The microarray data analysis process: from raw data to biological significance.

Abstract

Despite advances in microarray technology that have led to increased reproducibility and substantial reductions in the cost of microarrays, the successful use of this technology is still elusive for many researchers, and microarray data analysis in particular presents a substantial bottleneck for many biomedical researchers. There are many reasons for this, including the expense of and a lack of adequate training in the use of analysis software. An additional reason is that microarray data analysis has largely been treated in the past as a set of separate steps, with the majority of emphasis being placed on statistical analysis and visualization of the data. For many biomedical researchers determining the biological significance of the data has been the greatest challenge and in the last several years more emphasis has been placed on this aspect of the analysis process. Despite this broadening of the scope of analysis there are still several aspects of the process that continue to be neglected, including additional related and interdependent aspects, such as experimental design, data accessibility, and platform selection. Though not traditionally thought of as integral to the data analysis process, these factors have profound effects on the analysis process. This article will discuss the importance of these additional aspects, as well as statistical analysis and determination of biological significance of microarray data. A summary of currently available software options will also be presented with a focus on the aspects discussed.