John S H Danial1,2, Raed Shalaby3, Katia Cosentino4, Marwa M Mahmoud5, Fady Medhat6, David Klenerman1,2, Ana J Garcia Saez3. 1. Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, United Kingdom. 2. UK Dementia Research Institute, University of Cambridge, Cambridge, United Kingdom. 3. Institute of Genetics, University of Cologne, Cologne, Germany. 4. Department of Biology, University of Osnabruck, Osnabruck, Germany. 5. Department of Computer Science, University of Cambridge, Cambridge, United Kingdom. 6. Department of Computer Science, University of York, York, United Kingdom.
Abstract
MOTIVATION: 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. RESULTS: 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. AVAILABILITY: 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 INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: 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. RESULTS: 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. AVAILABILITY: 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 INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: John S H Danial; Yuri Quintana; Uris Ros; Raed Shalaby; Eleonora G Margheritis; Sabrina Chumpen Ramirez; Christian Ungermann; Ana J Garcia-Saez; Katia Cosentino Journal: J Phys Chem Lett Date: 2022-01-19 Impact factor: 6.475