Li Ting, Qian Kun, Wang Xiang, Li Wei Vivian, Li Hongwei
School of Mathematics and Physics, China University of Geosciences, Wuhan 430074, China.
Department of Statistics, University of California, Riverside, Riverside, CA 92507, USA.
NAR Genom Bioinform. 2024 Jan 29;6(1):lqae004. doi: 10.1093/nargab/lqae004. eCollection 2024 Mar.
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.
由于单细胞RNA测序（scRNA-seq）数据具有高维度、稀疏性和技术噪声等特点，对其进行分析仍然是一项挑战。认识到降维在简化复杂性和提高信噪比方面的好处，我们引入了scBiG，这是一种专为scRNA-seq数据的表示学习设计的新型图节点嵌入方法。scBiG建立了一个连接细胞和表达基因的二分图，然后构建一个多层图卷积网络来学习细胞和基因嵌入。通过一系列广泛的实验，我们证明scBiG在各种分析任务中优于常用的降维技术。下游任务包括无监督细胞聚类、细胞轨迹推断、基因表达重建和基因共表达分析。此外，scBiG还具有显著的计算效率和可扩展性。总之，scBiG为scRNA-seq数据的表示学习提供了一个有用的图神经网络框架，支持各种下游分析。
