Methodology article

Evaluating different methods of microarray data normalization

André Fujita^1,2 , João Ricardo Sato¹ , Leonardo de Oliveira Rodrigues² , Carlos Eduardo Ferreira¹ and Mari Cleide Sogayar²

¹Institute of Mathematics and Statistics, University of São Paulo, Rua do Matão, 1010 – São Paulo, 05508-090 SP, Brazil

²Chemistry Institute, University of São Paulo, Av. Lineu Prestes, 748 – São Paulo, 05513-970 SP, Brazil

author email corresponding author email

BMC Bioinformatics 2006, 7:469doi:10.1186/1471-2105-7-469

Published:	23 October 2006

Abstract

Background

With the development of DNA hybridization microarray technologies, nowadays it is possible to simultaneously assess the expression levels of thousands to tens of thousands of genes. Quantitative comparison of microarrays uncovers distinct patterns of gene expression, which define different cellular phenotypes or cellular responses to drugs. Due to technical biases, normalization of the intensity levels is a pre-requisite to performing further statistical analyses. Therefore, choosing a suitable approach for normalization can be critical, deserving judicious consideration.

Results

Here, we considered three commonly used normalization approaches, namely: Loess, Splines and Wavelets, and two non-parametric regression methods, which have yet to be used for normalization, namely, the Kernel smoothing and Support Vector Regression. The results obtained were compared using artificial microarray data and benchmark studies. The results indicate that the Support Vector Regression is the most robust to outliers and that Kernel is the worst normalization technique, while no practical differences were observed between Loess, Splines and Wavelets.

Conclusion

In face of our results, the Support Vector Regression is favored for microarray normalization due to its superiority when compared to the other methods for its robustness in estimating the normalization curve.