An Identification Method of Data-Specific GO Terms from a Microarray Data Set

Yoichi YAMADA  Ken-ichi HIROTANI  Kenji SATOU  Ken-ichiro MURAMOTO  

IEICE TRANSACTIONS on Information and Systems   Vol.E92-D   No.5   pp.1093-1102
Publication Date: 2009/05/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.E92.D.1093
Print ISSN: 0916-8532
Type of Manuscript: PAPER
Category: Data Mining
microarray data set,  differentially expressed genes,  data-specific GO terms,  cell cycle,  

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Microarray technology has been applied to various biological and medical research fields. A preliminary step to extract any information from a microarray data set is to identify differentially expressed genes between microarray data. The identification of the differentially expressed genes and their commonly associated GO terms allows us to find stimulation-dependent or disease-related genes and biological events, etc. However, the identification of these deregulated GO terms by general approaches including gene set enrichment analysis (GSEA) does not necessarily provide us with overrepresented GO terms in specific data among a microarray data set (i.e., data-specific GO terms). In this paper, we propose a statistical method to correctly identify the data-specific GO terms, and estimate its availability by simulation using an actual microarray data set.