GLAE: A graph-learnable auto-encoder for single-cell RNA-seq analysis

Single-cell RNA sequencing (scRNA-seq) methods based on graph neural networks (GNNs) can make good use of cell relation graphs. Considering the cell relation graph is unknown in most situations, some GNN-based methods generate a pre-fixed cell relation graph using all the features (i.e., genes) from a single perspective and input it into GNN models. However, these GNN-based models can be severely hurt by the pre-fixed relation graph especially when it is not well pre-obtained due to the scRNA-seq errors. In addition, such methods learn the cell relation graph from a single perspective using all the features, which ignores the different influences of different gene subsets on cell relations. In this paper, we propose a novel end-to-end GNN-based scRNA-seq method called GLAE to address the above shortcomings, which is capable of learning cell relation graphs from different perspectives adaptively during the training process. We compare GLAE with several recently proposed methods and the results on six scRNA-seq datasets show that GLAE outperforms most of the methods on clustering tasks and is able to learn a meaningful cell relation graph for downstream tasks.