IEEE/ACM Trans Comput Biol Bioinform. 2019 Jan-Feb;16(1):172-182. doi: 10.1109/TCBB.2017.2769647. Epub 2017 Nov 3.
Tumor samples clustering based on biomolecular data is a hot issue of cancer classifications discovery. How to extract the valuable information from high dimensional genomic data is becoming an urgent problem in tumor samples clustering. In this paper, we introduce manifold regularization into low-rank representation model and present a novel method named Mixed-norm Laplacian regularized Low-Rank Representation (MLLRR) to identify the differentially expressed genes for tumor clustering based on gene expression data. Then, in order to advance the accuracy and stability of tumor clustering, we establish the clustering model based on Penalized Matrix Decomposition (PMD) and propose a novel cluster method named MLLRR-PMD. In this method, the cancer clustering research includes three steps. First, the matrix of gene expression data is decomposed into a low rank representation matrix and a sparse matrix by MLLRR. Second, the differentially expressed genes are identified based on the sparse matrix. Finally, the PMD is applied to cluster the samples based on the differentially expressed genes. The experiment results on simulation data and real genomic data illustrate that MLLRR method enhances the robustness to outliers and achieves remarkable performance in the extraction of differentially expressed genes.
基于生物分子数据的肿瘤样本聚类是癌症分类发现的热门问题。如何从高维基因组数据中提取有价值的信息，成为肿瘤样本聚类中亟待解决的问题。在本文中，我们将流形正则化引入低秩表示模型，并提出了一种新的方法，即混合范数拉普拉斯正则化低秩表示（MLLRR），以基于基因表达数据识别用于肿瘤聚类的差异表达基因。然后，为了提高肿瘤聚类的准确性和稳定性，我们基于惩罚矩阵分解（PMD）建立了聚类模型，并提出了一种新的聚类方法，即 MLLRR-PMD。在该方法中，癌症聚类研究包括三个步骤。首先，通过 MLLRR 将基因表达数据矩阵分解为低秩表示矩阵和稀疏矩阵。其次，基于稀疏矩阵识别差异表达基因。最后，基于差异表达基因应用 PMD 对样本进行聚类。模拟数据和真实基因组数据的实验结果表明，MLLRR 方法增强了对离群值的鲁棒性，并在差异表达基因的提取方面取得了显著的性能。
