scBiG for representation learning of single-cell gene expression data based on bipartite graph embedding

Abstract

Abstract Analyzing single-cell RNA sequencing (scRNA-seq) data remains a challenge due to its high dimensionality, sparsity and technical noise. Recognizing the benefits of dimensionality reduction in simplifying complexity and enhancing the signal-to-noise ratio, we introduce scBiG, a novel graph node embedding method designed for representation learning in scRNA-seq data. scBiG establishes a bipartite graph connecting cells and expressed genes, and then constructs a multilayer graph convolutional network to learn cell and gene embeddings. Through a series of extensive experiments, we demonstrate that scBiG surpasses commonly used dimensionality reduction techniques in various analytical tasks. Downstream tasks encompass unsupervised cell clustering, cell trajectory inference, gene expression reconstruction and gene co-expression analysis. Additionally, scBiG exhibits notable computational efficiency and scalability. In summary, scBiG offers a useful graph neural network framework for representation learning in scRNA-seq data, empowering a diverse array of downstream analyses.