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Open Access Methodology article

A novel regulatory event-based gene set analysis method for exploring global functional changes in heterogeneous genomic data sets

Chien-Yi Tung1, Chih-Hung Jen2, Ming-Ta Hsu23, Hsei-Wei Wang124* and Chi-Hung Lin124*

Author Affiliations

1 Institute of Microbiology and Immunology, National Yang-Ming University, Taipei, Taiwan

2 VGH Yang-Ming Genome Research Center, Taipei, Taiwan

3 Institute of Biochemistry and Molecular Biology, National Yang-Ming University, Taipei, Taiwan

4 Medical Research & Education Division, Taipei City Hospital, Taipei, Taiwan

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BMC Genomics 2009, 10:26  doi:10.1186/1471-2164-10-26

Published: 16 January 2009



Analyzing gene expression data by assessing the significance of pre-defined gene sets, rather than individual genes, has become a main approach in microarray data analysis and this has promisingly derive new biological interpretations of microarray data. However, the detection power of conventional gene list or gene set-based approaches is limited on highly heterogeneous samples, such as tumors.


We developed a novel method, the regulatory

nalysis (eGSA), which considers not only the consistently changed genes but also every gene regulation (event) of each sample to overcome the detection limit. In comparison with conventional methods, eGSA can detect functional changes in heterogeneous samples more precisely and robustly. Furthermore, by utilizing eGSA, we successfully revealed novel functional characteristics and potential mechanisms of very early hepatocellular carcinoma (HCC).


Our study creates a novel scheme to directly target the major cellular functional changes in heterogeneous samples. All potential regulatory routines of a functional change can be further analyzed by the regulatory event frequency. We also provide a case study on early HCCs and reveal a novel insight at the initial stage of hepatocarcinogenesis. eGSA therefore accelerates and refines the interpretation of heterogeneous genomic data sets in the absence of gene-phenotype correlations.