ArticleHuman genomics2019
Robust hypergraph regularized non-negative matrix factorization for sample clustering and feature selection in multi-view gene expression data.
Article in Human genomics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Analyzing Single Cell RNA Sequencing with Topological Nonnegative Matrix Factorization.Journal of computational and applied mathematics · 2024Article
- Preprocessing of Single Cell RNA Sequencing Data Using Correlated Clustering and Projection.Journal of chemical information and modeling · 2024Article
- Analyzing scRNA-seq data by CCP-assisted UMAP and tSNE.PloS one · 2024Article
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Authors and funding
5 authors.
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Abstract
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 L
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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