Motivation: Gene clustering and gene set-based functional analysis are widely used for the analysis of expression profiles. The development of a comprehensive method jointly combining the two methods would allow for greater biological insights.
Results: We developed a software package, PathCluster for gene set-based clustering via an agglomerative hierarchical clustering algorithm. The distances between predefined gene sets are illustrated in a dendrogram in which the relationships between gene sets can be visually assessed. Valuable biological insights can be obtained according to the type of gene sets, e.g. coordinated action of molecular functions (functional gene sets) and putative motif synergy (promoter gene set) in a biological process. The combined use of gene sets further enables the interrogation of different biological themes and their putative relationships, such as function-versus-regulatory motif or drug-versus-function. PathCluster can also be used for knowledge-based sample partitioning or class categorization for clinical purposes. With extended applicability, PathCluster will facilitate the gleaning of meaningful biological insights and testable hypotheses in the contexts of given expression profiles.
Availability: PathCluster executable files can be freely downloaded at http://www.systemsbiology.co.kr/PathCluster/.