A New Method for Processing scRNA-seq Data by Coupling Low-Rank Representation and Concept Factorization

The advent and development of single-cell RNA sequencing (scRNA-seq) have provided new avenues for exploring cellular heterogeneity. Although many researchers have designed and developed efficient models to address cell heterogeneity and diversity by clustering cells into several groups, the performance of these methods may need improvement due to the characteristics of scRNA-seq data, such as high dimensionality, sparsity, and high dropout rates. In this paper, we propose a new method that couples low-rank representation (LRR) and concept factorization (CF) to learn a better clustering assignment matrix from both global and local perspectives, named SLRRGCF. Specifically, the LRR with similarity constraints based on tired random walk (TRW) can reduce the dimensionality of high-dimensional data while capturing more comprehensive global structure. At the same time, hypergraph regularization and CF are utilized to capture the local structure of the data further and directly obtain the clustering assignment matrix. We evaluated the performance of SLRRGCF on several real datasets, and comparisons with other competitive methods validated the effectiveness of our approach.