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A comparison of background correction methods for two-colour microarrays

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Microarray data must be background corrected to remove the effects of non-specific binding or spatial heterogeneity across the array, however this practice typically causes other problems such as negative corrected intensities and high variability of low intensity log-ratios. In this talk, I will present the results of a comparison of different estimators of background, and various model-based processing methods which aim to overcome these problems. 8 different background correction alternatives are compared, in terms of precision and bias of the resulting gene expression measures, and in terms of their ability to detect differentially expressed genes. Data sets where some independent truth in gene expression is known a priori are used in the comparison. Recommendations on the best methods to use for differential expression analyses of small microarray experiments will be given.

This talk is part of the Computational and Systems Biology series.

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