A meta-clustering analysis indicates distinct pattern alteration between two series of gene expression profiles for induced ischemic tolerance in rats.

We have developed a visualization methodology, called a "cluster overlap distribution map" (CODM), for comparing the clustering results of time series gene expression profiles generated under two different conditions. Although various clustering algorithms for gene expression data have been proposed, there are few effective methods to compare clustering results for different conditions. With CODM, the utilization of three-dimensional space and color allows intuitive visualization of changes in cluster set composition, changes in the expression patterns of genes between the two conditions, and relationship with other known gene information, such as transcription factors. We applied CODM to time series gene expression profiles obtained from rat four-vessel occlusion models combined with systemic hypotension and time-matched sham control animals (with sham operation), identifying distinct pattern alteration between the two. Comparisons of dynamic changes of time series gene expression levels under different conditions are important in various fields of gene expression profiling analysis, including toxicogenomics and pharmacogenomics. CODM will be valuable for various types of analyses within these fields, because it integrates and simultaneously visualizes various types of information across clustering results.