Title: Tri-mean-based statistical differential gene expression detection

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

Addresses: 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

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.

Keywords: bioinformatics; differential gene expression detection; statistic methodology; tri-mean; differentially expressed genes; cancer genes; absolute deviation.

DOI: 10.1504/IJDMB.2012.049245

International Journal of Data Mining and Bioinformatics, 2012 Vol.6 No.3, pp.255 - 271

Received: 13 Apr 2010
Accepted: 22 Jul 2010
Published online: 17 Dec 2014 *

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