Normalization benefits microarray-based classification

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

Research output: Contribution to journalArticlepeer-review

16 Scopus citations

Abstract

When using cDNA microarrays, normalization to correct labeling bias is a common preliminary step before further data analysis is applied, its objective being to reduce the variation between arrays. To date, assessment of the effectiveness of normalization has mainly been confined to the ability to detect differentially expressed genes. Since a major use of microarrays is the expression-based phenotype classification, 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. These synthetic expression values are subjected to typical normalization methods and passed through a set of classification rules, the objective being to carry out a systematic study of the effect of normalization on classification.Three normalization methods are considered: offset, linear regression, and Lowess regression. Seven classification rules are considered: 3-nearest neighbor, linear support vector machine,linear discriminant analysis, regular histogram, Gaussian kernel,perceptron, and multiple perceptron with majority voting. The results of the first three are presented in the paper, with the full results being given on a complementary website. The conclusion from the different experiment models considered in the study is that normalization can have a significant benefit for classification under difficult experimental conditions, with linear and Lowess regression slightly outperforming the offset method.

Original languageEnglish (US)
Article number43056
Pages (from-to)1-13
Number of pages13
JournalEurasip Journal on Bioinformatics and Systems Biology
Volume2006
DOIs
StatePublished - 2006

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Computer Science Applications
  • Computational Mathematics

Fingerprint Dive into the research topics of 'Normalization benefits microarray-based classification'. Together they form a unique fingerprint.

Cite this