Novel stepwise normalization method for two-channel cDNA microarrays

Microarray experiments contain many sources of systematic errors. In order to extract biologically relevant information from microarray data, normalization needs to be applied to remove such variations. Although a number of normalization models have been proposed, it has not been well researched on how to select the most appropriate model with respect to the observed data. To tackle this problem, we propose A new stepwise within-slide normalization method, STEPNORM. It is a normalization framework that integrates various models of different complexities to sequentially detect and adjust systematic variations associated with spot intensities, print-tips, plates and two-dimensional spatial effects. We demonstrate the utility of STEPNORM on a set of well-studied cDNA microarray experiment.