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Meta-analysis of microarray results: challenges, opportunities, and recommendations for standardization Cahan P; Rovegno F; Mooney D; Newman JC; St Laurent G 3rd; McCaffrey TAGene 2007[Oct]; 401 (1-2): 12-8Microarray profiling of gene expression is a powerful tool for discovery, but the ability to manage and compare the resulting data can be problematic. Biological, experimental, and technical variations between studies of the same phenotype/phenomena create substantial differences in results. The application of conventional meta-analysis to raw microarray data is complicated by differences in the type of microarray used, gene nomenclatures, species, and analytical methods. An alternative approach to combining multiple microarray studies is to compare the published gene lists which result from the investigators' analyses of the raw data, as implemented in Lists of Lists Annotated (LOLA: www.lola.gwu.edu) and L2L (depts.washington.edu/l2l/). The present review considers both the potential value and the limitations of databasing and enabling the comparison of results from different microarray studies. Further, a major impediment to cross-study comparisons is the absence of a standard for reporting microarray study results. We propose a reporting standard: standard microarray results template (SMART), which will facilitate the integration of microarray studies.|*Meta-Analysis as Topic[MESH]|Animals[MESH]|Computational Biology[MESH]|Data Interpretation, Statistical[MESH]|Databases, Genetic[MESH]|Gene Expression Profiling[MESH]|Gene Expression Regulation[MESH]|Humans[MESH]|Oligonucleotide Array Sequence Analysis/*standards[MESH]|Species Specificity[MESH] |
