Cross-platform analysis of cancer microarray data improves gene expression based classification of phenotypes

Background: The extensive use of DNA microarray technology in the characterization of the cell transcriptome is leading to an ever increasing amount of microarray data from cancer studies. Although similar questions for the same type of cancer are addressed in these different studies, a comparative analysis of their results is hampered by the use of heterogeneous microarray platforms and analysis methods. Results: In contrast to a meta-analysis approach where results of different studies are combined on an interpretative level, we investigate here how to directly integrate raw microarray data from different studies for the purpose of supervised classification analysis. We use median rank scores and quantile discretization to derive numerically comparable measures of gene expression from different platforms. These transformed data are then used for training of classifiers based on support vector machines. We apply this approach to six publicly available cancer microarray gene expressio...

• Type: Journal
• Year: 2005
• Where: BMCBI
• Authors: Patrick Warnat, Roland Eils, Benedikt Brors
• Keywords: BMCBI 2005, Cancer, Gene Expression, Microarray Data