Robust PCA based method for discovering differentially expressed genes

BMC Bioinformatics

Proceedings

Jin-Xing Liu¹  Yong Xu²  Jian-Xun Mi²  Wen Sha³  Chun-Hou Zheng³  Yu-Tian Wang⁴ 

[1] Bio-Computing Research Center, Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen, China;College of Information and Communication Technology, Qufu Normal University, Rizhao, China;Key Laboratory of Network Oriented Intelligent Computation, Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen, China;Bio-Computing Research Center, Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen, China;Key Laboratory of Network Oriented Intelligent Computation, Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen, China;College of Electrical Engineering and Automation, Anhui University, Hefei, China;College of Information and Communication Technology, Qufu Normal University, Rizhao, China;

关键词: Gene Ontology;    Gene Expression Data;    Independent Component Analysis;    Recognition Accuracy;    Sparse Matrix;   

DOI  :  10.1186/1471-2105-14-S8-S3

来源: Springer

【 摘 要 】

How to identify a set of genes that are relevant to a key biological process is an important issue in current molecular biology. In this paper, we propose a novel method to discover differentially expressed genes based on robust principal component analysis (RPCA). In our method, we treat the differentially and non-differentially expressed genes as perturbation signals S and low-rank matrix A, respectively. Perturbation signals S can be recovered from the gene expression data by using RPCA. To discover the differentially expressed genes associated with special biological progresses or functions, the scheme is given as follows. Firstly, the matrix D of expression data is decomposed into two adding matrices A and S by using RPCA. Secondly, the differentially expressed genes are identified based on matrix S. Finally, the differentially expressed genes are evaluated by the tools based on Gene Ontology. A larger number of experiments on hypothetical and real gene expression data are also provided and the experimental results show that our method is efficient and effective.

【 授权许可】

Unknown   
© Liu et al.; licensee BioMed Central Ltd. 2013. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.