Regularized Non-Negative Matrix Factorization for Identifying Differentially Expressed Genes and Clustering Samples: A Survey

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

doi:10.1109/tcbb.2017.2665557

Cited by 61 publications

(21 citation statements)

References 103 publications

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“…The definition of L 2, 1 -norm is first proposed in reference [ 18 ]. And the L 2, 1 -norm has been applied in many research direction such as the feature identification [ 19 , 20 ] and image direction [ 21 , 22 ]. The definition of L 2, 1 -norm can be written as .…”

Section: Methodsmentioning

confidence: 99%

Identifying drug-pathway association pairs based on L2,1-integrative penalized matrix decomposition

et al. 2017

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BackgroundTraditional drug identification methods follow the “one drug-one target” thought. But those methods ignore the natural characters of human diseases. To overcome this limitation, many identification methods of drug-pathway association pairs have been developed, such as the integrative penalized matrix decomposition (iPaD) method. The iPaD method imposes the L1-norm penalty on the regularization term. However, lasso-type penalties have an obvious disadvantage, that is, the sparsity produced by them is too dispersive.ResultsTherefore, to improve the performance of the iPaD method, we propose a novel method named L2,1-iPaD to identify paired drug-pathway associations. In the L2,1-iPaD model, we use the L2,1-norm penalty to replace the L1-norm penalty since the L2,1-norm penalty can produce row sparsity.ConclusionsBy applying the L2,1-iPaD method to the CCLE and NCI-60 datasets, we demonstrate that the performance of L2,1-iPaD method is superior to existing methods. And the proposed method can achieve better enrichment in terms of discovering validated drug-pathway association pairs than the iPaD method by performing permutation test. The results on the two real datasets prove that our method is effective.

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Section: Methodsmentioning

confidence: 99%

Identifying drug-pathway association pairs based on L2,1-integrative penalized matrix decomposition

et al. 2017

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“…As an effective matrix decomposition method, non-negative matrix factorization (NMF) [4] is widely prevalent in bioinformatics [5], image representation [6], and other fields [7]. NMF can learn part-based representations of objects.…”

Section: Introductionmentioning

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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“…It is intended to find two nonnegative matrices to learn the part-based representation of the standard data itself. NMF has been popular for decades and successfully implemented in a wide range of fields, including robotics control [6], image analysis [7], and biomedical engineering [8]. To this end, we will provide a brief introduction to the relevant methods.…”

Section: Introductionmentioning

confidence: 99%

Hypergraph Regularized Discriminative Nonnegative Matrix Factorization on Sample Classification and Co‐Differentially Expressed Gene Selection

et al. 2019

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Nonnegative Matrix Factorization (NMF) is a significant big data analysis technique. However, standard NMF regularized by simple graph does not have discriminative function, and traditional graph models cannot accurately reflect the problem of multigeometry information between data. To solve the above problem, this paper proposed a new method called Hypergraph Regularized Discriminative Nonnegative Matrix Factorization (HDNMF), which captures intrinsic geometry by constructing hypergraphs rather than simple graphs. The introduction of the hypergraph method allows high-order relationships between samples to be considered, and the introduction of label information enables the method to have discriminative effect. Both the hypergraph Laplace and the discriminative label information are utilized together to learn the projection matrix in the standard method. In addition, we offered a corresponding multiplication update solution for the optimization. Experiments indicate that the method proposed is more effective by comparing with the earlier methods.

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Regularized Non-Negative Matrix Factorization for Identifying Differentially Expressed Genes and Clustering Samples: A Survey

Cited by 61 publications

References 103 publications

Identifying drug-pathway association pairs based on L2,1-integrative penalized matrix decomposition

Identifying drug-pathway association pairs based on L2,1-integrative penalized matrix decomposition

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

Hypergraph Regularized Discriminative Nonnegative Matrix Factorization on Sample Classification and Co‐Differentially Expressed Gene Selection

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