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Microarrays can provide detailed information of the expression patterns of thousands of genes in tumours, but due to systematic variations such as dyes, hybridization and processing precedures, and technical attefacts, true biological differences in gene expression levels cannot easily be detected. If the vast quantity of these microarray data can be suitably normalized, the impact of this genomic expression data on the understanding of diagnosis and treatment of disease will be profound. Statistical normalization approaches to microarray data are not as yet fully explored and need to be continuously evaluated. We compared the currently much used normalization methods and recently proposed statiostical normalization methods of ANOVA with oligonucleotide microarray data of hepatic nocular lesions indluding hepatocellular carcinoma. This article proposes combined normalization methods of ANOVA after a robust local lowess, intensity and print- tip dependent, regression.
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