title: A class-information-based penalized matrix decomposition for identifying plants core genes responding to abiotic stresses.
abstract: In terms of making genes expression data more interpretable and comprehensible, there exists a significant superiority on sparse methods. Many sparse methods, such as penalized matrix decomposition (PMD) and sparse principal component analysis (SPCA), have been applied to extract plants core genes. Supervised algorithms, especially the support vector machine-recursive feature elimination (SVM-RFE) method, always have good performance in gene selection. In this paper, we draw into class information via the total scatter matrix and put forward a class-information-based penalized matrix decomposition (CIPMD) method to improve the gene identification performance of PMD-based method. Firstly, the total scatter matrix is obtained based on different samples of the gene expression data. Secondly, a new data matrix is constructed by decomposing the total scatter matrix. Thirdly, the new data matrix is decomposed by PMD to obtain the sparse eigensamples. Finally, the core genes are identified according to the nonzero entries in eigensamples. The results on simulation data show that CIPMD method can reach higher identification accuracies than the conventional gene identification methods. Moreover, the results on real gene expression data demonstrate that CIPMD method can identify more core genes closely related to the abiotic stresses than the other methods.
authors.affiliation: School of Information Science and Engineering, Qufu Normal University, Rizhao, Shandong, China; Bio-Computing Research Center, Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen, Guangdong, China.
authors.forename: Jin-Xing
authors.initials: JX
authors.lastname: Liu
authors.affiliation: School of Communication, Qufu Normal University, Rizhao, Shandong, China.
authors.forename: Jian
authors.initials: J
authors.lastname: Liu
authors.affiliation: Library of Qufu Normal University, Qufu Normal University, Rizhao, Shandong, China.
authors.forename: Ying-Lian
authors.initials: YL
authors.lastname: Gao
authors.affiliation: College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China; Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, China.
authors.forename: Jian-Xun
authors.initials: JX
authors.lastname: Mi
authors.affiliation: School of Information Science and Engineering, Qufu Normal University, Rizhao, Shandong, China.
authors.forename: Chun-Xia
authors.initials: CX
authors.lastname: Ma
authors.affiliation: School of Information Science and Engineering, Qufu Normal University, Rizhao, Shandong, China.
authors.forename: Dong
authors.initials: D
authors.lastname: Wang
mesh_terms: D000064:Acclimatization / Q000235:genetics; D000465:Algorithms; D003198:Computer Simulation; D030541:Databases, Genetic; D003890:Desiccation; D015972:Gene Expression Regulation, Neoplastic; D017343:Genes, Plant; D018869:Heat-Shock Response; D018517:Plant Roots / Q000235:genetics; D018520:Plant Shoots / Q000235:genetics; D010944:Plants / Q000235:genetics*; D013312:Stress, Physiological / Q000235:genetics*