Analyzing dwell times with the Generalized Method of Moments
Analyzing dwell times with the Generalized Method of Moments
复制标题
DOI：
10.1371/journal.pone.0197726
复制
发表时间：
2019-01-08
期刊：
影响因子：
3.7
通讯作者：
Price, Allen C.
中科院分区：
文献类型：
--
作者：
Piatt, Sadie;Price, Allen C.
广义矩量法 (GMM) 是一种用于分析随机过程样本的统计方法。该方法最初是为分析计量经济数据而开发的，这里制定的方法是从单分子停留时间的测量中提取隐藏的动力学参数。我们的方法基于对测量的停留时间的累积量的分析。我们开发了目标函数的一般形式，其最小化可以直接从数据返回任意数量的中间体的衰减参数的估计。我们使用模拟和实验数据来测试我们技术的性能。我们还将我们的方法的性能与非线性最小二乘最小化（NL-LSQM）进行比较，非线性最小二乘最小化是一种分析单分子停留时间的常用技术。我们的研究结果表明，在我们探索的大部分参数范围内，GMM 的表现与 NL-LSQM 相当。与 NL-LSQM 相比，它具有一些优势，因为它不需要分箱，在小样本量 (N
The Generalized Method of Moments (GMM) is a statistical method for the analysis of samples from random processes. First developed for the analysis of econometric data, the method is here formulated to extract hidden kinetic parameters from measurements of single molecule dwell times. Our method is based on the analysis of cumulants of the measured dwell times. We develop a general form of an objective function whose minimization can return estimates of decay parameters for any number of intermediates directly from the data. We test the performance of our technique using both simulated and experimental data. We also compare the performance of our method to nonlinear least-squares minimization (NL-LSQM), a commonly-used technique for analysis of single molecule dwell times. Our findings indicate that the GMM performs comparably to NL-LSQM over most of the parameter range we explore. It offers some benefits compared with NL-LSQM in that it does not require binning, exhibits slightly lower bias and variance with small sample sizes (N