Differentially expressed genes selection via Laplacian regularized low-rank representation method.
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.
School of Information Science and Engineering, Qufu Normal University, Rizhao, 276826, China. Electronic address: yxwsfz@126.com.
Ya-Xuan
YX
Wang
School of Information Science and Engineering, Qufu Normal University, Rizhao, 276826, China. Electronic address: sdcavell@126.com.
Jin-Xing
JX
Liu
Library of Qufu Normal University, Qufu Normal University, Rizhao, 276826, China. Electronic address: yinliangao@126.com.
Ying-Lian
YL
Gao
School of Information Science and Engineering, Qufu Normal University, Rizhao, 276826, China. Electronic address: zhengch99@126.com.
Chun-Hou
CH
Zheng
School of Information Science and Engineering, Qufu Normal University, Rizhao, 276826, China. Electronic address: shangjunliang110@163.com.
Jun-Liang
JL
Shang
D005786:Gene Expression Regulation; D008962:Models, Theoretical
Differentially expressed genes; Genomic data; Graph regularization; Low-rank representation