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

Nguyen TT, Song K, Tsoy Y, Kim JY, Kwon YJ, Kang M, Edberg Hansen MA - Source Code Biol Med (2014)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: University of California, Davis, USA.

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.

No MeSH data available.


A four-parameter dose-response curve.β1,β2,β3, and β4 are the floor, the window, the shift, and the slope, respectively.
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Fig1: A four-parameter dose-response curve.β1,β2,β3, and β4 are the floor, the window, the shift, and the slope, respectively.

Mentions: where x is the dose or concentration of a data point; β represents the four parameters β1, β2, β3, and β4; β1 is the floor - the efficacy - which shows the biological activity without a chemical compound; β2 is the window - the efficacy - which shows the maximum saturated activity at high concentration; β3 is the shift - the potency - of the DRC; and β4 is the slope - the kinetics. Figure 1 shows an illustration of a response curve and its four parameters.Figure 1


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

Nguyen TT, Song K, Tsoy Y, Kim JY, Kwon YJ, Kang M, Edberg Hansen MA - Source Code Biol Med (2014)

A four-parameter dose-response curve.β1,β2,β3, and β4 are the floor, the window, the shift, and the slope, respectively.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4279979&req=5

Fig1: A four-parameter dose-response curve.β1,β2,β3, and β4 are the floor, the window, the shift, and the slope, respectively.
Mentions: where x is the dose or concentration of a data point; β represents the four parameters β1, β2, β3, and β4; β1 is the floor - the efficacy - which shows the biological activity without a chemical compound; β2 is the window - the efficacy - which shows the maximum saturated activity at high concentration; β3 is the shift - the potency - of the DRC; and β4 is the slope - the kinetics. Figure 1 shows an illustration of a response curve and its four parameters.Figure 1

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: University of California, Davis, USA.

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.

No MeSH data available.