Tri-mean-based statistical differential gene expression detection

  • Authors:

  • Zhaohua Ji;Chunguo Wu;Yao Wang;Renchu Guan;Huawei Tu;Xiaozhou Wu;Yanchun Liang

  • Affiliations:

  • Key Laboratory of Symbolic Computation and Knowledge Engineering, College of Computer Science and Technology, Jilin University, 130012 Changchun, China;Key Laboratory of Symbolic Computation and Knowledge Engineering, College of Computer Science and Technology, Jilin University, 130012 Changchun, China;Key Laboratory of Symbolic Computation and Knowledge Engineering, College of Computer Science and Technology, Jilin University, 130012 Changchun, China;Key Laboratory of Symbolic Computation and Knowledge Engineering, College of Computer Science and Technology, Jilin University, 130012 Changchun, China;Key Laboratory of Symbolic Computation and Knowledge Engineering, College of Computer Science and Technology, Jilin University, 130012 Changchun, China;Key Laboratory of Symbolic Computation and Knowledge Engineering, College of Computer Science and Technology, Jilin University, 130012 Changchun, China;Key Laboratory of Symbolic Computation and Knowledge Engineering, College of Computer Science and Technology, Jilin University, 130012 Changchun, China

  • Venue:
  • International Journal of Data Mining and Bioinformatics
  • Year:
  • 2012

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Abstract

Based on the assumption that only a subset of disease group has differential gene expression, traditional detection of differentially expressed genes is under the constraint that cancer genes are up-or down-regulated in all disease samples compared with normal samples. However, in 2005, Tomlins assumed and discussed the situation that only a subset of disease samples would be activated, which are often referred to as outliers.