Block-Constraint Robust Principal Component Analysis and its Application to Integrated Analysis of TCGA Data

Liu, Jin-Xing; Gao, Ying-Lian; Zheng, Chun-Hou; Xu, Yong; Yu, Jiguo

doi:10.1109/tnb.2016.2574923

Cited by 17 publications

(8 citation statements)

References 23 publications

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“…Cancer is the most common type of modern diseases, and it is a serious threat to human life and health. Changes in the genome often lead to cancer [25, 26]. Therefore, we select com-abnormal genes on the PAAD_ESCA_CHOL_GE dataset (to save space, we only list the experimental results on the PAAD_ESCA_CHOL_GE dataset.).…”

Section: Resultsmentioning

confidence: 99%

Robust hypergraph regularized non-negative matrix factorization for sample clustering and feature selection in multi-view gene expression data

Gao

Liu

et al. 2019

Hum Genomics

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BackgroundAs one of the most popular data representation methods, non-negative matrix decomposition (NMF) has been widely concerned in the tasks of clustering and feature selection. However, most of the previously proposed NMF-based methods do not adequately explore the hidden geometrical structure in the data. At the same time, noise and outliers are inevitably present in the data.ResultsTo alleviate these problems, we present a novel NMF framework named robust hypergraph regularized non-negative matrix factorization (RHNMF). In particular, the hypergraph Laplacian regularization is imposed to capture the geometric information of original data. Unlike graph Laplacian regularization which captures the relationship between pairwise sample points, it captures the high-order relationship among more sample points. Moreover, the robustness of the RHNMF is enhanced by using the L2,1-norm constraint when estimating the residual. This is because the L2,1-norm is insensitive to noise and outliers.ConclusionsClustering and common abnormal expression gene (com-abnormal expression gene) selection are conducted to test the validity of the RHNMF model. Extensive experimental results on multi-view datasets reveal that our proposed model outperforms other state-of-the-art methods.

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Section: Resultsmentioning

confidence: 99%

Robust hypergraph regularized non-negative matrix factorization for sample clustering and feature selection in multi-view gene expression data

Gao

Liu

et al. 2019

Hum Genomics

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“…It is well known that genomic alterations and genetic mutations can cause cancer [20,21]. Therefore, research on cancer genomic data is an urgent task.…”

Section: Resultsmentioning

confidence: 99%

Co-differential Gene Selection and Clustering Based on Graph Regularized Multi-View NMF in Cancer Genomic Data

Gao

Liu

et al. 2018

Genes

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Cancer genomic data contain views from different sources that provide complementary information about genetic activity. This provides a new way for cancer research. Feature selection and multi-view clustering are hot topics in bioinformatics, and they can make full use of complementary information to improve the effect. In this paper, a novel integrated model called Multi-view Non-negative Matrix Factorization (MvNMF) is proposed for the selection of common differential genes (co-differential genes) and multi-view clustering. In order to encode the geometric information in the multi-view genomic data, graph regularized MvNMF (GMvNMF) is further proposed by applying the graph regularization constraint in the objective function. GMvNMF can not only obtain the potential shared feature structure and shared cluster group structure, but also capture the manifold structure of multi-view data. The validity of the proposed GMvNMF method was tested in four multi-view genomic data. Experimental results showed that the GMvNMF method has better performance than other representative methods.

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“…Since different types of molecular data contain information in various aspects that may complement each other, it is beneficial for leveraging different types of omics data simultaneously [10]. Several integrative frameworks have been proposed and gained success [9,10,11,12,13,14].…”

Section: Introductionmentioning

confidence: 99%

A Random Walk Based Cluster Ensemble Approach for Data Integration and Cancer Subtyping

Wang

Zheng

2019

Genes

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Availability of diverse types of high-throughput data increases the opportunities for researchers to develop computational methods to provide a more comprehensive view for the mechanism and therapy of cancer. One fundamental goal for oncology is to divide patients into subtypes with clinical and biological significance. Cluster ensemble fits this task exactly. It can improve the performance and robustness of clustering results by combining multiple basic clustering results. However, many existing cluster ensemble methods use a co-association matrix to summarize the co-occurrence statistics of the instance-cluster, where the relationship in the integration is only encapsulated at a rough level. Moreover, the relationship among clusters is completely ignored. Finding these missing associations could greatly expand the ability of cluster ensemble methods for cancer subtyping. In this paper, we propose the RWCE (Random Walk based Cluster Ensemble) to consider similarity among clusters. We first obtained a refined similarity between clusters by using random walk and a scaled exponential similarity kernel. Then, after being modeled as a bipartite graph, a more informative instance-cluster association matrix filled with the aforementioned cluster similarity was fed into a spectral clustering algorithm to get the final clustering result. We applied our method on six cancer types from The Cancer Genome Atlas (TCGA) and breast cancer from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC). Experimental results show that our method is competitive against existing methods. Further case study demonstrates that our method has the potential to find subtypes with clinical and biological significance.

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Block-Constraint Robust Principal Component Analysis and its Application to Integrated Analysis of TCGA Data

Cited by 17 publications

References 23 publications

Robust hypergraph regularized non-negative matrix factorization for sample clustering and feature selection in multi-view gene expression data

Robust hypergraph regularized non-negative matrix factorization for sample clustering and feature selection in multi-view gene expression data

Co-differential Gene Selection and Clustering Based on Graph Regularized Multi-View NMF in Cancer Genomic Data

A Random Walk Based Cluster Ensemble Approach for Data Integration and Cancer Subtyping

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