Transcription profiling by array of mouse preadipocytes after treatment with dexamethasone

DNA microarrays are popular tools for measuring gene expression of biological samples. This ever increasing popularity is ensuring that a large number of microarray studies are conducted, many of which with data publicly available for mining by other investigators. Under most circumstances, validation of differential expression of genes is performed on a gene to gene basis. Thus, it is not possible to generalize validation results to the remaining majority of non-validated genes or to evaluate the overall quality of these studies. <br></br> We have devised an approach for the global validation of DNA microarray experiments that will allow researchers to evaluate the general quality of their experiment and to extrapolate validation results of a subset of genes to the remaining non-validated genes. We applied this method to a microarray experiment validated with quantitative real time polymerase chain reaction. The experiment consists of three biological replicate treatments of mouse 3T3-L1 preadipocytes with the steroid hormone dexamethasone for 3 hours. Total RNA was extracted from each of our three treatment and three control samples, and we labeled and hybridized five aliquots of each sample to Affymetrix MGU74Av2 microarrays, for a total of 30 microarrays.<br></br> We illustrate why the popular strategy of selecting only the most differentially expressed genes for validation generally fails as a global validation strategy and propose random-stratified sampling as a better gene selection method. We also illustrate shortcomings of often-used validation indices such as overlap of significant effects and the correlation coefficient and recommend the concordance correlation coefficient (CCC) as an alternative.

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Miron M, Woody OZ, Marcil A, Murie C, Sladek R, Nadon R