An adaptive method for cDNA microarray normalization
- Equal contributors
1 Biometric Research Branch, National Cancer Institute, National Institutes of Health, Rockville, Maryland, USA
2 The EMMES Corporation, Rockville, Maryland, USA
BMC Bioinformatics 2005, 6:28 doi:10.1186/1471-2105-6-28Published: 11 February 2005
Normalization is a critical step in analysis of gene expression profiles. For dual-labeled arrays, global normalization assumes that the majority of the genes on the array are non-differentially expressed between the two channels and that the number of over-expressed genes approximately equals the number of under-expressed genes. These assumptions can be inappropriate for custom arrays or arrays in which the reference RNA is very different from the experimental samples.
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.