Normalization benefits microarray-based classification.

EURASIP Journal on Bioinformatics & Systems Biology
Jianping HuaEdward R Dougherty

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 p...Continue Reading

References

Nov 28, 2002·Nature Genetics·John Quackenbush
Feb 13, 2004·Bioinformatics·Ulisses M Braga-Neto, Edward R Dougherty
Dec 2, 2004·Bioinformatics·Jianping HuaEdward R Dougherty
Oct 1, 1997·Journal of Biomedical Optics·Y ChenM L Bittner

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Citations

Dec 5, 2019·Cancer Imaging : the Official Publication of the International Cancer Imaging Society·Qian LiYoganand Balagurunathan
Jun 14, 2019·Scientific Reports·Yoganand BalagurunathanRobert J Gillies

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