Effect of normalization on microarray-based classification Conference Paper uri icon

abstract

  • 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.

name of conference

  • 2006 IEEE International Workshop on Genomic Signal Processing and Statistics

published proceedings

  • 2006 IEEE INTERNATIONAL WORKSHOP ON GENOMIC SIGNAL PROCESSING AND STATISTICS

author list (cited authors)

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

citation count

  • 0

complete list of authors

  • Hua, Jianping||Balagurunathan, Yoganand||Chen, Yidong||Lowey, Daines||Bittner, Michael L||Xiong, Zixiang||Suh, Edward||Dougherty, Edward R

publication date

  • May 2006