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

Thuy Tuong Nguyen; Kyungmin Song; Yury Tsoy; Jin Yeop Kim; Yong-Jun Kwon; Myungjoo Kang; Michael Adsetts Edberg Hansen

doi:10.1186/s13029-014-0027-x

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

Source Code Biol Med. 2014 Dec 10;9(1):27. doi: 10.1186/s13029-014-0027-x. eCollection 2014.

Authors

Thuy Tuong Nguyen¹, Kyungmin Song², Yury Tsoy³, Jin Yeop Kim³, Yong-Jun Kwon⁴, Myungjoo Kang², Michael Adsetts Edberg Hansen⁵

Affiliations

¹ University of California, Davis, USA.
² Seoul National University, Seoul, South Korea.
³ Institut Pasteur Korea, Seongnam-si, Gyeonggi-do South Korea.
⁴ Samsung Medical Center, Seoul, South Korea.
⁵ Videometer A/S, Horsholm, Denmark.

Abstract

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.

Keywords: Curve fitting; Dose response curve; High content screening; Outlier detection; Sigmoidal function; Weighting function.