Characterizing the genome-wide dynamic regulation of gene expression is important and will be of much interest in the future. However, there is currently no established method for identifying differentially expressed genes in a time course study. Here we propose 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% FDR level, whereas several existing approaches fail to identify any genes. In another study, 417 genes are identified at a 10% FDR 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. The methodology proposed here has been implemented in the freely distributed and open-source EDGE software package.
Genetics | Longitudinal Data Analysis and Time Series | Microarrays | Multivariate Analysis
Storey, John D.; Xiao, Wenzhong; Leek, Jeffrey T.; Tompkins, Ronald G.; and Davis, Ron W., "Significance Analysis of Time Course Microarray Experiments" (August 2004). UW Biostatistics Working Paper Series. Working Paper 232.
Genetics Commons, Longitudinal Data Analysis and Time Series Commons, Microarrays Commons, Multivariate Analysis Commons
This paper is scheduled to appear in the Proceedings of the National Academy of Sciences in August - September 2005. Please see: http://www.pnas.org/http://faculty.washington.edu/jstorey/publications.html