Many algorithms that search for the determinants of gene co-regulation assign each gene to a single cluster (e.g. [4,9,23]), which is limiting, because genes can belong to different clusters under different biological conditions [24]. More recent network approaches overcome this problem by examining differential canonical correlation between multiple states, such as healthy and diseased, or with a reference [11]; these approaches, however, may rely on methods that are not robust to non-normal data to find correlated genes. This may be a problem for gene microarray expression data, which is often not normally distributed. Alternatively, robust, "mega-clustering" methods have been developed to provide improved estimates of co-regulation for microarray data [25]. One such algorithm-the 'Gene Recommender'-has successfully predicted previously unknown interactions that were verified experimentally in a multicellular organism [26]. A key property of the Gene Recommender is the categorization of genes into clusters under different conditions (i.e., allowing for "biclusters") where different samples' contributions to the given cluster may vary. Since inexpensive genotyping platforms can presently interrogate >1 million SNPs and we are rapidly shifting into the era of whole
