Tri-mean-based statistical differential gene expression detection. (1st October 2012)

• Record Type: Journal Article
• Authors: Ji, Zhaohua; Wu, Chunguo; Wang, Yao; Guan, Renchu; Tu, Huawei; Wu, Xiaozhou; Liang, Yanchun
• Is Part Of: International journal of data mining and bioinformatics. Volume 6:Number 3(2012)
• Page Start: 255
• Page End: 271
• Publication Date: 2012-10-01
• Subjects: bioinformatics -- differential gene expression detection -- statistic methodology -- tri-mean; Data mining -- Periodicals; Bioinformatics -- Periodicals; 006.312
• Languages: English
• ISSNs: 1748-5673

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.