Abraham Anish V, Ram Sripad, Chao Jerry, Ward E S, Ober Raimund J
Department of Immunology, University of Texas Southwestern Medical Center, 6000 Harry Hines Boulevard, MC9093, Dallas, TX 75390, USA.
Opt Express. 2009 Dec 21;17(26):23352-73. doi: 10.1364/OE.17.023352.
Estimating the location of single molecules from microscopy images is a key step in many quantitative single molecule data analysis techniques. Different algorithms have been advocated for the fitting of single molecule data, particularly the nonlinear least squares and maximum likelihood estimators. Comparisons were carried out to assess the performance of these two algorithms in different scenarios. Our results show that both estimators, on average, are able to recover the true location of the single molecule in all scenarios we examined. However, in the absence of modeling inaccuracies and low noise levels, the maximum likelihood estimator is more accurate than the nonlinear least squares estimator, as measured by the standard deviations of its estimates, and attains the best possible accuracy achievable for the sets of imaging and experimental conditions that were tested. Although neither algorithm is consistently superior to the other in the presence of modeling inaccuracies or misspecifications, the maximum likelihood algorithm emerges as a robust estimator producing results with consistent accuracy across various model mismatches and misspecifications. At high noise levels, relative to the signal from the point source, neither algorithm has a clear accuracy advantage over the other. Comparisons were also carried out for two localization accuracy measures derived previously. Software packages with user-friendly graphical interfaces developed for single molecule location estimation (EstimationTool) and limit of the localization accuracy calculations (FandPLimitTool) are also discussed.
从显微镜图像估计单分子的位置是许多定量单分子数据分析技术中的关键步骤。人们提倡使用不同的算法来拟合单分子数据，特别是非线性最小二乘法和最大似然估计器。我们进行了比较，以评估这两种算法在不同场景下的性能。我们的结果表明，在我们所研究的所有场景中，平均而言，这两种估计器都能够恢复单分子的真实位置。然而，在不存在建模误差和低噪声水平的情况下，通过估计的标准差衡量，最大似然估计器比非线性最小二乘估计器更准确，并且在测试的成像和实验条件集上达到了可实现的最佳精度。尽管在存在建模误差或错误指定的情况下，这两种算法都并非始终优于另一种，但最大似然算法作为一种稳健的估计器出现，在各种模型不匹配和错误指定的情况下都能产生具有一致精度的结果。在高噪声水平下，相对于点源的信号，这两种算法都没有明显的精度优势。我们还对先前推导的两种定位精度度量进行了比较。本文还讨论了为单分子位置估计（EstimationTool）和定位精度计算极限（FandPLimitTool）开发的具有用户友好图形界面的软件包。
