Effect of Normalization on Microarray-Based Classification

When using cDNA microarrays, normalization to correct biases is a common preliminary step before carrying out any data analysis, its objective being to reduce the systematic variations between the arrays. The biases are due to various systematic factors - scanner setting, amount of mRNA in the sample pool, and dye response characteristics between the channels. Since expression-based phenotype classification is a major use of microarrays, it is important to evaluate microarray normalization procedures relative to classification. Using a model-based approach, we model the systemic-error process to generate synthetic gene-expression values with known ground truth. Three normalization methods and three classification rules are then considered. Our simulation shows that normalization can have a significant benefit for classification under difficult experimental conditions. ©2006 IEEE.

Hua, J., Balagurunathan, Y., Chen, Y., Lowey, J., Bittner, M. L., Xiong, Z., Suh, E., & Dougherty, E. R.

Hua, Jianping||Balagurunathan, Yoganand||Chen, Yidong||Lowey, James||Bittner, Michael||Xiong, Zixiang||Suh, Edward||Dougherty, Edward

Effect of Normalization on Microarray-Based Classification Conference Paper