A Mixed-Norm Laplacian Regularized Low-Rank Representation Method for Tumor Samples Clustering

Abstract

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. Firstly, the matrix of gene expression data is decomposed into a low rank representation matrix and a sparse matrix by MLLRR. Secondly, 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.

Non-Negative Symmetric Low-Rank Representation Graph Regularized Method for Cancer Clustering Based on Score Function

“…For example, the original LRR method is improved by considering the geometric structures within the data, including the graph regularization method (Lu et al, 2013) and k-nearest neighbour graph method (Yin et al, 2016). The different norm items are used to improve the robustness of the original LRR method (Wang et al, 2018) and others.…”

“…However, the highdimensional and low-sample-size characteristics of the cancer gene expression dataset present a challenge for researchers in terms of data mining. To mitigate this problem, researchers have proposed many methods (Cui et al, 2013;Ge and Hu, 2014;Wang et al, 2016;Wang et al, 2018;Xu et al, 2019). Among the existing methods, feature selection is a reasonable method that has achieved great success.…”

“…Recently, LLRR was applied to cancer sample clustering (Wang et al, 2019a). Furthermore, Wang et al introduced the mixed-norm to increase the robustness of the LLRR method and proposed the mixed-norm Laplacian regularized LRR (MLLRR) method for tumour sample clustering based on penalized matrix decomposition (Wang et al, 2018). However, cancer sample clustering is processed on the obtained low-rank matrix, which is the global structural representation of the raw data.…”

Block-Constraint Laplacian-Regularized Low-Rank Representation and Its Application for Cancer Sample Clustering Based on Integrated TCGA Data

“…Gan et al applied latent low-rank representation to derive features for tumor clustering [39]. Wang et al proposed Mixednorm Laplacian regularized Low-Rank Representation (MLLRR) and applied it to tumor clustering [40]. Xia et al…”

Novel modelling of clustering for enhanced classification performance on gene expression data

“…Singular Value Decomposition using p-normalization approach as well as k-means clustering is another effective strategy to perform bimolecular clustering as seen in the work of Kong et al [23]. Apart from this other schemes toward clustering operation are usage of ensemble classifier (Pratama [24]), Laplacian regularization with mix-norm (Wang et al [25]), clustering on the basis of available information (Leale et al [26]), matrix factorization (Li et al [27]), random forest graph (Pouyan and Nourani [28]), integrated clustering using distance factor (Ushakov et al [29]), and weighted consensus matrix (Wu et al [30]) ]. Sudha V and Girijamma H A [31] has introduced a technique called SCDT for Gene study by using fuzzy cluster based closest neighbor categorization.…”