Abraham Anish V, Ram Sripad, Chao Jerry, Ward E Sally, Ober Raimund J
Dept. Electrical Engineering, University of Texas at Dallas, Richardson, TX 75083 ; Dept. Immunology, University of Texas Southwestern Medical Center, Dallas, TX 75390.
Dept. Immunology, University of Texas Southwestern Medical Center, Dallas, TX 75390.
Proc SPIE Int Soc Opt Eng. 2010 Feb 24;7570:757004. doi: 10.1117/12.842178.
Different techniques have been advocated for estimating single molecule locations from microscopy images. The question arises as to which technique produces the most accurate results. Various factors, e.g. the stochastic nature of the photon emission/detection process, extraneous additive noise, pixelation, etc., result in the estimated single molecule location deviating from its true location. Here, we review the results presented by [Abraham et. al, Optics Express, 2009, 23352-23373], where the performance of the maximum likelihood and nonlinear least squares estimators for estimating single molecule locations are compared. Our results show that on average both estimators recover the true single molecule location in all scenarios. Comparing the standard deviations of the estimates, we find that in the absence of noise and modeling inaccuracies, the maximum likelihood estimator is more accurate than the non-linear least squares estimator, and attains the best achievable accuracy for the sets of experimental and imaging conditions tested. In the presence of noise and modeling inaccuracies, the maximum likelihood estimator produces results with consistent accuracy across various model mismatches and misspecifications. At high noise levels, neither estimator has an accuracy advantage over the other. We also present new results regarding the performance of the maximum likelihood estimator with respect to the objective function used to fit data containing both additive Gaussian and Poisson noise. Comparisons were also carried out between two localization accuracy measures derived previously. User-friendly software packages were developed for single molecule location estimation (EstimationTool) and localization accuracy calculations (FandPLimitTool).
人们提出了不同的技术来从显微镜图像中估计单分子位置。问题在于哪种技术能产生最准确的结果。各种因素，例如光子发射/检测过程的随机性、外部加性噪声、像素化等，会导致估计的单分子位置偏离其真实位置。在此，我们回顾了[亚伯拉罕等人，《光学快报》，2009年，23352 - 23373]所展示的结果，其中比较了用于估计单分子位置的最大似然估计器和非线性最小二乘估计器的性能。我们的结果表明，平均而言，在所有情况下这两种估计器都能恢复单分子的真实位置。比较估计值的标准差，我们发现，在没有噪声和建模误差的情况下，最大似然估计器比非线性最小二乘估计器更准确，并且在所测试的实验和成像条件集下达到了最佳可实现精度。在存在噪声和建模误差的情况下，最大似然估计器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，两种估计器都没有精度优势。我们还给出了关于最大似然估计器在用于拟合同时包含加性高斯噪声和泊松噪声的数据的目标函数方面的新结果。还对先前推导的两种定位精度度量进行了比较。开发了用于单分子位置估计（EstimationTool）和定位精度计算（FandPLimitTool）的用户友好型软件包。
