A hidden Markov model approach to characterizing the photo-switching behavior of fluorophores

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Title: A hidden Markov model approach to characterizing the photo-switching behavior of fluorophores
Authors: Cohen, E
Patel, L
Ober, R
Lin, Y
Henriques, R
Gustafsson, N
Item Type: Journal Article
Abstract: Fluorescing molecules (fluorophores) that stochastically switch between photon-emitting and dark states underpin some of the most celebrated advancements in super-resolution microscopy. While this stochastic behavior has been heavily exploited, full characterization of the underlying models can potentially drive forward further imaging methodologies. Under the assumption that fluorophores move between fluorescing and dark states as continuous time Markov processes, the goal is to use a sequence of images to select a model and estimate the transition rates. We use a hidden Markov model to relate the observed discrete time signal to the hidden continuous time process. With imaging involving several repeat exposures of the fluorophore, we show the observed signal depends on both the current and past states of the hidden process, producing emission probabilities that depend on the transition rate parameters to be estimated. To tackle this unusual coupling of the transition and emission probabilities, we conceive transmission (transition-emission) matrices that capture all dependencies of the model. We provide a scheme of computing these matrices and adapt the forward-backward algorithm to compute a likelihood which is readily optimized to provide rate estimates. When confronted with several model proposals, combining this procedure with the Bayesian Information Criterion provides accurate model selection.
Date of Acceptance: 18-Jan-2019
URI: http://hdl.handle.net/10044/1/67167
ISSN: 1932-6157
Publisher: Institute of Mathematical Statistics
Journal / Book Title: Annals of Applied Statistics
Copyright Statement: This paper is embargoed until publication.
Keywords: 0104 Statistics
Statistics & Probability
Embargo Date: publication subject to indefinite embargo
Online Publication Date: 2019-01-21
Appears in Collections:Statistics
Faculty of Natural Sciences

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