Liu Jian, Liu Jin-Xing, Gao Ying-Lian, Kong Xiang-Zhen, Wang Xue-Song, Wang Dong
School of Information Science and Engineering, Qufu Normal University, Rizhao, 276826, Shandong, China; School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou, 221000, Jiangsu, China.
School of Information Science and Engineering, Qufu Normal University, Rizhao, 276826, Shandong, China; Bio-Computing Research Center, Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen, 518055, Guangdong, China.
PLoS One. 2015 Jul 22;10(7):e0133124. doi: 10.1371/journal.pone.0133124. eCollection 2015.
In current molecular biology, it becomes more and more important to identify differentially expressed genes closely correlated with a key biological process from gene expression data. In this paper, based on the Schatten p-norm and Lp-norm, a novel p-norm robust feature extraction method is proposed to identify the differentially expressed genes. In our method, the Schatten p-norm is used as the regularization function to obtain a low-rank matrix and the Lp-norm is taken as the error function to improve the robustness to outliers in the gene expression data. The results on simulation data show that our method can obtain higher identification accuracies than the competitive methods. Numerous experiments on real gene expression data sets demonstrate that our method can identify more differentially expressed genes than the others. Moreover, we confirmed that the identified genes are closely correlated with the corresponding gene expression data.
在当前的分子生物学中，从基因表达数据中识别与关键生物学过程密切相关的差异表达基因变得越来越重要。本文基于Schatten p-范数和Lp-范数，提出了一种新颖的p-范数鲁棒特征提取方法来识别差异表达基因。在我们的方法中，Schatten p-范数用作正则化函数以获得低秩矩阵，而Lp-范数用作误差函数以提高对基因表达数据中异常值的鲁棒性。模拟数据的结果表明，我们的方法比其他竞争方法能获得更高的识别准确率。在真实基因表达数据集上的大量实验表明，我们的方法比其他方法能识别出更多的差异表达基因。此外，我们证实所识别出的基因与相应的基因表达数据密切相关。
