An overview of the integration of gene expression datasets

Gene expression analysis holds incredible potential in the understanding and detection of human disease. While comparisons between studies are essential to the validation of findings, the accuracy of this data fusion relies on underlying assumptions about various measures of control. It is well accepted that separate microarray platforms demonstrate different expression patterns, and thus the accurate integration of data with respect to platform knowledge presents a significant challenge. This article is based on a survey of existing literature regarding the fusion of microarray datasets, and explores the success and limitations of various methodologies. It is designed to provide a framework for future comparisons between gene expression studies