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

Su-Wen Chua; Praveen Vijayakumar; Peter M Nissom; Chew-Yeam Yam; Victor V T Wong; He Yang

doi:10.1093/nar/gkl024

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

Su-Wen Chua·Praveen Vijayakumar·Peter M Nissom·Chew-Yeam Yam·Victor V T Wong·He Yang

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Abstract

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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Algorithms Animals Cell Line Gene Expression Profiling Mice Normal Distribution Oligonucleotide Array Sequence Analysis RNA, Messenger ROC Curve Reproducibility of Results Yeasts Comparative Study Journal Article Research Support, Non-U.S. Gov't Validation Study

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Su-Wen Chua, Praveen Vijayakumar, Peter M Nissom, Chew-Yeam Yam, Victor V T Wong, He Yang. 2006-03-09. A novel normalization method for effective removal of systematic variation in microarray data.. https://doi.org/10.1093/nar%2Fgkl024

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