Integration of biological knowledge encoded in various lists of functionally related genes has become one of the most important aspects of analyzing genome-wide functional genomics data. In the context of cluster analysis, functional coherence of clusters established through such analyses have been used to identify biologically meaningful clusters, compare clustering algorithms and identify biological pathways associated with the biological process under investigation.

We developed a computational framework for analytically and visually integrating knowledge-based functional categories with the cluster analysis of genomics data. The framework is based on the simple, conceptually appealing, and biologically interpretable gene-specific functional coherence score (CLEAN score). The score is derived by correlating the clustering structure as a whole with functional categories of interest. We directly demonstrate that integrating biological knowledge in this way improves the reproducibility of conclusions derived from cluster analysis. The CLEAN score differentiates between the levels of functional coherence for genes within the same cluster based on their membership in enriched functional categories. We show that this aspect results in higher reproducibility across independent datasets and produces more informative genes for distinguishing different sample types than the scores based on the traditional cluster-wide analysis. We also demonstrate the utility of the CLEAN framework in comparing clusterings produced by different algorithms. CLEAN was implemented as an add-on R package and can be downloaded at http://Clusteranalysis.org . The package integrates routines for calculating gene specific functional coherence scores and the open source interactive Java-based viewer Functional TreeView (FTreeView).

Our results indicate that using the gene-specific functional coherence score improves the reproducibility of the conclusions made about clusters of co-expressed genes over using the traditional cluster-wide scores. Using gene-specific coherence scores also simplifies the comparisons of clusterings produced by different clustering algorithms and provides a simple tool for selecting genes with a "functionally coherent" expression profile.