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Nucleic Acids Research 2006 34(5):e38; doi:10.1093/nar/gkl024
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Published online 9 March 2006

© The Author 2006. Published by Oxford University Press. All rights reserved
The online version of this article has been published under an open access model. Users are entitled to use, reproduce, disseminate, or display the open access version of this article for non-commercial purposes provided that: the original authorship is properly and fully attributed; the Journal and Oxford University Press are attributed as the original place of publication with the correct citation details given; if an article is subsequently reproduced or disseminated not in its entirety but only in part or as a derivative work this must be clearly indicated. For commercial re-use, please contact journals.permissions@oxfordjournals.org

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A novel normalization method for effective removal of systematic variation in microarray data

Su-Wen Chua1, Praveen Vijayakumar1, Peter M. Nissom2, Chew-Yeam Yam1, Victor V.T. Wong2 and He Yang1,*

1Bioinformatics Institute #07-01, Matrix, 30 Biopolis Street, Singapore 138671 2Bioprocessing Technology Institute #06-01, Centros, 20 Biopolis Way, Singapore 138668

*To whom correspondence should be addressed. Tel: 65 64788268; Fax: 65 64789047; Email: henryy{at}bii.a-star.edu.sg

Received October 14, 2005. Revised January 11, 2006. Accepted February 18, 2006.

Normalization of cDNA and oligonucleotide microarray data has become a standard procedure to offset non-biological differences between two samples for accurate identification of differentially expressed genes. Although there are many normalization techniques available, their ability to accurately remove systematic variation has not been sufficiently evaluated. In this study, we performed experimental validation of various normalization methods in order to assess their ability to accurately offset non-biological differences (systematic variation). The limitations of many existing normalization methods become apparent when there are unbalanced shifts in transcript levels. To overcome this limitation, we have proposed a novel normalization method that uses a matching algorithm for the distribution peaks of the expression log ratio. The robustness and effectiveness of this method was evaluated using both experimental and simulated data.


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