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Clustering GO Terms Applied to Differential Gene Expression Detection(PDF)

Chinese Journal of Applied & Environmental Biology[ISSN:1006-687X/CN:51-1482/Q]

2011 03
Research Field:
T & M
Publishing date:


Clustering GO Terms Applied to Differential Gene Expression Detection
TANG Qiuju XU Tao WANG Dong LI Lingjin DU Linfang
(1Key Laboratory of Bio-resources and Eco-environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu 610064, China)
(2Institute for Nanobiomedical Technology and Membrane Biology, Sichuan University, Chengdu 610041, China)
gene ontology hierarchy clustering differential gene expression semantic similarity

To improve individual GO term analysis algorithm for detecting differential gene expression, according to the directed acyclic graph structure property of gene classification system, Gene Ontology (GO), a novel and effective method named significant cluster analysis based on GO (ScaGO) was presented. The inputs of ScaGO were the expression values from a case-control microarrary experiment, aimed at detecting some novel differential expression changes. The results had shown some insights into gene expression difference at the functional level, towarded clarification of the process of pathological changes or mechanism of medicine. Both ScaGO and individual GO term analysis were applied to the acute lymphoblastic leukemia expression dataset and yeast Rap1 DNA-binding mutant dataset. Compared to individual GO term analysis, ScaGO was turned out to be more sensitive, and some novel differential expression changes which were mostly reported were mined successfully. It means that our ScaGO can provide the positive help in the experimental guidance. Fig 1, Tab 3, Ref 21


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Last Update: 2011-06-23