吉林大学学报(工学版)

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Using simple statistical model to identify differentially expressed genes in microarray experiments

WU Jia-nan1,2, ZHOU Chun-guang1,3, XIA Xue-fei4, LIU Gui-xia1,3, SHEN Wei1, 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;
    4. Jilin Communications Polytechnic College, Changchun 130012, China
  • Received:2011-09-20 Online:2013-07-01 Published:2013-07-01

Abstract:

One of the main objectives in the analysis of microarray data is the identification of Differentially Expressed Genes (DEGs) under different experiment conditions. A main approach for such analysis is to calculate a statistical value for each gene, and then rank the genes in accordance with their statistical values. A large ranking value is evidence of a differential expression. Inevitably, different methods generally produce different gene rankings, and the performance of each method depends on its evaluation metric, the dataset and data preprocessing method. A disadvantage shared by existing methods is that some top ranked genes, which are falsely detected as DEGs, tend to exhibit lower expression levels. Here, we present a novel technique named Matrix Rank Product (MRP) for identifying differentially expressed genes that originate from a simple statistical rank model. The algorithm can directly deal with the raw data of the microarray. As a result it can eliminate the interference of different data preprocessing methods. Meanwhile, the new technique is designed for accurate gene ranking by calculating the microarray data matrix of overall sorting.

Key words: computer application, bioinformatics, identification of differentially expressed genes, microarray data, rank

CLC Number: 

  • TP399

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