An adaptive method for cDNA microarray normalization

We propose a mixture model based normalization method that adaptively identifies non-differentially expressed genes and thereby substantially improves normalization for dual-labeled arrays in settings where the assumptions of global normalization are problematic. The new method is evaluated using both simulated and real data.The new normalization method is effective for general microarray platforms when samples with very different expression profile are co-hybridized and for custom arrays where the majority of genes are likely to be differentially expressed.Microarray technology provides simultaneous measurements of expression levels for thousands of genes. Each step from sample preparation to data analysis, however, contains potential sources of bias and variability. Proper normalization adjusts for differences which interfere with the comparison of intensities of different labels at a given probe and with the comparison of intensities of corresponding probes on different arrays. Proper data normalization should allow for the comparison of expression levels across different arrays. Subsequent data analysis results are heavily dependent on effective normalization.Normalization issues differ for dual-labeled platforms compared to single labeled platforms such as the Affymetrix GeneChip arrays. In this paper we address normalization for dual-labeled arrays with either cDNA or oligonucleotide probes. The objective of normalization for dual-labeled arrays is to correct for differences in intensities for the two labels on the same array. These differences arise from factors such as differences in sample concentrations, differences in photomultiplier tube setting, and differences in the affinity of the two labels for DNA.Median or mean based global normalization methods use a single normalization factor applied to all genes on the array to adjust for labeling bias [1,2]. Such methods are widely used because of their simplicity. Intensity-based and location-based methods t