SignatureClust: A tool for landmark gene-guided clustering

Pankaj Chopra, Hanjun Shin, Jaewoo Kang, Sunwon Lee

    Research output: Contribution to journalArticlepeer-review


    Over the last several years, many clustering algorithms have been applied to gene expression data. However, most clustering algorithms force the user into having one set of clusters, resulting in a restrictive biological interpretation of gene function. It would be difficult to interpret the complex biological regulatory mechanisms and genetic interactions from this restrictive interpretation of microarray expression data. The software package SignatureClust allows users to select a group of functionally related genes (called 'Landmark Genes'), and to project the gene expression data onto these genes. Compared to existing algorithms and software in this domain, our software package offers two unique benefits. First, by selecting different sets of landmark genes, it enables the user to cluster the microarray data from multiple biological perspectives. This encourages data exploration and discovery of new gene associations. Second, most packages associated with clustering provide internal validation measures, whereas our package validates the biological significance of the new clusters by retrieving significant ontology and pathway terms associated with the new clusters. SignatureClust is a free software tool that enables biologists to get multiple views of the microarray data. It highlights new gene associations that were not found using a traditional clustering algorithm. The software package 'SignatureClust' and the user manual can be downloaded from

    Original languageEnglish
    Pages (from-to)411-418
    Number of pages8
    JournalSoft Computing
    Issue number3
    Publication statusPublished - 2012 Mar

    ASJC Scopus subject areas

    • Software
    • Theoretical Computer Science
    • Geometry and Topology


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