Reproducibility of Differential Gene Detection across Multiple Microarray Studies

Although expression profiling of various diseases to identify interesting genes is a well-established methodology, it still faces many challenges. Labs often have difficulty reproducing results on different microarray platforms. Microarray manufacturers use different clones to represent similar genes on various platforms. Consequently, researchers struggle to integrate data published in literature and databases. Even results from identical microarray platforms may not correlate due to technical variability between labs. We seek some degree of congruity between the same microarray platforms implemented at multiple test sites. We analyze two prostate cancer datasets from commercially synthesized oligonucleotide arrays (Affymetrix HG-U95v2). Our analysis focuses on determining reproducibility in identifying differentially expressed genes using fold change and t-tests. We use p-values to compare specificity and sensitivity of the methods applied to each dataset. Findings indicate that, even though both datasets use the same microarray platform, differences in experimental design and test conditions result in variations when detecting differentially expressed genes.