Model selection and efficiency testing for normalization of cDNA microarray data
1 Institute for Theoretical Biology, Humboldt-Universität, Invalidenstraße 43, 10115 Berlin, Germany
2 Department of Information Science, University of Otago, PO Box 56, Dunedin, New Zealand
3 Otago School of Medical Sciences, Division of Health Science, University of Otago, PO Box 913, Dunedin, New Zealand
Genome Biology 2004, 5:R60 doi:10.1186/gb-2004-5-8-r60Published: 30 July 2004
In this study we present two novel normalization schemes for cDNA microarrays. They are based on iterative local regression and optimization of model parameters by generalized cross-validation. Permutation tests assessing the efficiency of normalization demonstrated that the proposed schemes have an improved ability to remove systematic errors and to reduce variability in microarray data. The analysis also reveals that without parameter optimization local regression is frequently insufficient to remove systematic errors in microarray data.