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Adaptive total variation constraint hypergraph regularized NMF for single-cell RNA-seq data analysis |
Ya-Li Zhu, Xiao-Ning Zhang, Chuan-Yuan Wang, Jin-Xing Liu, Xiang-Zhen Kong( ) |
School of Computer Science, Qufu Normal University, Rizhao 276826, China |
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Abstract Background: Single-cell RNA sequencing (scRNA-seq) data provides a whole new view to study disease and cell differentiation development. With the explosive increment of scRNA-seq data, effective models are demanded for mining the intrinsic biological information. Methods: This paper proposes a novel non-negative matrix factorization (NMF) method for clustering and gene co-expression network analysis, termed Adaptive Total Variation Constraint Hypergraph Regularized NMF (ATV-HNMF). ATV-HNMF can adaptively select the different schemes to denoise the cluster or preserve the cluster boundary information between clusters based on the gradient information. Besides, ATV-HNMF incorporates hypergraph regularization, which can consider high-order relationships between cells to reserve the intrinsic structure of the space. Results: Experiments show that the performances on clustering outperform other compared methods, and the network construction results are consistent with previous studies, which illustrate that our model is effective and useful. Conclusion: From the clustering results, we can see that ATV-HNMF outperforms other methods, which can help us to understand the heterogeneity. We can discover many disease-related genes from the constructed network, and some are worthy of further clinical exploration.
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Keywords
adaptive total variation
single-cell RNA sequencing
network analysis
nonnegative matrix factorization
hypergraph
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Corresponding Author(s):
Xiang-Zhen Kong
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Just Accepted Date: 21 June 2021
Online First Date: 19 July 2021
Issue Date: 01 December 2021
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