Pitching single-focus confocal data analysis one photon at a time with Bayesian nonparametrics

Tavakoli, Meysam; Jazani, Sina; Sgouralis, Ioannis; Shafraz, Omer; Donaphon, Bryan; Sivasankar, Sanjeevi; Levitus, Marcia; Pressé, Steve

doi:10.1101/749739

Cited by 12 publications

(36 citation statements)

References 112 publications

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“…Because we cannot set a prior on the number of fluorophores in each ROI, M r , we must use Bayesian nonparametrics in our analysis. Briefly, we will implement this using a nonparametric limit [50,51]. That is, we will assume an exceedingly large number of model fluorophores in the ROI, K M r , indexing each fluorophore with k = 1, .…”

Section: Inverse Modelmentioning

confidence: 99%

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Diffraction-Limited Molecular Cluster Quantification with Bayesian Nonparametrics

Sgouralis

Bryan

Pressé

2020

Preprint

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Determining the number of biomolecules within a small, diffraction-limited spot, is key to understanding intermolecular interactions that form the basis of all of information processing relevant to life. To enumerate the number of biomolecules within such a small region of interest (ROI), it is possible to illuminate an ROI containing a collection of fluorescently labeled biomolecules and observe the ROI's brightness over time. As most fluorescent labels (fluorophores) are initially in a fluorescently active state, the brightness diminishes in a step-like pattern as fluorophores photobleach one at a time. Naively, by counting steps, one could determine the number of fluorophores present. However, this analysis is complicated by photon shot noise, camera noise that amplifies intrinsic photon shot noise and fluorophore photophysics (i.e., states with variable emission visited by fluorophores). Current state of the art can typically enumerate as many as 20 fluorophores reliably. Yet in many biophysics problems, fluorophore numbers can vastly exceed 20 within an ROI. For this reason, we develop a method to learn the number of fluorophores alongside other parameters required to achieve high counts simultaneously and self-consistently (e.g., fluorophore photophysical rate parameters and camera parameters). As the number of fluorophores is initially unknown and may be very large, and as not all fluorophores may initially be emitting photons, we cannot rely on a traditional (parametric) Bayesian framework. As such, to achieve our goal of exceeding the state of the art by a factor of about 5, we must abandon the parametric Bayesian paradigm and invoke novel tools within Bayesian nonparametrics never previously used in step counting by photobleaching.

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Section: Inverse Modelmentioning

confidence: 99%

“…The result is that as K becomes large, formally at the limit K → ∞, the probability distribution converges to a distribution in which an infinite number of fluorophores are considered [50,51]. Furthermore, this choice of prior allows inference that is independent on our choice of loads.…”

Section: Inverse Modelmentioning

confidence: 99%

Diffraction-Limited Molecular Cluster Quantification with Bayesian Nonparametrics

Sgouralis

Bryan

Pressé

2020

Preprint

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“…Originally developed for applications in speech recognition (3,4), the relevance of HMMs to single-molecule time series analysis was quickly recognized (5)(6)(7)(8)(9)(10)(11)(12)(13)(14). Since then, HMMs and variants have successfully been used in the interpretation of ion channel patch-clamp data (15)(16)(17)(18)(19), fluorescence resonant energy transfer (FRET) (10,(20)(21)(22)(23)(24)(25)(26)(27)(28)(29), force spectroscopy (30)(31)(32), among many other physical applications (3,33,34).…”

Section: Introductionmentioning

confidence: 99%

Generalizing HMMs to Continuous Time for Fast Kinetics: Hidden Markov Jump Processes

Kilic

Sgouralis

Pressé

2021

Biophysical Journal

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The hidden Markov model (HMM) is a framework for time series analysis widely applied to single-molecule experiments. Although initially developed for applications outside the natural sciences, the HMM has traditionally been used to interpret signals generated by physical systems, such as single molecules, evolving in a discrete state space observed at discrete time levels dictated by the data acquisition rate. Within the HMM framework, transitions between states are modeled as occurring at the end of each data acquisition period and are described using transition probabilities. Yet, whereas measurements are often performed at discrete time levels in the natural sciences, physical systems evolve in continuous time according to transition rates. It then follows that the modeling assumptions underlying the HMM are justified if the transition rates of a physical process from state to state are small as compared to the data acquisition rate. In other words, HMMs apply to slow kinetics. The problem is, because the transition rates are unknown in principle, it is unclear, a priori, whether the HMM applies to a particular system. For this reason, we must generalize HMMs for physical systems, such as single molecules, because these switch between discrete states in ''continuous time''. We do so by exploiting recent mathematical tools developed in the context of inferring Markov jump processes and propose the hidden Markov jump process. We explicitly show in what limit the hidden Markov jump process reduces to the HMM. Resolving the discrete time discrepancy of the HMM has clear implications: we no longer need to assume that processes, such as molecular events, must occur on timescales slower than data acquisition and can learn transition rates even if these are on the same timescale or otherwise exceed data acquisition rates.

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“…By growing of machine learning ideas, 27,28 studies on MA detection using classification based approaches are mostly seen recently. Antal et al 29 applied a rule-based expert system for MAs detection.…”

Section: Introductionmentioning

confidence: 99%

Comparison different vessel segmentation methods in automated microaneurysms detection in retinal images using convolutional neural networks

Tavakoli¹,

Nazar²

2020

Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging

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Image processing techniques provide important assistance to physicians and relieve their workload in different tasks. In particular, identifying objects of interest such as lesions and anatomical structures from the image is a challenging and iterative process that can be done by computerized approaches in a successful manner. Microaneurysms (MAs) detection is a crucial step in retinal image analysis algorithms. The goal of MAs detection is to find the progress and at last identification of diabetic retinopathy (DR) in the retinal images. The objective of this study is to apply three retinal vessel segmentation methods, Laplacian-of-Gaussian (LoG), Canny edge detector, and Matched filter to compare results of MAs detection using combination of unsupervised and supervised learning either in the normal images or in the presence of DR. The steps for the algorithm are as following: 1) Preprocessing and Enhancement, 2) vessel segmentation and masking, 3) MAs detection and Localization using combination of Matching based approach and Convolutional Neural Networks. To evaluate the accuracy of our proposed method, we compared the output of our method with the ground truth that collected by ophthalmologists. By using the LoG vessel segmentation, our algorithm found sensitivity of more than 85% in detection of MAs for 100 color images in a local retinal database and 40 images of a public dataset (DRIVE). For the Canny vessel segmentation, our automated algorithm found sensitivity of more than 80% in detection of MAs for all 140 images of two databases. And lastly, using Matched filter, our algorithm found sensitivity of more than 87% in detection of MAs in both local and DRIVE datasets. arXiv:2005.09097v1 [physics.med-ph] 18 Apr 2020

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Pitching single-focus confocal data analysis one photon at a time with Bayesian nonparametrics

Cited by 12 publications

References 112 publications

Diffraction-Limited Molecular Cluster Quantification with Bayesian Nonparametrics

Diffraction-Limited Molecular Cluster Quantification with Bayesian Nonparametrics

Generalizing HMMs to Continuous Time for Fast Kinetics: Hidden Markov Jump Processes

Comparison different vessel segmentation methods in automated microaneurysms detection in retinal images using convolutional neural networks

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