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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Chang Sik Kim Seungwoo Hwang Shu-Dong Zhang |
| Copyright Year | 2014 |
| Description | Author affiliation: Korean Bioinformation Center (KOBIC), Korea Res. Inst. of Biosci. & Biotechnol. (KRIBB), Daejeon, South Korea (Seungwoo Hwang) || Centre for Cancer Res. & Cell Biol. (CCRCB), Queen's Univ. Belfast (QUB), Belfast, UK (Chang Sik Kim; Shu-Dong Zhang) |
| Abstract | Quantile normalization (QN) is a technique for microarray data processing and is the default normalization method in the Robust Multi-array Average (RMA) procedure, which was primarily designed for analysing gene expression data from Affymetrix arrays. Given the abundance of Affymetrix microarrays and the popularity of the RMA method, it is crucially important that the normalization procedure is applied appropriately. In this study we carried out simulation experiments and also analysed real microarray data to investigate the suitability of RMA when it is applied to dataset with different groups of biological samples. From our experiments, we showed that RMA with QN does not preserve the biological signal included in each group, but rather it would mix the signals between the groups. We also showed that the Median Polish method in the summarization step of RMA has similar mixing effect. RMA is one of the most widely used methods in microarray data processing and has been applied to a vast volume of data in biomedical research. The problematic behaviour of this method suggests that previous studies employing RMA could have been misadvised or adversely affected. Therefore we think it is crucially important that the research community recognizes the issue and starts to address it. The two core elements of the RMA method, quantile normalization and Median Polish, both have the undesirable effects of mixing biological signals between different sample groups, which can be detrimental to drawing valid biological conclusions and to any subsequent analyses. Based on the evidence presented here and that in the literature, we recommend exercising caution when using RMA as a method of processing microarray gene expression data, particularly in situations where there are likely to be unknown subgroups of samples. |
| Starting Page | 139 |
| Ending Page | 143 |
| File Size | 583616 |
| Page Count | 5 |
| File Format | |
| ISBN | 9781479956692 |
| DOI | 10.1109/BIBM.2014.6999142 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2014-11-02 |
| Publisher Place | UK |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Biological system modeling Microarray data analysis Breast cancer Mixing biological signals Robust Multi-array Average (RMA) Arrays Gene expression Probes Erbium Quantile normalization |
| Content Type | Text |
| Resource Type | Article |
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