Methodological study of affine transformations of gene expression data with proposed robust non-parametric multi-dimensional nor

Background: Low-level processing and normalization of microarray data are most important steps in microarray analysis, which have profound impact on downstream analysis. Multiple methods have been suggested to date, but it is not clear which is the best. It is therefore important to further study the different normalization methods in detail and the nature of microarray data in general. Results: A methodological study of affine models for gene expression data is carried out. Focus is on two-channel comparative studies, but the findings generalize also to single- and multi-channel data. The discussion applies to spotted as well as in-situ synthesized microarray data. Existing normalization methods such as curve-fit ("lowess") normalization, parallel and perpendicular translation normalization, and quantile normalization, but also dye-swap normalization are revisited in the light of the affine model and their strengths and weaknesses are investigated in this context. As a dire...

• Type: Journal
• Year: 2006
• Where: BMCBI
• Authors: Henrik Bengtsson, Ola Hössjer
• Keywords: Affine Model, BMCBI 2006, Microarray Data, Normalization Method