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Effect of normalization on microarray-based classification

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8 Author(s)
Jianping Hua ; Translational Genomics Res. Inst., Phoenix, AZ ; Balagurunathan, Y. ; Yidong Chen ; Lowey, J.
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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.

Published in:

Genomic Signal Processing and Statistics, 2006. GENSIPS '06. IEEE International Workshop on

Date of Conference:

28-30 May 2006