Adaptive total variation constraint hypergraph regularized NMF for single-cell RNA-seq data analysis

• 作者: Ya-Li Zhu Xiao-Ning Zhang Chuan-Yuan Wang Jin-Xing Liu Xiang-Zhen Kong
• 来源期刊: 定量生物学（英文版）
• 年，卷（期）: 2021,（4）
• 所属期刊栏目: METHOD
• 页码范围: 451-462
• 页数: 12页
• 语种: 英文

摘要

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.