Local-Global Graph Fusion to Enhance scRNA-Seq Clustering

Local-Global Graph Fusion to Enhance scRNA-Seq Clustering

Blog Article

Single-cell RNA sequencing (scRNA-seq) is delta blues vitex crucial for demystifying the cell heterogeneity and differentiation processes, enabling the identification of distinct cell subtypes within a population.However, most of the existing approaches are feeble to comprehensively investigate the interactive relationships between cells and exploit the topological structures of the scRNA-seq data, resulting in the accurate identification of cell types hard to ploughed.In this paper, we propose scLGF, a novel scRNA-seq deep clustering model with Local and Global Graph Fusion.Specifically, scLGF first generates a latent representation for each cell using the dual embedding learning module.Then, scLGF introduces a local and global graph fusion module to effectively capture underlying connections between cells to enhance the model’s representative capabilities.

Finally, scLGF proposes an optimized triplet graph self-supervised learning approach to learn the discriminative feature representations of cells.We use the fused consensus representation to generate reliable target distributions to supervise the dual embedding learning task.In this viqua-f4 way, the three modules can mutually enhance each other end-to-end.Experimental results demonstrate the superiority of scLGF over six alternative methods on ten widely used single-cell datasets.Moreover, scLGF exhibits scalability on large-scale datasets, making it a practical tool for scRNA-seq data analysis.

The source codes are available online at https://github.com/lijing2000/scLGF.