稳健剂量反应曲线估计应用于高内涵筛选数据分析。
Robust dose-response curve estimation applied to high content screening data analysis.
作者信息
Nguyen Thuy Tuong, Song Kyungmin, Tsoy Yury, Kim Jin Yeop, Kwon Yong-Jun, Kang Myungjoo, Edberg Hansen Michael Adsetts
机构信息
University of California, Davis, USA.
Seoul National University, Seoul, South Korea.
出版信息
Source Code Biol Med. 2014 Dec 10;9(1):27. doi: 10.1186/s13029-014-0027-x. eCollection 2014.
BACKGROUND AND METHOD
Successfully automated sigmoidal curve fitting is highly challenging when applied to large data sets. In this paper, we describe a robust algorithm for fitting sigmoid dose-response curves by estimating four parameters (floor, window, shift, and slope), together with the detection of outliers. We propose two improvements over current methods for curve fitting. The first one is the detection of outliers which is performed during the initialization step with correspondent adjustments of the derivative and error estimation functions. The second aspect is the enhancement of the weighting quality of data points using mean calculation in Tukey's biweight function.
RESULTS AND CONCLUSION
Automatic curve fitting of 19,236 dose-response experiments shows that our proposed method outperforms the current fitting methods provided by MATLAB®;'s nlinfit function and GraphPad's Prism software.
背景与方法
当应用于大型数据集时，成功实现S形曲线的自动拟合极具挑战性。在本文中，我们描述了一种稳健的算法，用于通过估计四个参数（下限、窗口、偏移和斜率）来拟合S形剂量反应曲线，并同时检测异常值。我们对当前的曲线拟合方法提出了两点改进。第一个改进是在初始化步骤中检测异常值，并相应地调整导数和误差估计函数。第二个方面是在Tukey双权函数中使用均值计算来提高数据点的加权质量。
结果与结论
对19236个剂量反应实验进行自动曲线拟合的结果表明，我们提出的方法优于MATLAB®的nlinfit函数和GraphPad的Prism软件所提供的当前拟合方法。
Zotero 插件