Towards multi-fusion graph neural network for single-cell RNA sequence clustering

Clustering analysis plays a crucial role in single-cell RNA sequencing (scRNA-seq) data analysis, in which the graph neural network (GNN)-based clustering methods have rapidly emerged as a promising technique. Despite considerable progress, the previous scRNA-seq clustering methods still suffer from two critical limitations. First, they mostly treat the node attributes and cell–cell topological information equally, neglecting their (probably) different reliability. Second, they usually only consider the learned representation of the last layer, lacking the ability to fuse multi-scale discriminative information embedded in different layers. In view of this, this paper presents a new single-cell multi-fusion graph neural network (scMFGNN) for scRNA-seq clustering. Particularly, we utilize a multi-fusion graph neural network (MFGNN) for learning discriminative representations while preserving the structural information latent in multi-scale network layers. To cope with the high-dispersion, high-heterogeneity, and high-dimensionality of scRNA-seq data, a zero-inflated negative binomial (ZINB) module is incorporated into the network structure. Furthermore, the consistency between node representations and graph topological information is constrained to guide the joint learning process. It is noteworthy that scMFGNN can dynamically fuse multi-scale representations from multiple layers and meanwhile adaptively combine node representations and topological structural information from the same layer for representation learning and clustering. Experiments on multiple scRNA-seq datasets demonstrate the superiority of scMFGNN over the state-of-the-art. Code available: https://github.com/youngcmm/scMFGNN.