Evaluating different methods of microarray data normalization

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.

Figure 1

The minimum mean square error for three different simulated microarray datasets. From left to right: 1) sinusoid shape; 2) banana shape; 3) mix shape. The Kernel regression was not included in this figure because its MSE is 10³ orders of magnitude greater than the other normalization methods.

Figure 2

Fitted normalization curves for actual cDNA microarray data using the five different normalization methods (Loess, Splines, Wavelets, Kernel, SVR).