scBiG for representation learning of single-cell gene expression data based on bipartite graph embedding.
scBiG for representation learning of single-cell gene expression data based on bipartite graph embedding.
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DOI：
10.1093/nargab/lqae004
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发表时间：
2024-03
影响因子：
4.6
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中科院分区：
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由于单细胞RNA测序数据的高维性、稀疏性和技术噪声，分析单细胞RNA测序数据仍然是一个挑战。考虑到降维在简化复杂性和提高信噪比方面的优势，我们引入了一种新的图节点嵌入方法scBiG，用于在scRNA-seq数据中进行表示学习。ScBiG建立连接细胞和表达基因的二部图，然后构造一个多层图卷积网络来学习细胞和基因的嵌入。通过一系列广泛的实验，我们证明了scBiG在各种分析任务中的降维效果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。总而言之，scBiG提供了一个有用的图形神经网络框架，用于在scRNA-seq数据中进行表示学习，从而支持各种下游分析。
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.