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

Background: Single-cell RNA sequencing(sc RNA-seq) data provides a whole new view to study disease and cell differentiation development. With the explosive increment of sc RNA-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 coexpression network analysis, termed Adaptive Total Variation Constraint Hypergraph Regularized NMF(ATVHNMF). 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.