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Reconstructing cell cycle and disease progression using deep learning / Philipp Eulenberg, Niklas Köhler, Thomas Blasi, Andrew Filby, Paul Rees, Anne E. Carpenter, Fabian J. Theis, F. Alexander Wolf

Nature Communications, Volume: 8, Issue: 1

Swansea University Author: Paul Rees

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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
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa35476
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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 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.
Item Description: 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.
Keywords: Cell division, Image processing, Machine learning
College: College of Engineering
Issue: 1