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 sampl...

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Bibliographic Details
Main Authors: Jianping Hua, Balagurunathan, Y., Yidong Chen, Lowey, J., Bittner, M.L., Zixiang Xiong, Suh, E., Dougherty, E.R.
Format: Conference Proceeding
Language:English
Subjects:
Additive noise
Background noise
Bioinformatics
Data analysis
Data engineering
Data mining
Fluorescence
Genomics
Labeling
Protocols
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Summary: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.
ISSN:2150-3001
DOI:10.1109/GENSIPS.2006.353129