A common goal in microarray experiments is to identify genes that are differentially expressed among two or more biological conditions. There is currently no standard methodology for detecting differential expression in time course studies. However, it is clear that monitoring the behavior of gene expression over time is important and will be a common experimental design in the future. Here we present a general statistical significance method for detecting temporal differential expression that can be applied to the typical types of comparisons and sampling schemes. We apply this method to two studies that we have carried out on humans. The goal of one study is to identify genes showing temporal differential expression between controls and endotoxin-treated individuals, and the other is to identify genes that show aging effects in the kidney. Genes identified in both studies corroborate previous findings and also provide novel insights. This methodology has been implemented in the freely distributed EDGE software package.