DeepSinse: deep learning-based detection of single molecules

Danial, John S H; Shalaby, Raed; Cosentino, Katia; Mahmoud, Marwa M; Medhat, Fady; Klenerman, David; Garcia Saez, Ana J.

doi:10.1093/bioinformatics/btab352

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DeepSinse: deep learning-based detection of single molecules

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Danial, J. S. H., Shalaby, R., Cosentino, K., Mahmoud, M. M., Medhat, F., Klenerman, D., et al. (2021). DeepSinse: deep learning-based detection of single molecules. Bioinformatics, 37(21), 3998-4000. doi:10.1093/bioinformatics/btab352.

Cite as: https://hdl.handle.net/21.11116/0000-000E-1182-B

Abstract

Imaging single molecules has emerged as a powerful characterization tool in the biological sciences. The detection of these under various noise conditions requires the use of algorithms that are dependent on the end-user inputting several parameters, the choice of which can be challenging and subjective.In this work, we propose DeepSinse, an easily trainable and useable deep neural network that can detect single molecules with little human input and across a wide range of signal-to-noise ratios. We validate the neural network on the detection of single bursts in simulated and experimental data and compare its performance with the best-in-class, domain-specific algorithms.Ground truth ROI simulating code, neural network training, validation code, classification code, ROI picker, GUI for simulating, training and validating DeepSinse as well as pre-trained networks are all released under the MIT License on www.github.com/jdanial/DeepSinse. The dSTORM dataset processing code is released under the MIT License on www.github.com/jdanial/StormProcessor.Supplementary data are available at Bioinformatics online.