scGDCF: Graphical Deep Clustering With Fused Common Information for Single-Cell RNA-Seq Data

Unsupervised deep clustering plays a crucial role in analyzing single-cell RNA sequencing data (scRNA-seq) as it helps to identify potential cell types. However, most existing clustering methods face challenges in effectively fusing common information between feature and topological structure information, and they may not perform well on the sparse data, which are common in single-cell analysis. To address these challenges, we propose a novel Graphical Deep Clustering with Fused Common Information method for scRNA-seq data, named scGDCF. This method can accurately segregate different cell types even in large and sparse scRNA-seq datasets. In scGDCF, we first introduce a sparse feature representation method that utilizes an adversarial loss function to address the sparsity problem in scRNA-seq data and improve the performance of the discriminator. Next, we design a mutual information extracting operator to deeply mine and fuse the common information from feature and topological structure data, thereby improving the clustering performance. Furthermore, we incorporate the varying degrees of contribution from different neighbor nodes and information sources. To handle this, we promote a dual adaptive attention mechanism that operates at both global and local levels. Finally, experiments on seven real-world datasets and two simulated datasets show scGDCF outperforms 17 state-of-the-art methods. We further extend the clustering results for visualization, analysis of gene differential expression and enrichment, showing scGDCF provides novel insights into cell developmental lineages and preserved inter-cluster distances.