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This article is part of the supplement: Proceedings of the 2012 International Conference on Intelligent Computing (ICIC 2012)

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Robust PCA based method for discovering differentially expressed genes

Jin-Xing Liu124, Yu-Tian Wang2, Chun-Hou Zheng3, Wen Sha3, Jian-Xun Mi14 and Yong Xu14*

Author Affiliations

1 Bio-Computing Research Center, Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen, China

2 College of Information and Communication Technology, Qufu Normal University, Rizhao, China

3 College of Electrical Engineering and Automation, Anhui University, Hefei, China

4 Key Laboratory of Network Oriented Intelligent Computation, Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen, China

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BMC Bioinformatics 2013, 14(Suppl 8):S3  doi:10.1186/1471-2105-14-S8-S3

Published: 9 May 2013


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.