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Does logarithm transformation of microarray data affect ranking order of differentially expressed genes?

Authors
Li W.Suh Y.J.Zhang J.
Issue Date
Aug-2006
Publisher
IEEE
Citation
Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings, pp 6593 - 6596
Pages
4
Journal Title
Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
Start Page
6593
End Page
6596
URI
https://scholarworks.sookmyung.ac.kr/handle/2020.sw.sookmyung/15413
DOI
10.1109/IEMBS.2006.260896
ISSN
0589-1019
Abstract
A common practice in microarray analysis is to transform the microarray raw data (light intensity) by a logarithmic transformation, and the justification for this transformation is to make the distribution more symmetric and Gaussian-like. Since this transformation is not universally practiced in all microarray analysis, we examined whether the discrepancy of this treatment of raw data affect the high level analysis result. In particular, whether the differentially expressed genes as obtained by t-test, regularized t-test, or logistic regression have altered rank orders due to presence or absence of the transformation. We show that as much as 20%-40% of significant genes are discordant (significant only in one form of the data and not in both), depending on the test being used and the threshold value for claiming significance. The t-test is more likely to be affected by logarithmic transformation than logistic regression, and regularized t-test more affected than t-test. On the other hand, the very top ranking genes (e.g. up to top 20-50 genes, depending on the test) are not affected by the logarithmic transformation. © 2006 IEEE.
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