Abstract:

Identifying similarities and differences in expression patterns across
multiple time series can provide a better understanding of the relationships
among various chemical treatments or the effects induced by a gene knockout.
We consider the task of identifying sets 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. 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. We examine the validity of this
multi-level clustering method by performing a GO-term enrichment analysis of
the clusters. Additionally, we use permutation testing to determine if our
clusters that have alignment scores that are unlikely to occur by chance.