Nguyen TT, Song K, Tsoy Y, Kim JY, Kwon YJ, Kang M, Edberg Hansen MA. Robust dose-response curve estimation applied to high content screening data analysis.
SOURCE CODE FOR BIOLOGY AND MEDICINE 2014;
9:27. [PMID:
25614758 PMCID:
PMC4279979 DOI:
10.1186/s13029-014-0027-x]
[Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/14/2014] [Accepted: 11/14/2014] [Indexed: 11/25/2022]
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.
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