Blythe Durbin

A model for measurement error for gene expression arrays

We introduce a model for measurement error in gene expression arrays as a function of the expression level. This model, together with analysis methods, data transformations, and weighting, allows much more precise comparisons of gene expression, and provides guidance for analysis of background, determination of confidence intervals, and preprocessing data for multivariate analysis.

Approximate variance-stabilizing transformations for gene-expression microarray data

MOTIVATION A variance stabilizing transformation for microarray data was recently introduced independently by several research groups. This transformation has sometimes been called the generalized logarithm or glog transformation. In this paper, we derive several alternative approximate variance stabilizing transformations that may be easier to use in some applications. RESULTS We demonstrate that the started-log and the log-linear-hybrid transformation families can produce approximate variance stabilizing transformations for microarray data that are nearly as good as the generalized logarithm (glog) transformation. These transformations may be more convenient in some applications.

Estimation of transformation parameters for microarray data.

MOTIVATION AND RESULTS Durbin et al. (2002), Huber et al. (2002) and Munson (2001) independently introduced a family of transformations (the generalized-log family) which stabilizes the variance of microarray data up to the first order. We introduce a method for estimating the transformation parameter in tandem with a linear model based on the procedure outlined in Box and Cox (1964). We also discuss means of finding transformations within the generalized-log family which are optimal under other criteria, such as minimum residual skewness and minimum mean-variance dependency. AVAILABILITY R and Matlab code and test data are available from the authors on request.

Modeling uncertainty in the measurement of low-level analytes in environmental analysis

The use of analytical chemistry measurements in environmental monitoring is dependent on an assessment of measurement error. Models for variation in measurements are needed to quantify uncertainty in measurements, set limits of detection, and preprocess data for more sophisticated analysis in prediction, classification, and clustering. This article explains how a two-component error model can be used to accomplish all of these objectives. In addition, we present applications to quantitating biomarkers of exposure to toxic substances using gene expression microarrays.