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Identification of Differentially Expressed Genes Using Disjoint Principal Component Analysis Coupled with Genetic Algorithm

SU Zhen-Qiang1,3, HONG Hui-Xiao2, TONG Wei-Da3*, PERKINS Roger2, SHAO Xue-Guang4, CAI Wen-Sheng1,4*   

    1. Department of Chemistry, University of Science and Technology of China, Hefei 230026, China;
    2. Division of Bioinformatics, Z-Tech at FDA's National Center for Toxicological Research, Jefferson, AR 72079, USA;
    3. Center for Toxicoinformatics, National Center for Toxicological Research(NCTR), US Food and Drug Administration(FDA), Jefferson, AR 72079, USA;
    4. Department of Chemistry, Nankai University, Tianjin 300071, China
  • Received:2007-01-08 Revised:1900-01-01 Online:2007-09-10 Published:2007-09-10
  • Contact: CAI Wen-Sheng

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Abstract: A new method for the feature selection using disjoint principal component analysis(PCA) coupled with genetic algorithm(GA) was proposed and was used to identify differentially expressed genes based on microarray gene expression profiles. The discriminatory power of combination of genes is assessed with using disjoint PCA, the combinatorial optimization problem of genes is solved by using GA, and the chance correlation of genes is assessed by a statistic method. Due to considering the cooperation between genes which is a way to approximate the synergistic regulation by genes during the biological processes, the genes identified by our method are capable of powerful ability to express the differences. This method has been applied to analyze the gene microarray data of hepatocellular caricinoma(HCC). It is found that the genes identified by the proposed method has more discriminatory power in distinguishing two-class samples than those identified by SAM(significance analysis of microarrays), which is very popular in the analysis of microarray data.

Key words: Microarray, Principal component analysis(PCA), Genetic algorithm(GA), Significance analysis of microarrays(SAM), Chance correlation

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