Wang Ya-Xuan, Liu Jin-Xing, Gao Ying-Lian, Zheng Chun-Hou, Shang Jun-Liang
School of Information Science and Engineering, Qufu Normal University, Rizhao, 276826, China.
Library of Qufu Normal University, Qufu Normal University, Rizhao, 276826, China.
Comput Biol Chem. 2016 Dec;65:185-192. doi: 10.1016/j.compbiolchem.2016.09.014. Epub 2016 Sep 28.
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.
随着DNA微阵列技术和下一代技术的快速发展，产生了大量的基因组数据。因此，如何从基因组数据中提取更多差异表达基因已成为当务之急。由于低秩表示（LRR）在研究低维子空间结构方面具有高性能，近年来受到了广泛关注。然而，它没有考虑数据中的内在几何结构。本文提出了一种名为拉普拉斯正则化低秩表示（LLRR）的新方法，并将其应用于基因组数据，该方法将图正则化引入LRR。通过充分利用图正则化，LLRR方法可以捕获数据之间的内在非线性几何信息。LLRR方法通过求解一个优化问题，将基因组数据的观测矩阵分解为一个低秩矩阵和一个稀疏矩阵。由于显著基因可被视为稀疏信号，差异表达基因被视为稀疏扰动信号。因此，可以根据稀疏矩阵选择差异表达基因。最后，我们使用GO工具分析所选基因，并将P值与其他方法进行比较。模拟数据和两个真实基因组数据的结果表明，该方法在差异表达基因选择方面优于其他一些方法。
