Integrative gene expression analysis of lung cancer based on a technology-merging approach

Nowadays there are many microarray gene expression datasets which are available by means of public repositories. Nevertheless, the fact is that most of them are low-scale analysis with a little number of samples. Consequently, integrating different data from several independent studies which try to treat a similar disease may be seen as a good option to tackle with the scarce number of samples that can be collected by only one study. In this paper we take a step beyond this approach and we merge also datasets from different microarray technologies, namely both Illumina and Affymetrix. Thanks to that we achieve a bigger database of lung cancer studies in order to obtain more significantly expressed genes in the differential gene expression analysis.