Novel and simple transformation algorithm for combining microarray data sets |
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| Authors: | Ki-Yeol Kim Dong Hyuk Ki Ha Jin Jeong Hei-Cheul Jeung " target="_blank">Hyun Cheol Chung " target="_blank">Sun Young Rha |
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| Institution: | (1) Oral Cancer Research Institute, Yonsei University College of Dentistry, Seoul, 120-752, Korea;(2) Cancer Metastasis Research Center, Yonsei University College of Medicine, Seoul, 120-752, Korea;(3) National Biochip Research Center, Yonsei University College of Medicine, Seoul, 120-752, Korea;(4) Brain Korea 21 Project for Medical Science, Yonsei University College of Medicine, Seoul, 120-752, Korea;(5) Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, 120-752, Korea;(6) Department of Internal Medicine, Yonsei University College of Medicine, Seoul, 120-752, Korea |
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| Abstract: | Background With microarray technology, variability in experimental environments such as RNA sources, microarray production, or the use
of different platforms, can cause bias. Such systematic differences present a substantial obstacle to the analysis of microarray
data, resulting in inconsistent and unreliable information. Therefore, one of the most pressing challenges in the field of
microarray technology is how to integrate results from different microarray experiments or combine data sets prior to the
specific analysis. |
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