A differential biclustering algorithm for comparative analysis of gene expression

Convergences and divergences among related organisms (S.cerevisiae and C.albicans for example) or same organisms (healthy and disease tissues for example) can often be traced to the differential expression of specific group of genes. Yet, algorithms to characterize such differences and similarities using gene expression data are not well developed. Given two related organisms A and B, we introduce and develop a differential biclustering algorithm, that aims at finding convergent biclusters, divergent biclusters, partially conserved biclusters, and split conserved biclusters. A convergent bicluster is a group of genes with similar functions that are conserved in A and B. A divergent bicluster is a group of genes with similar function in A (or B) but which play different role in B (or A). Partially conserved biclusters and split conserved biclusters capture more complicated relationships between the behavior and functions of the genes in A and B. Uncovering such patterns can elucidate new insides about how related organisms have evolved or the role played by some group of genes during the development of some diseases. Our differential biclustering algorithm consists of two steps. The first step consists of using a parallel biclustering algorithm to uncover all valid biclusters with coherent evolutions in each set of data. The second step consists of performing a differential analysis on the set of biclusters identified in step one, yielding sets of convergent, divergent, partially conserved and split conserved biclusters.