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

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.

Graphical abstract

Keywords

adaptive total variation / single-cell RNA sequencing / network analysis / nonnegative matrix factorization / hypergraph

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