CLEAN: CLustering Enrichment ANalysis

Johannes Freudenberg, Vineet Joshi, Zhen Hu, Mario Medvedovic

Laboratory for Statistical Genomics and Systems Biology

Department of Environmental Health,

University of Cincinnati College of Medicine,

3223 Eden Av. ML 56, Cincinnati OH 45267-0056,




Freudenberg JM, Joshi VK, Hu Z, Medvedovic M. CLEAN: CLustering Enrichment ANalysis. BMC Bioinformatics (2009) 10:234. Pubmed. Poster presented at OCCBIO 2008.



Background: 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.
Results: 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 The package integrates routines for calculating gene specific functional coherence scores and the open source interactive Java-based viewer Functional TreeView (FTreeView).
Conclusion: 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.



The CLEAN R package contains functions to compute the R functions to perform the Clustering Enrichment Analysis. In addition, it provides a number of tools to import and export files in TreeView format (i.e. .cdt, .gtr, and .atr files), and to match gene identifiers across species using HomoloGene.

  • R package CLEAN download (Linux,Windows)

  • CLEAN annotation R packages for Human, Mouse, and Rat (Linux,Windows)

  • FTreeView clustering browser (Run using JWS)

  • Learn more about FTreeView

  • NEW! The LRpath function has been incorporated within the R package CLEAN. The function has been updated to use the new Bioconductor library formats for the functional annotations (i.e. GO and KEGG) as well as to use other built-in and external functional categories accessible through CLEAN annotation packages.

  • NEW! Functions to compute the Random Set statistic and the Generalized Random Set statistic have been incorporated.

Supplemental Materials for the paper

  • FTreeView display of genes with statistically significant CLEAN scores in all four breast cancer datasets (Figure 8).

  • CLEAN analysis for different breast cancer datasets (GSE3494, GSE7390)

  • Top functionally coherent genes from analysis of different breast cancer datasets (fTreeView)

  • Many additional results can be accessed through our Genomics Portals


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