Font Size:     

There is a vast amount of gene expression data that has been gathered in microarray studies all over the world. Many of these studies use different experimentation plans, different platforms, different methodologies, etc. Merging information of different studies is an important part of current research in bio-informatics and several algorithms have been proposed recently. There is a need to create large data sets which will allow more statistically relevant analysis. In this article we concisely describe several data merging techniques and apply them on cancer microarray data sets. We study three cases of increasing complexity and test all methods by using a number of popular validation criteria. Furthermore, we test the compatibility of the transformed data sets by performing cross-study classification.