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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 MATLABĀ®;'s nlinfit function and GraphPad's Prism software.

No MeSH data available.


Influence of noise (the arrows show the outliers). Fitting on the left assigns low weights to the outliers to disregard them. Fitting on the right considers the outliers as useful data points and gives higher weights to these points.
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Fig2: Influence of noise (the arrows show the outliers). Fitting on the left assigns low weights to the outliers to disregard them. Fitting on the right considers the outliers as useful data points and gives higher weights to these points.

Mentions: In most cases, it is difficult to estimate the parameters, either due to noise in the observations or because the experimental design might give rise to ambiguities in the parameters of the DRC. There is a need for an outlier detection mechanism to cope with noise before fitting curves. Figure 2 shows the effect of outliers in the data. There are eight different concentrations, five replicates at the first concentration, and three replicates at the remaining concentrations. It is likely to become noisy when the number of data points increases. Seven outliers (the solid arrows show these outliers in the figure) can change the fit of the curve dramatically. These seven points in the left figure have lower weights (outliers detected) than those in the right figure.Figure 2


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)

Influence of noise (the arrows show the outliers). Fitting on the left assigns low weights to the outliers to disregard them. Fitting on the right considers the outliers as useful data points and gives higher weights to these points.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Fig2: Influence of noise (the arrows show the outliers). Fitting on the left assigns low weights to the outliers to disregard them. Fitting on the right considers the outliers as useful data points and gives higher weights to these points.
Mentions: In most cases, it is difficult to estimate the parameters, either due to noise in the observations or because the experimental design might give rise to ambiguities in the parameters of the DRC. There is a need for an outlier detection mechanism to cope with noise before fitting curves. Figure 2 shows the effect of outliers in the data. There are eight different concentrations, five replicates at the first concentration, and three replicates at the remaining concentrations. It is likely to become noisy when the number of data points increases. Seven outliers (the solid arrows show these outliers in the figure) can change the fit of the curve dramatically. These seven points in the left figure have lower weights (outliers detected) than those in the right figure.Figure 2

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 MATLABĀ®;'s nlinfit function and GraphPad's Prism software.

No MeSH data available.