Key Laboratory of Machine Perception (MOE), School of Artificial Intelligence, Peking University, Beijing 100871, China.
Bioinformatics. 2022 Apr 12;38(8):2187-2193. doi: 10.1093/bioinformatics/btac099.
Emerging single-cell RNA sequencing (scRNA-seq) technology empowers biological research at cellular level. One of the most crucial scRNA-seq data analyses is clustering single cells into subpopulations. However, the high variability, high sparsity and high dimensionality of scRNA-seq data pose lots of challenges for clustering analysis. Although many single-cell clustering methods have been recently developed, few of them fully exploit latent relationship among cells, thus leading to suboptimal clustering results.
Here, we propose a novel unsupervised clustering method, scGAC (single-cell Graph Attentional Clustering), for scRNA-seq data. scGAC firstly constructs a cell graph and refines it by network denoising. Then, it learns clustering-friendly representation of cells through a graph attentional autoencoder, which propagates information across cells with different weights and captures latent relationship among cells. Finally, scGAC adopts a self-optimizing method to obtain the cell clusters. Experiments on 16 real scRNA-seq datasets show that scGAC achieves excellent performance and outperforms existing state-of-art single-cell clustering methods.
Python implementation of scGAC is available at Github (https://github.com/Joye9285/scGAC) and Figshare (https://figshare.com/articles/software/scGAC/19091348).
Supplementary data are available at Bioinformatics online.
新兴的单细胞 RNA 测序 (scRNA-seq) 技术使细胞水平的生物学研究成为可能。scRNA-seq 数据分析中最关键的一项是将单细胞聚类成亚群。然而，scRNA-seq 数据的高度可变性、高度稀疏性和高维性给聚类分析带来了诸多挑战。尽管最近已经开发了许多单细胞聚类方法，但它们很少能够充分利用细胞之间的潜在关系，从而导致聚类结果不理想。
在这里，我们提出了一种新的无监督聚类方法，scGAC（单细胞图注意聚类），用于 scRNA-seq 数据。scGAC 首先构建细胞图，并通过网络去噪对其进行优化。然后，它通过图注意自动编码器学习细胞的聚类友好表示，通过不同的权重在细胞之间传播信息，并捕获细胞之间的潜在关系。最后，scGAC 采用自优化方法获得细胞簇。在 16 个真实的 scRNA-seq 数据集上的实验表明，scGAC 具有优异的性能，优于现有的单细胞聚类方法。
scGAC 的 Python 实现可在 Github（https://github.com/Joye9285/scGAC）和 Figshare（https://figshare.com/articles/software/scGAC/19091348）上获得。
补充数据可在生物信息学在线获得。
