Robust regression for high throughput drug screening

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.

Theoretical and Experimental Relationships between Percent Inhibition and IC50 Data Observed in High-Throughput Screening

“…Several alternative but mathematically equivalent expressions are in practical use. 4,5,10 We have performed Monte Carlo simulation experiments that were designed to obtain a better quantitative understanding of the expected correlation between inhibition values at some constant concentration and the experimentally derived IC 50 values from the same experiment (within-curve correlation) and also from independently performed experiments. The latter will mirror the situation encountered when comparing single data point primary screening results with the corresponding IC 50 determinations based on different stock solutions or fresh solutions prepared from powder.…”

“…1,2 In a second step, the primary hits (i.e., the compounds passing a fixed threshold or having a larger than 3-6 standard deviation distance from the zero effect level) are selected for concentration-response curve (CRC) validation experiments. 2,3 The determination of an IC 50 ("potency") value is based on fitting the four-parameter logistic Hill equation 4,5 to the data, although more sophisticated models are sometimes used in detailed pharmacological investigations. 6 The basic assumption of this stepwise HTS design is a reasonable correlation between the percent inhibition and IC 50 values, as can be expected from the Hill equation for ideal inhibitors.…”

Differential effects of COMT on gait and executive control in aging

“…associations between the Val 158 Met polymorphism in COMT, gait velocity and Executive Attention were examined with linear or robust regression (Fomenko, Durst, & Balaban, 2006;Motulsky & Brown, 2006) as appropriate. To elucidate the associations between the COMT genotype and the outcome variables, gait velocity and Executive Attention were dichotomized to the best performance quartile vs. the remaining lower three quartiles.…”

Robust kernel ridge regression based on M-estimation

“…Fomenko et al [4] proposed a nonlinear robust prediction based on the M-estimation, where the method needs a specific nonlinear regression model in advance. In many situations, however, an appropriate nonlinear regression model for a set of data is unknown in advance.…”

“…M-estimation can be considered as a modification of both regression based on OLS and maximum likelihood estimation that eliminate the effects of outlying observation on the regression estimation. In the meantime, Fomenko [4] proposed a nonlinear robust prediction based on M-estimation by specifying a nonlinear regression model in advance. In many situations, however, an appropriate nonlinear regression model for a set of data is unknown in advance.…”