Differentially expressed genes selection via Laplacian regularized low-rank representation method. (December 2016)

• Record Type: Journal Article
• Authors: Wang, Ya-Xuan; Liu, Jin-Xing; Gao, Ying-Lian; Zheng, Chun-Hou; Shang, Jun-Liang
• Is Part Of: Computational biology and chemistry. Volume 65(2016)
• Page Start: 185
• Page End: 192
• Publication Date: 2016-12
• Subjects: Differentially expressed genes -- Low-rank representation -- Graph regularization -- Genomic data; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2016.09.014
• Languages: English
• ISSNs: 1476-9271

Abstract

Graphical abstract: The LLRR method decomposes the original data matrix into one low-rank matrix and one sparse matrix. And the low-rank matrix is represented by dictionary learning. The differentially expressed genes can be discovered from the sparse matrix. Highlights: The Laplacian regularized low-rank representation method is introduced. The LLRR method is applied on genomic data to discover differentially expressed genes. The experiments are conducted on The Cancer Genome Atlas data. Abstract: With the rapid development of DNA microarray technology and next-generation technology, a large number of genomic data were generated. So how to extract more differentially expressed genes from genomic data has become a matter of urgency. Because Low-Rank Representation (LRR) has the high performance in studying low-dimensional subspace structures, it has attracted a chunk of attention in recent years. However, it does not take into consideration the intrinsic geometric structures in data. In this paper, a new method named Laplacian regularized Low-Rank Representation (LLRR) has been proposed and applied on genomic data, which introduces graph regularization into LRR. By taking full advantages of the graph regularization, LLRR method can capture the intrinsic non-linear geometric information among the data. The LLRR method can decomposes the observation matrix of genomic data into a low rank matrix and a sparse matrix through solving an optimization problem. Because the significant genes can be considered as sparse signals, the differentially expressed genes are viewed as the sparse perturbation signals. Therefore, the differentially expressed genes can be selected according to the sparse matrix. Finally, we use the GO tool to analyze the selected genes and compare the P-values with other methods. The results on the simulation data and two real genomic data illustrate that this method outperforms some other methods: in differentially expressed gene selection.