| Info. | Vol.4 - No.3 (2010.09.20) |
|---|---|
| Title | Identification of significant gene-sets differentially expressed in a specific disease by co-expressed functional gene modules generation |
| Authors | Jaeyoung Kim1 & Miyoung Shin2 |
| Institutions | 1Graduate School of Electrical Engineering and Computer Science,
Kyungpook National University, Daegu, Korea 2School of Electronics Engineering, Kyungpook National Universit Daegu, Korea Correspondence and requests for materials should be addressed to M. Shin (shinmy@knu.ac.kr) |
| Abstract | In recent microarray studies, the gene-set analysis is one of the most popular computational approaches to find significant gene-sets that show significantly differential expression between case and control groups of samples. For this purpose, it employs a variety of biological resources such as pathway databases, gene ontology, literatures, and etc., to generate candidate functional gene-sets at the first step. Out of these candidates, then, the most significant ones are identified by taking such gene-sets that have sufficiently high statistical significance in expression difference between case and control groups. Here the significance of each gene-set is usually evaluated based on its representative score obtained from the expression profiles of its constituent genes. In practice, however, the representative score for a gene-set may not be easily able to capture overall characteristics of the expression patterns of its constituent genes. For example, it can occur that some genes in a specific functional gene-set show very different expression pattern from a majority of genes in the same gene-set. In such a case, those genes cause the problem that the representative score for a gene-set gets weakened, eventually leading to the hindrance in estimating the statistical significance of the gene-set. To handle this problem, thus, we propose an approach to employ gene modules, a group of genes which do not only share a specific function in common but are also strongly correlated to each other, as the candidate functional gene-sets for the gene-set analysis. Specifically, from each gene-set of the same functionality, we attempt to filter out the ?쐀ad??genes, of which expression patterns in a functional gene-set are not strongly correlated to those of a majority of genes in the same gene-set, by generating co-expressed functional gene modules from each gene-set. Also, for the significance evaluation of these gene modules, a nonparametric Wilcoxon rank-sum test is employed. From our experiments, it is observed that our proposed approach to coexpressed functional modules generation for gene-set analysis can greatly improve the performance on the identification of significant gene-sets differentially expressed in a specific disease. |
| Keyword | Microarray, Significant gene-sets, Gene-set analysis, Gene modules, Wilcoxon rank-sum test |
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