Evaluating different methods of microarray data normalization.
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.


CONCLUSIONS
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.
Fujita
André
A
Institute of Mathematics and Statistics, University of São Paulo, Rua do Matão, 1010--São Paulo, 05508-090 SP, Brazil. fujita@ime.usp.br
Sato
João Ricardo
JR
Rodrigues
Leonardo de Oliveira
Lde O
Ferreira
Carlos Eduardo
CE
Sogayar
Mari Cleide
MC
D000818:Animals; D002478:Cells, Cultured; D003627:Data Interpretation, Statistical; D005069:Evaluation Studies as Topic; D020869:Gene Expression Profiling; D051379:Mice; D020411:Oligonucleotide Array Sequence Analysis; D012016:Reference Values; D012044:Regression Analysis