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Robust regression for high throughput drug screening.

Igor Fomenko·Mark Durst·David Balaban
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Abstract

Effective analysis of high throughput screening (HTS) data requires automation of dose-response curve fitting for large numbers of datasets. Datasets with outliers are not handled well by standard non-linear least squares methods, and manual outlier removal after visual inspection is tedious and potentially biased. We propose robust non-linear regression via M-estimation as a statistical technique for automated implementation. The approach of finding M-estimates by Iteratively Reweighted Least Squares (IRLS) and the resulting optimization problem are described. Initial parameter estimates for iterative methods are important, so self-starting methods for our model are presented. We outline the software implementation, done in Matlab and deployed as an Excel application via the Matlab Excel Builder Toolkit. Results of M-estimation are compared with least squares estimates before and after manual editing.

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AlgorithmsDiagnosis, Computer-AssistedHumansRegression AnalysisSubstance Abuse DetectionUnited StatesJournal Article

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BibTeXRIS

Igor Fomenko, Mark Durst, David Balaban. 2006-03-23. Robust regression for high throughput drug screening.. https://doi.org/10.1016/j.cmpb.2006.01.008

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