Hooghoudt Jan-Otto, Barroso Margarida, Waagepetersen Rasmus
Aalborg University.
Albany Medical College.
Ann Appl Stat. 2017 Sep;11(3):1711-1737. doi: 10.1214/17-AOAS1054. Epub 2017 Oct 5.
Förster resonance energy transfer (FRET) is a quantum-physical phenomenon where energy may be transferred from one molecule to a neighbor molecule if the molecules are close enough. Using fluorophore molecule marking of proteins in a cell, it is possible to measure in microscopic images to what extent FRET takes place between the fluorophores. This provides indirect information of the spatial distribution of the proteins. Questions of particular interest are whether (and if so to which extent) proteins of possibly different types interact or whether they appear independently of each other. In this paper we propose a new likelihood-based approach to statistical inference for FRET microscopic data. The likelihood function is obtained from a detailed modeling of the FRET data-generating mechanism conditional on a protein configuration. We next follow a Bayesian approach and introduce a spatial point process prior model for the protein configurations depending on hyperparameters quantifying the intensity of the point process. Posterior distributions are evaluated using Markov chain Monte Carlo. We propose to infer microscope-related parameters in an initial step from reference data without interaction between the proteins. The new methodology is applied to simulated and real datasets.
荧光共振能量转移（FRET）是一种量子物理现象，如果分子足够接近，能量可以从一个分子转移到相邻分子。通过对细胞中的蛋白质进行荧光团分子标记，可以在微观图像中测量荧光团之间发生FRET的程度。这提供了蛋白质空间分布的间接信息。特别令人感兴趣的问题是，可能不同类型的蛋白质是否相互作用（如果相互作用，程度如何），或者它们是否彼此独立出现。在本文中，我们提出了一种基于似然性的新方法，用于对FRET微观数据进行统计推断。似然函数是从以蛋白质构象为条件的FRET数据生成机制的详细建模中获得的。接下来，我们采用贝叶斯方法，针对蛋白质构象引入一个空间点过程先验模型，该模型取决于量化点过程强度的超参数。使用马尔可夫链蒙特卡罗方法评估后验分布。我们建议在第一步从无蛋白质间相互作用的参考数据中推断与显微镜相关的参数。新方法应用于模拟数据集和真实数据集。
