GSECA (Gene Set Expression Coherence Analysis)

: Comprehensive and integrative analytic tool for identification of functional themes
  and putative transcriptional logics in microarray-based expression profiles

Upgrade version of GSECA software is now available (version 1.2)

The major improvements are (1) easy-to-use and advanced graphic interface for data representation and (2) extended gene sets covering four annotation categories (functional gene set, promoter gene set, miRNA gene set, and gene perturbation gene sets). Several bugs are also fixed and current version is generally recommended.

    - Download the install package of GSECA-V1.2 (require VB.NET) and Manual (require Adobe Acrobat).

    - Older version is still available here with corresponding manual.

- To deal with high-throughput large-scale gene expression profile of microarray experiment, diverse analytic algorithms have been proposed. One of them, gene clustering has been commonly used to classify genes with similar expression changes (co-expressed genes or gene cluster). The gene members of a cluster are likely to be regulated by a common (at least, similar) regulatory mechanisms and they were investigated for 'enriched functional annotation' or 'enriched transcription factor binding sites (TFBS)' by using enrichment analysis.

- In spite of the promising utility, two methods have been performed separately (in most cases, gene clustering is followed by subsequent enrichment analysis) raising a question: 'It it possible to develop the integrative/comprehensive method to link both methods, clustering and enrichment analysis?".

- Thus, we propose an algorithm, gene set expression coherence analysis (GSECA) that identifies functional categories and associated transcriptional regulatory logics in terms of two kinds of gene information (functional gene sets and promoter gene sets). The GSECA algorithm performs in three major steps:
(1) it first determines expression coherence for individual functional gene sets,
(2) gene sets with significant coherence are further processed into a number of functionally related themes using clustering algorithm,
(3) each functional theme is then, investigated for the enrichment of transcriptional regulatory motifs using modified gene set enrichment analysis with promoter gene sets.

- GSECA algorithm is currently available with standalone program running on Microsoft Windows (VB.NET).

Contact developer for any suggestion or trouble in using GSECA or this page.