Rao Jiahua, Zhou Xiang, Lu Yutong, Zhao Huiying, Yang Yuedong
School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 510000, China.
Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou 510000, China.
iScience. 2021 Apr 2;24(5):102393. doi: 10.1016/j.isci.2021.102393. eCollection 2021 May 21.
Single-cell RNA sequencing technology promotes the profiling of single-cell transcriptomes at an unprecedented throughput and resolution. However, in scRNA-seq studies, only a low amount of sequenced mRNA in each cell leads to missing detection for a portion of mRNA molecules, i.e. the dropout problem which hinders various downstream analyses. Therefore, it is necessary to develop robust and effective imputation methods for the increasing scRNA-seq data. In this study, we have developed an imputation method (GraphSCI) to impute the dropout events in scRNA-seq data based on the graph convolution networks. Extensive experiments demonstrated that GraphSCI outperforms other state-of-the-art methods for imputation on both simulated and real scRNA-seq data. Meanwhile, GraphSCI is able to accurately infer gene-to-gene relationships and the inferred gene-to-gene relationships could also provide powerful assistance for imputation dynamically during the training process, which is a key promotion of GraphSCI compared with other imputation algorithms.
单细胞RNA测序技术以前所未有的通量和分辨率推动了单细胞转录组的分析。然而，在单细胞RNA测序研究中，每个细胞中测序的mRNA量很少，导致一部分mRNA分子漏检，即脱落问题，这阻碍了各种下游分析。因此，有必要为不断增加的单细胞RNA测序数据开发强大而有效的插补方法。在本研究中，我们开发了一种插补方法（GraphSCI），基于图卷积网络对单细胞RNA测序数据中的脱落事件进行插补。大量实验表明，GraphSCI在模拟和真实的单细胞RNA测序数据上的插补性能均优于其他现有方法。同时，GraphSCI能够准确推断基因与基因之间的关系，并且推断出的基因与基因之间的关系在训练过程中也能够为动态插补提供有力帮助，这是GraphSCI相对于其他插补算法的关键改进。
