconstructing the cell cycle of Jurkat cells and disease progression in diabetic retinopathy. In further analysis of Jurkat cells, we detect and separate a subpopulation of dead cells in an unsupervised manner and, in classifying discrete cell cycle stages, we reach a sixfold reduction in error rate compared to a recent approach based on boosting on image features. In contrast to previous methods, deep learning based predictions are fast enough for on-the-fly analysis in an imaging flow cytometer.
published_date 2017-12-01T03:44:09Z
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score 11.024221

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