Abstract

Motivation: Pixel saturation occurs when the pixel intensity exceeds a threshold and the recorded pixel intensity is truncated. Microarray experiments are commonly afflicted with saturated pixels. As a result, estimators of gene expression are biased, with the amount of bias increasing as a function of the proportion of pixels saturated. Saturation is directly related to the photomultiplier tube (PMT) voltage settings and RNA abundance and is not necessarily associated with poor array or poor spot quality. When choosing PMT settings, higher PMT settings are desired because of improved signal-to-noise ratios of low-intensity spots. This improved signal is somewhat offset by saturation of high-intensity spots. In practice, spots with saturated pixels are discarded or the biased value is used. Neither of these approaches is appealing, particularly the former approach when a highly expressed gene is discarded because of saturation.

Results: We present a method to correct for saturation using pixel-level data. The method is based on a censored regression model. Evaluations on several arrays indicate that the method performs well. Simulation studies suggest that the method is robust under certain model violations.

Supplementary material: Supplementary tables and figures can be found at http://linus.nci.nih.gov/Data/doddl/saturation/extras.pdf

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To whom correspondence should be addressed.

Author notes

1Biometric Research Branch, DCTD, National Cancer Institute, Bethesda, MD 20892-7434, USA, 2Center for Cancer Research, National Cancer Institute, 8717 Grovemont Circle, Gaithersburg, MD 20877, USA and 3Radiation Oncology Branch, CCR, National Cancer Institute, Bethesda, MD 20892-4605, USA

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