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用广义矩法分析停留时间。

Analyzing dwell times with the Generalized Method of Moments.

机构信息

Department of Chemistry and Physics, Emmanuel College, Boston, MA, United States of America.

出版信息

PLoS One. 2019 Jan 8;14(1):e0197726. doi: 10.1371/journal.pone.0197726. eCollection 2019.

DOI:10.1371/journal.pone.0197726
PMID:30620735
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6324800/
Abstract

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<20), and is somewhat superior in identifying fast decay times with these same low count data sets. Additionally, a comparison with the Classical Method of Moments (CMM) shows that the CMM can fail in many cases, whereas the GMM always returns estimates. Our results show that the GMM can be a useful tool and complements standard approaches to analysis of single molecule dwell times.

摘要

广义矩方法(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学数据而开发的,现在被制定为从单个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停留时间的累积量的分析。我们开发了一种目标函数的一般形式,其最小化可以直接从数据中返回任意数量中间产物的衰减参数的估计值。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与非线性最小二乘最小化(NL-LSQM)进行比较,NL-LSQM 是分析单个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 的性能与 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<20)下表现出略低的偏差和方差,并且在使用相同低计数数据集识别快速衰减时间方面稍占优势。此外,与经典矩方法(CMM)的比较表明,CMM 在许多情况下可能会失败,而 GMM 总是返回估计值。我们的结果表明,GMM 可以成为分析单个分子停留时间的有用工具,并补充标准方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fcff/6324800/eb60d09d5e03/pone.0197726.g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fcff/6324800/9e6e2efc94ca/pone.0197726.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fcff/6324800/cb69093bc5ff/pone.0197726.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fcff/6324800/f0cd1ec54dea/pone.0197726.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fcff/6324800/5efc58932bf7/pone.0197726.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fcff/6324800/4c1e66748f28/pone.0197726.g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fcff/6324800/0cea06fc438d/pone.0197726.g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fcff/6324800/eb60d09d5e03/pone.0197726.g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fcff/6324800/9e6e2efc94ca/pone.0197726.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fcff/6324800/cb69093bc5ff/pone.0197726.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fcff/6324800/f0cd1ec54dea/pone.0197726.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fcff/6324800/5efc58932bf7/pone.0197726.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fcff/6324800/4c1e66748f28/pone.0197726.g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fcff/6324800/0cea06fc438d/pone.0197726.g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fcff/6324800/eb60d09d5e03/pone.0197726.g007.jpg

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