Characterizing the genome-wide dynamic regulation of gene expression is important and will be of much interest in the future. Here I develop a significance method for analyzing time course microarray studies that can be applied to the typical types of comparisons and sampling schemes. This method is applied to two studies on humans. In one study, genes are identified that show differential expression over time in response to in vivo endotoxin administration. Using our method 7409 genes are called significant at a 1% false discovery rate level, whereas several existing approaches fail to identify any genes. In another study, 417 genes are identified at a 10% false discovery rate level that show expression changing with age in the kidney cortex. Here it is also shown that as many as 47% of the genes change with age in a manner more complex than simple exponential growth or decay.
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