Robust dose-response curve estimation applied to high content screening data analysis

Source Code for Biology and Medicine

Michael Adsetts Edberg Hansen³  Myungjoo Kang⁵  Yong-Jun Kwon¹  Jin Yeop Kim²  Yury Tsoy²  Kyungmin Song⁵  Thuy Tuong Nguyen⁴ 

[1] Samsung Medical Center, Seoul, South Korea;Institut Pasteur Korea, Seongnam-si, Gyeonggi-do, South Korea;Videometer A/S, Horsholm, Denmark;University of California, Davis, USA;Seoul National University, Seoul, South Korea

关键词: Outlier detection;    Weighting function;    Sigmoidal function;    Curve fitting;    Dose response curve;    High content screening;   

Others  :  1139268
DOI  :  10.1186/s13029-014-0027-x

 received in 2014-07-14, accepted in 2014-11-14,  发布年份 2014

【 摘 要 】

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 nlinfit function and GraphPad’s Prism software.

【 授权许可】

   
2014 Nguyen et al.; licensee BioMed Central.

【 图 表 】

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