Abstract
Identifying similarities and differences in expression patterns across multiple time series can provide a better understanding of the relationships among various normal biological and experimentally induced conditions such as chemical treatments or the effects induced by a gene knockout/ suppression. We consider the task of identifying sets of genes that have a high degree of similarity both in their (i) expression profiles within each condition, and (ii) changes in expression responses across conditions. Previously, we developed an approach for aligning time series that computes clustered alignments. In this approach, an alignment represents the correspondences between two gene expression time series. Portions of one of the time series may be compressed or stretched to maximize the similarities between the two series. A clustered alignment groups genes such that the genes within a cluster share a common alignment, but each cluster is aligned independently of the others. Unlike standard gene-expression clustering, which groups genes according to the similarity of their expression profiles, the clustered-alignment approach clusters together genes that have similar changes in expression responses across treatments. We have now extended the clustered alignment approach to produce multi-level clusterings that identify subsets of genes that have a high degree of similarity both in their (i) expression profiles within each treatment, and (ii) changes in expression responses across treatments.