Department of Physics and Astronomy, University of New Mexico, Albuquerque, New Mexico, USA.
Department of Mathematics and Statistics, University of New Mexico, Albuquerque, New Mexico, USA.
Sci Rep. 2019 Sep 24;9(1):13791. doi: 10.1038/s41598-019-50232-x.
In single molecule localization-based super-resolution imaging, high labeling density or the desire for greater data collection speed can lead to clusters of overlapping emitter images in the raw super-resolution image data. We describe a Bayesian inference approach to multiple-emitter fitting that uses Reversible Jump Markov Chain Monte Carlo to identify and localize the emitters in dense regions of data. This formalism can take advantage of any prior information, such as emitter intensity and density. The output is both a posterior probability distribution of emitter locations that includes uncertainty in the number of emitters and the background structure, and a set of coordinates and uncertainties from the most probable model.
在基于单分子定位的超分辨率成像中，高标记密度或更大的数据采集速度的需求可能导致原始超分辨率图像数据中出现重叠发射体图像的簇。我们描述了一种用于多发射器拟合的贝叶斯推断方法，该方法使用可逆跳跃马尔可夫链蒙特卡罗 (Reversible Jump Markov Chain Monte Carlo) 来识别和定位数据密集区域中的发射器。这种形式主义可以利用任何先验信息，例如发射器强度和密度。输出结果既是发射器位置的后验概率分布，其中包括发射器数量和背景结构的不确定性，也是最可能模型的一组坐标和不确定性。
