An expectation–maximisation algorithm for the deconvolution of the intrinsic distribution of single molecule's parameters

Werner Baumgartner, Detlev Drenckhahn
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引用次数: 7

Abstract

Values obtained from single molecule techniques exhibit distinct distributions comprising an uncertainty due to random noise convoluted with the intrinsic distribution of the molecule's properties. In the fields of single molecule light microscopy and spectroscopy, force microscopy and spectroscopy as well as other techniques like electrophysiology, sophisticated data analysis algorithms are available which extract the interesting parameters and their uncertainties from the noisy data set. The intrinsic distributions of these parameters contain valuable information about the molecules’ physical and chemical properties, that need to be deconvoluted from the data. Here, we present an expectation–maximisation (EM-) algorithm that estimates the intrinsic distribution in single molecule experiments. The performance is tested by using computer simulations and the application of the method is demonstrated for data from single molecule force spectroscopy.

单分子参数本征分布反褶积的期望最大化算法
从单分子技术获得的值表现出明显的分布,其中包括由于随机噪声与分子性质的固有分布相缠绕而产生的不确定性。在单分子光学显微镜和光谱学、力显微镜和光谱学以及电生理学等其他技术领域,可以使用复杂的数据分析算法从嘈杂的数据集中提取有趣的参数及其不确定性。这些参数的内在分布包含有关分子物理和化学性质的宝贵信息,这些信息需要从数据中解卷积。在这里,我们提出了一种期望最大化(EM-)算法,用于估计单分子实验中的固有分布。通过计算机仿真验证了该方法的性能,并对单分子力谱数据进行了验证。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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