›› 2012, Vol. 42 ›› Issue (05): 1262-1266.

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RSDM:A method to identify differentially expressed genes based on meta-analysis

WU Jia-nan1,2, ZHOU Chun-guang1,3, LIU Gui-xia1,3, SHEN Wei1, ZHENG Ming1, ZHOU You1,3   

  1. 1. College of Computer Science and Technology, Jilin University, Changchun 130022, China;
    2. College of Computer Science and Technology, Changchun University, Changchun 130022, China;
    3. Symbolic Computation and Knowledge Engineering Laboratory of the Ministry of Education, Jilin University, Changchun 130022, China
  • Received:2011-08-23 Online:2012-09-01 Published:2012-09-01

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Abstract: Traditional methods of Differentially Expressed Genes (DEGs) analysis can not be used to deal with heterogeneous data sets that the analysis results are inconsistent usually. A new method, named Rank Standard Deviation Meta (RSDM) analysis, is proposed in this paper for detecting DEGs. The method is based on the meta-analysis and rank standard deviation filtering technology. The proposed method can detect True Differentially Expressed Genes (TDEGs) and filter Pseudo Differentially Expressed Genes (PDEGs), both TDEGs and PDEGs coming from experimental datasets. The experiment results show that the propose method is of high efficiency.

Key words: computer application, bioinformatics, meta-analysis, identification of differentially expressed genes, microarray data, standard deviation

CLC Number: 

  • TP399
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