Institutional Repository
Technical University of Crete
Technical University of Crete
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| URI: | http://purl.tuc.gr/dl/dias/2FE6CD0E-0F52-4B21-85FF-E225ABD8412B | ||
| Year | 2011 | ||
| Type of Item | Peer-Reviewed Journal Publication | ||
| License |
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| Bibliographic Citation | G. Tsiliki, M. Zervakis, M. Ioannou, E. Sanidas, E. Stathopoulos and G. Potamias, "Multi-platform data integration in microarray analysis," IEEE Trans. Inf. Techn. Biom., vol. 15, no.6, pp. 806-812, Nov. 2011. doi: 10.1109/TITB.2011.2158232 https://doi.org/10.1109/TITB.2011.2158232 | ||
| Appears in Collections |
An increasing number of studies have profiled gene expressions in tumor specimens using distinct microarray plat forms and analysis techniques. One challenging task is to develop robust statistical models in order to integrate multi-platform findings. We compare some methodologies on the field with respect to estrogen receptor (ER) status, and focus on a unified-among platforms scale implemented by Shen et at. in 2004, which is based on a Bayesian mixture model. Under this scale, we study the ER intensity similarities between four breast cancer datasets derived from various platforms. We evaluate our results with an independent dataset in terms of ER sample classification, given the derived gene ER signatures of the integrated data. We found that integrated multi-platform gene signatures and fold-change variability similarities between different platform measurements can assist the statistical analysis of independent microarray datasets in terms of ER classification.
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