| The power of a microarray experiment derives from the identification of genes differentially regulated across biological conditions. To date, differential regulation is most often taken to mean differential expression, and a number of methods for identifying differentially expressed (DE) genes or gene sets are available. Although useful in thousands of studies, such methods are not able to identify many important classes of differentially regulated genes. One example concerns differentially co-expressed genes.;We propose an approach, gene set co-expression analysis (GSCA), to identify differentially co-expressed gene sets. The GSCA approach provides an FDR controlled list of interesting gene sets, does not require that genes be highly correlated in at least one biological condition, and is readily applied to data from individual or multiple experiments, as we demonstrate using data from studies of lung cancer and diabetes.;The GSCA approach is implemented in R and available upon request. |
