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 i...

• Type: Journal
• Year: 2006
• Where: BMCBI
• Authors: André Fujita, João Ricardo Sato, Leonardo de Oliveira Rodrigues, Carlos Eduardo Ferreira, Mari Cleide Sogayar
• Keywords: BMCBI 2006, Expression Levels, Support Vector Regression, Vector Regression